Deliverable 1.2. Report on Region Specific Data Models · 2020. 9. 30. · Deliverable 1.2. Report on Region Specific Data Models Page 8 of 35 4. Deliverable Aims The aim of this

This project has received funding from the Bio Based Industries Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 792221.

Deliverable 1.2. Report on Region Specific Data Models

Acronym: ICT-BIOCHAIN

Project title: ICT-BIOCHAIN - ICT Tools in Efficient Biomass Supply Chains for

Sustainable Chemical Production

Contract Nº: 792221

Start date: 1.6.2018

Duration: 24 months

Deliverable number D1.2

Deliverable title Report on Region Specific Data Models

Submission due date M18 – November 2019

Actual submission date 29/11/2019

Work Package WP1

WP Lead Beneficiary VTT

Dissemination Level Public

Version 00

Authors Catriona Clark (STR), Johan Belfrage (STR),

Filippo Giancarlo Martinelli (IBF), Sol Cuenca

Ref. Ares(2019)7364696 - 29/11/2019

This project has received funding from the Bio Based Industries Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 792221.

(CAGPDS), Natividad Pérez (CAGPDS), Antonio

Giráldez (CAGPDS), Timo Erler (IML), Jacek Flak

(VTT), Jennifer Attard (ITT), James Gaffey (ITT)

Marta Macías Aragonés (CTA)

Deliverable 1.2. Report on Region Specific Data Models Page 3 of 35

DOCUMENT CONTROL PAGE

Author(s)

Catriona Clark (STR), Johan Belfrage (STR), Filippo Giancarlo Martinelli (IBF), Sol Cuenca (CAGPDS), Natividad Pérez (CAGPDS), Antonio Giráldez (CAGPDS), Timo Erler (IML), Jacek Flak (VTT), Jennifer Attard (ITT), James Gaffey (ITT) Marta Macías Aragonés (CTA)

Version history

# Reviewer Comments

00 CC Document creation


1. Executive Summary

This report details the methodology used to create Bioresource Mapping Tools for the two demonstrator regions of South East Ireland and Andalusia and will provide examples of the output that can be extracted from the model. This was a multi-step process as follows:

Determine the biomass available at a county/provincial level for three selected

value chains for each region.

Characterise the biomass composition into useful biorefinery products e.g.

protein, fat, carbohydrate, C5, C6 sugars.

Determine the accessibility of biomass considering its location and current

uses.

The models highlight the main sources of feedstock in the two regions and the bioresources that can be derived. The work to determine the biomass available at a county/provincial level for three selected material categories for each region has been successfully completed and can form part of the digital innovation hubs for each region and the data can also be further integrated into the platform where users can search selected parts of the model. Data of the composition for the defined material categories and streams from each region was further collected to allow the modelling of bioresource availability per county or province while also considering current uses. For the materials in the Andalusian region data on C5 sugars and C6 sugars was not available and it was also not possible to determine some of the prices of the mapped materials in this region. Both regions have clearly identifiable geographical areas where material streams and bioresources are clustered and thus provide potential suitable locations for future biorefining operations. The county of Cork stands out as a specifically strong area and hold almost one third of the total materials arising on Ireland and is predominantly from manure. In Andalusia, Almería, Jaén and Córdoba stands out as particularly strong provinces. Almería is dominated by horticulture resources and Jaén and Córdoba hold the majority of olive derived material streams. Algae based materials are only located in Cádiz and Huelva. This data was captured into a model that allows for the calculation and interpretation of data in a graphical and geographical form. This model will later be used as the input for the ICT-BIOCHAIN Platform, where it will be analysed alongside the collection of ICT, IoT and Industry 4.0 technologies.


Content 1. Executive Summary ................................................................................................... 4

2. Acronyms and abbreviations ..................................................................................... 6

3. Introduction............................................................................................................... 7

4. Deliverable Aims........................................................................................................ 8

5. Methodology ............................................................................................................. 8

5.1. Quantifying Materials Arising ............................................................................ 9

5.2. Bioresource Content ........................................................................................ 16

5.3. Quantifying Accessibility of Material ............................................................... 18

5.4. Modelling ......................................................................................................... 18

6. Results and Opportunities ....................................................................................... 19

6.1. Irish Region ...................................................................................................... 20

6.1.1. Bioresource ................................................................................................... 22

6.2. Andalusian Region ........................................................................................... 30

6.3. Region Comparison .......................................................................................... 36

7. Conclusions.............................................................................................................. 37


2. Acronyms and abbreviations

GIS Geographic Information System

CSO Central Statistics Office

IFA Irish Farmers Association

DAFM Department of Agriculture, Food and the Marine

COFORD Council for Forest Research and Development


3. Introduction

The circular economy is an ambitious vision for a smarter way of managing resources through new business models, product redesign and new supply chain collaborations. The concept has developed against a backdrop of climate change and increasing resource pressure and therefore focuses on addressing both environmental and economic goals. It helps to secure businesses against risks (such as resource scarcity) and ultimately will help make supply chains more sustainable and competitive, whilst demonstrating businesses’ social responsibility, and minimising the environmental impact of products and services. The circular bioeconomy can also offer new business growth opportunities for organisations willing to innovate. Therefore, the adoption of a new innovative circular economy business model in Europe provides a very attractive opportunity to businesses to both become more competitive and to protect themselves against future increases to resource prices. Concentration of economic activity in a few, highly networked sectors lowers the cost of collaboration and increases the viability of cross sectoral projects and resource use. One example of how these cross sectoral projects can help maximise resource use and material productivity is that of biorefining. Biorefining offers the chance to extract as much value as possible from (usually biological) material streams via the production of chemical compounds that can be recycled directly back into industry. These compounds include materials such as proteins, carbohydrates, C5 and C6 sugars and lignin, all of which are valuable resources to feed back into industrial production loops. Advocates of biorefining make the point that turning biotic materials into energy by digestion can be inferior to reusing or recycling them in biorefinery processes.

One potential feedstock within a biorefinery is food waste. Biorefining offer a way of valorising food waste and therefore increasing the economic value of the waste material, allowing it to go from negative (a financial liability to the producer) to positive (a source of revenue for the producer).

The value chains of the two demonstrator regions contained within this report (South-East Ireland and Andalusia) differ greatly however they can be modelled using the same methodology to easily identify similarities, gaps and interlinking opportunities. By mapping key value chains within each region, this identifies waste, by-products and agricultural residues that are, or could be, available as feedstock for a biorefining processing. The bioresource mapping tools developed as part of this deliverable will therefore identify the scale of the opportunity for the bioeconomy sector in the two demonstrator regions by quantifying and mapping bioresource arisings to understand the scale and shape of potential markets.


4. Deliverable Aims

The aim of this project deliverable is to meet a defined need of the industry, namely to identify the material arisings and characterising them for their biorefining potential. Key aims of the deliverable are therefore:

To identify key data sources on the arisings of materials streams valuable to the

biorefining industry – three value chains were selected for each region.

To map the material arisings that can be quantified regionally across the two demonstrator regions of South East Ireland and Andalusia, to enable assessment at the level of county/province of where key materials arise

Utilising the known fate of these arisings, to estimate the quantity of available arisings for industry when current market conditions are considered

To generate indicative figures of available bioresource arisings regionally across the two demonstrator regions.

To deliver the above analysis, a core output for the deliverable is a dynamic model (the bioresource mapping tool for each region) that will enable operators to quickly assess the viability of facilities looking to extract and exploit a given bioresource, providing clear visualisation of the density of both material arisings required as a feedstock, as well as the bioresource content that might be available for them to pursue.

5. Methodology

In order to generate meaningful estimates to allow assessment of the potential for biorefining in Ireland and Andalusia, a phased methodology was adopted to data collection, material characterisation, modelling of data and interpretation of outputs. This methodology was designed with the full expectation that data gaps would be encountered, and therefore that flexibility would be required to arrive at a usable model based on the best currently available data. Therefore, a key aspect throughout the methodology was a pragmatic approach to project delivery, identifying weaknesses in the data where they existed, recommending how these could be improved going forwards, but resolving to produce indicative outputs for industry use. All local data from the two regions was collated by the respective local partner: ITT for the Irish region and CAGPDS (former CAPDER) for the Andalusian region. The manipulation of the data into the Bioresource Mapping Model was conduction by a sub-contractor, Ricardo, through STR. The methodology developed was a four-step process:

Material arising: material arising from the selected three value chains within

each region were quantified with geographical breakdown to county/provincial

level


Characterisation (bioresource content): composition of the

bioresource was identified (dry matter, calorific value, protein, fat,

carbohydrate etc.)

Accessibility: temporal availability (transport) and existing uses and economic

restraints were assessed

Modelling: the data was incorporated into Bioresource Mapping Tool for each

region to easily asses the results.

The three value chains assessed within the two regions are as follows: Table 1: Selected value chains for model demonstrator regions

South-East Ireland Lignocellulose Andalusia Olive and Olive Oil

waste

Horticulture Vegetable waste

Manure Algae

The data was limited to three value chains to allow the models to act as proof of concept for each region with the opportunity to expand the models with further value chain in the future. For each value chain the three levels of data (materials arising, bioresource content and accessibility constraints) were collected. This data was then manipulated in the model as shown in Figure 1 to provide an output of the bioresource availability.

Figure 1: Simple overview of model calculation

5.1. Quantifying Materials Arising

In order to build the bioresource mapping tool for each region a large amount of data has been collated from various sources. The specification of the type of information required was based on the Bioresource Mapping Tool previously developed for the Scottish Region and owned by project partner IBioIC (STR). The cumulative total of these material arisings is over 30 million tonnes for the Irish Region and 4.8 million tonnes for the Andalusian region of potential feedstocks for bio–refining which was mapped within the dynamic tool. Table 2 and Table 3 provides a breakdown of the sources per value chain for the Irish and Andalusian region materials arising respectively.


Table 2: Source of Data for Irish Region Materials Arisings

Value Chain Biomass Arising Source of Data Year of Data Collection

Lignocellulose Winter wheat straw CSO 2010

Summer wheat straw CSO 2010

Winter barley straw CSO 2010

Summer barley straw CSO 2010

Winter oat straw CSO 2010

Summer oat straw CSO 2010

Winter oil seed rape CSO 2010

Summer oil seed rape CSO 2010

Spruce COFORD 2016

Broadleaf COFORD 2016

Other conifers COFORD 2016

Lodgepole pine COFORD 2016

Horticulture Spent mushroom compost IFA 2019

Mushroom offcuts IFA 2019

Manure Pig manure

DAFM, Agrocycle

2017

Poultry manure CSO, Agrocycle 2017

Dairy cattle CSO, Agrocycle 2017


Table 3: Source of Data for Andalusian Region Materials Arisings

Value Chain

Biomass Arising

Source of Data Year of Data Collection

Olive and Olive Oil Waste

Wood “Potencial energético de la biomasa residual agrícola y ganadera en Andalucía. Secretaría General de Agricultura, Ganadería y Desarrollo Rural. 2008”. Link: https://juntadeandalucia.es/export/drupaljda/Potencial%20energetico%20de%20la%20biomasa.pdf

2008

Leaves and branches

“Potencial energético de la biomasa residual agrícola y ganadera en Andalucía. Secretaría General de Agricultura, Ganadería y Desarrollo Rural. 2008”. Link: https://juntadeandalucia.es/export/drupaljda/Potencial%20energetico%20de%20la%20biomasa.pdf

2008

Hojín

“Evaluación de la producción y usos de los subproductos de las agroindustrias del olivar en Andalucía. Servicio de Estudios y Estadísticas, de la Consejería de Agricultura, Pesca y Desarrollo Rural. 2015”. Link: https://www.juntadeandalucia.es/agriculturaypesca/observatorio/servlet/FrontController?ec=default&action=DownloadS&table=11030&element=1585171&field=DOCUMENTO

2015

Pit


2015

Orujillo


2015

https://juntadeandalucia.es/export/drupaljda/Potencial%20energetico%20de%20la%20biomasa.pdf




https://www.juntadeandalucia.es/agriculturaypesca/observatorio/servlet/FrontController?ec=default&action=DownloadS&table=11030&element=1585171&field=DOCUMENTO







Value Chain

Biomass Arising


Vegetable Waste

Greenhouse Tomato Vegetal Waste

“Balance entre los restos vegetales generados por la horticultura intensiva andaluza y la capacidad de gestión de las plantas existentes”. Plan específico de gestión de restos vegetales de cultivos de invernadero. Departamento de Prospectiva. Agencia de Gestión Agraria y Pesquera de Andalucía. Publication pending from 2017.

2017

Greenhouse Pepper Vegetal Waste


2017

Greenhouse Zucchini Vegetal Waste


2017

Greenhouse Cucumber Vegetal Waste


2017

Greenhouse Eggplant Vegetal Waste


2017

Strawberry Vegetal Waste

“Anuarios de Estadísticas Agrarias y Pesqueras de Andalucía de la CAGPDS de 2014 a 2017” and “Potencial energético de la biomasa residual agrícola y ganadera en Andalucía. Secretaría General de Agricultura, Ganadería y Desarrollo Rural. 2008”. Link: https://juntadeandalucia.es/export/drupaljda/Potencial%20energetico%20de%20la%20biomasa.pdf

2017




Value Chain

Biomass Arising


Industry Tomato Vegetal Waste

“El sector del tomate de industria en Andalucía. Características y evolución del cultivo. Industria asociada” (Consejería de Agricultura y Pesca. Secretaría General de Agricultura y Ganadería. Departamento de Prospectiva de la Agencia de Gestión Agraria y Pesquera de Andalucía. Febrero 2007).

2007

Central Tomato Fruit Discard


2017

Central Pepper Fruit Discard


2017

Central Zucchini Fuit Discard


2017

Central Cucumber Fruit Discard


2017

Central Eggplant Fruit Discard


2017

Strawberry Fruit Discard

“Anuarios de Estadísticas Agrarias y Pesqueras de Andalucía de la CAGPDS de 2014 a 2017” and “Potencial energético de la biomasa residual agrícola y ganadera en Andalucía. Secretaría

2017


Value Chain

Biomass Arising


General de Agricultura, Ganadería y Desarrollo Rural. 2008”. Link: https://juntadeandalucia.es/export/drupaljda/Potencial%20energetico%20de%20la%20biomasa.pdf

Industry Tomato Fruit Discard


2007

Industry Tomato Processing Waste


2007

Algae

Microalgae nep.

“AGAPA. 2019. La acuicultura marina en Andalucía 2018. Agencia de Gestión Agraria y Pesquera de Andalucía. Consejería de Agricultura, Ganadería, Pesca y Desarrollo Sostenible. Sevilla España. 56 pp.” https://juntadeandalucia.es/export/drupaljda/20190612%20Acuicultura%20Marina%20Andaluc%C3%ADa%202018.pdf

2018

Micro Isochrysis galbana


2018

Micro Nannochloropsis gaditana

“AGAPA. 2019. La acuicultura marina en Andalucía 2018. Agencia de Gestión Agraria y Pesquera de Andalucía. Consejería de Agricultura, Ganadería, Pesca y Desarrollo Sostenible. Sevilla España. 56 pp.”

2018



https://juntadeandalucia.es/export/drupaljda/20190612%20Acuicultura%20Marina%20Andaluc%C3%ADa%202018.pdf





Value Chain

Biomass Arising



Micro Tetraselmis chuii


2018

Macro Gracilariopsis spp.


2018

Macro Ulva lactuca


2018










5.2. Bioresource Content

The model allows for key biochemical components to be reviewed. The team decided on 24 key components that would by collated to define each biomass arising including Protein, Fat and Carbohydrate including C5 and C6 sugars. The source of this data was varied with data from existing literature sources including United State Department of Agriculture, Energy Research Centre of the Netherlands, Oleícola el Tejar and Teagasc and data calculated for the project by Cellignis. Table 4: Source of Data for Irish Region Bioresource Content

Value Chain Biomass Arising Source of Data

Lignocellulose Winter wheat ECN, Teagasc, Celignis

Spring wheat ECN, Teagasc, Celignis

Winter barley ECN, Teagasc, Feedipedia, Celignis

Spring barley ECN, Teagasc, Feedipedia, Celignis

Winter oat ECN, Feedipedia, Celignis

Spring oat ECN, Feedipedia, Celignis

Rapeseed ECN, Teagasc, Feedipedia, Celignis

Sitka Spruce ECN, Celignis,

Lodgepole Pine ECN, Celignis,

Broadleaf ECN, Celignis,

Other conifers ECN

Manure Pig ECN, Celignis,

Chicken ECN, Dibanet, Feedipedia

Dairy cattle ECN, Feedpedia, Celignis

Horticulture Spent Compost Teagasc, Celignis, published references

Offcuts USDA, published references

Table 5: Data for Andalusian Region Bioresource Content


Olive Hojin El Tejar

Alperujo El Tejar

Pit El Tejar

Orujillo El Tejar

Prunning El Tejar

Algae Microalgae nep. Phyllis


Phyllis


Phyllis


Phyllis




El.Din, Soad & Din, El & Hassan, Shimaa. (2018). The Promotive effect of different concentrations of marine algae on Spinach Plants (Spinacia oleracea L.).

Macro Ulva lactuca

El.Din, Soad & Din, El & Hassan, Shimaa. (2018). The Promotive effect of different concentrations of marine algae on Spinach Plants (Spinacia oleracea L.).

Horticulture Greenhouse Tomato Vegetal Waste

Phyllis


Phyllis


Phyllis


Phyllis


Phyllis


Phyllis


Phyllis


USDA

Central Pepper Fruit Discard

USDA


USDA

Central Cucumber Fruit Discard

USDA

Central Eggplant Fruit Discard

USDA


USDA

Industry Tomato Fuit Discard

USDA


Agrowaste, EU


5.3. Quantifying Accessibility of Material

In order to quantify the total potential of any bioresource material for use in a biorefinery two factors were considered namely, the existing uses of the material and the distance of the material from a potential biorefinery site, these were defined as the accessibility constraints in Figure 1 above. Information was gathered on the existing fate of the bioresource material arising, both the quantity and cost associated with the current outcome. The availability and accuracy of data varied across both demonstrator regions according to the material and their fates, however estimates were formed using existing knowledge from the two regions. Information on fates were provided by the Irish Farmers Association and Coillte for the Irish region and from CAGPDS for the Andalusian region. In order to address the question of is the material too far away the following calculation was performed. Firstly, centroid locations were identified for each county/province using GIS. A straight-line distance between counties/provinces was then calculated which was then augmented by factor (1.3 for the Irish region and 1.2 for the Andalusian region) to account for the deviation of roads from straight-line distances. Finally, a unit price for freight was applied. These calculations yielded in a table of prices (in euro/tonne) to move material between any two local counties/provinces.

5.4. Modelling

Two Bioresource Mapping Tools were developed as part of this deliverable, one corresponding to each region. Each model consists of a series of data sheets to capture materials arising from each of the three value chains for each region including current price and fate information, bioresource content for each material and freight costs as described above. These inputs formed the background data sheets as illustrated in Figure 2 below. The flow of data through the model is controlled using Excel’s Power Pivot functionality.


Figure 2: Bioresource Mapping Tool Model

The model contains three calculation sheets where the user can manipulate the data from the background data sheets to get the output they require. Selections within the model that may be of interest include the selection of a single bioresource to be modelled. The user may also not be interested in considering all possible materials, therefore the Arisings PT and Bioresources PT can be used to screen for certain categories, materials and fates as desired. In order to update the model for the desired output “Refresh All” buttons are used to update any changes. Another possible adjustment is the assumed location of a biorefinery. On the Arisings PT sheet the model calculates the optimum location for a biorefinery that minimises the tonnes-kilometers of freight required to bring all arisings to a single location. Alternatively, the user can edit the freight sheet to switch between a “calculated centre” to a chosen location of the biorefinery. Once the user has manipulated the calculation data sheets the results can be viewed in the results data sheets. The results data sheets primarily produce visual outputs in graphic form as illustrated below.

6. Results and Opportunities

The two bioresource mapping tool models contain a large quantity of data that can be manipulated and analysed in several ways depending on the desired outcome. This section looks at three possible scenarios for each region and how this alters the results. A comparison between the two regions was also completed to look at similarities and differences.


6.1. Irish Region

The model for this region compiles information for the whole of Ireland for the three material categories: Lignocellulose, Horticulture and Manure. The total of these materials arisings is 30 million tonnes (wet weight) of potential feedstocks for biorefining, the summary of which is shown in Figure 3. This information can also be viewed as a geographical heat map that visualise the distribution of the materials (dry weight) as shown in Figure 4.

Figure 3: Total potential feedstocks for biorefining in the Irish region

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nes

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Ireland

Manure

Lignocellulose

Horticulture


Figure 4: Geographical heat map of potential feedstock distribution.

The heat map distribution of the materials in Ireland reveal that especially Cork stands out the main contributor with over seven million tonnes of dry weight materials. Figure 5 below show the six counties with the highest amount of materials from the three materials categories of Lignocellulose, Horticulture and Manure. The dominant resource in these counties are found to be manure from either dairy or pig.

Figure 5: Six counties with highest quantity of biomass materials

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Cork Kerry Kilkenny Limerick Tipperary Waterford

Winter wheat straw

Winter oil rapeseed straw

Winter oat straw

Winter barley straw

Spruce

Spring wheat straw

Spring Oil rapeseed straw

Spring oat straw

Spring barley straw

Spent Mushroom compost

Pig manure


6.1.1. Bioresource

The available bioresource from the different materials that has been mapped in the region can be calculated based on the compositional data for each material arising. The individual components called bioresource can be calculated for each county and presented in the graphs below and the heat map over the modelled area. Section 6.1.1 below show the output of the most important bioresources from the Irish region that can be produced by biorefining operations from the material categories that have been mapped (Manure, Lignocellulose and Horticulture). The chosen bioresources selected for this report are protein, fat, carbohydrate, C5 sugars and C6 sugars and total lignin. Some of the applications that are relevant for these bioresources are for example protein into food and feed products, fat into biodiesel or surfactants or other specialty chemicals. Carbohydrates are an important source for fermentable sugars that can be further converted into chemicals, materials, food, feed and fuels. The further breakdown of carbohydrates to C5 sugars (pentoses) and C6 sugars (hexoses) are important for the different suitable conversion routes available. Glucose and mannose are for example easily fermented into products whereas xylose and arabinose are potentially easier to convert through chemical catalysis routes. Total lignin is interesting for the biomaterials applications and new innovative concepts are being commercialised where lignin is used as resins or building blocks for bioplastics. The table below show the dry weight tonnage of each selected bioresource per county and the material vs Bioresource graphic is there to show how the selected materials from a specific region contribute the bioresource being analysed. The scale is relative so that the highest value will always be given the value of 100%. The plot data in the table is the input values for the heap map graph.


Figure 6: Proteins

Council Country Plot Data Material BioResource

Carlow Ireland 40,830

Cavan Ireland 167,314

Clare Ireland 69,065

Cork Ireland 841,118

Donegal Ireland 50,578

Dublin Ireland 8,662

Galway Ireland 75,206

Kerry Ireland 216,869

Kildare Ireland 48,322

Kilkenny Ireland 187,581

Laois Ireland 99,278

Leitrim Ireland 5,198

Limerick Ireland 282,988

Longford Ireland 33,862

Louth Ireland 47,440

Mayo Ireland 43,964

Meath Ireland 142,611

Monaghan Ireland 244,773

Offaly Ireland 78,061

Roscommon Ireland 18,906

Sligo Ireland 17,888

Tipperary Ireland 377,544

Waterford Ireland 190,341

Westmeath Ireland 96,127

Wexford Ireland 170,174

Wicklow Ireland 52,727

3,607,426


Figure 7: Fat





Cork Ireland 249,337












Mayo Ireland 12,132





Sligo Ireland 5,487






986,326


Figure 8: Carbohydrate





Cork Ireland 2,816,080












Mayo Ireland 147,851






Tipperary Ireland 1,297,426





11,784,524


Figure 9: C6 sugars

















Mayo Ireland 90,729











7,074,843


Figure 10: C5 sugars

















Mayo Ireland 57,158











4,712,617


Figure 11: Total Lignin

















Mayo Ireland 103,518











7,873,027


Figure 12a and 12b below is shown to exemplify the model functionality to generate price curve of a given bioresource (in this case Total Lignin) calculated from the price associated with its current use (fate) and also including the cost of transport (freight). This information provide insight into the cost side of the business case and how much bioresource can be accessed depending on how much a business is willing to pay.

Figure 12a: Price curve for Total Lignin in Ireland

Figure 12b: Price curve (including freight cost) for Total Lignin in Ireland

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6.2. Andalusian Region

The model for this region compiles information for the whole of Andalusia for the three value chains: Olive, Horticulture and Algae. The total of these materials arisings is 4.8 million tonnes (wet weight) of potential feedstocks for biorefining, the summary of which is shown in Figure 13. This information can also be viewed as a geographical heat map that visualise the distribution of the materials (dry weight) as shown in Figure 14.

Figure 13: Total materials arising in Andalusia

Figure 14: Heat map distribution of dry matter from Total materials arising in Andalusia

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Olive

Horti

Algae


Table 6: Total materials arising in Andalusia (dry matter)

The heat map distribution in Figure 14 and Table 6 above of the materials in Andalusia reveal that especially Almeria stands out the main contributor with over 1million tonnes of dry weight materials. Figure 15a-d below show graphs with a breakdown of all materials over all the 8 counties in the three materials categories of Olive, Horticulture and Algae. The figure reveals that Almería, Jaén and Córdoba stands out large contributors of feedstock for biorefining. The dominant resource in these counties are found to be from olive production in Córdoba and Jaén and from vegetable production in Almería.

Model Province District Community Country Plot Data

Almería Almeria Andalucia Spain 1,058,270

Cádiz Cadiz Andalucia Spain 13,965

Córdoba Cordoba Andalucia Spain 658,302

Granada Granada Andalucia Spain 323,742

Huelva Huelva Andalucia Spain 123,902

Jaén Jaen Andalucia Spain 707,988

Málaga Malaga Andalucia Spain 130,927

Sevilla Sevilla Andalucia Spain 205,025

3,222,121


Figure 15a: All material categories

Figure 15b: Olive

Figure 15c: Horticulture

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Almería Cádiz Córdoba Granada Huelva Jaén Málaga Sevilla

Wood

Pit

Orujillo

Leaves and branches

Hojín








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1,000,000

1,200,000

1,400,000


Wood

Pit

Orujillo

Leaves and branches

Hojín

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000















Figure 15d: Algae

6.2.1. Bioresource

Section 6.2.1 show the output of the most important bioresources from the Andalusian region that can be produced by biorefining operations from the material categories that have been mapped (Olives, Horticulture and Algae). The chosen bioresources selected for this report for this region are protein, fat and carbohydrate. The tables below show the dry weight tonnage of each selected bioresource per county and the material vs Bioresource graphic is there to show how the selected materials from a specific region contribute the bioresource being analysed. The scale is relative so that the highest value will always be given the value of 100%. The plot data in the table is the input values for the heap map graph.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000


Microalgae nep.




Macro Ulva lactuca



Figure 16: Protein

Model Province District Community Country Plot Data Material BioResource

Almería Almeria Andalucia Spain 110,750

Cádiz Cadiz Andalucia Spain 3,887

Córdoba Cordoba Andalucia Spain 0


Huelva Huelva Andalucia Spain 350

Jaén Jaen Andalucia Spain 0

Málaga Malaga Andalucia Spain 5,881


163,968


Figure 17: Fat


Almería Almeria Andalucia Spain 318

Cádiz Cadiz Andalucia Spain 17


Granada Granada Andalucia Spain 52

Huelva Huelva Andalucia Spain 58


Málaga Malaga Andalucia Spain 6

Sevilla Sevilla Andalucia Spain 500

951


Figure 18: Carbohydrate

6.3. Region Comparison

By comparing the total amount of materials arising from the two model demonstrator regions it is apparent that there is a large different with the more than 30 million tonnes (wet weight) from Ireland (84,000 km2) and only around 4.8 million tonnes from the Andalusian region (87,000 km2). On a dry weight basis the comparison is 23 million versus 3.2 million tonnes indicating that the difference between the regions are independent of the water content of the selected materials The distribution of material in the Irish region is very dominated by the county of Cork that holds almost a quarter of the total material resulting in that all the major bioresources identified as protein, fat, carbohydrate, C5 sugars, C6 sugars and total lignin are heavily distributed to this part of Ireland. The source of material in Ireland is also heavily dominated by dairy, cattle and pig manure. The Andalusian region show a more diverse distribution of materials with the three counties Almería, Jaén and Córdoba contributing strongly to the total amount of material. Compared to Ireland that is strongly influenced by the category of manure


Almería Almeria Andalucia Spain 6,568

Cádiz Cadiz Andalucia Spain 423



Huelva Huelva Andalucia Spain 1,629


Málaga Malaga Andalucia Spain 115


22,342


both olives and horticulture contribute significantly to the total amount in the Andalusian region with 3 and 1.7 million tonnes respectively. Less than 1 percent comes from Algae. The region of Almeria holds the most amount of protein and the region of Seville holds the most amount of fat and carbohydrates. On Ireland, the counties of Cork, Tipperary, Limerick, Kerry, Waterford and Kilkenny rank at the top for all major bioresources (protein, fat, carbohydrate, C5 sugars, C6 sugars and total lignin) except for protein where Monaghan replaces Kilkenny in the top 6. In Andalusia the concentration of the different categories of materials are very distinct and offer opportunities for biorefining from olives in mainly Jaén and Córdoba and from horticulture in Almería. The algae resources, which comes with its own distinct opportunities of high value products are very focused to Cádiz and Huelva. On Ireland, Monaghan stands out as a potential for biorefining operations based on spent mushroom compost and mushroom off cuts. With regards to manure and lignocellulose these are more distributed than mushroom but with an increased concentration around Cork, Tippery and Limerick and a few other counties following closely behind

7. Conclusions

The work to determine the biomass available at a county/provincial level for three selected material categories for each region has been successfully completed and can form part of the digital innovation hubs for each region and the data can also be further integrated into the platform where users can search selected parts of the model. Data of the composition for the defined material categories and streams from each region was further collected to allow the modelling of bioresource availability per county or province while also considering current uses. For the materials in the Andalusian region data on C5 sugars and C6 sugars was not available and it was also not possible to determine some of the prices of the mapped materials in this region. Both regions have clearly identifiable geographical areas where material streams and bioresources are clustered and thus provide potential suitable locations for future biorefining operations. The county of Cork stands out as a specifically strong area and hold almost one third of the total materials arising on Ireland and is predominantly from manure. In Andalusia, Almería, Jaén and Córdoba stands out as particularly strong provinces. Almería is dominated by horticulture resources and Jaén and Córdoba hold the majority of olive derived material streams. Algae based materials are only located in Cádiz and Huelva.

Documents

Deliverable 1.2. Report on Region Specific Data Models · 2020. 9. 30. · Deliverable 1.2. Report on Region Specific Data Models Page 8 of 35 4. Deliverable Aims The aim of this