Combining machine learning with multi-physics modelling for multi-objective optimisation and techno-economic analysis of electrochemical CO2 reduction process

As a carbon capture and utilization (CCU) technology, gas diffusion electrode (GDE) based electrochemical CO2 reduction reaction (eCO2RR) can convert CO2 to valuable products, such as formate and CO. However, the elec-trode parameters and operational conditions need to be studied and optimised to enhance the performance and reduce the net cost of the eCO2RR process before its industrial application. In this work, a machine learning algorithm, i.e., extended adaptive hybrid functions (E-AHF) is combined with a multi-physics model for the data -driven three-objective optimisation and techno-economic analysis of the GDE-based eCO2RR process. The effects of eight design variables on the product yield (PY), CO2 conversion (CR) and specific electrical energy consump-tion (SEEC) of the process are analysed. The results show that the R 2 of the E-AHF model for the prediction of PY, CR and SEEC are all higher than 0.96, indicating the high accuracy of the developed machine learning al-gorithm for the prediction of the eCO2RR process. The process performance experiences a notable improvement after optimisation and is affected by a combination of eight variables, amongst which the electrolyte concentra-tion having the most significant impact on PY and CR. The optimal trade-off single-pass PY, CR and SEEC are 3.25 x10 - 9 kg s - 1, 0.663% and 9.95 kWh kg- 1 based on flow channels with 1 cm in length, respectively. The SEEC is reduced by nearly half and PY and CR are improved more than two times after optimisation. The production cost of the GDE-based eCO2RR process was approximately $378 t- 1product (CO and formate), much lower than that of traditional CO2 utilisation factories ($835 t- 1product). The electricity cost accounted for more than 80% of the total cost, amounting to $318 t- 1, indicating that cheaper and cleaner electricity sources would further reduce the production cost of the process, which is the key to the economics of this technology.