Chuo, H. S. E. and Tan, M. K. and Tham , Heng Jin and Teo, Kenneth Tze Kin (2011) Optimization of fed-batch Baker’s yeast fermentation process using learning algorithm. In: Proceedings of 18th regional symposium on Chemical Engineering (RSCE2011), 27-28 Oktober 2011, Ho Chi Minh City, Vietnam.
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Official URL: http://ums.academia.edu/MinKengTan/Papers/1199076/...
Industrial fed-batch yeast fermentation process is a typical highly nonlinear dynamic process that requires good controlling technique and monitoring to achieve optimization. Due to the various uncertainties in the process, optimization to get the desired product quality with less capital and without compromising the environment is therefore, and has always been an issue of interest in chemical process control. This research aims to report an optimization in substrate feeding to maximize the yeast production and to minimize the production of ethanol under the influence of process disturbance using Q-learning (QL) algorithm. QL is a form of reinforcement learning. It is of interest to investigate the ability of QL in exploring and handling the uncertainties of a dynamic nonlinear system without prior experience of the system. The algorithm applies step-to-step learning to estimate the effect of the system upsets and seek for the best path to optimize the process. On the other hand, monitoring on concentration of ethanol is needed to prevent the quality of yeast from deterioration with the increasing ethanol concentration. The performance of the proposed controller is evaluated based on the boosting of the amount of final yeast produced and the disturbance rejection.
|Item Type:||Conference Paper (UNSPECIFIED)|
|Uncontrolled Keywords:||Fed-batch Baker’s, Fermentation process, Learning alorithm, Alorithm|
|Subjects:||Q Science > QA Mathematics|
|Divisions:||SCHOOL > School of Engineering and Information Technology|
|Deposited By:||IR Admin|
|Deposited On:||20 Jul 2012 16:47|
|Last Modified:||30 Dec 2014 09:42|
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