title: Assessment of bioethanol yield by S. cerevisiae grown on oil palm residues: Monte Carlo simulation and sensitivity analysis.
abstract: Oil palm trunk (OPT) sap was utilized for growth and bioethanol production by Saccharomycescerevisiae with addition of palm oil mill effluent (POME) as nutrients supplier. Maximum yield (YP/S) was attained at 0.464g bioethanol/g glucose presence in the OPT sap-POME-based media. However, OPT sap and POME are heterogeneous in properties and fermentation performance might change if it is repeated. Contribution of parametric uncertainty analysis on bioethanol fermentation performance was then assessed using Monte Carlo simulation (stochastic variable) to determine probability distributions due to fluctuation and variation of kinetic model parameters. Results showed that based on 100,000 samples tested, the yield (YP/S) ranged 0.423-0.501g/g. Sensitivity analysis was also done to evaluate the impact of each kinetic parameter on the fermentation performance. It is found that bioethanol fermentation highly depend on growth of the tested yeast.
authors.affiliation: School of Chemical Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia.
authors.forename: Mohd Dinie Muhaimin
authors.initials: MD
authors.lastname: Samsudin
authors.affiliation: School of Chemical Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Pulau Pinang, Malaysia. Electronic address: chmashitah@usm.my.
authors.forename: Mashitah
authors.initials: M
authors.lastname: Mat Don
mesh_terms: D028023:Arecaceae / Q000737:chemistry / Q000378:metabolism*; D001709:Biotechnology / Q000379:methods*; D003198:Computer Simulation; D000431:Ethanol / Q000378:metabolism*; D005285:Fermentation; D005947:Glucose / Q000378:metabolism; D007220:Industrial Waste; D007700:Kinetics; D008962:Models, Theoretical; D009010:Monte Carlo Method; D012441:Saccharomyces cerevisiae / Q000378:metabolism*
chemical_list: D007220:Industrial Waste; D000431:Ethanol; D005947:Glucose
keywords: Bioethanol; Modeling; Monte Carlo simulation; Oil palm residues; Sensitivity analysis