Abstract

Microbial electrolysis cells (MECs) have become more attractive because they can produce hydrogen (H2) from various organic sources with a low input electrical energy requirement. In this study, a dynamic model for the single-chamber anode brush MEC with acetic acid as a carbon source was developed for the first time. The dynamic model was calibrated and validated using both experimental and literature data, with a high coefficient of determination (R2) of 0.87–0.99, a high Pearson correlation coefficient (PCC) of 0.93–0.99, and a low normalized root mean squared error (NRMSE) of 0.221–0.078. For rapid simulation and optimization, the validated dynamic model was reproduced using an artificial neural network (ANN), and particle swarm optimization (PSO) was then applied to optimize the fitness functions derived from this ANN model (i.e., hydrogen production yield, hydrogen production rate, hydrogen fraction in biogas, and total energy recovery). As a result, the proposed method took about 3 s with an absolute error of <3.5 %. The optimization results revealed that an optimal condition (including applied voltage, operating temperature, substrate concentration, and reactor working volume) cannot exist to obtain the maximum values of all performance parameters simultaneously. Under the same importance, the co-optimal condition was determined at an applied voltage of 0.9 V, temperature of 36.2 °C, substrate concentration of 0.87 g/L, and reactor working volume of 0.05 L. At such co-optimal conditions, MEC achieved an H2 yield of 1168.6 ± 13.3 mL/g with a production rate of 185.6 ± 2.7 mL/L/h, an H2 fraction of 63.6 ± 0.2 %, and a total energy recovery (including electrical, substrate, heat, and mixing input energies) of 16.7 ± 0.2 %. Although these performances increased 1.5–2.3 times compared to non-optimal conditions, the low energy efficiency indicates the need for further investigation into combining MEC with sustainable energy sources, such as solar, wind and osmosis.

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