Genetic Algorithms Using for a Batch Fermentation Process Identification

Benjamin, Kouame Kan; Emmanuel, Assidjo Nogbou; Alonso, David; Yao, Benjamin

doi:10.3923/jas.2008.2272.2278

Cited by 13 publications

(3 citation statements)

References 14 publications

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“…The tools and methods reviewed here are ones that have proved to be useful with a sensor system. Currently, with profound studies into the theory of soft sensor control and the ongoing advancement of engineering technology, some modern methods have appeared and developed rapidly to solve the problems which are hard to measure for soft sensor models, such as FL [43], partial least squares (PLS) [44], SVM [45], support vector regression (SVR), radial basis function (RBF), NN [23], GA [46], and PLVMs [32]. Some important soft-sensing modeling methods, shown in Figure 4 are used for the prediction of quality variables.…”

Section: Review Of Modern Soft-sensing Models and Optimization Technimentioning

confidence: 99%

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Zhu

Rehman

Wang

et al. 2020

Sensors

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For effective monitoring and control of the fermentation process, an accurate real-time measurement of important variables is necessary. These variables are very hard to measure in real-time due to constraints such as the time-varying, nonlinearity, strong coupling, and complex mechanism of the fermentation process. Constructing soft sensors with outstanding performance and robustness has become a core issue in industrial procedures. In this paper, a comprehensive review of existing data pre-processing approaches, variable selection methods, data-driven (black-box) soft-sensing modeling methods and optimization techniques was carried out. The data-driven methods used for the soft-sensing modeling such as support vector machine, multiple least square support vector machine, neural network, deep learning, fuzzy logic, probabilistic latent variable models are reviewed in detail. The optimization techniques used for the estimation of model parameters such as particle swarm optimization algorithm, ant colony optimization, artificial bee colony, cuckoo search algorithm, and genetic algorithm, are also discussed. A comprehensive analysis of various soft-sensing models is presented in tabular form which highlights the important methods used in the field of fermentation. More than 70 research publications on soft-sensing modeling methods for the estimation of variables have been examined and listed for quick reference. This review paper may be regarded as a useful source as a reference point for researchers to explore the opportunities for further enhancement in the field of soft-sensing modeling.

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Section: Review Of Modern Soft-sensing Models and Optimization Technimentioning

confidence: 99%

Modern Soft-Sensing Modeling Methods for Fermentation Processes

Zhu

Rehman

Wang

et al. 2020

Sensors

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“…[15] The mathematical model of the batch fermentation process is presented by the following nonlinear differential equation system [25]:…”

Section: Problem Formulationmentioning

confidence: 99%

Kinetic parameters estimation of protease production using penalty function method with hybrid genetic algorithm and particle swarm optimization

Ghovvati

Khayati

Attar

et al. 2016

Biotechnology & Biotechnological Equipment

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Almost all optimization techniques are restricted by the problems' dimensions and large search spaces. This research focuses on a special hybrid method combining two meta-heuristic techniques, genetic algorithms (GA) and particle swarm optimization (PSO) that aims at overcoming this issue. This method investigates the potential impact of constraints (or the feasible regions) on the search pattern of GA and PSO. The proposed algorithm was applied for parameter estimation of batch fermentation process for alkaline protease production by Bacillus licheniformis in submerged culture. Furthermore, a comparison of proposed hybrid GA/PSO with pure GA and pure PSO was carried out. The results revealed that combination of these two meta-heuristic algorithms speeds up the search (about two-fold faster) in comparison to pure algorithms, since it benefits from synergy. Hence, the proposed method can be considered as an applicable method for parameter estimation of biological models in particular for large search space problems. Also, it was concluded that PSO has a slightly better performance and possesses better convergence and computational time than GA.

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“…GAs are highly relevant for industrial applications, because they can handle problems with nonlinear constraints, multiple objectives, and dynamic components -properties that frequently occur in real-life problems (20,25). GA have been successfully applied in a number of areas (2,5,12,31) and still find increasing acceptance; they are often employed as an alternative optimization tool to the conventional methods (20). the effectiveness and robustness of GAs have already been demonstrated in fed-batch cultivation processes (8,32,33,34,35,36,39).…”

Section: Introductionmentioning

confidence: 99%