Digital Circular Electrochemical Economy (DCEE)
Lead Research Organisation:
Imperial College London
Department Name: Chemical Engineering
Abstract
This project focuses on a radical change to chemical manufacturing with a view to effective step changes in environmental sustainability and in circularity of materials. We shall focus on the emerging electrochemical sector which is expected to grow strongly and within which there are many opportunities for the deployment of digital technologies to underpin system design and operation.
In response to this call, we have united a cross-disciplinary team of leading researchers from three UK universities (Imperial College, Loughborough, and Heriot-Watt) to create a digital circular electrochemical economy.
The chemical sector is a "hard to decarbonise" sector. Its high embedded carbon comes from two aspects: (1) the intensive energy use; and (2) the use of fossil feedstock. Therefore, the decarbonisation requires the substitution of both two with renewable energy (electrifying the chemical processes) and feedstock (e.g., H2O, CO2). We foresee a closer integration of the electrical energy system with the industrial chemistry system, with the former providing reducing energy formerly available in fossil fuels and which enables the processing of highly oxidised but abundant feedstocks. The intermittency of renewable electricity supply and the economic benefits of flexible processing and closer integration between these two sectors will give rise to opportunities for new digital technologies. These will enable improved design and operation of emerging electrochemical processing technologies and provide new pathways to chemical building blocks (e.g. olefins) and fuels. The integration of the sectors also provides opportunities for cost savings in the electrical system through improved flexibility and demand management.
We propose three work packages (WP) to look at the challenges at different levels, and finally integrate as a whole solution:
- WP1 Digital twins of key electrochemical operation units and processes.
- WP2 Digitalisation of the value chain encompassing the integration between the chemical and electrical systems
- WP3 Policy, Society and Finance, including business models to capture value generation opportunities from industrial integration
In response to this call, we have united a cross-disciplinary team of leading researchers from three UK universities (Imperial College, Loughborough, and Heriot-Watt) to create a digital circular electrochemical economy.
The chemical sector is a "hard to decarbonise" sector. Its high embedded carbon comes from two aspects: (1) the intensive energy use; and (2) the use of fossil feedstock. Therefore, the decarbonisation requires the substitution of both two with renewable energy (electrifying the chemical processes) and feedstock (e.g., H2O, CO2). We foresee a closer integration of the electrical energy system with the industrial chemistry system, with the former providing reducing energy formerly available in fossil fuels and which enables the processing of highly oxidised but abundant feedstocks. The intermittency of renewable electricity supply and the economic benefits of flexible processing and closer integration between these two sectors will give rise to opportunities for new digital technologies. These will enable improved design and operation of emerging electrochemical processing technologies and provide new pathways to chemical building blocks (e.g. olefins) and fuels. The integration of the sectors also provides opportunities for cost savings in the electrical system through improved flexibility and demand management.
We propose three work packages (WP) to look at the challenges at different levels, and finally integrate as a whole solution:
- WP1 Digital twins of key electrochemical operation units and processes.
- WP2 Digitalisation of the value chain encompassing the integration between the chemical and electrical systems
- WP3 Policy, Society and Finance, including business models to capture value generation opportunities from industrial integration
Publications
Jiang H
(2023)
Machine learning based techno-economic process optimisation for CO2 capture via enhanced weathering
in Energy and AI
Leonzio G
(2023)
Towards ethylene production from carbon dioxide: Economic and global warming potential assessment
in Sustainable Production and Consumption
Leonzio G
(2023)
33rd European Symposium on Computer Aided Process Engineering
Leonzio G
(2022)
A Comparative Study of Different Sorbents in the Context of Direct Air Capture (DAC): Evaluation of Key Performance Indicators and Comparisons
in Applied Sciences
Leonzio G
(2022)
Environmental performance of different sorbents used for direct air capture
in Sustainable Production and Consumption
Tai X
(2023)
Dynamic optimisation of CO2 electrochemical reduction processes driven by intermittent renewable energy: Hybrid deep learning approach
in Digital Chemical Engineering
Xing L
(2023)
Data-driven surrogate modelling and multi-variable optimization of trickle bed and packed bubble column reactors for CO2 capture via enhanced weathering
in Chemical Engineering Journal
Xing L
(2023)
Combining machine learning with multi-physics modelling for multi-objective optimisation and techno-economic analysis of electrochemical CO2 reduction process
in Carbon Capture Science & Technology
Xuan J
(2022)
The equality, diversity and inclusion in energy and AI: Call for actions
in Energy and AI
| Title | Supplementary information files for: Data-driven surrogate modelling and multi-variable optimization of trickle bed and packed bubble column reactors for CO2 capture via enhanced weathering |
| Description | Supplementary information files for: Data-driven surrogate modelling and multi-variable optimization of trickle bed and packed bubble column reactors for CO2 capture via enhanced weathering Enhanced weathering (EW) of minerals could potentially absorb atmospheric CO2 at gigaton scale per year and store it as bicarbonate and carbonate in the ocean. However, this process must be accelerated by engineered reactors, in which optimal reaction conditions maximise the CO2 capture rate and minimise the energy and water consumption. In this work, trickle beds (TBs) and packed bubble columns (PBCs), operated with fresh water and CO2 -rich flue gas, are chosen as typical chemical reactors to perform the EW-based CO2 capture. We firstly develop experimentally validated physics-based mechanistic models then generate data to train data-driven surrogate models to achieve rapid prediction of performance and multi-variable optimization. Two surrogate models, namely, response surface methodology (RSM) and extended adaptive hybrid functions (E-AHF), are developed and compared, in which the effect of five design variables on three objective functions are investigated. Results show that the R2 for the prediction of CO2 capture rate (CR) and water consumption (WC) through RSM and E-AHF is higher than 0.84. For TB reactors, in particular, the calculated R2 is higher than 0.96. The prediction accuracy of energy consumption (EC) through the RSM approach is, however, relatively poor (R2 ~ 0.79), but is improved by using the E-AHF surrogate model, increasing to R2 ~ 0.89. The developed data-driven surrogate model can rapidly predict the performance indicators of TB and PBC reactors without solving complex mechanistic models consisting of many partial differential equations. After optimization using the surrogate models, improvements were achieved in the objectives for TB and PBC reactors as follows: CR increased by 37.8% and 13.1%, EC reduced by 37.4% and 23.8%, and WC reduced by 12.5% and 40.7%, respectively. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://repository.lboro.ac.uk/articles/dataset/Supplementary_information_files_for_Data-driven_surr... |
| Title | Supplementary information files for: Data-driven surrogate modelling and multi-variable optimization of trickle bed and packed bubble column reactors for CO2 capture via enhanced weathering |
| Description | Supplementary information files for: Data-driven surrogate modelling and multi-variable optimization of trickle bed and packed bubble column reactors for CO2 capture via enhanced weathering Enhanced weathering (EW) of minerals could potentially absorb atmospheric CO2 at gigaton scale per year and store it as bicarbonate and carbonate in the ocean. However, this process must be accelerated by engineered reactors, in which optimal reaction conditions maximise the CO2 capture rate and minimise the energy and water consumption. In this work, trickle beds (TBs) and packed bubble columns (PBCs), operated with fresh water and CO2 -rich flue gas, are chosen as typical chemical reactors to perform the EW-based CO2 capture. We firstly develop experimentally validated physics-based mechanistic models then generate data to train data-driven surrogate models to achieve rapid prediction of performance and multi-variable optimization. Two surrogate models, namely, response surface methodology (RSM) and extended adaptive hybrid functions (E-AHF), are developed and compared, in which the effect of five design variables on three objective functions are investigated. Results show that the R2 for the prediction of CO2 capture rate (CR) and water consumption (WC) through RSM and E-AHF is higher than 0.84. For TB reactors, in particular, the calculated R2 is higher than 0.96. The prediction accuracy of energy consumption (EC) through the RSM approach is, however, relatively poor (R2 ~ 0.79), but is improved by using the E-AHF surrogate model, increasing to R2 ~ 0.89. The developed data-driven surrogate model can rapidly predict the performance indicators of TB and PBC reactors without solving complex mechanistic models consisting of many partial differential equations. After optimization using the surrogate models, improvements were achieved in the objectives for TB and PBC reactors as follows: CR increased by 37.8% and 13.1%, EC reduced by 37.4% and 23.8%, and WC reduced by 12.5% and 40.7%, respectively. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://repository.lboro.ac.uk/articles/dataset/Supplementary_information_files_for_Data-driven_surr... |