To cite this article: Kasbawati, Rusni Samsir, Sulfahri, Andi Kresna Jaya & Anisa Kalondeng(2018): Determining an appropriate unstructured kinetic model for batch ethanol fermentationdata using a direct search method, Biotechnology & Biotechnological Equipment, DOI:10.1080/13102818.2018.1503563
To link to this article: https://doi.org/10.1080/13102818.2018.1503563
Determining an appropriate unstructured kinetic model for batch ethanolfermentation data using a direct search method
Kasbawatia, Rusni Samsira, Sulfahrib, Andi Kresna Jayaa and Anisa Kalondenga
aDepartment of Mathematics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Makassar, Indonesia; bDepartmentof Biology, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Makassar, Indonesia
ABSTRACTIn this paper, we study an extracellular process of a biochemical system such as batch ethanolfermentation system by considering an unstructured kinetic model with four different well-known models for the specific growth rate of the yeast cells. Then, we fit the unstructured mod-els to the experimental data for determining the appropriate model that can capture thedynamic behaviour of the batch ethanol fermentation experimental data. The fitting procedureis proceeded by minimising a least-squared error between the model solutions and the experi-mental data using a direct search method. Our simulations show that the unstructured modelwith Aiba-type structured model for the specific growth rate of the yeast cell has the bestapproximating ability to describe the dynamic of the batch ethanol fermentation data.
ARTICLE HISTORYReceived 25 November 2017Accepted 19 July 2018
Renewable energy has become an increasingly inter-esting research topic in recent years. It plays animportant role in providing alternative energy sourceswith a guarantee of sustainability. Bioethanol is one ofthe highly recommended energy sources, as it is arenewable and environmentally friendly alternative[1–4]. As one of the renewable and sustainable energysources, bioethanol has become a potential candidatefor replacing fossil fuels, which have greatly contrib-uted to generating high levels of pollution. Along withthe development of bioethanol production, optimalproduction processes and economic feasibility of theethanol industry are needed. Therefore, optimisationstudies through operating the variable design are acommon practice in the bioethanol industry, whichhas an unfeasible complexity.
In studying the optimal production of ethanol, severalaspects are investigated, including the fermentation pro-cess of the sugars to ethanol performed by yeast. This isa key process in the bioethanol industry where generat-ing a fermentation process with stable performancebecomes the biggest challenge for all researchers due tothe dependency of the system on the operating varia-bles. Besides that, producing rapid fermentation alsobecomes the main concern in the ethanol industry.
Several mathematical models have been proposedto investigate the optimal growth of the yeast cells[5–15]. Among the models, the unstructured model isthe simple model proposed to describe the growth ofyeast cells. A suitable design for the operating param-eters of the system can be identified when appropri-ate models are applied to study the biochemicalsystem. The suitable models are quantified whetherthey correctly reproduce the dynamic behaviour ofthe biochemical system. More deeply, the modelshould fit the experimental data generated from anexperimental process. In this research, several modelswere investigated to find the best model that can cap-ture the behaviour of the experimental data. Fourwell-known unstructured models were considered tostudy our batch ethanol fermentation data. Weassumed that the yeast cells are entities in solutionwhich interact with the environment in a way that thebiomass is described only by its concentration. Thefour studied models are Monod [15], Tiessier, Aiba [16]and Tyagi [17] model type for the growth of the yeastcells. Our goal was to carry out a comprehensive ana-lysis of the four mathematical expressions for model-ling our batch ethanol fermentation data. This studyalso aims to understand the impact of some modellingassumptions underlying the system and to provide
CONTACT Kasbawati [email protected]� 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis GroupThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permitsunrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
