Flexible operation of modular electrochemical CO2 reduction processes

Electrochemical CO2 reduction (eCO(2)R) is an emerging technology that is capable of producing various organic chemicals from CO2, but its high electricity cost is a big economic obstacle. One solution to reduce the cumulative electricity cost is demand side management, i.e., to adjust the power load based on time-variant electricity prices. However, varying the power load of CO2-electrolyzers often leads to changes in Faraday efficiency towards target components and thereby influences the product composition. Such deviations from the target product composition may be undesired for downstream processes. We tackle this challenge by proposing a flexible operating scheme for a modular eCO(2)R process. We formulate the economically optimal operation of an eCO(2)R process with multiple electrolyzer stacks as a parallel-machine scheduling problem. Adjusting the power load of each sub-process properly, we can save electricity costs while the desired product composition is met at any time. We apply an algorithm based on wavelet transform to solve the resulting large-scale nonlinear scheduling problem in tractable time. We solve each optimization problem with a deterministic global optimization software MAiNGO. We examine flexible operation of a modular eCO(2)R process for syngas production. The case studies show that the modular structure enables savings in the cumulative electricity cost of the eCO(2)R process via flexible operation while deviations in the syngas composition could be reduced. Also, the maximum ramping speed of the entire process is found to be a key parameter that strongly influences the cost saving. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND license(https://creativecommons.org/licenses/by-nc-nd/4.0/)

Automated summary

Electrochemical CO2 reduction (eCO2R) is an emerging technology that can produce organic chemicals from CO2, but its high electricity cost is a significant economic obstacle. To reduce electricity costs, researchers propose a flexible operating scheme for a modular eCO2R process. By adjusting the power load of each sub-process, they can save electricity costs while meeting the desired product composition. They use a wavelet transform-based algorithm to solve the scheduling problem and find that the maximum ramping speed of the entire process is a key parameter that strongly influences cost savings.