Digitalization of microbial electrosynthesis: Understanding operational variables for ethanol production optimization

Microbial electrosynthesis (MES) is a bioelectrochemical technology devoted to carbon dioxide (CO2) conversion into value-added organic products. Traditionally, it has been addressed through trial-and-error approaches, with limited experimental data. This study introduces a data-driven framework to advance the MES digitalization through systematic analysis of operational variables influencing CO2 conversion. CO2 conversion to acetic acid and ethanol was investigated in MES cells integrated within an automated bench-scale platform. It enabled real-time control of key operational variables, i.e. dissolved CO2, pH, and pressure, while monitoring electrical conductivity, cell voltage and temperature. The ethanol production rate was improved by 35% (14.80 g m−2d−1), obtained controlling dissolved CO2 at 100–300 mg L−1, pH at 4.5–4.8 and pressure at 1.8 bar. Process data were analyzed through pattern discovery (based on both operation and production variables) and unsupervised clustering (based solely on operational variables). The latter allowed to identify distinct operational phases, which matched the real production stages, solely based on operation variables. Principal component analysis revealed that acetic acid production was primarily dependent on dissolved CO2, with secondary contributions from pH and electrical conductivity. Electrical conductivity emerged as an identifying signal for ethanol production. The proposed data-based methodology elucidates the key variable interactions and supports phase-specific monitoring and control strategies for MES optimization in the absence of time-consuming, punctual product measurements.