Increasing bio-hydrogen production from microbial electrolysis cell using artificial gorilla troops optimization - Rezk, Hegazy; Sayed, Enas Taha (Frontiers in Energy Research, 2024)
The target of this paper is to improve the performance of the microbial electrolysis cell (MEC).Background. The performance of MEC including bio-hydrogen production and energy recovery is depending on the values of three controlling parameters including buffer concentration, dilution factor, and applied voltage.Problem. Therefore, defining the optimal values of three controlling parameters is the challenge of the work.In this paper the artificial gorilla troops optimization has been combined with and ANFIS modelling to increase the bio-hydrogen production from MEC. At first, using measured data, a model is created to simulate the MEC in terms of three controlling parameters. Then, for first time, an artificial gorilla troops optimization (AGTO) has been used to determine the optimal values of buffer concentration, dilution factor, and applied voltage to boost simultaneously bio-hydrogen production and energy recovery of MEC. To demonstrate the superiority of integration between ANFIS modelling and AGTO, the obtained results are compared with RSM methodology, and artificial neural network integrated with particle swarm optimization. Findings. For hydrogen yield model, the RMSE lowered from 67.5 using RSM to 5.562 using ANFIS (decreased by 91.7%) as compared to RSM. The R-square for prediction rises from 0.94 (using RSM) to 0.99 (using ANFIS) by about 5.32%. For the ANFIS model of energy recovery, the RMSE decreased from 31.7 to 2.83 utilising ANFIS, a decrease of 91%.The R-square for prediction rises from 0.95 (using RSM) to 0.986 (using ANFIS) by about 3.8%. Compared with measured data, the integration between ANFIS and AGTO succeed to increase the hydrogen yield from 576.3 mL/g-VS to 843.32 mL/g-VS. in sum, the total performance of the MEC has been increased by 34.74%, 29.9% and 24.38 % respectively compared to measured data, RSM and ANN-PSO.