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Zhang D, Wang Z, Oberschelp C, Bradford E, Hellweg S. Enhanced Deep-Learning Model for Carbon Footprints of Chemicals. ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2024; 12:2700-2708. [PMID: 38389904 PMCID: PMC10880087 DOI: 10.1021/acssuschemeng.3c07038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 01/17/2024] [Accepted: 01/17/2024] [Indexed: 02/24/2024]
Abstract
Millions of chemicals have been designed; however, their product carbon footprints (PCFs) are largely unknown, leaving questions about their sustainability. This general lack of PCF data is because the data needed for comprehensive environmental analyses are typically not available in the early molecular design stages. Several predictive tools have been developed to estimate the PCF of chemicals, which are applicable to only a narrow range of common chemicals and have limited predictive ability. Here, we propose FineChem 2, which is based on a novel transformer framework and first-hand industry data, for accurately predicting the PCF of chemicals. Compared to previous tools, FineChem 2 demonstrates significantly better predictive power, and its applicability domains are improved by ∼75% on a diverse set of chemicals on the global market, including the high-production-volume chemicals identified by regulators, daily chemicals, and chemical additives in food and plastics. In addition, through better interpretability from the attention mechanism, FineChem 2 may successfully identify PCF-intensive substructures and critical raw materials of chemicals, providing insights into the design of more sustainable molecules and processes. Therefore, we highlight FineChem 2 for estimating the PCF of chemicals, contributing to advancements in the sustainable transition of the global chemical industry.
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Affiliation(s)
- Dachuan Zhang
- National
Centre of Competence in Research (NCCR) Catalysis, Ecological Systems
Design, Institute of Environmental Engineering, ETH Zürich, Zürich 8093, Switzerland
| | - Zhanyun Wang
- National
Centre of Competence in Research (NCCR) Catalysis, Ecological Systems
Design, Institute of Environmental Engineering, ETH Zürich, Zürich 8093, Switzerland
- Technology
and Society Laboratory, Empa-Swiss Federal
Laboratories for Materials Science and Technology, St. Gallen CH-9014, Switzerland
| | - Christopher Oberschelp
- National
Centre of Competence in Research (NCCR) Catalysis, Ecological Systems
Design, Institute of Environmental Engineering, ETH Zürich, Zürich 8093, Switzerland
| | - Eric Bradford
- National
Centre of Competence in Research (NCCR) Catalysis, Ecological Systems
Design, Institute of Environmental Engineering, ETH Zürich, Zürich 8093, Switzerland
| | - Stefanie Hellweg
- National
Centre of Competence in Research (NCCR) Catalysis, Ecological Systems
Design, Institute of Environmental Engineering, ETH Zürich, Zürich 8093, Switzerland
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2
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Baaqel H, Bernardi A, Hallett JP, Guillén-Gosálbez G, Chachuat B. Global Sensitivity Analysis in Life-Cycle Assessment of Early-Stage Technology using Detailed Process Simulation: Application to Dialkylimidazolium Ionic Liquid Production. ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2023; 11:7157-7169. [PMID: 37180025 PMCID: PMC10170515 DOI: 10.1021/acssuschemeng.3c00547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 04/04/2023] [Indexed: 05/15/2023]
Abstract
The ability to assess the environmental performance of early-stage technologies at production scale is critical for sustainable process development. This paper presents a systematic methodology for uncertainty quantification in life-cycle assessment (LCA) of such technologies using global sensitivity analysis (GSA) coupled with a detailed process simulator and LCA database. This methodology accounts for uncertainty in both the background and foreground life-cycle inventories, and is enabled by lumping multiple background flows, either downstream or upstream of the foreground processes, in order to reduce the number of factors in the sensitivity analysis. A case study comparing the life-cycle impacts of two dialkylimidazolium ionic liquids is conducted to illustrate the methodology. Failure to account for the foreground process uncertainty alongside the background uncertainty is shown to underestimate the predicted variance of the end-point environmental impacts by a factor of two. Variance-based GSA furthermore reveals that only few foreground and background uncertain parameters contribute significantly to the total variance in the end-point environmental impacts. As well as emphasizing the need to account for foreground uncertainties in LCA of early-stage technologies, these results illustrate how GSA can empower more reliable decision-making in LCA.
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Affiliation(s)
- Husain
A. Baaqel
- Department
of Chemical Engineering, Imperial College
London, South Kensington Campus, London SW7 2AZ, United Kingdom
- Sargent
Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
| | - Andrea Bernardi
- Department
of Chemical Engineering, Imperial College
London, South Kensington Campus, London SW7 2AZ, United Kingdom
- Sargent
Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
| | - Jason P. Hallett
- Department
of Chemical Engineering, Imperial College
London, South Kensington Campus, London SW7 2AZ, United Kingdom
| | - Gonzalo Guillén-Gosálbez
- Institute
for Chemical and Bioengineering, Swiss Federal Institute of Technology, Vladimir-Prelog-Weg 1, Zurich 8093, Switzerland
| | - Benoît Chachuat
- Department
of Chemical Engineering, Imperial College
London, South Kensington Campus, London SW7 2AZ, United Kingdom
- Sargent
Centre for Process Systems Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
- E-mail:
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3
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Sun Y, Wang X, Ren N, Liu Y, You S. Improved Machine Learning Models by Data Processing for Predicting Life-Cycle Environmental Impacts of Chemicals. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:3434-3444. [PMID: 36537350 DOI: 10.1021/acs.est.2c04945] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Machine learning (ML) provides an efficient manner for rapid prediction of the life-cycle environmental impacts of chemicals, but challenges remain due to low prediction accuracy and poor interpretability of the models. To address these issues, we focused on data processing by using a mutual information-permutation importance (MI-PI) feature selection method to filter out irrelevant molecular descriptors from the input data, which improved the model interpretability by preserving the physicochemical meanings of original molecular descriptors without generation of new variables. We also applied a weighted Euclidean distance method to mine the data most relevant to the predicted targets by quantifying the contribution of each feature, thereby the prediction accuracy was improved. On the basis of above data processing, we developed artificial neural network (ANN) models for predicting the life-cycle environmental impacts of chemicals with R2 values of 0.81, 0.81, 0.84, 0.75, 0.73, and 0.86 for global warming, human health, metal depletion, freshwater ecotoxicity, particulate matter formation, and terrestrial acidification, respectively. The ML models were interpreted using the Shapley additive explanation method by quantifying the contribution of each input molecular descriptor to environmental impact categories. This work suggests that the combination of feature selection by MI-PI and source data selection based on weighted Euclidean distance has a promising potential to improve the accuracy and interpretability of the models for predicting the life-cycle environmental impacts of chemicals.
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Affiliation(s)
- Ye Sun
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin150090, P. R. China
| | - Xiuheng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin150090, P. R. China
| | - Nanqi Ren
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin150090, P. R. China
| | - Yanbiao Liu
- College of Environmental Science and Engineering, Textile Pollution Controlling Engineering Center of the Ministry of Ecology and Environment, Donghua University, Shanghai201620, China
| | - Shijie You
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin150090, P. R. China
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4
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Digitizing sustainable process development: From ex-post to ex-ante LCA using machine-learning to evaluate bio-based process technologies ahead of detailed design. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2021.117339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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5
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Baxevanidis P, Papadokonstantakis S, Kokossis A, Marcoulaki E. Group contribution‐based
LCA
models to enable screening for environmentally benign novel chemicals in
CAMD
applications. AIChE J 2021. [DOI: 10.1002/aic.17544] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Pantelis Baxevanidis
- System Reliability and Industrial Safety Lab, National Centre for Scientific Research “Demokritos” Athens Greece
- School of Chemical Engineering National Technical University of Athens Athens Greece
| | - Stavros Papadokonstantakis
- Department of Space, Earth, and Environment, Division of Energy Technology Chalmers University of Technology Gothenburg Sweden
| | - Antonis Kokossis
- School of Chemical Engineering National Technical University of Athens Athens Greece
| | - Effie Marcoulaki
- System Reliability and Industrial Safety Lab, National Centre for Scientific Research “Demokritos” Athens Greece
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6
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COSMO-susCAMPD: Sustainable solvents from combining computer-aided molecular and process design with predictive life cycle assessment. Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2021.116863] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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7
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Fantke P, Cinquemani C, Yaseneva P, De Mello J, Schwabe H, Ebeling B, Lapkin AA. Transition to sustainable chemistry through digitalization. Chem 2021. [DOI: 10.1016/j.chempr.2021.09.012] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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8
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Weber JM, Guo Z, Zhang C, Schweidtmann AM, Lapkin AA. Chemical data intelligence for sustainable chemistry. Chem Soc Rev 2021; 50:12013-12036. [PMID: 34520507 DOI: 10.1039/d1cs00477h] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
This study highlights new opportunities for optimal reaction route selection from large chemical databases brought about by the rapid digitalisation of chemical data. The chemical industry requires a transformation towards more sustainable practices, eliminating its dependencies on fossil fuels and limiting its impact on the environment. However, identifying more sustainable process alternatives is, at present, a cumbersome, manual, iterative process, based on chemical intuition and modelling. We give a perspective on methods for automated discovery and assessment of competitive sustainable reaction routes based on renewable or waste feedstocks. Three key areas of transition are outlined and reviewed based on their state-of-the-art as well as bottlenecks: (i) data, (ii) evaluation metrics, and (iii) decision-making. We elucidate their synergies and interfaces since only together these areas can bring about the most benefit. The field of chemical data intelligence offers the opportunity to identify the inherently more sustainable reaction pathways and to identify opportunities for a circular chemical economy. Our review shows that at present the field of data brings about most bottlenecks, such as data completion and data linkage, but also offers the principal opportunity for advancement.
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Affiliation(s)
- Jana M Weber
- Department of Chemical Engineering and Biotechnology, University of Cambridge, West Cambridge Site, Philippa Fawcett Drive, Cambridge CB3 0AS, UK. .,Chemical Data Intelligence (CDI) Pte Ltd, Robinson Road, #02-00, 068898, Singapore
| | - Zhen Guo
- Chemical Data Intelligence (CDI) Pte Ltd, Robinson Road, #02-00, 068898, Singapore.,Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, 138602, Singapore
| | - Chonghuan Zhang
- Department of Chemical Engineering and Biotechnology, University of Cambridge, West Cambridge Site, Philippa Fawcett Drive, Cambridge CB3 0AS, UK.
| | - Artur M Schweidtmann
- Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, The Netherlands
| | - Alexei A Lapkin
- Department of Chemical Engineering and Biotechnology, University of Cambridge, West Cambridge Site, Philippa Fawcett Drive, Cambridge CB3 0AS, UK. .,Chemical Data Intelligence (CDI) Pte Ltd, Robinson Road, #02-00, 068898, Singapore.,Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. 1 CREATE Way, CREATE Tower #05-05, 138602, Singapore
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9
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Ioannou I, D'Angelo SC, Galán-Martín Á, Pozo C, Pérez-Ramírez J, Guillén-Gosálbez G. Process modelling and life cycle assessment coupled with experimental work to shape the future sustainable production of chemicals and fuels. REACT CHEM ENG 2021; 6:1179-1194. [PMID: 34262788 PMCID: PMC8240698 DOI: 10.1039/d0re00451k] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/03/2021] [Indexed: 12/17/2022]
Abstract
Meeting the sustainable development goals and carbon neutrality targets requires transitioning to cleaner products, which poses significant challenges to the future chemical industry. Identifying alternative pathways to cover the growing demand for chemicals and fuels in a more sustainable manner calls for close collaborative programs between experimental and computational groups as well as new tools to support these joint endeavours. In this broad context, we here review the role of process systems engineering tools in assessing and optimising alternative chemical production patterns based on renewable resources, including renewable carbon and energy. The focus is on the use of process modelling and optimisation combined with life cycle assessment methodologies and network analysis to underpin experiments and generate insight into how the chemical industry could optimally deliver chemicals and fuels with a lower environmental footprint. We identify the main gaps in the literature and provide directions for future work, highlighting the role of PSE concepts and tools in guiding the future transition and complementing experimental studies more effectively.
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Affiliation(s)
- Iasonas Ioannou
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich Vladimir-Prelog-Weg 1 8093 Zürich Switzerland
| | - Sebastiano Carlo D'Angelo
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich Vladimir-Prelog-Weg 1 8093 Zürich Switzerland
| | - Ángel Galán-Martín
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich Vladimir-Prelog-Weg 1 8093 Zürich Switzerland
| | - Carlos Pozo
- LEPAMAP Research Group, University of Girona C/Maria Aurèlia Capmany 61 17003 Girona Spain
| | - Javier Pérez-Ramírez
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich Vladimir-Prelog-Weg 1 8093 Zürich Switzerland
| | - Gonzalo Guillén-Gosálbez
- Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich Vladimir-Prelog-Weg 1 8093 Zürich Switzerland
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10
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Adjiman CS, Sahinidis NV, Vlachos DG, Bakshi B, Maravelias CT, Georgakis C. Process Systems Engineering Perspective on the Design of Materials and Molecules. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.0c05399] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Claire S. Adjiman
- Department of Chemical Engineering, Centre for Process Systems Engineering and Institute for Molecular Science and Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, U.K
| | - Nikolaos V. Sahinidis
- H. Milton Stewart School of Industrial & Systems Engineering and School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Dionisios G. Vlachos
- Department of Chemical and Biomolecular Engineering, Catalysis Center for Energy Innovation, RAPID Manufacturing Institute, and Delaware Energy Institute (DEI), University of Delaware, Newark, Delaware 19716, United States
| | - Bhavik Bakshi
- Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States
| | - Christos T. Maravelias
- Department of Chemical & Biological Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
| | - Christos Georgakis
- Department of Chemical and Biological Engineering Systems Research Institute of Chemical and Biological Processes, Tufts University, Medford, Massachusetts 02155, United States
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11
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Kleinekorte J, Fleitmann L, Bachmann M, Kätelhön A, Barbosa-Póvoa A, von der Assen N, Bardow A. Life Cycle Assessment for the Design of Chemical Processes, Products, and Supply Chains. Annu Rev Chem Biomol Eng 2020; 11:203-233. [PMID: 32216728 DOI: 10.1146/annurev-chembioeng-011520-075844] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Design in the chemical industry increasingly aims not only at economic but also at environmental targets. Environmental targets are usually best quantified using the standardized, holistic method of life cycle assessment (LCA). The resulting life cycle perspective poses a major challenge to chemical engineering design because the design scope is expanded to include process, product, and supply chain. Here, we first provide a brief tutorial highlighting key elements of LCA. Methods to fill data gaps in LCA are discussed, as capturing the full life cycle is data intensive. On this basis, we review recent methods for integrating LCA into the design of chemical processes, products, and supply chains. Whereas adding LCA as a posteriori tool for decision support can be regarded as established, the integration of LCA into the design process is an active field of research. We present recent advances and derive future challenges for LCA-based design.
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Affiliation(s)
- Johanna Kleinekorte
- Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany;
| | - Lorenz Fleitmann
- Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany;
| | - Marvin Bachmann
- Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany;
| | - Arne Kätelhön
- Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany;
| | - Ana Barbosa-Póvoa
- Centre for Management Studies, Instituto Superior Técnico, University of Lisbon, 1649-004, Lisbon, Portugal
| | - Niklas von der Assen
- Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany;
| | - André Bardow
- Institute of Technical Thermodynamics, RWTH Aachen University, 52062 Aachen, Germany; .,Institute of Energy and Climate Research, Forschungszentrum Jülich, 52428 Jülich, Germany
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12
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Process systems engineering thinking and tools applied to sustainability problems: current landscape and future opportunities. Curr Opin Chem Eng 2019. [DOI: 10.1016/j.coche.2019.11.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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13
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Integrating Life Cycle Inventory and Process Design Techniques for the Early Estimate of Energy and Material Consumption Data. ENERGIES 2018. [DOI: 10.3390/en11040970] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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