A Novel Model Predictive Control Scheme for Sustainability

A Novel Model Predictive Control Scheme for Sustainability: Application to

Biomass/Coal Co-gasification SystemShuyun Li1, Gerardo J. Ruiz-Mercado2 and Fernando V. Lima1

1 Department of Chemical and Biomedical Engineering, West Virginia University, Morgantown, WV 2 U.S. Environmental Protection Agency, Cincinnati, OH

2018 AIChE Annual MeetingNovember 2 2018, Pittsburgh, PA

Presenter

Presentation Notes

Hello everyone, today the topic I’m going to discuss is process control for sustainability and LCI monitoring with application of biomass/coal co-gasification system.

Background Motivation Challenges and Objectives

Process Systems Approach Proposed Framework Software Communication

Case Study Biomass/coal co-gasification Process Modeling Multi-objective Optimization (MOO) Dynamic Sustainability Performance Analysis MPC Formulation and Implementation Results

Conclusions

TitleOutline

BackgroundFrameworkCase Study Conclusions

1

Presentation Outline

Presenter

Presentation Notes

First, I’ll give a brief introduction on the motivation, challenges and objectives of this work and then talk about the proposed approach, including process modeling, multi-objective optimization, control and LCI monitoring. Finally, I’ll show some preliminary result of the case study.

TitleOutline

BackgroundFrameworkCase StudyConclusions

2

Current Methods and ChallengesCurrent Methods:

Green Chemistry and Engineering:Pollution Prevention (P2); Waste Reduction; End of Pipe Technologies;

Sustainability Evaluation:Risk and Impact Assessment; Life Cycle Assessment (LCA); GREENSCOPE*

Process Systems Engineering (PSE): Optimization; Sustainable Process Design

PSE Challenges: High-dimensionality and nonlinearities of

chemical process models Limited ability of dealing with multiple and

conflicting objectives Additional complexity of adding sustainability

objectives to process controllers

Ruiz-Mercado GJ, Smith RL, Gonzalez MA. GREENSCOPE.xlsm User’s Guide. Excel Version 1.1 2013.Sikdar SK. Sustainable Development and Sustainability Metrics. AIChE journal 2003; 49(8): 1928-32.

Presenter

Presentation Notes

In recent years, due to the increasing global energy demand and environmental awareness, biomass has received considerable attention. Biomass is a renewable feedstock alternative to produce energy and chemicals with potential for ameliorating the environmental effects of using fossil fuels. The processes of converting biomass to energy or chemicals release carbon dioxide (CO2). However, the plants that are the source of biomass capture a great amount of CO2 through photosynthesis while they are growing, which can make biomass a sustainable and clean energy source. In addition, biomass, as the main renewable resources, provides about 5 % of the primary energy used in the united states last year. Biomass could play more important role on national energy security and environmental sustainability if some more efficient, and commercial viable ways can be developed or improved to convert biomass into biofuel, biopower and biochemicals.

Motivation and Objectives Motivation:

Limited process systems studies on sustainability performance of biomass/coal conversion process (No control and Dynamic performance were done)

Co-gasification technology has some advantages to address

-- Low energy density and low quality of biomass

-- Biomass limited and intermittent supply

TitleOutline

BackgroundFrameworkCase StudyConclusions

3

Objectives: Evaluate the performance of biomass/coal co-gasification system in

terms of economic and environmental aspects

Develop a systematic framework to control co-gasification process systems at the most sustainable operating region

Presenter

Presentation Notes

Since there is little literature on systematically evaluating the sustainability performance of biomass use, especially on the thermal conversion processes, so one of the motivation of this research is to find the optimal balance between the economic and environmental aspect via multi-optimization algorithm. In addition, Biomass/coal co-gasification with coal is chosen as the case study since this technology can overcome some undesirable features of biomass, like low energy density and low quality, as well as limited and intermittent supply. Overall, co-gasification is economically attractive and technically viable way to convert biomass into energy or other valuable chemicals. Based on the aforementioned motivation, here are the objectives of this work: Evaluate the performance of biomass/coal co-gasification system in terms of economic and environmental aspects Develop a systematic framework to optimize and control chemical process systems at the most sustainable operating region considering efficiency, environmental, economic and energy aspects

Proposed Framework Title

OutlineBackgroundFrameworkCase Study Conclusions

5

Presenter

Presentation Notes

Here is the overall proposed framework. In the proposed approach, a process model and sustainability assessment model will be developed. Based on the developed model, a multi-objective optimization problem will be formulated, considering minimizing the operating cost and environmental waste emissions as objective functions. By solving this multi-objective optimization problem using genetic algorithm, Pareto front can be obtained. After the analysis of the optimal points on the pareto front, desired operating point can either based on the preference of the policy holder or the gate-to-gate LCI results. Once the desired operating point is determined, the implemented process control can take the system to this setpoints.

Software CommunicationTitle

OutlineBackgroundFrameworkCase StudyConclusions

6

Presenter

Presentation Notes

This figure shows the software environments of the components of the proposed framework and also the data communications between different components. Steady-state and dynamic will be developed in aspen Hysys while the MOO will be coded in Matlab. For solving the MOO, Presents a multiobjective optimization to obtain the optimal planning of the chemical process, considering the optimal selection of feedstock and emissions. Multi-objective methodology was applied during both gasification and water gas shift processes. The multiobjective optimization problem considers minimizing the annual cost and environmental impact as objective function. The economic objective function takes into account the availability of raw resources, the cost of feedstocks, fermentation conditions and separation units while the environmental assessment includes the overall impact measured through the eco-indicator

Biomass/Coal To Methanol Process TitleOutline

BackgroundFrameworkCase Study ConclusionsFuture Work

7* Li, S., Feliachi, Y., Agbleze, S., Ruiz-Mercado, G.J., Smith, R.L., Meyer, D.E., Gonzalez, M.A. & Lima, F.V. A Process Systems Framework for Rapid Generation of Life Cycle Inventories for Pollution Control and Sustainability. Clean Technologies and Environmental Policy, 2018, 7, 1543-1561.

Presenter

Presentation Notes

Entrained flow gasifier simulation High temperature, pressure 3 CSTRs in series Coal: assume pseudo-component (C18H20) Biomass: employ yield reactor to decompose biomass into smaller species for reactions Empirical functions are used to estimate power for grinding and feeding*

Sustainability Indicators (SI) Model* TitleOutline

BackgroundFrameworkCase Study ConclusionsFuture Work

7

* Ruiz-Mercado GJ, Smith RL, Gonzalez MA. GREENSCOPE.xlsm User’s Guide. Excel Version 1.1 2013.

Category Indicator DefinitionReference Value

Best case Worst case

Efficiency Reaction yield (RY) 1.0 0

Economic Economic potential(EP) 0.5 0

Envi

ronm

enta

l

Global warming potential (GWP) 0 2.5

Specific solid waste mass (𝑚𝑚𝑠𝑠,𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠) 0 50

Specific liquid waste volume (𝑉𝑉𝐿𝐿,𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠) 0 100

Energy Specific energyintensity (RSEI)

0 100

𝑆𝑆𝑆𝑆 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 =𝐴𝐴𝐴𝐴𝐴𝐴𝑉𝑉𝑉𝑉𝑉𝑉 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 −𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝐴𝐴 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝐵𝐵𝑉𝑉𝑊𝑊𝐴𝐴 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 −𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝐴𝐴 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 × 100%

( )cf , RM, UT, L,

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Optimization Problem Formulation Title

OutlineBackgroundFrameworkCase Studies Conclusions

10

Minimize Environmental Waste Index (𝑓𝑓1) Global warming potential (GWP) Specific solid waste mass (𝑚𝑚𝑠𝑠,𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠) Specific liquid waste volume (𝑉𝑉𝐿𝐿,𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠)

𝑓𝑓1 = (𝑤𝑤1 � 𝑆𝑆𝑆𝑆𝐺𝐺𝐺𝐺𝐺𝐺 + 𝑤𝑤2 � 𝑆𝑆𝑆𝑆𝑚𝑚𝑠𝑠,𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 + 𝑤𝑤3 � 𝑆𝑆𝑆𝑆𝑉𝑉𝐿𝐿,𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠)/3

Minimize Economic Index (𝑓𝑓1) :𝑓𝑓1 = 𝑆𝑆𝑆𝑆𝐸𝐸𝐺𝐺

𝑊𝑊. 𝐴𝐴. 𝑝𝑝𝑊𝑊𝑊𝑊𝐴𝐴𝑉𝑉𝑊𝑊𝑊𝑊 𝑚𝑚𝑊𝑊𝑚𝑚𝑉𝑉𝑉𝑉

constraints: RSE > 0.7; RY > 0.95;1800 <𝐹𝐹𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑠𝑠𝑜𝑜< 4000 kmol/h;1800 < 𝐹𝐹𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑚𝑚 < 5400 kmol/h;1000 <𝑇𝑇𝑜𝑜𝑠𝑠𝑠𝑠𝑔𝑔𝑔𝑔𝑔𝑔𝑠𝑠𝑔𝑔< 1500 ℃;𝐹𝐹𝑠𝑠𝑜𝑜𝑠𝑠𝑐𝑐 fixed at 923.5 lbmol/h

Optimization: Genetic Algorithm Title


11

MOO ResultsTitle


12

Genetic algorithm is an efficient method to solve the multi-objective optimization problems by finding a set of converged and well-diversified solutions

Initial Generation Population 100th Generation Population

MOO Results AnalysisTitle


13

Genetic algorithm is capable of finding trade-offs between economic and environmental objectives

Pareto front shows the trends: better economic performance requires higher waste/emissions

Population size: 120Generation number: 150

Dynamic Sustainability performance Analysis TitleOutline

BackgroundFrameworkCase Studies Conclusions

17

Feedback controllers (9 PIs): take the process to the selected setpoint based on optimal points from MOO

Reference case conditions Optimal case conditions Coal flow rate (923.5 lbmol/hr..)Oxygen flow rate (7000 lbmol/hr.)Water flow rate (7200 lbmol/hr.)Biomass flow rate (0 lbmol/hr.)

Coal flow rate (923.5 lbmol/hr.)Oxygen flow rate (7800 lbmol/hr.)Water flow rate (8200 lbmol/hr.)Biomass flow rate (80 lbmol/hr.)

TitleOutline


14

Sustainability Performance during the transient part

Dynamic Sustainability performance Analysis

Linear MPC Implementation TitleOutline


15

Control structure:

Continuous state-space model obtained from System Identification:

Manipulate Variables Control Variables

Coal flow rateOxygen flow rateWater flow rateBiomass flow rate (Disturbance)

Syngas production rate POX temperature H2/CO ratio

Syngas production rate (y1) measured (in blue) and modeled (in black)

POX temperature (y2) measured (in blue) and modeled (in black)

H2/CO ratio (y3) measured (in blue) and modeled (in black)

MPC Results: Setpoint tracking TitleOutline


16

Setpoints tracking scenarios [-10; 0; 5]:

Conclusions TitleOutline


17

The effectiveness of proposed framework was illustrated through evaluation of MOO considering conflicting objectives in terms of environmental and economic aspects

The proposed control structure can keep the system sustainability performance in a certain predefined range in the transient scenarios

Proposed framework can bridge existing gaps between sustainability/LCI and process systems (simulation, optimization, control)

Still working on the results of time-explicit SI value

AcknowledgmentsWest Virginia University and U.S. Environmental Protection Agency for the

financial support through contract Ref. EP-16-C-000049.

DisclaimerThe views expressed in this presentation are those of the authors and do not

represent the views or policies of the U.S. Environmental Protection Agency.

Thank you!

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A Novel Model Predictive Control Scheme for Sustainability