2024/01/01 by Phan Khanh Thinh Nguyen, Thi Thu Ha Tran, Tuan Loi Nguyen
Engineering · Environmental Science · #Anaerobic Digestion and Biogas Production #Biodiesel Production and Applications #Wastewater Treatment and Nitrogen Removal
paper · pdf · doi:10.1155/2024/5630435
openalex publication_date 2024/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30
Dark fermentative hydrogen (H 2 ) production from water hyacinth (WH) is considered a potentially sustainable process that helps minimize this weed’s harmful effects on the ecosystem and dependence on fossil fuels. To create a quick and precise tool for simulating and optimizing this process, this study applied the combination of the physics‐based model and artificial intelligence approaches for the first time. The physics‐based model was used as a computational experimental dataset generator to save time and cost in acquiring experimental data. Such a synthetic dataset was used to train the artificial neural network (ANN) model, which can predict the performance of dark fermentation fed with water hyacinth (DF@WH) in a fraction of the time. The particle swarm optimization (PSO) algorithm was then integrated to identify the ideal conditions for DF@WH. H 2 productivity and total energy recovery were selected as objectives based on basic operating parameters such as substrate concentration, initial pH, temperature, and operating time. The optimization results revealed that the maximum values of H 2 productivity (i.e., the maximum yield of 266.8 mL/g‐TS and the maximum rate of 80.5 mL/L/h) and energy efficiency (i.e., 11.4%) cannot be achieved simultaneously under a specific optimal condition. Instead, when these targets were considered equally important, the balance optimal condition was determined at a substrate concentration of 8.9 g‐TS/L, an initial pH of 6.5, a temperature of 33.9°C, and an operating time of 28.2 h. Under such conditions, H 2 productivity can be achieved with a yield of 200.2 mL/g‐TS at a production rate of 62.9 mL/L/h and a total energy recovery of 11.0%.