Control of Batch Evaporative Crystallization of Sugar by Means of Adaptive Simulation

Fagervik, Kaj; Konstari, Olli; Schalien, Randolf von

doi:10.23919/acc.1988.4789805

Cited by 5 publications

(3 citation statements)

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“…We choose, as can be seen from matrix A, R,, R,, and R, as free variables and assume that we can describe the kinetics of the yeast fermentation process in the glucose consumption range by means of only reaction numbers I (ethanol production), 2 (maintenance), and 11 (biomass growth) in matrix B. For reaction numbers 11 and 1 we choose reaction rate expressions of the Monod type: (4) For the maintenance reaction we choose a reaction rate which only depends on the concentration of biomass:…”

Section: Phenomenological Expressionsmentioning

confidence: 99%

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Adaptive on‐line model for aerobic Saccharomyces cerevisiae fermentation

Schalien

Fagervik

Saxén

et al. 1995

Biotech & Bioengineering

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In order to study and control fermentation processes, indirect on-tine measurements and mathematical models can be used. In this article we present a mathematical on-line model for fermentation processes. The model is based on atom and partial mass balances as well as on equations describing the acid-base system. The model is brought into an adaptive form by including transport equations for mass transfer and unstructured expressions for the fermentation kinetics. The state of the process, i.e., the concentrations of biomass, substrate, and products, can be estimated on-line using the balance part of the model completed with measurement equations for the input and output flows of the process. Adaptivity is realized by means of on-line estimation of parameters in the transport and kinetic expressions using recursive regression analysis. These expressions can thus be used in the model as valid equations enabling prediction of the process. This makes model-based automation of the process and testing of the validity of the measurement variables possible. The model and the on-line principles are applied to a 3.5-L laboratory tormentor in which Saccharomyces cerevisiae is cultivated. The experimental results show that the model-based estimation of the state and the predictions of the process correlate closely with high-performance liquid chromatography (HPLC) analyses. (c) 1995 John Wiley & Sons, Inc.

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Section: Phenomenological Expressionsmentioning

confidence: 99%

“…where N is the corrent sampling number. According to C~p p e l l a ,~ we have chosen pLX,max = 0.45 h-' and K, = 0.5 g/L in Equation (4). The corresponding kinetic constants in Equation (5) have, according to performed fermentation runs at our laboratory, been found to be pp,max = 1.0 h-' and K, = 0.3 g/L.…”

Section: Estimation Of Kinetic Parametersmentioning

confidence: 99%

Adaptive on‐line model for aerobic Saccharomyces cerevisiae fermentation

Schalien

Fagervik

Saxén

et al. 1995

Biotech & Bioengineering

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“…On the contrary, batch evaporative crystallization is economically more favorable than batch cooling crystallization if the solubility of solute depends on slightly temperature. With the object of enhancing the productivity of batch crystallization process, there are a number of researches focused on optimization and controlling both of batch crystallization by cooling and evaporation processes [1][2][3][4][5][6]. Paengjuntuek et al [7] studied the optimization of batch crystallization processes.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Modeling and Control for a Batch Heating/Cooling Evaporative Crystallization Process

Daosud

Thampasato

Kittisupakorn

2017

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Abstract. Crystallization processes have been widely used for separation in many fields to provide a high purity product. In this work, dynamic optimization and neural network (NN) have been applied to improve the quality of the product: citric acid. In the dynamic optimization, optimization problems maximizing both crystal yield and crystal size have been formulated. The neural networks have been developed to provide NN models to be used in the formulation of not only neural network inverse control (NNDIC) but also neural network model predictive control (NNMPC) strategies. The Levenberg Marquadt algorithm has been used to train the network and optimal neural network architectures have been determined by a mean squared error (MSE) minimization technique. In addition, a neural network model has been designed to provide estimates of the temperature and the concentration of the crystallizer. These estimates have been incorporated into the NNMPC controller. In the NNDIC controller, another neural network model has been applied to predict the set point of jacket temperature. The simulation results have shown that the obtained crystal size is increased by 19% and 30% compared to that by cooling and evaporation methods respectively and the obtained yield is increased more than 50%. The robustness of the proposed controller is investigated with respect to parameters mismatches. The results have shown that the NNMPC controller provides superior control performances in all case studies.

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