One of the most urgent challenges facing society today is the continuously rising atmospheric CO₂ level, caused by anthropogenic emissions from various point sources such as cement factories, automobiles, and airplanes. The electrochemical CO₂ reduction reaction (CO₂RR) offers a promising pathway for the chemical and energy industries to convert CO₂ into valuable products, including carbon monoxide, methane, and ethylene, providing a value-added approach to CO₂ mitigation.

Long‑term stability is essential for the development and scale-up of CO₂ electrolysis technologies; however, meaningful lifetime measurements can only be achieved when the electrolyzer has reached a well‑defined and reproducible operational state. Achieving stable, and energy‑efficient CO₂RR operation remains a major challenge due to the complex interplay of operational parameters and the typically short duration of laboratory-scale experiments. Here, we emphasize the importance of standardized conditioning protocols and present a machine‑learning‑driven methodology that enables precise prediction, optimization, and adaptive control of CO₂ electrolyzers.

The conditioning or break‑in step plays a critical role in achieving a true baseline performance. This initial activation period allows electrode surfaces, ion‑conducting membranes, and gas-liquid interfaces to equilibrate under operating conditions, minimizing transient artifacts that could generate degradation mechanism. Without controlled conditioning, transient shifts in cell voltage, selectivity and conversion may occur.

The artificial neural network (ANN) was trained using four controllable input parameters: applied current density, gas humidifier temperature, cell temperature, and CO₂ inlet flow rate. After training, the model could predict key performance indicators, including cell voltage, product gas composition (CO, CO₂, H₂), and outlet flow rate. The developed model enabled identification of optimal parameter combinations that simultaneously improved several performance metrics, including CO selectivity, single‑pass conversion (SPC), and energy efficiency.

In this work, we employed zero‑gap electrolyzer cells in which catalyst‑coated electrodes are separated only by an ion exchange membrane. During operation, an anolyte was continuously recirculated through the anode side, and humidified CO₂ was supplied to the cathode.

Overall, this work highlights the role of standardized conditioning protocols in CO₂RR research, while machine learning can provide continuous, data-driven optimization. Integration of these protocols offers a strategy for development and scale-up of stable, high-performance CO₂ electrolyzer technology.