Machine learning-based prediction and optimization of green hydrogen production technologies from water industries for a circular economy
- Publisher:
- Elsevier
- Publication Type:
- Journal Article
- Citation:
- Desalination, 2023, 567, pp. 116992
- Issue Date:
- 2023-12-01
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| Machine learning-based prediction and optimization of green hydrogen production technologies from water industries for a circular economy.pdf | Accepted version | 1.98 MB |
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Currently, there exists a significant number of green hydrogen production (GHP) technologies based on scaling-up issues (SCUI). Optimal prediction and process optimization could be one of the most substantial SCUI of GHP. Machine learning (ML)-based prediction and optimization of GHP technologies from water industries for a circular economy (CRE) could be a plausible solution for these SCUI. We studied a detailed techno-economic and environmental feasibility study, which recommended proton exchange membrane (PEM) and dark fermentation (DF) as the most promising and environment-friendly technologies for GHP. Thus, the present investigation aims to apply different ML models to predict and optimize the GHP of DF and PEM technologies to solve the SCUI. The results revealed K-nearest neighbor and random forest are the best-fitted models to predict GHP for DF and PEM, correspondingly based on the regression co-efficient (R2), root mean squared error (RMSE) and mean absolute error (MEA). The permutation variable index (PVI) recommended that chemical oxygen demand (COD), butyrate, temperature, pH and acetate/butyrate ratio are the most influential process parameters in decreasing order for DF, while temperature, cell areas, cell pressure, cell voltage and catalysts loadings are the most effective process parameters for PEM in reducing order. The partial dependency analysis (PDA) demonstrated GHP increases with increasing COD values up to 10 mg/L, and the optimal temperature range in the DF process is between 25 and 30 °C. On the other hand, cell temperature up to 35 °C should be considered optimum for PEM, and 40–70 cm2 cell areas could produce a significant GHP. In summary, the present study underscores the potential of machine learning (ML) and artificial intelligence (AI) as promising techniques for optimizing GHP, ultimately addressing scaling-up challenges in large-scale industrial GHP production and ensuring a sustainable hydrogen economy (HE).
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