Dynamic optimisation of CO2 electrochemical reduction processes driven by intermittent renewable energy: Hybrid deep learning approach

Abstract

The increasing demand for net zero solutions has prompted the exploration of electrochemical CO₂ reduction reaction (eCO₂RR) systems powered by renewable energy sources. Here, we present a comprehensive AI-enabled framework for the adaptive optimisation of the dynamic eCO₂RR processes in response to the intermittent renewable energy supply. The framework includes (1). a Bi-LSTM (bidirectional long-short-term memory) to predict the meteorological data for renewable energy input; (2). a deep learning surrogate model to predict the eCO₂RR process performance; and (3). a NSGA-II algorithm for multi-objective optimisation, targeting the trade-off of the single-pass Faraday efficiency (FE), product yield (PY) and conversion. The framework seamlessly integrates the three different AI modules, enabling adaptive optimisation of the eCO₂RR system composed of electrolyser stacks and renewable energy sources, and providing insights into system's performance and feasibility under real-world conditions.