OPTIMIZATION OF MATHEMATICAL MODELING OF MICROBIAL ELECTROLYSIS CELL FOR THE PRODUCTION OF HYDROGEN FROM SAGO WASTEWATER SUBSTRATE - Mohamad Afiq, Mohd Asrul; Mohd Farid, Atan; Hafizah, Abdul Halim Yun; Noraziah, Abdul Wahab; Hung, Hu Hin; Josephine Lai, Chang Hui; Ivy Tan, Ai Wei (Penerbit UTM Press, 2024)
The experimental runs of a 4-L double-chamber microbial electrolysis cell (MEC) produce hydrogen from sago wastewater within the retention time of 16 days. The simulation of the simplified microbial biofilm growth model provides the results to validate the experimental data. However, the comparable profiles have a nonlinear phenomenon, such as the data deviation in substrate concentration and hydrogen production rate. The stoichiometric reaction and kinetics affect the behavior of the substrate concentration profile. In addition, the bioelectrochemical factors also affect the hydrogen production rate profile. The artificial neural network (ANN) predicts the experimental hydrogen production rate according to the input of pH of the catholyte at controlled applied potential of 0.8 V and current density of 0.632 A‧m-2. The convex method assists the model in finding the optimal input values that lead to the minimum mean square error (MSE) between modelling and experimental data. Evaluation of the COD removal efficiency, coulombic efficiency, and energy efficiency determines the process limit of the model MEC. At an optimum applied potential of 0.45 V, anode surface area of 0.06 m2, anodic chamber volume of 5.2 L, and initial substrate concentration of 2,476.14 mg‧L-1, the MEC model reached maximum steady-state percentage at 100.0% of COD removal efficiency, 50.0% of Coulombic efficiency, and 7.8% of energy efficiency.