# Neural Algorithm Aided Operation of CO2 Electrolyzers

## TL;DR

This paper introduces a machine learning method to optimize CO2 electrolyzers for stable and efficient long-term operation.

## Contribution

A novel high-throughput testing and machine learning approach for adaptive optimization of CO2 electrolyzer operations.

## Key findings

- A high-throughput testing methodology combined with machine learning enables precise predictions of CO2 electrolyzer performance.
- The neural network model accurately predicts cell operation under new and untrained conditions.
- Adaptive optimization based on machine learning improves long-term stability and efficiency of CO2 electrolysis.

## Abstract

While the number
of reports on the electrochemical carbon dioxide
reduction increases at an ever-accelerating rate, achieving long-term
stable, selective, and energy efficient operation is still challenging.
This can be attributed mostly to the short length of lab-scale measurements
and the complexity of cell operation parameters. Here we introduce
a high-throughput cell operation testing methodology, including data
evaluation and process optimization by machine learning algorithms.
An autonomously operating test station allowed collection of enough
data to develop an artificial neural network model. When the model
is trained on a fraction of a large data set, predictions for the
operation of the same cell under different conditions are very precise.
Accurate predictions can also be made for newly assembled cells and
at parameter settings outside of the training parameter space. Our
results pave the way for the long-term stable operation of CO2 electrolyzers by the adaptive optimization of the process
conditions based on machine-learning-based holistic data evaluation.