D8.1 Preliminary environmental LCA of the …...2017/03/02 · WP8 – LCA and safety analysis D8.1 Preliminary environmental LCA of the developed technology Topic: FCH-02.2-2014

BIONICO

BIOGAS MEMBRANE REFORMER FOR DECENTRALIZED HYDROGEN PRODUCTION

FCH JU GRANT AGREEMENT NUMBER: 671459

Start date of project: 01/09/2015 Duration: 3 years

WP8 – LCA and safety analysis

D8.1 Preliminary environmental LCA of the developed technology

Topic: FCH-02.2-2014 - Decentralized hydrogen production from clean CO2-containing biogas Type of Action: FCH2-RIA Research and Innovation action Call identifier: H2020-JTI-FCH-2014-1

Due date of deliverable: 2017-02-28

Actual submission date: 2017/03/02

Reference period:

Document name: BIONICO-WP8-D81-DLR-QUANTIS-20170227-v01.docx

Prepared by (*): QUANTIS

Version DATE Changes CHECKED APPROVED

V1 2017-02-26 First release QUANTIS

V2 2017-02-27 Second release QUANTIS

V3 2017-02-28 Third release QUANTIS

V4 2017-03-02 Fourth release QUANTIS

Dissemination Level

PU Public X

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the Commission Services)

CO Confidential, only for members of the consortium (including the Commission Services)

CON Confidential, only for members of the Consortium

This project has received funding from the Fuel Cells and Hydrogen 2 Joint Undertaking under grant agreement No 671459. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and Hydrogen Europe and Hydrogen Europe Research.

__________________________________________________________________________________ (*) indicate the acronym of the partner that prepared the document


Proj. Ref.: BIONICO - 671459 Doc. Ref.: BIONICO-WP8-D81-DLR-QUANTIS-20170302-v04 Date: 2017/03/02 Page Nº: 2 of 49

_________________________________________________________________________________________________________________ PU

PUBLISHABLE SUMMARY Hydrogen production from biogas is a promising technology with potentials to scale up biogas utilization and decarbonize our energy supply; however conventional conversion systems involving several process steps, through steam membrane reforming (SMR) or autothermal reforming (ATR), are both energy and capital intensive. In light of that, a process intensification technology is developed that integrates H2 production and separation in a single vessel using catalytic membrane reactor (CMR) in order to achieve a higher overall energy efficiency. Due to the large variations embedded in biogas-to-hydrogen system, its environmental implications need to be carefully scrutinized before large scale applications are implemented. Using multi-indicator life cycle assessment (LCA) approach, this study aims to investigate environmental implications of adopting hydrogen production systems to landfill sites with following focuses: 1) sourcing variability of biogas and electricity generation; 2) approaches of attributing landfill impact to biogas production; 3) consequence resulting from marginal changes of existing conditions of biogas and electricity generation and utilization; 4) trade-off between biogas input and electricity consumption and their implications on adopting different conversion technologies. Overall, it shows that higher system energy efficiency of technologies is not necessarily translated into better environmental performance, due to large difference in environmental impacts of feedstock or energy types and their sourcing variations. Also, climate change impact indicator is a poor proxy to represent all impact categories. CMR technology can be either better or worse than alternatives, depending on specific situations considered and chosen indicators. The CMR technology has a lower impact on climate change: i) when biogas is taken away from those otherwise would be flared, also electricity comes from additional generation from biogas; the less CO2 emitted directly from H2 conversion is better; ii) when biogas is taken away from those otherwise would be used for bioelectricity production, resulting in marginal carbon-intensive electricity generated to satisfy energy demand; the less biogas input is better; iii) when part of impact from landfill is allocated to biogas that dominates the life cycle GHG impact; the less biogas input is better. On the other hand, the CMR technology may have a higher impact on climate change: i) when fugitive biogas is additionally captured for H2 production, resulting in avoided methane emissions. Counter-intuitively, the more biogas input the better; and ii) when biogas is taken away from those otherwise would be flared and electricity comes from carbon-intensive grid mix; as biogas bears no climate change impact, the more electricity consumption is worse. With sensitivity analysis, key influencing parameters are identified, including: i) landfill gas emission and utilization rate, leachate rate, and price of green electricity; ii) yield of biogas, biodegradability and fossil carbon content from degraded waste; iii) time horizon; iv) variation of biogas impact accounting; v) variation of marginal electricity supply from different locations, timing, technologies, and fuel efficiencies. The LCA results presented are limited to the predefined scenarios, just preliminary based on BIONICO CMR concepts that will be further updated. Also, the choice of 1 MJ of H2 as function unit will be further discussed within the consortium and might be changed later. Other key limitations include omission of infrastructure and biogas pre-cleaning, which will be also improved in the second phase of the project. When this study is communicated to stakeholders, the magnitude and nature of the limitations should be communicated at the same time. The next step will also explore the trade-off among techno-economic and environmental aspects to guide the design of CMR concepts.



_________________________________________________________________________________________________________________ PU

Content

PUBLISHABLE SUMMARY .................................................................................................................. 2

EXECUTIVE SUMMARY ....................................................................................................................... 5

1. INTRODUCTION ............................................................................................................................ 6

1.1. Context and background ............................................................................................................. 6

1.2. Life cycle assessment approach ................................................................................................ 7

2. GOAL AND SCOPE ...................................................................................................................... 8

2.1. Objectives ..................................................................................................................................... 8

2.2. Intended audience ........................................................................................................................ 8

2.3. Function and functional unit ....................................................................................................... 8

2.4. System generic description ......................................................................................................... 8

2.5. System boundaries ...................................................................................................................... 10

2.6. Life cycle Inventory ...................................................................................................................... 12

2.7. Life cycle impact assessment method ....................................................................................... 12

2.8. Calculation tool ............................................................................................................................ 13

3. APPROACH AND LIFE CYCLE INVENTORY DATA .................................................................... 14

3.1. Key process data ......................................................................................................................... 14

3.2. Main data assumptions ................................................................................................................ 15

3.3. Landfill modelling ........................................................................................................................ 15

3.4. Allocation of landfill impact to biogas production..................................................................... 17

3.5. Landfill gas sourcing effect ......................................................................................................... 18

3.6. Electricity sourcing effect............................................................................................................ 19

3.7. Biogas pre-cleaning step ............................................................................................................. 20

3.8. Substitution of hydrogen production from average European market .................................... 20

3.9. Scenario and sensitivity analysis ............................................................................................... 21

3.9.1. Default scenario ............................................................................................................. 21

3.9.2. Scenarios for sensitivity analysis .................................................................................... 21

4. LIFE CYCLE IMPACT RESULTS .................................................................................................. 23

4.1. Default scenario analysis ............................................................................................................ 23

4.1.1. Overall comparison ........................................................................................................ 23

4.1.2. Detailed results- contribution analysis ............................................................................ 24

4.1.3. Energy efficiency and trade-off between biogas and electricity consumption in LCA ...... 27

4.2. Sensitivity analysis ...................................................................................................................... 29

5. CONCLUSIONS AND RECOMMENDATIONS .............................................................................. 33

5.1. Key findings ................................................................................................................................. 33



_________________________________________________________________________________________________________________ PU

5.2. Key issues for further explorations ............................................................................................ 33

5.3. Limitations and outlook ............................................................................................................... 34

6. REFERENCES .............................................................................................................................. 35

APPENDIX A – ACRONYMS AND ABBREVIATIONS ......................................................................... 36

APPENDIX B – Layout of BIONICO and reference technologies ...................................................... 37

APPENDIX C – Project parameters used for modelling .................................................................... 39

APPENDIX D – Links and differences in membrane development among FCH-JU projects ......... 41

APPENDIX E – Life cycle inventory of disposal 1 kg of municipal solid waste in sanitary landfill 42

APPENDIX F – Life cycle inventory of electricity generation from landfill gas ............................... 47

APPENDIX G – Life cycle inventory of hydrogen production........................................................... 48

APPENDIX H – Scenario and parameter input ................................................................................... 48

APPENDIX I – detailed LCIA results ................................................................................................... 49



_________________________________________________________________________________________________________________ PU

EXECUTIVE SUMMARY The present phase of work Task 8.1.1 aims to conduct, on lower level of details, the screening environmental LCA to help identify the most probable hotspots in the life cycle and where more in-depth information is necessary, and provide recommendations on the most promising options from an environmental point of view. The screening LCA also aims at preparing the framework for the subsequent final LCA. In particular, it has defined i) the goal and scope of the study, ii) the reference conventional technologies as alternatives, iii) the framework of the data collection and calculation, iv) the environmental indicators to be addressed, and v) key issues to be further explored in the detailed LCA. Hydrogen production from biogas is a promising technology with potentials to scale up biogas utilization and decarbonize our energy supply; however conventional conversion systems involving several process steps, through steam membrane reforming (SMR) or autothermal reforming (ATR), are both energy and capital intensive. In light of that, a process intensification technology is developed that integrates H2 production and separation in a single vessel using catalytic membrane reactor (CMR) in order to achieve a higher overall energy efficiency. Due to the large variations embedded in biogas-to-hydrogen system, its environmental implications need to be carefully scrutinized before large scale applications are implemented. Using multi-indicator life cycle assessment (LCA) approach, this study aims to investigate environmental implications of adopting hydrogen production systems to landfill sites with following focuses: 1) sourcing variability of biogas and electricity generation; 2) approaches of attributing landfill impact to biogas production; 3) consequence resulting from marginal changes of existing conditions of biogas and electricity generation and utilization; 4) trade-off between biogas input and electricity consumption and their implications on adopting different conversion technologies. Overall, it shows that higher system energy efficiency of technologies is not necessarily translated into better environmental performance, due to large difference in environmental impacts of feedstock or energy types and their sourcing variations. Also, climate change impact indicator is a poor proxy to represent all impact categories. CMR technology can be either better or worse than alternatives, depending on specific situations considered and chosen indicators. The CMR technology has a lower impact on climate change: i) when biogas is taken away from those otherwise would be flared, also electricity comes from additional generation from biogas; the less CO2 emitted directly from H2 conversion is better; ii) when biogas is taken away from those otherwise would be used for bioelectricity production, resulting in marginal carbon-intensive electricity generated to satisfy energy demand; the less biogas input is better; iii) when part of impact from landfill is allocated to biogas that dominates the life cycle GHG impact; the less biogas input is better. On the other hand, the CMR technology may have a higher impact on climate change: i) when fugitive biogas is additionally captured for H2 production, resulting in avoided methane emissions. Counter-intuitively, the more biogas input the better; and ii) when biogas is taken away from those otherwise would be flared and electricity comes from carbon-intensive grid mix; as biogas bears no climate change impact, the more electricity consumption is worse. With sensitivity analysis, key influencing parameters are identified, including: i) landfill gas emission and utilization rate, leachate rate, and price of green electricity; ii) yield of biogas, biodegradability and fossil carbon content from degraded waste; iii) time horizon; iv) variation of biogas impact accounting; v) variation of marginal electricity supply from different locations, timing, technologies, and fuel efficiencies. The LCA results presented are limited to the predefined scenarios, just preliminary based on BIONICO CMR concepts that will be further updated. Also, the choice of 1 MJ of H2 as function unit will be further discussed within the consortium and might be changed later. Other key limitations include omission of infrastructure and biogas pre-cleaning, which will be also improved in the second phase of the project. When this study is communicated to stakeholders, the magnitude and nature of the limitations should be communicated at the same time. The next step will also explore the trade-off among techno-economic and environmental aspects to guide the design of CMR concepts.



_________________________________________________________________________________________________________________ PU

1. INTRODUCTION 1.1. Context and background The increasing awareness of the importance of sustainability and the potential environmental impact associated with products and services has sparked the innovation of methods to better understand, measure and reduce this impact. The leading tool for achieving this is life cycle assessment (LCA), a method standardized by the International Organization for Standardization (ISO) 14040-44 standards (ISO 14040:2006, ISO 14044:2006). LCA is an internationally recognized approach which evaluates the potential environmental and human health impact associated with products and services throughout their life cycle, from raw material extraction and including transportation, production, use, and end-of-life treatment. Among other uses, LCA is used to identify opportunities to improve the environmental performance of products at various stages along their life cycle, inform decision-making, and support marketing and communication. The aim of the BIONICO project is to develop, build and demonstrate at a real biogas plant (TRL6) with a novel Catalytic Membrane Reactor (CMR) that integrates H2 production and separation in a single vessel. The main idea of BIONICO is to design and demonstrate an efficient biogas-to-hydrogen conversion system at real plant conditions (in the ENC Landfill plant at Chamusca, Santarém, Portugal) using process intensification. The BIONICO process will demonstrate to achieve an overall efficiency up to 72% thanks to the process intensification. Compared with any other membrane reactor project in the past, BIONICO will demonstrate the membrane reactor at a much larger scale, so that more than 100 membranes will be implemented in a single fluidized bed membrane reactor. The hydrogen production capacity will be of 100 kg/day. More project background information is available in detail in Appendix D. The goal of the present study focusses on the sustainability of the processes, and is (i) to assess the preliminary environmental performances through a life cycle perspective of the CMR technologies developed within the BIONICO project, and (ii) to compare it with two reference technologies, Steam membrane reforming (SMR) and Autothermal Reforming (ATR) for producing hydrogen from biogas. This deliverable 8.1 presents the results of the first step of the LCA analysis (screening LCA), which allows to calculate a global environmental assessment of the new technologies and to identify the hotspots for each process and each environmental indicator, based on the data provided by WP2 (D2.2 Definition of the reference case) and POLIMI (CMR modelling and process simulation). For the second step of the LCA analysis (detailed LCA, 8.2 due M42), the step 1 will be repeated, but using the data provided by pilot plant as well as improved modelling data. Emphasis will be set on the process units generating the largest impacts (“hotspots”) and key parameters that identified in the present screening analysis D8.1. The environmental LCA in this project will follow the ISO norms 14040-14044. In addition, a special attention is given to the FC-HyGuide “Guidance document for performing life cycle assessment on fuel cells and hydrogen technologies”. In particular, the life cycle inventory data collection will be based on the questionnaire from the FC-HyGuide, adapted to the specific context of the present project. The results of this project are intended for internal use by BIONICO partners. The study at this stage does not comply with all the ISO 14040 requirements to make competitive public statements or marketing claims. While it is not intended to support such purposes, it provides a foundation for additional work aiming at meeting such purposes. Communication of the results presented in this report outside BIONICO should be conducted with caution and accompanied by a statement that the findings are based on a LCA that doesn’t support public claims.



_________________________________________________________________________________________________________________ PU

1.2. Life cycle assessment approach An LCA is comprised of four phases, as shown in Figure 1:

a) Goal and scope definition: defining the purposes of the study, determining the boundaries of the system life cycle in question and identifying important assumptions that will be made;

b) Inventory analysis: compiling a complete record of the important material and energy flows throughout the life-cycle, in additional to releases of pollutants and other environmental aspects being studied;

c) Impact assessment: using the inventory compiled in the prior stage to create a clear and concise picture of environmental impacts among a limited set of understandable impact categories; and

d) Interpretation: identifying the meaning of the results of the inventory and impact assessment relative to the goals of the study.

LCA is best practiced as an iterative process, where the findings at each stage influence changes and improvements in the others to arrive at a study design that is of adequate quality to meet the defined goals. The principles, framework, requirements and guidelines to perform an LCA are described by the international standards ISO 14040 series (ISO 2006).

Figure 1: Life cycle assessment methodology

This deliverable 8.1 presents the LCA methodology and the scope of the study (chapter 1), the data used and the assumptions (chapter 2), the screening LCA results of the BIONICO CMR technologies compared to conventional technologies (chapter 3), and conclusions and recommendation (chapter 4).



_________________________________________________________________________________________________________________ PU

2. GOAL AND SCOPE 2.1. Objectives The LCA in WP8 will be articulated around i) Task 8.1.1 a screening environmental LCA performed over the first half of the project (M1-M18) and aimed at identifying the main areas of environmental impacts, and ii) Task 8.1.2 a more detailed final environmental LCA (M19-M42) aimed at providing a complete LCA of the developed CMR technology for hydrogen production from biogas. The investigated membranes and overall technology will be compared to reference conventional alternatives in terms of their environmental performance. The present phase (Task 8.1.1) aims to conduct, on lower level of details, the screening environmental LCA to help identify the most probable hotspots in the life cycle and where more in-depth information is necessary, and provide recommendations on the most promising options (e.g. reactor configuration) from an environmental point of view. The screening LCA also aims at preparing the framework for the subsequent final LCA. In particular, it defines i) the goal and scope of the study, ii) the reference conventional technologies, iii) the framework of the data collection process, and iv) the priority environmental indicators to be addressed. Data collection at this stage will rely mostly upon expert judgment from the technical partners as well as data from the literature. The results of the screening environmental will be used as much as possible to guide the design and development strategy towards more environmentally-friendly solutions and provide best-practice recommendations. 2.2. Intended audience The results of this screening LCA are not intended for public disclosure but only destined to the members of the consortium (including the Commission Services). 2.3. Function and functional unit The function of BIONICO system is to provide pure hydrogen. The functional unit quantifies the performance of a product system and is used as a reference unit for which the life cycle assessment study is performed and the results are presented. It is therefore critical that this parameter is clearly defined and measurable. Following the FC-HyGuide, the function unit in this project for all comparing systems is defined below:

1 MJ of hydrogen (NCV= 120 MJ/kg) @13.3 bar, @15 °C @99.9 % purity, at production plant 2.4. System generic description This project studies processes converting biogas into hydrogen through catalytic membrane reactor or conventional reforming. Biogas as feedstock or heat source will come from landfill sites. As stated in D2.1, raw biogas cannot be used directly as raw material in conventional reforming processes mostly due to the presence of hydrogen sulphide that will cause the poisoning and loose of activity of the reforming catalyst, therefore a pre-cleaning biogas stage will be required to remove some contaminants as hydrogen sulphide, water, ammonia, siloxanes, particulates, etc. Subsequently, cleaned biogas will be sent to reactors for hydrogen production. Figure 2.1 below illustrates the generic process of biogas production and conversion through BIONICO process for pure hydrogen production. BIONICO process is an innovative system that integrates H2 production and separation in an intensified single autothermal



_________________________________________________________________________________________________________________ PU

fluidized bed catalytic membrane reactor (CMR). Also, through process intensification of integrating the separation of hydrogen in situ during the reforming reaction, the methane in the biogas will be converted to hydrogen at a much lower temperature compared with a conventional system, due to the shifting effect on the equilibrium conversion.

Figure 2.1 The process of converting biogas to hydrogen through BIONICO process

In order to benchmark the BIONICO technology, two mainstream technologies for the processing of biogas in the context of hydrogen production are defined as reference technologies for comparison, namely, conventional high temperature steam methane reforming (SMR) and autothermal reformer (ATR) where the endothermic reforming reaction is balanced with partial oxidation of the feed biogas. Figure 2.2 below illustrates major differences of the three technology systems.

Figure 2.2 System diagram illustrating the difference between conventional systems and BIONICO CMR system

The main characteristics of the reference and CMR technologies assessed in the present study are presented in Table 2.1



_________________________________________________________________________________________________________________ PU

Table 2.1 General description of the studied hydrogen production systems

Condition and inputs SMR ATR CMR

Type of Process Steam reforming Auto-thermal reforming Catalytic Membrane reactor

Type of reactor Packed bed reactor Packed bed membrane reactor Fluidized bed membrane

reactor

# of WGS 2 2 None

HT-WGS °C / bar 350 / 0.05 350 / 0.05 -

# of temperature swing 4 4 1

Reforming temperature °C 800 800 550

Purification process PSA PSA None

System efficiency (including aux) %LHV

59.2 55.4 70.8

Detailed descriptions available in deliverable

D2.2 D2.2 POLIMI

In this project, a demonstration pilot plant using BIONICO technology will be built in the ENC Landfill plant at Chamusca, Santarém, Portugal. The hydrogen production capacity will be of 100 kg/day. The location of pilot plant and site information is depicted in Figure 2.3 below.

Figure 2.3 Illustration of location of landfill site (Chamusca, Portugal) and biogas cleaning unit

2.5. System boundaries The setting of system boundaries identifies the stages, processes and flows considered in the LCA and should include:

All activities relevant to achieve the present LCA study objectives and therefore necessary to carry out the studied function; and

All the processes and flows that significantly contribute to the potential environmental impacts. This section describes the life cycle stages of the studied systems and determines which processes and flows are included in the LCA, i.e., what is considered to be in the system and is therefore analysed, and what is outside the system boundaries and therefore not included in the assessment. For this project, the LCAs of the three processes cover the life cycle stages from the extraction and processes of raw materials needed for the process (e.g., biogas, electricity, water) to the production of pure hydrogen (direct emissions). Within each stage of the three processes taken into account, the LCA considers all identifiable “upstream” inputs to provide a view as comprehensive as practical for the product system. For example, electricity consumption not only includes the operation of the plant, but



_________________________________________________________________________________________________________________ PU

also upstream processes such as fuel production/extraction, transport to the power plant and maintenance. Thus, the production chains of all inputs are traced back to the original extraction of raw materials. The different stages taken into account for the three systems are presented in Figure 2.4 below.

Figure 2.4 System boundaries for the reference technologies (SMR &ATR) and BIONICO CMR technology

In this study, all product components and production processes have been included in cases where the necessary information is readily available or reasonable estimate can be made. In cases where important information is uncertain, it has been specified and these aspects will be improved during the detailed LCA (Task 8.1.2 and D8.2 due at M42) through a new data collection and updated modelling or adding sensitivity analyses to evaluate the potential significance of the data used. For this phase of project, the system boundaries do not include the distribution and use stage of the product (hydrogen), infrastructures and pre-cleaning of biogases. The reasons are as following:

the product distribution and use stage are equivalent for the compared systems (reference and novel technology);

the infrastructures here refer to the construction and end-of-life of buildings, reactors, and other pieces of equipment used in the process (incl. membranes). The associated impacts are generally distributed over the entire lifetime of the individual elements. It should be noted that, depending on the environmental indicators considered, it is common practice in LCA not to include the contribution of the infrastructure, which is reported to be negligible in a number of situations. Given the scope of the BIONICO project and although it is considered to be less relevant for the purposes of the preliminary LCA, infrastructure will eventually be addressed in the final (more detailed) LCA;

the biogas pretreatment cleaning is considered as out of the scope as stated in the Grant Agreement; however, this might be explored during the detailed LCA (Task 8.1.2, D8.2 due at M42). See further discussion in section 3.5.



_________________________________________________________________________________________________________________ PU

2.6. Life cycle Inventory The life cycle inventory (LCI) is an inventory of input/output data that relates to the functional unit of the system being studied (ISO 14040, 2006). The foreground processes are based on activity data collected from project partners and literature. In particular, in this project, the life cycle inventory data collection followed the questionnaire from the FC-HyGuide, with adaptation to the specific context of the present project (e.g. biogas as a feed). The BIONICO project allows constructing detailed and consistent LCIs for the different studied processes for both reference and CMR process. The foreground data are described in detail in section 3.1 and 3.2. The secondary LCI data describing background processes (e.g., transportation, grave or electricity production) are in large part from the latest ecoinvent database (version 3.3) with adaptations if necessary for this project context (e.g. switching electricity grid to Portugal, etc). Ecoinvent database is consider as a particularly robust and complete database, both in terms of technological and environmental coverage. It surpasses other commercial databases, from quantitative (number of included processes) and qualitative (quality of the validation processes, data completeness, transparency, etc.) perspectives. This database can be used in ISO-compatible LCAs and it is internationally recognized by experts in the LCA field. Also, a special focus was given to landfill life cycle inventory modelling and electricity generation. The model used in this project is based on the Excel tool '13_MSWLFv2.xls' (acronym for 'municipal solid waste landfill') developed by Doka G. (2008) to specifically calculate inventories for waste landfill. Some parameters and input are adapted to generate specific landfill inventory data for this project. Electricity from biogas model is based on the one developed by PSI, with adaptations to fit this project. The quality of LCA results is dependent on the quality of data used in the study. For this reason, the data quality of landfill will be further improved during the detailed LCA (Task 8.1.2, D8.2 due at M42). 2.7. Life cycle impact assessment method The life cycle impact assessment (LCIA) provides the basis for analysing the potential contributions of resource extractions and emissions in a LCI to a number of potential impacts. The impacts are calculated using characterization factors recommended in internationally-recognized impact assessment methods. According to ISO 14044 (2006), LCI flows of materials, energy and emissions into and out of each product system are classified into impact categories by the type of impact their use or release has on the environment. Then, they are characterized into their contribution to an indicator representing the impact category. The category indicator can be located at any intermediate position between the life cycle inventory results and the resulting damage (where the environmental effect occurs) in the cause-and-effect chain. The damage represents changes in environmental quality and a category indicator is a quantifiable representation of this change. In this detailed LCA, the IMPACT 2002+ LCIA method is considered. The IMPACT 2002+ framework links all types of life cycle inventory results via several midpoint categories to five endpoints (damage-oriented) categories (human health, ecosystem quality, climate change, resources, and water withdrawal). It was originally developed at the Swiss Federal Institute of Technology of Lausanne (EPFL), Switzerland. Subsequently, Quantis made some updates to the original IMPACT 2002+ methodology version 2.1. The main difference between IMPACT 2002+ v2.1 and IMPACT 2002+ vQ2.2 (adapted by Quantis) are (i) climate change characterization factors are adapted with global warming potentials for a 100 year time horizon (ii) water withdrawal, water consumption and water turbined are



_________________________________________________________________________________________________________________ PU

added as the midpoint categories, (iii) aquatic acidification, aquatic eutrophication and water turbined are brought to the damage category ecosystem quality, and (iv) normalization factors are updated. This adapted version is referred to as “IMPACT 2002+ version Q2.2 (adapted by Quantis)”. In this project, vQ2.7 is used that further updated climate change impact with IPCC 2013 characterization factors. The detailed life cycle assessment focuses on the five IMPACT 2002+ end-point indicators (described in the table below) over the entire life cycle of the processes.

Table 2.2 IMPACT 2002+ endpoint indicators description.

Indicator Definition

Greenhouse gas emissions

This indicator measures the potential impact on climate change from greenhouse gas emissions associated with a product, process or organization. It takes into account the midpoint category “global warming". The impact metric is expressed in kg CO2-eq.

Resources depletion

This indicator measures the potential impact on resource depletion from resource use (e.g. fossil fuels and minerals) associated with a product, process or organization. It takes into account non-renewable energy and mineral extraction. These factors are simply the sum of the endpoint categories non-renewable energy consumption and mineral extraction. The impact metric is expressed in MJ ("measures the amount of energy extracted plus the amount needed to extract the resource itself").

Water withdrawal

This indicator measures the amount of water withdrawal associated with a product, process or organization. It takes into account water (whether it is evaporated, consumed or released again downstream) excluding turbined water (i.e., water flowing through hydropower generation). It considers drinking water, irrigation water and water for and in industrialized processes (including cooling water), fresh water and sea water. This indicator is actually based and expressed on volumes (m3) of water withdrawal.

Human health

This indicator measures the potential impact on human health caused by emissions associated with a product, process or organization. It takes into account human toxicity (carcinogenic and non-carcinogenic), respiratory inorganics, ionizing radiation, ozone layer depletion and respiratory organics. It characterizes disease severity, accounting for both mortality (years of life lost due to premature death) and morbidity (rate of incidence of a disease). The impact metric is expressed in DALY (“disability-adjusted life years”).

Ecosystem quality

This indicator measures the potential impact on ecosystems (biodiversity, species and their inhabitant) caused by emissions or resource use associated with a product, process or organization. It takes into account aquatic ecotoxicity, terrestrial ecotoxicity, terrestrial acidification & nutrification, aquatic eutrophication, aquatic acidification, water turbined and land occupation. It characterizes the fraction of species disappeared on one m2 surface during one year. The impact metric is expressed in PDF.m².y (“potentially disappeared fraction of species over one m2 and during one year”).

2.8. Calculation tool SimaPro 8.3 software, developed by PRé Consultants (www.pre.nl) was used to assist the LCA modelling and link the reference flows with the LCI database and link the LCI flows to the relevant characterization factors.



_________________________________________________________________________________________________________________ PU

3. APPROACH AND LIFE CYCLE INVENTORY DATA This section presents main data, hypothesis, and allocation approaches related to landfill biogas production, electricity production and conversion of biogas to hydrogen through different technologies, together with the choices of sensitivity analysis of key parameters. Key process conversion data are provided in section 3.1 that includes biogas feed input, energy and water input, emissions and hydrogen production quantity, used as foreground data input of LCA model. Main assumptions and hypothesis are presented in section 3.2 that is used as data to compute foreground data input for LCA model as well as data input to build specific inventories, such as landfill and electricity inventory modelling. In particular, specific landfill LCI model is customized for this project, as detailed in section 3.3. Different ways of allocating landfill impact to biogas production is discussed in 3.4. Modelling and consideration of biogas sources and electricity are presented in section 3.5 and section 3.6, respectively. Choices of sensitivity analysis of key parameters is given in section 3.9. A full list of parameters used for modelling is presented in APPENDIX C – Project parameters used for modelling. Detailed life cycle inventory modelling can be found in APPENDIX E-G, for landfill biogas production, electricity generation and hydrogen generation, respectively. Scenario and parameter input is presented in APPENDIX H. 3.1. Key process data

Key process data of converting biogas to hydrogen for three technologies is given in Table 3.1 below. All data will be normalized into hourly-basis (500 MJ of hydrogen production / hour) in LCA model, and then further normalized into the function unit that is 1MJ of hydrogen @13.3 bar, @15 °C @99.9 % purity.

Table 3.1 Key conversion data of different technologies systems under evaluation*

Conditions and Inputs Unit SMR ATR CMR

Hydrogen prod. capacity kg/day 100 100 100

Fuel type - Biogas from landfill (ENC)

Reforming temperature °C 800 800 550

Hydrogen delivery pressure bar 13.3 13.3 13.3

Total biogas input kW 221 207 162

Biogas feed Nm3/h 35.7+14.6** 47.0 36.8

Electricity kW 6.2 8.1+11.6*** 4.9+4.0+6.3+5.2****

Water g/s 12.6 13.5 6.95

CO2 emissions g/s 21.43 20.03 16

Seconds per hour s/h 3600 3600 3600

Hydrogen production MJ/h 500 500 500

* Detailed data for reference technology are available in deliverable D2.2 , CMR data are preliminary from POLIMI

** 14.6 Nm3/h is used for heating rather than feed stock input *** Electricity for ATR includes biogas compression and air compression **** Electricity for SMR includes biogas compression, air compression, vacuum, pressure from 1.2 to 13.3 bar



_________________________________________________________________________________________________________________ PU

3.2. Main data assumptions

Key property and parameters of waste composition, biogas, methane, hydrogen, and electricity generation efficiency is given in Table 3.2 below.

Table 3.2 Main assumptions related to property parameters *

Parameter units value Sources

Feed & operating conditions

Biogas composition

CH4 44.2 CO2 34 D2.1 N2

% mol 16

O2 2.7 H2 0.0165 CO 0.0006 H2O Saturated

Biogas LHV MJ/kg 12.7 D2.1

Biogas density at STP kg/ Nm3 1.246

Density of CH4 at STP kg/ Nm3 0.0892 calculated

Mass of CH4 per Nm3 of biogas from landfill kg/ Nm3 0.31552 calculated

Mass of CH4 per kg of MSW kg/kg 0.0407

Ecoinvent

'13_MSWLFv2.xls' (acronym for 'municipal solid waste landfill'), which as similar

waste composition and LHV value as what's provided by

BIONICO ENC landfill

% biogenic carbon in degraded waste % 95.63 Same as above

H2 production target kg/day 100 D2.1

H2 purity % 99.99 D2.1

LHV of H2 @13.3 bar, @15 °C @99.9 % purity MJ/kg 120 D2.1

kg of waste required for 1 Nm3 biogas kg/ Nm3 7.7454 calculated

Biogas input for electricity generation Nm3/kWh 0.495 Estimated from ecoinvent

Landfill gas: used for hydrogen production** % 100 own estimate

* Detailed data are available in deliverable D2.2 and via contacting POLIMI

** In default ecoinvent assumption: 17% flared, 21% used for energy, 62% are direct emissions

3.3. Landfill modelling In this project, landfill gas captured from waste landfill is used as feedstock for hydrogen production and also used as heat sources. Given the paramount importance of biogas modelling to the overall quality of LCA results, a robust high-quality environmental LCI modelling of landfilling processes is thus required. The landfill inventory model used in this project is built based on a detailed calculation tool developed by Gabor Doka (2002, 2008) for waste disposal in Municipal Sanitary Waste Landfill (MSWLF.xls). This tool is also used for generating the ecoinvent landfill dataset “Disposal, municipal solid waste, 22.9% water, to sanitary landfill”.



_________________________________________________________________________________________________________________ PU

It takes into consideration of energy and land use of landfill facility and operations, construction of landfill facility, waste water treatment, waste compositions, waste-specific short-term emissions to air via landfill gas incineration if any and landfill leachate, burdens from treatment of short-term leachate (0-100 year) in wastewater treatment plant (including WWTP sludge disposal in municipal incinerator), long-term emissions from landfill to groundwater (after base lining failure). The interface of the model is illustrated in Figure 3.1, and key parameters in this tool are listed below:

Waste composition to estimate biodegradability within 100 years, share of biogenic carbon in waste and landfill gas that would significantly affect climate change impact of direct carbon dioxide emissions from both landfill site and hydrogen production processes

Amount of biogas yield and its composition, mass of CH4 generation per kg of MSW. This will affect the mass of waste required per unit of biogas input

Share of landfill gas that is directly emitted, captured for either flaring, or utilization (generally for electricity and heat production), as discussed in greater details in section 3.5

Amount of electricity and heat that is generated from CHP plant on landfill sites if any

Figure 3.1 Illustration of the landfill modeling tool (Gabor Doka, 2008)



_________________________________________________________________________________________________________________ PU

Key assumptions used in the current modelling input are listed below and also stated in Table 3.2 above.

Inventoried waste: 21% paper; 8% Mixed cardbord; 15% plastics; 3% laminated materials; 2% laminated packaging, e.g. tetra bricks; 3% combined goods e.g. dipers; 3% glass; 2% textiles; 8% minerals; 9% natural products; 22% compostable material; 2.65% inert metals; 1% volatile metals; 0.0065% batteries; 0.34% electronic goods

Waste composition (wet, in ppm): upper heating value 13.27 MJ/kg; lower heating value 11.74 MJ/kg; H2O 228830; O 257060; H 48250; C 334230; S 1119; N 3123.8; P 893.79; B 7.1933; Cl 6866.2; Br 13.552; F 56.358; I 0.0121; Ag 0.714; As 0.62521; Ba 149.04; Cd 11.748; Co 1.3453; Cr 315.21; Cu 1212.8; Hg 1.4424; Mn 259.36; Mo 1.9551; Ni 107.38; Pb 502.43; Sb 22.564; Se 0.31969; Sn 73.44; V 9.2147; Zn 1311.2; Be n.a.; Sc n.a.; Sr n.a.; Ti n.a.; Tl n.a.; W n.a.; Si 48510; Fe 29996; Ca 14062; Al 12420; K 2059.7; Mg 3377.7; Na 5143.9

Overall degradability of waste during 100 years: 18.73%

Share of biogenic carbon in degraded waste: 95.63%

In this project, it’s assumed 100% of biogases are captured for hydrogen production, i.e. no fugitive CH4 or CO2 emissions; however, in the landfill modeling tool above ( Figure 3.1), it’s treated as “100% emitted” to avoid air emissions generation from biogas combustion for flaring or energy generation purpose. The methane or carbon dioxide emissions will be later subtracted from the final life cycle inventory, as they will be used for feedstock input.

For the screening LCA model, the amount of energy demand, independent from waste composition, for landfill operations is based on data taken from ecoinvent report ‘Life Cycle Inventory for residual material landfills’, as cited below in Italic context. For the detailed LCA phase, this will be updated with data provided by our partner ENC if available. During the landfill operation, loaders are used to place the solidified residual material. Unlike for MSW in sanitary landfills, compaction of the waste is less an issue here. An average consumption figure of 0.75 litre diesel per ton of waste (0.027 MJ/kg waste) is inventoried. The 3240 MJ electricity and 96'600 MJ fuel oil per year during the 30 year operation time equate to 0.0002 MJ electricity and 0.006 MJ fuel oil per kilogram waste. The background electricity and heat generation life cycle inventory data are adapted to the specific Portugal context. The detailed full landfill life cycle inventory data can be found in the APPENDIX E – Life cycle inventory of disposal 1 kg of municipal solid waste in sanitary landfill. 3.4. Allocation of landfill impact to biogas production In this project, feedstock for hydrogen production are sourced from landfill gas (biogas) produced from municipal solid waste. For a landfill facility, it provides both waste disposal services and biogas production. Depending on local regulations, landfill gas is either directly emitted or capture for flaring and energy production. Whether or not utilize landfill gas is often considered not having any influence of landfill disposal rates for a given region. Biogas or energy production is clearly not the major goal of landfill, but rather a by-product. Therefore, the full burden of the landfill is allocated to the disposal function of the landfill. This is in accordance with the approach taken by the ecoinvent center. Utilization of landfill biogas is either free of any burden or generating carbon credits by avoiding methane in landfill gas that would be otherwise emitted. On the other hand, allocation according to economic value is also feasible between the functions 'waste disposal' (disposal fee) and the function 'biogas generation' (biogas price) for sensitivity analyses. In this case, landfill processes can be formulated as multi-output process. Both the default and alternative allocation factors based on economic revenues are shown in Table 3.3.



_________________________________________________________________________________________________________________ PU

Table 3.3 Alternative allocation factors for sensitivity analysis (adapted from ecoinvent report No. 13 – part II)

Allocation of product Default allocation factors Suggested alternative allocation factors

based on economic revenues *

Landfill gas 0% 7.3% (4.8–9.8%)

Disposal function 100% 92.7% (90.2%–95.2%)

*Proxy from energy (electricity and heat) price generated from MSW incineration plants per ecoinvent report 13

3.5. Landfill gas sourcing effect In many cases, landfill gas is directly emitted to air without capture. Percentages of landfill gas captured vary significantly depending on regulations and conditions of different regions. The diagram below illustrates how landfill gas is captured for either flare without energy recovery or for electricity production.

Figure 3.2 Illustration of capturing landfill gas for flare or energy generation (source: wakegov.com)

Landfill gas used for hydrogen production can come from different sources, namely 1) those directly emitted as fugitive gases; 2) those captured however flared without energy recovery; 3) those captured for utilization, generally for electricity and heat production. Effect of different biogas sourcing routes are discussed below:

Biogas are taken from those would be otherwise directly emitted to air

In this case, the use of biogas for hydrogen production effectively increase biogas capture rate. In the other words, it will gain credits by avoiding fugitive gas emissions including methane that has 28 times of global warming potentials compared to carbon dioxide from fossil sources.

Biogas are taken from those are captured for flaring

In this case, the use of biogas for hydrogen production does not avoid methane emissions, nor increase biogas capture rate. This is considered as the neutral scenario of using biogas.

Biogas are taken from those are captured for energy generation (electricity or heat)

In this case, the use of biogas for hydrogen production does not avoid methane emissions by increasing biogas capture rate. Instead, it enters direct competitions with electricity generation that could potentially gain environmental credits by replacing grid electricity or other marginal electricity generations such as natural gas or coal-fired power. This would be considered the worst scenario of using biogas. As discussed above, different treatments of biogas sources therefore are critically important for us to understand the environmental burden or benefit of biogas used for hydrogen production.



_________________________________________________________________________________________________________________ PU

Nevertheless, no matter how biogas is used, the basic assumption for all scenarios is that utilization of biogas from landfill does not increase the rate of waste disposed through landfill facilities. It means harvesting biogas from landfill will not change the equilibrium of how wastes are disposed through various ways, such as incineration, composting, recycling or digestions. The reason is that biogas generation from landfill is not the main purpose of landfill facilities. Also, the cost of landfill is also considered much cheaper than incineration. The benefit of harvesting biogas will not change the dynamics between decisions if waste should be sent to landfill or incineration facilities. In the other words, the increase of biogas utilization rate from landfill sites will not divert wastes that would be otherwise sent to other disposal routes, such as incineration sites (with energy recovery possibly). 3.6. Electricity sourcing effect In this project, electricity used for hydrogen production could either come from grid mix or on-site biogas CHP plants if it exists. Use of different sources of electricity could potentially bear significantly different environmental consequences, as it’s discussed above in section 3.4, due to allocation of landfill impact to biogas, electricity from biogas production could either bear almost very low impact or large impact depending on both where biogas come from and how allocation is made. Therefore, it’s important to considered the sourcing effect of electricity used for hydrogen production. When electricity can only be taken from grid or on-site CHP plants, electricity will come from wherever it’s possible, as there would be no other choices. When electricity can either taken from grid or on-site CHP plants, two scenarios are differentiated:

If feed-in tariff or certificate exists for green electricity, such as the electricity generated from landfill gas, electricity price from biogas CHP plants would be higher than electricity purchased from national or local grid mix. In this case, from economic perspective, electricity used for hydrogen production would be taken from grid instead of from on-site CHP plants, because it’s cheaper. This is likely the actual situation in most cases.

If feed-in tariff or certificate do not exist for green electricity, such as the electricity generated from landfill gas, electricity price from biogas CHP plants is considered as the same as those purchased from grid mix. From economic perspective, electricity used for hydrogen production can be either taken from grid or from on-site CHP plants, because it’s the same price.

For electricity taken from grid, the marginal technology is assumed as “grid average” as a simplification. For electricity taken from on-site biogas CHP plants, the consumption of bio-electricity on-site will reduce green electricity that would be otherwise used for other uses, therefore two scenarios are further differentiated:

the reduced amount will be substituted by electricity generated from market average the reduced amount will be substituted by additional bioelectricity generated from the landfill site

For the prior case, market average electricity grid mix should still be used, only the latter case creates benefits of using “green electricity, as they’re additionally produced. The different scenarios of electricity sourcing are incorporated in the LCA modelling in the approach as described in Table 3.4, where Elec_h is electricity used for hydrogen production, and Grid is a binary variable (1= use grid mix; 0= use green electricity generation from landfill gas on-site). Electricity taken from national grid is modelled as “Electricity, low voltage {PT}| market for | Alloc Rec, U” from ecoinvent v3.3 database. On the other hand, electricity from landfill gas is modelled based on the ecoinvent model created by Paul Scherrer Institut (PSI), named as“Electricity, on-site {PT}| heat and power co-generation, biogas, gas engine | Alloc Rec, U BIONICO” in SimaPro.



_________________________________________________________________________________________________________________ PU

Table 3.4 Choice of electricity used for hydrogen production

Items Value Unit

Electricity/heat

Electricity, low voltage {PT}| market for | Alloc Rec, U Elec_h*Grid kWh

Electricity, on-site {PT}| heat and power co-generation, biogas, gas engine | Alloc Rec, U BIONICO, adapted Elec_h*(1-Grid) kWh

A detailed life cycle inventory raw data for electricity generated onsite from landfill gas is given in APPENDIX F – Life cycle inventory of electricity generation from landfill gas. 3.7. Biogas pre-cleaning step

Despite the fact that biogas pre-cleaning is necessary to be suitable as feedstock input, however, as

stated in the Grant Agreement, the cleaning of the biogas is out of the scope of Bionico project and will

not be funded. For the screening phase of study, due to lack of data, the biogas pretreatment cleaning is

considered as out of system boundaries in the LCA analysis; however, this might be included as

sensitivity analysis during the detailed LCA (Task 8.1.2, D8.2 due at M42).

3.8. Substitution of hydrogen production from average European market

Hydrogen produced from landfill gas could potentially substitute hydrogen provided from existing

technologies in European market. The main technologies of generating hydrogen in the market are

through refinery or steam cracking or integrated technologies that has both refinery and steam cracking

as shown in Figure 3.3, where yellow dots represent refineries, red ones represent steam cracker and

the blues represent refineries integrated with steam cracker.

Figure 3.3 Refineries and steam cracker in EU

aWhen data for marginal displacing technologies are not available, market average data will be used.

The life cycle inventory of current European market average of hydrogen production is modelled as

“Hydrogen, liquid {RER}| market for | Alloc Rec, U” from the ecoinvent v3.3.



_________________________________________________________________________________________________________________ PU

3.9. Scenario and sensitivity analysis The parameters, methodological choices and assumptions used when modelling the systems present a certain degree of uncertainty and variability. It is important to evaluate whether the choice of parameters, methods, and assumptions significantly influences the study’s conclusions and to what extent the findings are dependent upon certain sets of conditions. Following the ISO 14044 standard, sensitivity analyses are used to study the influence of the uncertainty and variability of modelling assumptions and data on the results and conclusions, thereby evaluating their robustness and reliability. Sensitivity analyses help in the interpretation phase to understand the uncertainty of results and identify limitations. 3.9.1. Default scenario The default scenario used in is project is given below:

0% landfill impact to allocated to biogas, per standard practice, in accordance with the approach taken in the ecoinvent database

electricity input is additionally produced from on-site CHP plant from biogas burning, assuming that there is no feed-in tariffs or green certificate to make it more attractive to use power from grid in Portugal instead, per communication with ENC, also “additionality” is assumed

96.63% of biogenic carbon in degraded waste per ecoinvent assumption

no substitution effect of hydrogen production from the average European market, as hydrogen produced in this project are directly flared at this moment

Biogas sourcing: those captured that would be otherwise flared, thus no avoided methane emissions, nor competition with CHP energy generation from biogas.

3.9.2. Scenarios for sensitivity analysis Various scenarios are constructed to model sensitivity of key parameters, as listed in table below. Table 3.5 Default scenarios and sensitivity test for three technologies

Parameter Default Value

Sensitivity alternative

Unit Descriptions

alloc_biogas 0 10 % Allocation of landfill impact to biogas

Grid 0 1 - Electricity: 0 = additional electricity from on-

site landfill CHP plants; 1= other situations

C_biogenic 96.63 50 % % of biogenic carbon in degraded waste

CH4_yield_MSW 0.04074 0.08148 kg/kg Mass of methane in biogas per kg of MSW

Sub_hydrogen 0 1 - displacing hydrogen (1=yes, 0=no)

Avoid_CH4 0 1 kg/Nm3 1= avoiding biogas that will be otherwise

directly emitted to air

CHP_comp 0 1 Binary 1= in competition with using biogas for

energy production; 0= no competition

Other potential key factors that are omitted from sensitivity test will be further analysed in Task 8.1.2, such as the definition of functional unit (fixing hydrogen production amount vs fixing biogas input), leachate treat rate in landfill facility, short-term or long-term time horizon, etc. In additional, the best and worst scenarios are also modelled with the following configurations. For the worst scenarios, due to the competition of biogas used by CHP, it’s considered to avoid both electricity



_________________________________________________________________________________________________________________ PU

production impact from biogas and also benefit of displacing grid electricity, which makes it less straightforward how biogenic carbon content in degraded waste will affect the total results, therefore three sub-scenarios are created, for 0%, 50%, 100% biogenic carbon, respectively.

Best scenario: biogas bearing no landfill impact + avoiding methane that would be otherwise emitted+ 100% biogenic carbon in degraded waste+ additional bioelectricity from onsite biogas CHP+ replacing 1 MJ of hydrogen produced from the average market

Worst scenario a): biogas bearing 10% of landfill impact + in competition with CHP energy generation+ 0% biogenic carbon in degraded waste + electricity from national grid+ no substitution of hydrogen produced from the average market

Worst scenario b): biogas bearing 10% of landfill impact + in competition with CHP energy generation+50% biogenic carbon in degraded waste + electricity from national grid+ no substitution of hydrogen produced from the average market

Worst scenario c): biogas bearing 10% of landfill impact + in competition with CHP energy generation+100% biogenic carbon in degraded waste + electricity from national grid+ no substitution of hydrogen produced from the average market

The results of sensitivity analysis can be found in section 4.2. A full list of parameter input used for all scenarios including sensitivity analysis can be found in Appendix H. The life cycle results for both default and sensitivity scenario are presented in section 4 LIFE CYCLE IMPACT RESULTS below.

4. LIFE CYCLE IMPACT RESULTS This section presents first the life cycle impact assessment (LCIA) results for default and sensitivity scenarios. The goal is to identify and understand the most influencing stages or parameters to overall comparative LCA results. 4.1. Default scenario analysis 4.1.1. Overall comparison Figure 4.1 shows the impacts of producing 1 Nm3 of H2 from three technologies based on the input given in section 3.9.1 from the Default scenario, also attached below again for convenience.

0% landfill impact to allocated to biogas, per standard practice, in accordance with the approach taken in the ecoinvent database

electricity input is additionally produced from on-site CHP plant from biogas burning, assuming that there is no feed-in tariffs or green certificate to make it more attractive to use power from grid in Portugal instead, per communication with ENC, also “additionality” is assumed

96.63% of biogenic carbon in degraded waste per ecoinvent assumption

no substitution effect of hydrogen production from the average European market

Biogas sourcing: those captured that would be otherwise flared, thus no avoided methane emissions, nor competition with CHP energy generation from biogas.

Due to the multi-indicator approach, results in the chart are presented in a relative way, normalized to the highest impact of each environmental impact categories among three technologies; however absolute value and also relative value in percentage are available in Table 4.1 for transparency.

Figure 4.1 Life cycle impact results for producing 1 MJ of hydrogen using different technologies

Overall, it appears that the production of 1 MJ of H2 using BIONICO CMR technology will have less greenhouses gas emissions and water withdrawal compared to reference technologies; however it has the highest impact for human health and ecosystem quality, and also very high impact of resource

0%

20%

40%

60%

80%

100%

120%

Environmentalimpacts(%

)

CMR SMR ATR

GREENHOUSEGASEMISSIONS

WATERWITHDRAWAL

RESOURCESDEPLETION

HUMANHEALTH

ECOSYSTEMQUALITY



_________________________________________________________________________________________________________________ PU

consumption compared to the SMR technology (also see detailed data with color coding in Table 4.1 below). Table 4.1 Life cycle impact results of three technologies under default scenario *

Scenario Technology Climate change

Human health

Ecosystem quality

Resources Freshwater withdrawal

Unit kg CO2-eq DALY PDF.m2.y MJ m3

Baseline SMR 0.0073 4.37E-10 5.59E-05 0.0016 1.13E-04

Baseline ATR 0.0081 1.31E-09 1.56E-04 0.0039 1.27E-04

Baseline CMR 0.0069 1.33E-09 1.56E-04 0.0037 7.08E-05

Percentage normalized to the highest value per impact category

Baseline SMR 90% 33% 36% 42% 89%

Baseline ATR 100% 98% 100% 100% 100%

Baseline CMR 85% 100% 100% 95% 56%

*Green colors stand for the best for a chosen impact category, whereas a red color the worst.

The reason for this will be discussed in section 4.1.2 that decompose contributions of impact from different input or outputs (such as biogas, electricity input, etc. )

4.1.2. Detailed results- contribution analysis Figure 4.2 presents the detailed contributions of the different inputs and outputs to various environmental impacts of BIONICO CMR process. Overall, climate change impact is dominated by direct emissions, whereas human health, ecosystems quality, and resources are dominated by electricity generation. The reason that CMR technology under default scenario only proves advantage on GHG emissions, as shown in Figure 4.1, is due to the fact that although CMR technology has least carbon dioxide emissions from biogas conversion, but it uses the highest amount of electricity consumption that dominates most impact from human health to resource depletion.

Figure 4.2 Contribution analysis of using CMR technology to generate 1 MJ of hydrogen under default scenario

GREENHOUSEGASEMISSIONS

WATERWITHDRAWAL

RESOURCESDEPLETION

HUMANHEALTH

ECOSYSTEMQUALITY



_________________________________________________________________________________________________________________ PU

For climate change impact, it shows more than 70% of climate change impact come from direct emissions of carbon dioxide during hydrogen production process. The rest 30% are associated with air emissions from burning landfill gas for electricity generation. These values might change depending on the biogenic carbon % from degraded waste. In the default scenario, it’s assumed to be 95.63%. For human health impact, 65% of impact come from air pollutants emitted from electricity generation processes and 35% from infrastructure manufacturing as shown in the Figure 4.2 below. Notable pollutants are sulfur dioxide, nitrogen oxides, PM 2.5, as shown in the APPENDIX I LCIA results spreadsheet, tab: “HumanHealth_Subs”.

Figure 4.3 Process contributions to human health impact from electricity generation from landfill gas

For Ecosystem quality, it is again dominated by electricity generation. Figure 4.4 below shows majority of impact come from infrastructure and equipment. Main pollutants are nitrogen oxides, aluminum, sulfur dioxide emitted to air, as shown in APPENDIX I LCIA results spreadsheet, tab: “Ecosystems_Subs”.

Figure 4.4 Process contributions to Ecosystem quality impact from electricity generation from landfill gas



_________________________________________________________________________________________________________________ PU

For Resources, electricity again dominates the total impact. Similar to ecosystem quality, majority impact come from infrastructure and equipment as shown in Figure 4.5. Main elementary flow contributors are Coal, hard; Oil, crude; Gas, natural/m3; Uranium; Coal, brown.

Figure 4.5 Process contributions to Resouces impact from electricity generation from landfill gas

For water withdrawal impact, it mainly comes from process water input as shown in Figure 4.6 below.

Figure 4.6 Process contributions to water withdrawal impact from hydrogen generation from landfill gas



_________________________________________________________________________________________________________________ PU

Based on data given in Table 4.2 - Table 4.4, the same pattern also holds for SMR and ATR systems. Table 4.2 Life cycle impact results of different components of CMR under default scenario

Impact category 1. Biogas production 2. Water 3. Electricity

4.Direct emissions

Climate change [kg CO2-eq] 0 0.0000 0.0018 0.0050

Human health [DALY] 0 2.18E-11 1.31E-09 0

Ecosystem quality [PDF.m2.y] 0 5.65E-06 1.50E-04 0

Resources [MJ] 0 0.0003 0.0034 0

Freshwater withdrawal [m3] 0 0.0001 0.0000 0

Table 4.3 Life cycle impact results of different components of SMR under default scenario


4.Direct emissions




Resources [MJ] 0 0.0006 0.0010 0


Table 4.4 Life cycle impact results of different components of ATR under default scenario


4.Direct emissions




Resources [MJ] 0 0.0006 0.0033 0


As shown above, a significant trade-off exists between emitting less CO2/ less biogas or using less electricity. The section below will put this trade-off together with energy efficiency into environmental LCA perspective. 4.1.3. Energy efficiency and trade-off between biogas and electricity consumption in LCA As shown in Table 2.1, CMR achieves the highest system efficiency (including aux) number, 70.8, measured as %LHV, compared to SMR and ATR, 59.2 and 55.4, respectively. The system efficiency number for different systems are calculated based on the ratios between hydrogen energy output and total system energy input that mainly includes energy embodied in electricity and landfill gas. Although CMR has clear advantage in terms of total primary energy efficiency by using least total combined electricity and biogas primary energy, however, as shown in Figure 4.7, it might not have the most favourable portfolio of energy combination. Compared to conventional steam membrane reforming processes, CMR uses 27% less biogas energy, however, biogas is considered to have little or no environmental burdens; meanwhile it uses 229% more electricity that has much higher environmental burdens associated with combustion emissions, infrastructure and fuel input. This environmental quality difference is demonstrated in Table 4.5 and Table 4.6 considering different allocation factors for biogas.



_________________________________________________________________________________________________________________ PU

Figure 4.7 Biogas and electricity input per hour production of hydrogen

Table 4.5 Impact per kWh of energy when 0% of landfill impact is allocated to biogas

Damage category Unit Biogas [kWh]

Electricity, from biogas

[kWh]

Electricity, from grid

[kWh]

Climate change kg CO2-eq 0 0.045207718 0.56303641

Human health DALY 0 3.21E-08 4.26E-07

Ecosystem quality PDF.m2.y 0 0.003682918 0.31495877

Resources MJ 0 0.083151831 7.3581873

Freshwater withdrawal m3 0 0.000250882 0.041176175

Table 4.6 Impact per kWh of energy when 10% of landfill impact is allocated to biogas

Damage category Unit Biogas [kWh]

Electricity, from biogas

[kWh]

Electricity, from grid

[kWh]

Climate change kg CO2-eq 0.00960 0.06613 0.56304

Human health DALY 2.60E-08 8.87E-08 4.26E-07

Ecosystem quality PDF.m2.y 0.24136 0.52968 0.31496

Resources MJ 0.05925 0.21227 7.35819

Freshwater withdrawal m3 0.00015 0.00058 0.04118



_________________________________________________________________________________________________________________ PU

Also, as shown in Table 4.5, when no impact of landfill impact is allocated to biogas production, electricity generated on-site from landfill gas is much more favorable than electricity taken from grid; however, when 10% of landfill impact is allocated to biogas production, albeit most impact categories are still considerably more favourable than grid electricity mix, however, ecosystem quality becomes much worse than that from grid mix as presented in the Table 4.6. This is due to copper in waste input from long-term leachate (>100a) of landfill site. Thus, sources of electricity matter significantly. To summarize, this section has discussed the importance of considering the difference and trade off of environmental property of different energy sources, i.e., biogas vs electricity; the significant difference of different electricity generation technologies; the influence of allocation factor between waste disposal and biogas, and also the influence of time-horizon (100 year short term or long-term perspective). The section 4.2 below will explore further the influence of key factors on comparative conclusions. 4.2. Sensitivity analysis As presented in section 3.9.2, the following sensitivity test are conducted to understand the magnitude of importance of their influence to total LCA results.

10% Landfill impact allocated to biogas production Substitute hydrogen production from European market average conditions Half fossil carbon in degraded waste Double biogas Yield Electricity taken from national grid Electricity taken from national grid+ Substitute hydrogen production Avoid biogas emissions Best scenario: biogas bearing no landfill impact + avoiding methane that would be otherwise

emitted+ 100% biogenic carbon in degraded waste+ additional bioelectricity from onsite biogas CHP+ replacing 1 MJ of hydrogen produced from the average market

Worst scenario a): biogas bearing 10% of landfill impact + in competition with CHP energy generation+ 0% biogenic carbon in degraded waste + electricity from national grid+ no substitution of hydrogen produced from the average market

Worst scenario b): biogas bearing 10% of landfill impact + in competition with CHP energy generation+50% biogenic carbon in degraded waste + electricity from national grid+ no substitution of hydrogen produced from the average market

Worst scenario c): biogas bearing 10% of landfill impact + in competition with CHP energy generation+100% biogenic carbon in degraded waste + electricity from national grid+ no substitution of hydrogen produced from the average market

Detailed scenario and parameter input are listed in APPENDIX H – Scenario and parameter inputTable 4.7 lists detailed impact results for all scenarios. Figure 4.8 gives an overview of the importance and influence of parameters or choices. Figure 4.9 further illustrates the influence on climate change indicator from various factors. Overall, in most cases, CMR still performs best for climate change indicator with the following exceptions:

when national grid is used, in this case, SMR will be the best as it uses least electricity

when landfill gas emissions are avoided, in this case, SMR will be the best as it uses the most biogas as feedstock input or heat source

In additional, specific observations related to each scenario are presented below:

Allocating the impact of landfill to biogas mainly influence the impact on ecosystem qualities, because copper is emitted through long-term leachate processes of landfill facilities (> 100 years). It leads to more burdens to ATR and SMR, but less for CMR



_________________________________________________________________________________________________________________ PU

Substituting hydrogen from average European market can generate large resources benefit, moderate human health benefit, but has relatively less significant impact on climate change or ecosystems quality. It’s considered equivalent for all technologies when function unit is fixed as the same amount of hydrogen production.

It leads to significantly higher GHG burdens when carbon in degraded waste input are not biogenic; The slope of increase is lesser for CMR, as it uses less biogas as feedstock input.

Double the yield of biogas from waste (e.g. via food waste or manure) has negligible impact when impact from landfill is not allocated to landfill gas and when carbon from degraded waste are mainly biogenic; however, it could be influential when allocation or carbon properties change.

The use of national grid mix will largely increase the footprint for ATR and CMR, but lesser extent for SMR, as it has the least electricity consumption

Avoiding biogas emissions would provide the largest benefit to climate change impact, though it barely has any influence on environmental impacts. In this case, the most beneficial technology is SMR, as it consumes most biogas.

For the best scenario, SMR will be the best choice for almost all indicators, due to the fact that it avoids the most fugitive biogas emissions and consume the least amount of electricity

For the worst scenario, CMR will be the best, mainly due to it displaces the least of bio-energy that would be otherwise produced through CHP plant, as it uses least biogas. Bio-energy through CHP plant from landfill is considered to be more environmentally friendly than using landfill gas for hydrogen production when bioelectricity can substitute grid mix and hydrogen produced from landfill gas cannot substitute hydrogen from the average market. It’s also found in the worst scenario, counter-intuitively, greener carbon leads to worse impact, as this means bioenergy generated from CHP biogas plant has lesser environmental burdens. It makes bioenergy production more environmentally competitive than using biogas for hydrogen production.

Figure 4.8 Influence of chosen scenarios to comparative impact results



_________________________________________________________________________________________________________________ PU

Figure 4.9 Greenhouses gas impact for different scenarios

Table 4.7 Sensitivity results *

Scenario Sys Climate change

Human health

Ecosystem quality

Resources Freshwater withdrawal

kg CO2-eq DALY PDF.m2.y MJ m3

Baseline SMR 0.007 4.37E-10 0.000 0.002 1.13E-04

Baseline ATR 0.008 1.31E-09 0.000 0.004 1.27E-04

Baseline CMR 0.007 1.33E-09 0.000 0.004 7.08E-05

10% Landfill impact SMR 0.012 1.27E-08 0.114 0.029 1.83E-04

10% Landfill impact ATR 0.013 1.43E-08 0.121 0.034 2.02E-04

10% Landfill impact CMR 0.011 1.21E-08 0.100 0.028 1.32E-04

Sub H2 SMR -0.011 -7.17E-09 -0.002 -0.662 -1.19E-03

Sub H2 ATR -0.011 -6.31E-09 -0.001 -0.660 -1.17E-03

Sub H2 CMR -0.012 -6.28E-09 -0.001 -0.660 -1.23E-03

Half fossil carbon SMR 0.082 4.37E-10 0.000 0.002 1.13E-04



_________________________________________________________________________________________________________________ PU

Half fossil carbon ATR 0.088 1.31E-09 0.000 0.004 1.27E-04

Half fossil carbon CMR 0.074 1.33E-09 0.000 0.004 7.08E-05

Double biogas Yield SMR 0.007 4.37E-10 0.000 0.002 1.13E-04

Double biogas Yield ATR 0.007 1.31E-09 0.000 0.004 1.27E-04

Double biogas Yield CMR 0.006 1.33E-09 0.000 0.004 7.08E-05

National grid SMR 0.014 5.32E-09 0.004 0.092 6.20E-04

National grid ATR 0.029 1.68E-08 0.012 0.291 1.74E-03

National grid CMR 0.028 1.74E-08 0.013 0.301 1.74E-03

National grid+Sub H2 SMR -0.005 -2.29E-09 0.002 -0.572 -6.79E-04

National grid+Sub H2 ATR 0.010 9.22E-09 0.011 -0.373 4.40E-04

National grid+Sub H2 CMR 0.009 9.79E-09 0.011 -0.363 4.41E-04

Avoid biogas emissions SMR -0.965 4.37E-10 0.000 0.002 1.13E-04

Avoid biogas emissions ATR -0.900 1.31E-09 0.000 0.004 1.27E-04

Avoid biogas emissions CMR -0.704 1.33E-09 0.000 0.004 7.08E-05

Best Scenario SMR -0.987 -7.17E-09 -0.002 -0.662 -1.19E-03

Best Scenario ATR -0.923 -6.31E-09 -0.001 -0.660 -1.17E-03

Best Scenario CMR -0.727 -6.28E-09 -0.001 -0.660 -1.23E-03

Worst, 100% fossil carbon SMR 0.117 8.54E-08 0.067 1.571 8.94E-03

Worst, 100% fossil carbon ATR 0.125 9.17E-08 0.072 1.673 9.51E-03

Worst, 100% fossil carbon CMR 0.106 7.60E-08 0.059 1.383 7.83E-03







* green color stands for the best for a given combination of scenario setting and impact indicator , whereas red the worst.



_________________________________________________________________________________________________________________ PU

5. CONCLUSIONS AND RECOMMENDATIONS 5.1. Key findings Overall, it shows that higher system energy efficiency of technologies is not necessarily translated into better environmental performance, due to large difference in environmental impacts of feedstock or energy types and their sourcing variations. Also, climate change impact indicator is a poor proxy to represent all impact categories. CMR technology can be either better or worse than alternatives, depending on specific situations considered and chosen indicators. The CMR technology can have a lower impact on climate change:

i) when biogas is taken away from those otherwise would be flared, also electricity comes from additional generation from biogas; the less CO2 emitted directly from H2 conversion is better;

ii) when biogas is taken away from those otherwise would be used for bioelectricity production, resulting in marginal carbon-intensive electricity generated to satisfy energy demand; the less biogas input is better;

iii) when part of impact from landfill is allocated to biogas that dominates the life cycle GHG impact; the less biogas input is better.

On the other hand, the CMR technology may have a higher impact on climate change:

when fugitive biogas is additionally captured for H2 production, resulting into large carbon credits by avoiding methane emissions. Counter-intuitively, the more biogas input the better; and

ii) when biogas is taken away from those otherwise would be flared and electricity comes from carbon-intensive grid mix; as biogas bears no impact, the more electricity consumption is worse.

5.2. Key issues for further explorations Through sensitivity analysis, key issues are identified below for further explorations in the detailed LCA.

Landfill modelling: o energy use onsite o leachate rate (short-term and long-term) o landfill gas capture situation o bioenergy generation o wastewater treatment o price effect of electricity price: it affects decision of using grid mix or internal bio-electricity

Waste composition: o biodegradability within 100 years o fossil carbon content from degraded waste o yield of biogas from waste

Time horizon: o 100 years vs more than 100 years for assessing landfill impact. In this screening LCA

study, it affects ecosystem quality significantly through long-term copper leachate.

Accounting the biogas impact or benefit: o How to handle allocation of landfill impact to biogas o How to treat avoiding biogas emissions o How to treat competition from bioenergy production

Electricity demand: marginal generation from national mix, or on-site electricity. Geography, timing, technology, fuel input all play important roles for electricity environmental impact.

Choice of function unit: fixed biogas amount vs. fixed hydrogen amount. This will affect resources biogas impact/credits and product substitution impact or credits.



_________________________________________________________________________________________________________________ PU

5.3. Limitations and outlook The LCA results presented are limited to the predefined scenarios, just preliminary based on BIONICO CMR concepts that will be further updated. Also, the choice of 1 MJ of H2 as function unit will be further discussed within the consortium and might be changed later. As discussed in detail below, other key limitations include omission of infrastructure and biogas pre-cleaning, which will be improved in the second phase of the project (D8.2 the detailed LCA).

The infrastructures (including reactor, membrane and catalysts) are not taken into account. Adding those infrastructures could increase comparative results, given the fact that feedstock’s impact can be negligible in this project. This will require additional data collection and will be included in the detailed LCA (D8.2).

The environmental impacts of landfill were calculated based on data from literature and model, plant data should be collected when that’s possible.

Bio-gas cleaning was excluded as being considered as out of scope.

The data used for the BIONICO CMR technologies is based on preliminary modelling results that will be further updated. The CMR technologies pilot prototypes testing will enable to refine those models and have more robust data on the different processes and the effects of the CMR technologies.

This study is based on available primary data combined with generic data from a preliminary literature research, existing commercial databases or best estimates.

When this study is communicated to stakeholders, the magnitude and nature of the limitations should be communicated at the same time. The second phase of this project that will also explore the trade-off among techno-economic and environmental aspects to guide the design of CMR concepts under different scenarios.



_________________________________________________________________________________________________________________ PU

6. REFERENCES [1] Doka G. (2009) Life Cycle Inventories of Waste Treatment Services. ecoinvent report No. 13.

Swiss Centre for Life Cycle Inventories, Dübendorf, 2009 [2] FCH JU, Guidance Document for performing LCAs on Fuel Cells and H2 Technologies (2011). [3] FCH-JU, Study on hydrogen from renewable resources in the EU. Final report., (2015). [4] Humbert S, Margni M, Jolliet O (2010). IMPACT 2002+ User guide: draft for version 2.2. Quantis, Lausanne, Switzerland. Available at www.impactmodeling.org [5] ISO 14044, 2006. Environmental management – life cycle assessment – principles and

framework. International Standard Organization, Geneva, Switzerland

[6] PFC Energy; Available at http://www.fuellingeuropesfuture.eu/es

http://www.impactmodeling.org/



_________________________________________________________________________________________________________________ PU

APPENDIX A – ACRONYMS AND ABBREVIATIONS ATR Autothermal reforming

C2H4 Ethylene (also referred to as ethene)

CH4 Methane

CHP Combined heat and power

CMR Catalytic membrane reactor

CO Carbon monoxide

CO2 Carbon dioxide

GHG Greenhouse gas

GJ Gigajoule

H2 Hydrogen

H2O water

ISO International Organization for Standardization

JRC-IES Joint Research Centre – Institute for Environment and Sustainability

LCA Life cycle assessment

LCI Life cycle inventory

LCIA Life cycle impact assessment

MJ Megajoule

N2 Nitrogen (gas)

n/a Not applicable

NG Natural gas

Nm3 Normal cubic metre

NOx Nitrogen oxides

O2 Oxygen (gas)

OCM Oxidative coupling of methane

PM Particulate matter

PSA Pressure swing adsorption

SMR Steam methane reforming

SOx Sulphur oxides

t CO2-eq Tonne carbon dioxide equivalent

WGS Water gas shift

WGS FB Water gas shift fluidized bed

WGS PB Water gas shift packed bed

WP Work Package



_________________________________________________________________________________________________________________ PU

APPENDIX B – Layout of BIONICO and reference technologies Figure 0.1 shows the layout of the BIONICO system, an innovative system for hydrogen production from biogas that integrates H2 production and separation in an intensified single autothermal fluidized bed catalytic membrane reactor (CMR). In particular, by integrating the separation of hydrogen in situ during the reforming reaction, the methane in the biogas will be converted to hydrogen at a much lower temperature compared with a conventional system, due to the shifting effect on the equilibrium conversion.

Figure 0.1 Layout of the BIONICO system

The layout for the conventional system based on high temperature steam methane reforming is shown in Figure 0.2. The scheme shows the hydrogen production and purification processes as follows: in the first step a mixture of compressed BG and steam is fed to the steam methane reforming reactor (SMR), where the reactions take place (R.1)- (R.2).

CH4 + H2O CO + 3 H2 ΔH298 K0 = 206 kJ/mol (R.1)

CO + H2O CO2 + H2 ΔH298 K0 = -41 kJ/mol (R.2)

This endothermic reaction is favoured by high temperatures, and therefore in a typical situation the system would be designed to operate within 800 to 900 °C. After a first cooling step (HX-4), the syngas is sent to water gas shift reactors (WGS) to promote the CO conversion in CO2, increasing hydrogen

P-1

H2O

Air+H2O

CMPBG

Retentate

Exhaust

waterrec.

H2

CMPAir

AirATR

ATR-MR

Sep

Burner

HX-4HX-2

Airbrn

HX-0

HX-1

Vacuum

Pump

Sep

PURE

HYDROGEN

BIOGAS

H2Ofeed



_________________________________________________________________________________________________________________ PU

production through the moderately exothermic reaction (R.2). Two WGSRs at different temperature (350°C and 200°C) are considered in order to push the CO conversion in H2 as much as possible. The resulting stream consists of a H2-rich syngas diluted with inert gases such as CO2, steam and N2. After the water removal, a pure H2 flow is recovered by a pressure swing adsorption system. The heat required by the endothermic reaction at the SMR is supplied by the combustion of the off-gas from the PSA and an additional amount of biogas (BGbrn). The heat exchange within the SMR reactor is implemented as counter-current with a minimum pinch-point of about 100°C. The feed water is pumped by P-1 and is evaporated through HX-1, HX-2 by the reformate gas and trough HX-3 by the flue gas and finally superheated in HX-4 by the reformate gas at the outlet of the SMR. The process water, recovered from the condensation in all the three separators downstream the cooling of the reformate and flue gas, is recycled back into the system.

Figure 0.2: Layout of the SMR reference case

Figure 0.3 shows the layout of the second reference case with ATR. The main difference with the previous case relies on the reforming reactor for biogas-to-hydrogen conversion. The stream at the inlet of the reactor is a mixture of compressed BG, steam and air. in this case, the heat required by the endothermic reforming reaction is balanced by the partial oxidation of the feed fuel with air.

CH4 + 2 O2 CO2 + 2 H2O ΔH298 K0 = -802 kJ/mol (R.3)

The intake of air is below stoichiometry, and regulated to control the temperature in the reactor. The compressed air is mixed with the steam before entering the reactor. Thanks to the partial oxidation, there

Water

P-1

SMR

(800 °C)

HT-WGS

LT-WGS

HX-2

HX-4

(SH)

CMPBG

BGfeed

BGbrn

AIRbrn

Exhaust

SEP-1

H2

HX-3

(EV)

H2Ofeed

Burner

HX-1

PSA

CMPH2

SEP-2

Flue gas

Reformate

Offgas

1

3

4

5 8

2

6

7



_________________________________________________________________________________________________________________ PU

is no need to supply an additional amount of biogas to the burner: in the ATR configuration, the burner is fed only with the off-gas from the PSA.

Figure 0.3: Layout of the ATR reference case

APPENDIX C – Project parameters used for modelling

Input parameters Value /formula Comment

hydrogen 1 1= 100% of biogas from landfill are captured for hydrogen production

alloc_biogas 0 see report D8.1

CH4_yield_MSW 0.040735965 kg CH4/ kg of MSW

Mass_CH4_Biogas 0.315517088 kg of CH4 per 1 Nm3 of biogas; calcuated from biogas composition

Biogas_kWh 0.494813535 Nm3 of biogas needed for producing 1 kWh of electricity, according to LHV value

C_MSW 0.054605024 kg/kg waste

CH4_C_inc 0.000025219 g compound per g element

CO2_C_inc 3.665706983 g compound per g element

C_biogenic 0.9563 % biogenic carbon in degraded waste, default=0.9563

Water

P-1

ATR

(800 °C)

SEP-2

HT-WGS

LT-WGS

HX-2

HX-4

(SH)

HX-1

CMPBG

BGfeed

AIRbrn

Exhaust

PSA

SEP-1

H2

CMPAIR

HX-3

(EV)

Burner

AIRfeed

H2Ofeed

CMPH2

Flue gas

Reformate Offgas

1

4

5

69

2

7

8

3



_________________________________________________________________________________________________________________ PU

LHV_H2 120 MJ/kg

Hours_day 24 24 hours per day

Biogas_SMR_h 50.3 Nm3/h, data from D2.2 &POLIMI

Biogas_ATR_h 47 Nm3/h, data from D2.2 &POLIMI

Biogas_CMR_h 36.8 Nm3/h, data from D2.2 &POLIMI

Elec_SMR_h 6.2 kWh/h, data from D2.2 &POLIMI

Elec_ATR_h 19.7 kWh/h, data from D2.2 &POLIMI

Elec_CMR_h 20.4 kWh/h, data from D2.2 &POLIMI

WAT_SMR_s 12.6 g/s, data from D2.2 &POLIMI

WAT_ATR_s 13.5 g/s, data from D2.2 &POLIMI

WAT_CMR_s 6.95 g/s, data from D2.2 &POLIMI

CO2_SMR_s 21.43 g/s, data from D2.2 &POLIMI

CO2_ATR_s 20.03 g/s, data from D2.2 &POLIMI

CO2_CMR_s 16 g/s, data from D2.2 &POLIMI

SMR 0 Binary parameter, 1=chosen for analysis

ATR 0 Binary parameter, 1=chosen for analysis

CMR 1 Binary parameter, 1=chosen for analysis

Sub_hydrogen 0 1=consider substituting market hydrogen

Grid 0 1=national grid; 0= electricity from biogas

Avoid_CH4 0 1= avoiding fugitive methane emissions

CHP_comp 0 1=competition with biogas CHP energy

Calculated parameters

Value /formula Comment

CH4_biogenic_MSW C_MSW*CH4_C_inc*C_biogenic

Methane emissions, biogenic

CH4_fossil_MSW C_MSW*CH4_C_inc*(1-C_biogenic)

Methane emissions, fossil

CO2_biogenic_MSW C_MSW*CO2_C_inc*C_biogenic

Carbon emissions, biogenic

CO2_fossil_MSW C_MSW*CO2_C_inc*(1-C_biogenic)

Carbon emissions, fossil

Biogas_h Biogas_SMR_h*SMR+Biogas_ATR_h*ATR+Biogas_CMR_h*CMR

Biogas input per hour

Elec_h Elec_SMR_h*SMR+Elec_ATR_h*ATR+Elec_CMR_h*CMR

Electricity input per hour

WAT_h (WAT_SMR_s*SMR+WAT_ATR_s*ATR+WAT_CMR_s*CMR)*3600/1000

Water input per hour

CO2_h (CO2_SMR_s*SMR+CO2_ATR_s*ATR+CO2_CMR_s*CMR)*3600/1000

CO2 emissions per hour



_________________________________________________________________________________________________________________ PU

APPENDIX D – Links and differences in membrane development among FCH-JU projects

Project ReforCELL FERRET FluidCELL BIONICO

Start / End of the project

01-02-2012 / 31-12- 2015

01-04-2014 / 31-03- 2017

01-04-2014 / 31-03- 2017

01-09-2015 /

Membrane development achieved

31-May-2014 31-July-2015

30-September-2015

Month 12

Ceramic support

Zirconia on Alumina 100 nm pore size (OD 10 mm)

YSZ or YSZ-

Alumina/alumina <

100 nm pore size (OD 10mm)

Alumina < 100

nm pore size (OD 10mm)

YSZ or YSZ- Alumina/alumin

a < 100 nm

pore size (OD 10mm)

Metallic support Interface

Hastelloy or Nickel YSZ

Hastelloy or Nickel YSZ, YSZ-Alumina

SS 316L Alumina

Hastelloy

X YSZ, YSZ-

Alumina

Selective layer Pd Pd-Ag Pd-Ag Pd-Ag-Au Pd-Ag

Pd-Ag Pd-Ag-Au

Thickness of selective layer

4 um 4um 2um

4um 2um;<1um

4um 1um

Preparation techniques

Electroless plating

Electroless plating

Pore-filling Direct

PVD-Magnetron Sputtering

Electroless plating Pore-

filling Direct

PVD-Magnetron Sputtering

Electroless plating (main

option) Or best

of previous projects

Length of selective layer (project target)

< 8 cm (ELP,PVD)

15 cm (ELP,PVD) >15 cm (ELP,PVD)

25-30 cm (ELP, PVD)

Selective layer Length

22-23 cm ELP 15 cm ELP 20 cm ELP

-

Process scale up (maximum number of membranes per batch)

No (6 PVD; 1 ELP)

Yes (60 PVD; 1 ELP) Yes (13 PVD, 1 ELP)

Yes (60 PVD, 6 ELP)

Total number of membranes for pilot- scale (target length)

30 30 21

100

Sealing

Swagelok+graphite gasket (leaks at 600oC). Sealing is being improved.

Swagelok+graphite gasket (leaks at 600oC). Sealing is being improved.

Swagelok+graphite gasket (suitable for the operating conditions)

Swagelok+graphite gasket+silver protection;-Ceramic- metallic join



_________________________________________________________________________________________________________________ PU

APPENDIX E – Life cycle inventory of disposal 1 kg of municipal solid waste in sanitary landfill This presents the life cycle inventory of landfill based on Garbor’s model. The default assumption for parameter “hydrogen=100%” , which assumes 100% of methane and CO2 generated from landfill are captured for hydrogen production. Accordingly, combustion emissions (PM2.5, NO2, etc) related to biogas for energy production are corrected.

Materials/fuels input Sub-compartment Quantity Unit

Sanitary landfill facility {RoW}| construction | Alloc Rec, U 5.56E-10 p

Sodium hydroxide, without water, in 50% solution state {GLO}| market for | Alloc Rec, U 0.000000132 kg

Aluminium sulfate, powder {GLO}| market for | Alloc Rec, U 0.0000174 kg

Quicklime, milled, packed {GLO}| market for | Alloc Rec, U 2.39E-08 kg

Chemical, organic {GLO}| market for | Alloc Rec, U 0.00000022 kg

Sewer grid, 5E9l/year, 110 km {RoW}| market for sewer grid, 5E9l/year, 110 km | Alloc Rec, U 5.45E-10 km

Municipal waste incineration facility {RoW}| market for municipal waste incineration facility | Alloc Rec, U 5.8E-13 p

Cement, unspecified {RoW}| market for cement, unspecified | Alloc Rec, U 0.0000366 kg

Process-specific burdens, municipal waste incineration {RoW}| market for process-specific burdens, municipal waste incineration | Alloc Rec, U 0.00232 kg

Chemical, inorganic {GLO}| market for chemicals, inorganic | Alloc Rec, U 6.71E-09 kg

Slag landfill {RoW}| market for slag landfill | Alloc Rec, U 1.34E-12 p

Residual material landfill {RoW}| market for residual material landfill | Alloc Rec, U 1.91E-13 p

Process-specific burden, sanitary landfill {PT}| processing | Alloc Rec, U BIONICO 1 kg

Ammonia, liquid {RoW}| market for | Alloc Rec, U 0.00000222 kg

Process-specific burdens, slag landfill {RoW}| market for process-specific burdens, slag landfill | Alloc Rec, U 0.000753 kg

Titanium dioxide {RoW}| market for | Alloc Rec, U 6.35E-08 kg

Process-specific burdens, residual material landfill {RoW}| market for process-specific burdens, residual material landfill | Alloc Rec, U 0.0000915 kg

Iron (III) chloride, without water, in 40% solution state {GLO}| market for | Alloc Rec, U 0.0000881 kg

Wastewater treatment facility, capacity 5E9l/year {RoW}| market for wastewater treatment facility, capacity 5E9l/year | Alloc Rec, U 1.42E-11 p

Hydrochloric acid, without water, in 30% solution state {RoW}| market for | Alloc Rec, U 4.03E-09 kg

Iron sulfate {GLO}| market for | Alloc Rec, U 0.0000643 kg



_________________________________________________________________________________________________________________ PU

Chromium oxide, flakes {GLO}| market for | Alloc Rec, U 1.29E-09 kg

Electricity/heat

Heat, district or industrial, natural gas {PT}| heat and power co-generation, natural gas, combined cycle power plant, 400MW electrical | Alloc Rec, U 0.00094224 MJ

Electricity, high voltage {PT}| market for | Alloc Rec, U 0.000333 kWh

Heat, district or industrial, other than natural gas {PT}| heat, from municipal waste incineration to generic market for heat district or industrial, other than natural gas | Alloc Rec, U 0.00194 MJ

Heat, central or small-scale, other than natural gas {RoW}| market for | Alloc Rec, U 0.00054238 MJ

Electricity, low voltage {PT}| market for | Alloc Rec, U 0.00864 kWh

Waste to treatment

Waste plastic, mixture {RoW}| market for waste plastic, mixture | Alloc Rec, U 0.0000388 kg

Waste graphical paper {RoW}| market for waste graphical paper | Alloc Rec, U 0.0000388 kg

Waste cement, hydrated {RoW}| market for waste cement, hydrated | Alloc Rec, U 0.0000915 kg

Emissions to air

Cyanide high. pop. 3.95E-08 kg

Copper low. pop. 8.32E-11 kg

Cobalt high. pop. 1.82E-15 kg

Carbon dioxide, biogenic high. pop. 0.0045 kg

Calcium low. pop. 7.84E-08 kg

Carbon monoxide, fossil high. pop. 0.000000145 kg

Nitrogen oxides low. pop. 0.000000407 kg

Magnesium high. pop. 5.52E-08 kg

Manganese high. pop. 1.59E-14 kg

Iron high. pop. 2.31E-09 kg

Hydrogen chloride low. pop. 0.0000179 kg

Cadmium low. pop. 9.3E-10 kg

Manganese low. pop. 1.49E-09 kg

Tin high. pop. 1.05E-11 kg

Phosphorus high. pop. 8.6E-09 kg

Nickel low. pop. 9.6E-11 kg

Methane, fossil high. pop. 4.03E-7*(1-hydrogen) kg

Aluminium low. pop. 0.000000024 kg

Molybdenum low. pop. 1.41E-11 kg

NMVOC, non-methane volatile organic compounds, unspecified origin low. pop. 0.000000151 kg

Lead high. pop. 5.58E-12 kg

Carbon monoxide, fossil low. pop. 3.49E-7*(1-hydrogen) kg



_________________________________________________________________________________________________________________ PU

NMVOC, non-methane volatile organic compounds, unspecified origin high. pop.

4.18E-8*(1-hydrogen) kg

Barium high. pop. 1.03E-08 kg

Lead low. pop. 5.62E-11 kg

Carbon dioxide, fossil high. pop. 0.000205*(1-hydrogen) kg

Antimony high. pop. 6.36E-12 kg

Tin low. pop. 3.41E-12 kg

Carbon dioxide, biogenic low. pop. 0.135*(1-hydrogen) kg

Calcium high. pop. 5.13E-08 kg

Zinc high. pop. 2.59E-11 kg

Methane, biogenic low. pop. 0.0206*(1-hydrogen) kg

Methane, fossil low. pop. 0.000944 kg

Chromium low. pop. 8.43E-11 kg

Carbon monoxide, biogenic low. pop. 0.00000763 kg

Nickel high. pop. 6.52E-15 kg

Hydrogen fluoride low. pop. 0.00000582 kg

Arsenic low. pop. 4.21E-10 kg

Arsenic high. pop. 8.46E-12 kg

Silicon low. pop. 5.19E-08 kg

Chromium high. pop. 1.23E-14 kg

Aluminium high. pop. 0.000000145 kg

Mercury low. pop. 1.2E-09 kg

Sulfur dioxide low. pop. 0.0000274 kg

Zinc low. pop. 5.09E-10 kg

Ammonia high. pop. 0.00000141 kg

Selenium low. pop. 1.52E-12 kg

Magnesium low. pop. 0.000000102 kg

Boron high. pop. 0.000000425 kg

Iron low. pop. 7.77E-09 kg

Carbon monoxide, biogenic high. pop. 0.00000317 kg

Methane, biogenic high. pop. 0.00000881 kg

Cadmium high. pop. 3.81E-12 kg

Antimony low. pop. 3.23E-11 kg

Molybdenum high. pop. 5.55E-11 kg

Particulates, < 2.5 um low. pop. 2.68E-6*(1-hydrogen) kg

Bromine low. pop. 2.37E-08 kg

Cobalt low. pop. 2.9E-11 kg

Nitrogen oxides high. pop. 1.37E-5*(1-hydrogen) kg

Carbon dioxide, fossil low. pop. 0.00615 kg

Copper high. pop. 1.59E-12 kg

Vanadium high. pop. 8.99E-12 kg

Silicon high. pop. 0.000000454 kg

Dinitrogen monoxide high. pop. 0.00000388 kg

Mercury high. pop. 5.07E-15 kg

Barium low. pop. 2.74E-09 kg



_________________________________________________________________________________________________________________ PU

Vanadium low. pop. 4.57E-11 kg

Iodine low. pop. 1.15E-10 kg

Sodium low. pop. 0.000000263 kg

Potassium low. pop. 0.000000107 kg

Emissions to water

Vanadium groundwater, long-term 0.00000265 kg

Sodium river 0.00105 kg

Tin groundwater, long-term 0.0000734 kg

Bromine river 0.00000169 kg

Iodide groundwater, long-term 3.76E-09 kg

Boron groundwater, long-term 0.00000234 kg

TOC, Total Organic Carbon river 0.000261 kg

Bromine groundwater, long-term 0.0000118 kg

Nickel groundwater, long-term 0.000107 kg

Selenium river 4.07E-09 kg

Ammonium, ion groundwater, long-term 0.000631 kg

Nitrogen river 0.0000207 kg

Nitrate river 0.00278 kg

Lead groundwater, long-term 0.000502 kg

Fluoride groundwater, long-term 0.0000498 kg

Molybdenum groundwater, long-term 0.000000773 kg

Aluminium river 0.00000576 kg

Silver groundwater, long-term 0.000000714 kg

Nickel river 0.000000233 kg

Arsenic groundwater, long-term 0.000000598 kg

Hydrogen sulfide groundwater, long-term 0.0000748 kg

Cadmium groundwater, long-term 0.0000116 kg

Chromium river 5.29E-10 kg

Potassium groundwater, long-term 0.00163 kg

Nitrite groundwater, long-term 0.0000344 kg

COD, Chemical Oxygen Demand groundwater, long-term 0.0779 kg

Phosphate groundwater, long-term 0.0000193 kg

Magnesium river 0.000369 kg

Chloride groundwater, long-term 0.00561 kg

Nitrogen, organic bound groundwater, long-term 0.00103 kg

Sulfate river 0.000294 kg

BOD5, Biological Oxygen Demand groundwater, long-term 0.0185 kg

Tin river 5.71E-09 kg

Mercury river 9.48E-10 kg

Zinc river 0.000000709 kg

Copper river 7.55E-08 kg

BOD5, Biological Oxygen Demand river 0.000325 kg

Manganese groundwater, long-term 0.00025 kg

Boron river 0.0000042 kg

Magnesium groundwater, long-term 0.00301 kg

Mercury groundwater, long-term 0.00000144 kg

Iron groundwater, long-term 0.00213 kg



_________________________________________________________________________________________________________________ PU

Iodide river 8.23E-09 kg

Manganese river 0.00000302 kg

TOC, Total Organic Carbon groundwater, long-term 0.0713 kg

Nitrite river 0.000016 kg

Chromium VI river 0.000000176 kg

Cobalt groundwater, long-term 0.00000128 kg

Nitrate groundwater, long-term 0.000068 kg

Vanadium river 9.27E-08 kg

Sodium groundwater, long-term 0.00409 kg

Cobalt river 0.000000059 kg

Molybdenum river 3.33E-08 kg

Cadmium river 7.08E-08 kg

Lead river 1.85E-08 kg

DOC, Dissolved Organic Carbon groundwater, long-term 0.0713 kg

Potassium river 0.000428 kg

COD, Chemical Oxygen Demand river 0.00103 kg

Fluoride river 0.00000107 kg

Antimony river 8.76E-08 kg

Ammonium, ion river 0.000763 kg

Silicon groundwater, long-term 0.000941 kg

Copper groundwater, long-term 0.00121 kg

Phosphate river 0.0000146 kg

Silicon river 0.0000125 kg

Sulfate groundwater, long-term 0.00287 kg

Barium groundwater, long-term 0.000147 kg

Zinc groundwater, long-term 0.00108 kg

Chromium VI groundwater, long-term 0.00000144 kg

Aluminium groundwater, long-term 0.0124 kg

Antimony groundwater, long-term 0.00000229 kg

Calcium river 0.000283 kg

Chloride river 0.0013 kg

Arsenic river 2.65E-08 kg

Calcium groundwater, long-term 0.0138 kg

Iron river 0.0000158 kg

Barium river 0.00000066 kg

Selenium groundwater, long-term 9.04E-08 kg

DOC, Dissolved Organic Carbon river 0.000251 kg



_________________________________________________________________________________________________________________ PU

APPENDIX F – Life cycle inventory of electricity generation from landfill gas The biogas electricity generation model is based on the model created by Paul Scherrer Institut (PSI). Some key parameters are adapted based on Garbor’s landfill model and specific biogas composition in this project. A detailed life cycle inventory raw data is given in the table below.

Materials/fuels input Quantity Uni

t Comments

Heat and power co-generation unit, 160kW electrical, common components for heat+electricity {GLO}| market for | Alloc Rec, U 3.91368E-08 p

value for cogeneration of natural gas used as approximination

Heat and power co-generation unit, 160kW electrical, components for heat only {GLO}| market for | Alloc Rec, U 3.91368E-08 p

Heat and power co-generation unit, 160kW electrical, components for electricity only {GLO}| market for | Alloc Rec, U 3.91368E-08 p

Biogas production of 1 Nm3 from landfill of MSW {PT} | Recycled content U, BIONICO Biogas_kWh m3

adapted according to specific LHV value

Lubricating oil {GLO}| market for | Alloc Rec, U 0.000234819 kg value for cogen of natural gas used as approximation

Waste to treatment

Waste mineral oil {Europe without Switzerland}| market for waste mineral oil | Alloc Rec, U 0.000234819 kg

value for cogeneration of natural gas used as approximation

Emissions to air

NMVOC, non-methane volatile organic compounds, unspecified origin 1.56547E-05 kg

value for cogeneration of natural gas used as approximination

Sulfur dioxide 0.000195682 kg

own calculations based on sulphur content in biogas (assumption: 300mg/m3)

Nitrogen oxides 0.000117409 kg

value for cogeneration of natural gas used as approximination.

Dinitrogen monoxide 1.95684E-05 kg

Carbon dioxide, biogenic

CO2_biogenic_MSW*Mass_CH4_Biogas/CH4_yield_MSW*Biogas_kWh kg

own calculations based on carbon content in biogas. See Tab. 13.10, which results in CO2 emissions of 0.0835 kg/MJin

Carbon dioxide, fossil

CO2_fossil_MSW*Mass_CH4_Biogas/CH4_yield_MSW*Biogas_kWh kg

Methane, biogenic

CH4_biogenic_MSW*Mass_CH4_Biogas/CH4_yield_MSW*Biogas_kWh kg

Methane, fossil

CH4_fossil_MSW*Mass_CH4_Biogas/CH4_yield_MSW*Biogas_kWh kg

Carbon monoxide, biogenic 0.000375713 kg

Platinum 5.47915E-11 kg



_________________________________________________________________________________________________________________ PU

APPENDIX G – Life cycle inventory of hydrogen production The final life cycle inventory model for hydrogen production is constructed as below that includes biogas input, water input ,electricity consumption, air emissions as well as avoided hydrogen production from European average market. Parameters are used to assess different scenarios and sensitivity of data input and value choices.

Items Value Unit

Products

Producing 100 kg of hydrogen BIONICO per day, i.e.500 MJ/ hour 100*LHV_H2/24 MJ

Displacing products

1 kg of hydrogen from EU market average 100/24*Sub_Hydrogen kg

1 Nm3 of fugitive biogas emission Avoid_CH4*biogas_h m3

Electricity generation displaced by 1 Nm3 of biogas CHP_comp*biogas_h m3

Electricity generation impact from 1 Nm3 of biogas CHP_comp*biogas_h m3

Materials/fuels

Biogas production of 1 Nm3 from landfill of MSW {PT} | Recycled content U, BIONICO Biogas_h m3

Tap water {Europe without Switzerland}| market for | Alloc Rec, U WAT_h kg

Electricity/heat

Electricity, low voltage {PT}| market for | Alloc Rec, U Elec_h*Grid kWh

Electricity, on-site {PT}| heat and power co-generation, biogas, gas engine | Alloc Rec, U BIONICO, adapted Elec_h*(1-Grid) kWh

Emissions to air

Carbon dioxide, fossil CO2_h*(1-C_biogenic) kg

Carbon dioxide, biogenic CO2_h*C_biogenic kg

APPENDIX H – Scenario and parameter input

Scenarios alloc_ CH4_yield

C_biogenic Grid Sub_H2 Avoid CHP

SMR ATR CMR biogas _MSW _CH4 _comp

SMR ,Baseline 0 0.04074 0.9563 0 0 0 0 1 0 0

ATR ,Baseline 0 0.04074 0.9563 0 0 0 0 0 1 0

CMR ,Baseline 0 0.04074 0.9563 0 0 0 0 0 0 1

SMR ,10% Landfill impact

0.1 0.04074 0.9563 0 0 0 0 1 0 0

ATR ,10% Landfill impact

0.1 0.04074 0.9563 0 0 0 0 0 1 0

CMR ,10% Landfill impact

0.1 0.04074 0.9563 0 0 0 0 0 0 1

SMR ,Sub H2 0 0.04074 0.9563 0 1 0 0 1 0 0

ATR ,Sub H2 0 0.04074 0.9563 0 1 0 0 0 1 0

CMR ,Sub H2 0 0.04074 0.9563 0 1 0 0 0 0 1

SMR ,Half fossil carbon

0 0.04074 0.5 0 0 0 0 1 0 0

ATR ,Half fossil 0 0.04074 0.5 0 0 0 0 0 1 0



_________________________________________________________________________________________________________________ PU

carbon

CMR ,Half fossil carbon

0 0.04074 0.5 0 0 0 0 0 0 1

SMR ,Double biogas Yield

0 0.081472 0.9563 0 0 0 0 1 0 0

ATR ,Double biogas Yield

0 0.081472 0.9563 0 0 0 0 0 1 0

CMR ,Double biogas Yield

0 0.081472 0.9563 0 0 0 0 0 0 1

SMR ,National grid

0 0.04074 0.9563 1 0 0 0 1 0 0

ATR ,National grid 0 0.04074 0.9563 1 0 0 0 0 1 0

CMR ,National grid

0 0.04074 0.9563 1 0 0 0 0 0 1

SMR ,grid, Sub H2 0 0.04074 0.9563 1 1 0 0 1 0 0

ATR ,grid, Sub H2 0 0.04074 0.9563 1 1 0 0 0 1 0

CMR ,grid, Sub H2 0 0.04074 0.9563 1 1 0 0 0 0 1

SMR, Avoid CH4e 0 0.04074 0.9563 0 0 1 0 1 0 0

ATR, Avoid CH4e 0 0.04074 0.9563 0 0 1 0 0 1 0

CMR, Avoid CH4e 0 0.04074 0.9563 0 0 1 0 0 0 1

SMR ,Best Scenario

0 0.081472 1 0 1 1 0 1 0 0

ATR ,Best Scenario

0 0.081472 1 0 1 1 0 0 1 0

CMR ,Best Scenario

0 0.081472 1 0 1 1 0 0 0 1

SMR , Worst scenario a)

0.1 0.04074 0 1 0 0 1 1 0 0

ATR , Worst scenario a)

0.1 0.04074 0 1 0 0 1 0 1 0

CMR , Worst scenario a)

0.1 0.04074 0 1 0 0 1 0 0 1

SMR , Worst scenario b)

0.1 0.04074 0.5 1 0 0 1 1 0 0

ATR , Worst scenario b)

0.1 0.04074 0.5 1 0 0 1 0 1 0

CMR , Worst scenario b)

0.1 0.04074 0.5 1 0 0 1 0 0 1

SMR , Worst scenario c)

0.1 0.04074 1 1 0 0 1 1 0 0

ATR , Worst scenario c)

0.1 0.04074 1 1 0 0 1 0 1 0

CMR , Worst scenario c)

0.1 0.04074 1 1 0 0 1 0 0 1

APPENDIX I – detailed LCIA results Detailed life cycle impact results are available in the spreadsheet “BIONICO-WP8-D81-DLR-QUANTIS-20170302-v04_LCIAresults.xlsx”.

Documents

D8.1 Preliminary environmental LCA of the …...2017/03/02 · WP8 – LCA and safety analysis D8.1 Preliminary environmental LCA of the developed technology Topic: FCH-02.2-2014