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Integrating multi-objective superstructure optimization and multi-criteria assessment: a novel methodology for sustainable process design

  • Philipp Kenkel ORCID logo EMAIL logo , Christian Schnuelle , Timo Wassermann ORCID logo and Edwin Zondervan
Published/Copyright: June 23, 2022
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Physical Sciences Reviews
From the journal Physical Sciences Reviews Volume 8 Issue 9

Abstract

This work presents a novel methodology for integrated multi-objective superstructure optimization and multi-criteria assessment. The method is tailored for sustainable process synthesis utilizing mixed-integer linear programming (MILP). The six-step algorithm includes 1) superstructure formulation, 2) criteria definition and implementation, 3) criteria weighting, 4) single-criterion optimization, 5) reformulation and 6) multi-criteria optimization. It is automated in the O pen s U perstruc T ure mo D eling and O ptimizati O n f R amework (OUTDOOR) and tested on integrated power-to-X and biomass-to-X processes for methanol production. Three criteria are considered, namely net production costs (NPC), net production greenhouse gas emissions (NPE) and net production fresh water demand (NPFWD). The optimization indicates NPC of 1307 €/tMeOH with NPE of −2.23 tCO2/tMeOH and NPFWD of −3.42 tH2O/tMeOH for an optimal trade-off plant. The plant configuration features low-pressure alkaline electrolysis for hydrogen supply, absorption-based CO2 capture and steam production from methanol purge gas for internal heat supply. Conducted variation and sensitivity analyses indicate that methanol costs can drop to about 500 €/tMeOH if electricity is free of charge, or to 805 €/tMeOH if biogas is available at large quantities, if a least-cost process layouts are considered. However, all performed multi-criteria analyses imply a robust optimal process design utilizing electricity-based methanol production.

Keywords: biomass-to-X; MCDA; methanol; power-to-X; superstructure optimization
Corresponding author: Philipp Kenkel, Advanced Energy Systems Institute, University of Bremen, Enrique-Schmidt Straße 7, 28359 Bremen, Germany; and artec Sustainability Research Center, University of Bremen, Enrique-Schmidt-Straße 7, 28359 Bremen, Germany, E-mail:

Funding source: German Federal Ministry for Economic Affairs and Energy

Award Identifier / Grant number: 03EIV051A

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: Funding of this research by the German Federal Ministry for Economic Affairs and Climate Action within the KEROSyN100 project (funding code 03EIV051A) is gratefully acknowledged.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

Appendix A: Superstructure formulation and representation in OUTDOOR

OUTDOOR utilizes different unit-operations, which provide different tasks. The fundamental tasks in OUTDOOR include mixing, reaction, separation and distribution. Detailed descriptions and visualizations of these are given in Table A1. A set of different unit-operation classes is defined in OUTDOOR, which combine different tasks. These unit-operation classes are splitters, reactors, distributors, product pools and raw material sources. The detailed description and visualization of these unit-operations are depicted in Table A2. In the actual representation of the given example superstructure Figure B1 the individual tasks of the unit-operations are not visualized Table B1. Unit-operations are simplified as blocks, with subscripts in the bottom right corner indicating the unit-operation class.

Table A1:

General tasks in OUTDOOR.

Task Description Visualisation
Mixing Mixing takes different inlet streams with given states/compositions (visualized by small circles) and calculates the resulting outlet flow and state/composition.
Example: Simple Mixers.
Separation Separation takes a single inlet flow with a given state/composition and calculates different outlet streams by given (pre-defined) split factors.
Examples: Pre-calculated distillation columns or flash evaporators.
Distribution Distribution takes a single inlet flow with a given state/composition and distributes this stream to a set of given (pre-defined) target processes. The state/composition of every outlet flow is equal to the inlet flow. The splitting ratio is not pre-defined as with the separation task but a variable solved by the system during optimization.
Examples: Valves.
Reaction Reaction calculates the outlet state/composition from given inlet flow based on stochiometric (st) or yield functions (Y). For stoichiometric reactions there are additional subclasses which are turbine mode (T) and furnace mode (F) which use stoichiometric functions together with given efficiencies and lower heating values of inlet components to calculate produced electricity or steam.
Examples: Methanol reactor, electricity steam turbine, steam producing furnace.
Table A2:

Unit-operations implemented in OUTDOOR.

Unit operation Name Discription Visualisation
Simple process/Splitter (S) A simple process is a combination of the mixing and the separation task. Most of the time the main goal is to split incoming streams into pre-defined output streams. If there is only one input stream the mixing part is omitted without any loss of generality. However, if mixing is part of the simple process, it is important that the state of the intermediate mixed stream is fitting to the pre-defined split factors in the separation task.
Distributor unit (D) The distributor unit combines the mixing and the distributing unit. Different inlet streams form an intermediate stream which is afterwards distributed to given target processes.
Raw material source Raw material sources are pre-defined flows with given costs and emissions per ton as well as minimum and maximum availability. These sources are connected to target processes by the distributor task, so that one source can feed different processes.
Product pool The product pool is a mass flow sink which mixes incoming flows and calculates profits and avoided emissions. It is possible to pre-define required minimum and maximum inlet flows for given products. It is important that the intermediate state after the mixing state meets the characteristics of the defined product.
Stoichiometric (St-R)/Yield reactor (Y-R), turbine (TUR) and furnace (FUR). The different reactor units display a combination process of mixing, reaction and separation tasks. Different input streams are mixed to an intermediate stream which is afterwards converted by either stoichiometric or yield reaction and finally separated to different outlet flows by given split factors. If only one inlet or one outlet flow (target process) is defined the mixing or separation task is omitted without loss of generality. If the reactors are defined as turbine or furnace, the reaction is calculated as stoichiometric reaction; while electricity or steam production is calculated from efficiencies and the lower heating value of reaction compounds.
Figure B1: Full superstructure representation of integrated biomass- and power-to-methanol plant.
Figure B1:

Full superstructure representation of integrated biomass- and power-to-methanol plant.

Appendix B: Case study superstructure representation

Table B1:

Technology options short cuts and names.

Shortcut Name Shortcut Name
MEA-CC MEA CO2 capture MEOH FLASH Flash evaporation unit
LT-DAC Low temperature direct air capture MEOH DC Distillation column
OXY Oxyfuel cement factory EL GEN Electricity generation (combined gas and steam turbine)
CPU CO2 purification unit HEAT GEN Steam generation furnace
CO2-COMP CO2 compressor to 70 bar WWT Waste water treatment process
CO2-DIST CO2 distributor BG-PSA Pressure swing adsorption for biogas purification
HP-PEM Polymer electrolyte membrane electrolysis at 30 bar BM-DIST Bio-methane distributor
HP-AEL High-pressure alkaline electrolysis at 30 bar VP Vacuum pump
AEL Low-pressure alkaline electrolysis at 1 bar SMR Steam methane reforming system
SOEL Solid oxide (high temperature) electrolysis at 1 bar ATR Autothermal reforming system
H2-MH-COMP Single-stage hydrogen compressor from 30 to 70 bar TRIR Biogas tri-reforming system
H2-LH-COMP Multi-stage hydrogen compressor from 1 bar to 70 bar H2-PSA Hydrogen pressure swing adsorption
H2-DIST Hydrogen distributor ATR-SEL ATR selexol unit
O2-DIST Oxygen distributor TRIR-SEL TRIR selexol unit
SMR-COMP Compressor for SMR SynFeed SMR-DIST SMR-SynFeed distributor
ATR-COMP Compressor for ATR SynFeed ATR-DIST ATR-SynFeed distributor
TRIR-COMP Compressor for TRIR SynFeed TRIR-DIST TRIR-SynFeed distributor
H2O-DIST Purified water distributor
MEOH SYN Methanol synthesis reactor SYNFEED COMP SynFeed compressor

Appendix C: Additional results

Table C1:

Basic results for variation scenario 1 (biogas availability = 3.5 t/h, Electricity price = 0 €/MWh).

NPC-optimized NPE-optimized NPFWD-optimized MCO
Calculated NPC 501 600 829 501
Calculated NPE −2.23 −2.26 −2.015 −2.2
Calculated NPFWD −3.39 −3.696 −4.717 −3.39
Table C2:

Basic results for variation scenario 2 (biogas availability = 60 t/h, electricity price = 72.3 €/MWh).

NPC-optimized NPE-optimized NPFWD -optimized MCO
Calculated NPC 5804 2245 2479 1348
Calculated NPE −0.4 −2.679 −2.479 −2.234
Calculated NPFWD 2.581 −4.99 −5.55 −3.616
Table C3:

Basic results for variation scenario 3 (Biogas availability = 60 t/h, Electricity price = 0 €/MWh).

NPC-optimized NPE-optimized NPFWD -optimized MCO
Calculated NPC 501 1446 1672 501
Calculated NPE −2.23 −2.679 −2.479 −2.234
Calculated NPFWD −3.39 −4.993 −5.55 −3.39

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Received: 2022-02-17
Accepted: 2022-05-02
Published Online: 2022-06-23

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/psr-2020-0058
Keywords for this article
biomass-to-X; MCDA; methanol; power-to-X; superstructure optimization

Articles in the same Issue

  1. Frontmatter
  2. Reviews
  3. Magnetic characterization of magnetoactive elastomers containing magnetic hard particles using first-order reversal curve analysis
  4. Microscopic understanding of particle-matrix interaction in magnetic hybrid materials by element-specific spectroscopy
  5. Biodeinking: an eco-friendly alternative for chemicals based recycled fiber processing
  6. Bio-based polyurethane aqueous dispersions
  7. Cellulose-based polymers
  8. Biodegradable shape-memory polymers and composites
  9. Natural substances in cancer—do they work?
  10. Personalized and targeted therapies
  11. Identification of potential histone deacetylase inhibitory biflavonoids from Garcinia kola (Guttiferae) using in silico protein-ligand interaction
  12. Chemical computational approaches for optimization of effective surfactants in enhanced oil recovery
  13. Social media and learning in an era of coronavirus among chemistry students in tertiary institutions in Rivers State
  14. Techniques for the detection and quantification of emerging contaminants
  15. Occurrence, fate, and toxicity of emerging contaminants in a diverse ecosystem
  16. Updates on the versatile quinoline heterocycles as anticancer agents
  17. Trends in microbial degradation and bioremediation of emerging contaminants
  18. Power to the city: Assessing the rooftop solar photovoltaic potential in multiple cities of Ecuador
  19. Phytoremediation as an effective tool to handle emerging contaminants
  20. Recent advances and prospects for industrial waste management and product recovery for environmental appliances: a review
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  22. A conversation on the quartic equation of the secular determinant of methylenecyclopropene
  23. Recent developments in the synthesis and anti-cancer activity of acridine and xanthine-based molecules
  24. An overview of in silico methods used in the design of VEGFR-2 inhibitors as anticancer agents
  25. Fragment based drug design
  26. Advances in heterocycles as DNA intercalating cancer drugs
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  34. Dicarbonyl compounds in the synthesis of heterocycles under green conditions
  35. Green synthesis of triazolo-nucleoside conjugates via azide–alkyne C–N bond formation
  36. Anaerobic digestion fundamentals, challenges, and technological advances
  37. Survival is the driver for adaptation: safety engineering changed the future, security engineering prevented disasters and transition engineering navigates the pathway to the climate-safe future
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