article · Biotechnology & Biotechnological Equipment
Improving biohydrogen yields in microbial electrolysis cells requires models capable of capturing complex, non-linear relationships between reactor conditions and hydrogen output. A committee of five artificial neural networks was developed to predict biohydrogen production using six input parameters: substrate type, substrate concentration, pH, temperature, applied voltage, and reactor configuration. Built with fifty data points drawn from fifteen published studies, the individual networks achieved coefficients of determination ranging from 0.70 to 0.90 between predicted and experimental yields. Testing on new microbial electrolysis processes demonstrated strong agreement between observed and predicted outputs. Further sensitivity analysis revealed that substrate type had the largest impact on hydrogen yield, followed by applied voltage, substrate concentration, pH, reactor configuration, and temperature. Overall, the committee model successfully mapped these non-linear dynamics and can assist in identifying optimal conditions for scaling up microbial electrolysis systems.
Microbial electrolysis cells offer a method for producing clean hydrogen fuel from organic materials, but managing their operating conditions is difficult. Using computational models to predict how factors like voltage, acidity, and feedstocks affect hydrogen output allows engineers to forecast reactor performance without running numerous costly physical trials. This aids the design of more efficient biohydrogen systems.
This computational tool is relevant to process engineers and developers working on bioenergy systems. By predicting biohydrogen yields from variables such as feedstock type and applied voltage, it can help operators identify optimal operating windows during microbial electrolysis cell scale-up. The research remains at an early stage, as it was established using data from published studies and requires further testing in larger physical systems.
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The enhancement of hydrogen yield in microbial electrolysis cells (MECs) requires a robust process model that accurately relates the effect of anodic physicochemical input variables to the process output. Artificial neural networks (ANNs) have been used for the modelling of complex and non-linear processes. This paper reports the modelling of biohydrogen yield in MECs by using a committee of five ANNs. A topology of 6–(6, 8, 11, 12, 14)–1 was adopted, corresponding to the number of neurons of inputs, hidden (varied) and output layers. The ANN inputs were substrate type, substrate concentration, pH, temperature, applied voltage and reactor configuration. Model development was carried out with 50 data points from 15 published studies. The coefficients of determination (R2) between the experimental and predicted hydrogen yields for the five models were as follows: 0.90, 0.81, 0.85, 0.70 and 0.80. Model validation on new MEC processes showed a strong correlation between the observed and predicted hydrogen yields. Sensitivity analysis revealed that the performance of MEC was highly affected by variations in the substrate type, followed by applied voltage, substrate concentration, pH, MEC configuration and temperature in decreasing order. This study showed that the committee model accurately modelled the non-linear relationship between the considered physicochemical parameters of MEC and hydrogen yield, and thus could be used to navigate the optimization window in MEC scale-up processes.
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DOI: 10.1080/13102818.2015.1062732
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