eprintid: 16517
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/65/17
datestamp: 2023-12-19 03:23:01
lastmod: 2023-12-19 03:23:01
status_changed: 2023-12-19 03:06:24
type: article
metadata_visibility: show
creators_name: Hossain, S.K.S.
creators_name: Ayodele, B.V.
creators_name: Almithn, A.
title: Predictive Modeling of Bioenergy Production from Fountain Grass Using Gaussian Process Regression: Effect of Kernel Functions
ispublished: pub
keywords: Bioethanol; Ethanol; Fountains; Gaussian distribution; Gaussian noise (electronic); Regression analysis, Bio-energy; Bio-ethanol production; Bio-ethanols; Bioenergy productions; Exponentials; Gaussian process regression; Kernel function; Performance; Predictive models; Regression effects, Biomass
note: cited By 2
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 (R2) 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 R2 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. © 2022 by the authors.
date: 2022
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136976610&doi=10.3390%2fen15155570&partnerID=40&md5=082492228648d37bd80e394a7d2cad9e
id_number: 10.3390/en15155570
full_text_status: none
publication: Energies
volume: 15
number: 15
refereed: TRUE
issn: 19961073
citation:   Hossain, S.K.S. and Ayodele, B.V. and Almithn, A.  (2022) Predictive Modeling of Bioenergy Production from Fountain Grass Using Gaussian Process Regression: Effect of Kernel Functions.  Energies, 15 (15).   ISSN 19961073