Predictive Modeling of Bioenergy Production from Fountain Grass Using Gaussian Process Regression: Effect of Kernel Functions

Safdar Hossain Sk, Bamidele Victor Ayodele and Abdulrahman Almithn
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Safdar Hossain Sk: Department of Chemical Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia
Bamidele Victor Ayodele: Department of Chemical Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia
Abdulrahman Almithn: Department of Chemical Engineering, College of Engineering, King Faisal University, P.O. Box 380, Al-Ahsa 31982, Saudi Arabia

Energies, 2022, vol. 15, issue 15, 1-13

Abstract: Experimental studies have shown that bioethanol production from biomass sources has been reported to be influenced by several process parameters. It is not entirely known, however, how the interaction of these factors affects the concentration of bioethanol production. In this study, the use of Gaussian Process Regression (GPR) in predictive modeling of bioethanol production from fountain grass has been investigated. Parametric analysis showing the interaction effect of time, pH, temperature, and yeast extract on the bioethanol production was examined. The effect of kernel functions on the performance of the GPR in modeling the prediction of bioenergy output was also examined. The study shows that the kernel function, namely, rotational quadratic (RQGPR), squared exponential (SEGPR), Matern 5/2 (MGPR), exponential (EGPR), and the optimizable (Opt.GPR.), had varying effects on the performance of the GPR. Coefficients of determination (R 2 ) of 0.648, 0.670, 0.667, 0.762, and 0.993 were obtained for the RQGPR, SEGPR, MGPR, EGPR, OptGPR, respectively. The OptGPR with R 2 of 0.993 and RMSE of 45.13 displayed the best performance. The input parameters analysis revealed that the pH of the fermentation medium significantly influences bioethanol production. A proper understanding of how the various process variables affect bioethanol production will help in the real-time optimization of the process in the eventuality of scale-up.

Keywords: bioenergy; bioethanol; biomass; Gaussian process regression; kernel functions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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