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

<p>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 electrode 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 consumption (SEEC) of the process are analysed. The results show that the R² 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 algorithm 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, among which the electrolyte concentration having the most significant impact on PY and CR. The optimal trade-off single-pass PY, CR and SEEC are 3.25×10^-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 GDEbased 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.</p>