Modeling and control of a continuous crystallization process Part 1. Linear and non-linear modeling

Single-input–single-output (SISO) and multi-input–single-output (MISO) models of a cooling crystallization process are developed using time series and neural network approaches. The process description is simulated using first principles including the population balance to describe the crystal size...

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Bibliographic Details
Published inComputers & chemical engineering Vol. 23; no. 3; pp. 263 - 277
Main Authors Rohani, S., Haeri, M., Wood, H.C.
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 28.02.1999
Elsevier
Subjects
Applied sciences
Auto-regressive exogenous linear models
Chemical engineering
Continuous cooling crystallizers
Crystallization, leaching, miscellaneous separations
Exact sciences and technology
Modeling
Neural network non-linear models
Continuous cooling crystallizers
Auto-regressive exogenous linear models
Modeling
Neural network non-linear models
Continuous process
Non linear model
Crystallization
Time series
Potassium Chlorides
Autoregressive model
Neural network
Linear model
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Summary:Single-input–single-output (SISO) and multi-input–single-output (MISO) models of a cooling crystallization process are developed using time series and neural network approaches. The process description is simulated using first principles including the population balance to describe the crystal size distribution. The process model is identified from the input–output data of the process simulation using either a linear ARX model or a non-linear model constructed using a recurrent or a feedforward neural network.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0098-1354
1873-4375
DOI:10.1016/S0098-1354(98)00271-3