An interpretable deep learning framework for photofermentation biological hydrogen production and process optimization

Abstract

The pursuit of sustainable and clean energy solutions has intensified research into photo-biological hydrogen production (PFHP), which offers a promising approach for converting biological waste into renewable hydrogen fuel. PFHP, however, presents considerable challenges due to the complex, non-linear biochemical reactions involved, making it difficult to accurately model and optimize using conventional techniques. This study introduces an advanced computational framework that integrates a CNN-LSTM-Attention neural network to efficiently model and optimize PFHP processes, addressing both the chemical engineering challenge of process non-linearity and the environmental imperative of waste utilization. The proposed framework utilizes convolutional layers for extracting spatial features, LSTM networks to capture time-dependent data, and attention mechanisms to focus on the most critical process variables, resulting in a highly accurate and efficient predictive model. Experimental validation shows that the CNN-LSTM-Attention model outperforms traditional methods, such as random forest, back propagation neural networks, and support vector machines, with a prediction accuracy of 98% for training data and 85% for testing data. Furthermore, the integration of the model with particle swarm optimization (PSO) predicted a maximum hydrogen production rate of 42.31 mL/h under optimized conditions, including temperature (29.44 °C), pressure (27.91 kPa), and pH (6.59), with an error margin of 0.3%. The findings underscore the potential of combining deep learning with heuristic optimization in enhancing PFHP processes, contributing to advancements in chemical process optimization and waste-to-energy conversion. This research provides a significant contribution to chemical engineering by offering a robust framework for optimizing renewable hydrogen production from organic waste, aligning with global objectives to reduce reliance on fossil fuels and lower environmental impact.