• Mohamad Afiq Mohd Asrul Department of Chemical Engineering and Energy Sustainability, Faculty of Engineering, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia.
  • Mohd Farid Atan Institute of Sustainable and Renewable Energy (ISuRE), Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia.
  • Hafizah Abdul Halim Yun Centre for Applied Learning and Multimedia, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia
  • Noraziah Abdul Wahab Institute of Social Informatics and Technological Innovations (ISITI), Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia.
  • Hu Hin Hung Department of Chemical Engineering and Energy Sustainability, Faculty of Engineering, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia.
  • Josephine Chang Hui Lai Institute of Sustainable and Renewable Energy (ISuRE), Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia.
  • Ivy Ai Wei Tan Department of Chemical Engineering and Energy Sustainability, Faculty of Engineering, Universiti Malaysia Sarawak, 94300, Kota Samarahan, Sarawak, Malaysia.




Biohydrogen, microbial electrolysis cell, biofilm growth, artificial neural network, mathematical model, optimization


The nonlinear phenomenon of the profile of substrate concentration and hydrogen production rate over 16 retention days in a 4 L double chamber of a microbial electrolysis cell (MEC) for bioelectrochemical production of hydrogen from sago wastewater validates the mathematical modeling results based on simplified microbial biofilm growth. The stoichiometric reaction and kinetics affect the substrate concentration curve behaviour, but the effects also include the bioelectrochemical balance for hydrogen production rate. 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 removed efficiency, coulombic efficiency, and energy efficiency determines the process limit of the model MEC. At an optimum applied potential of 0.485 V, anode surface area of 0.098 m2, anodic chamber volume of 4 L, and initial substrate concentration of 2,500.99 mg‧L-1, the MEC model reached maximum steady-state percentage at 81.99% of COD removed efficiency, 69.01% of Coulombic efficiency, and 7.47% of energy efficiency.


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