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DYNAMIC MODELING OF RETORT PROCESSING USING NEURAL NETWORKS
Author(s) -
CHEN C. R.,
RAMASWAMY H. S.,
PRASHER S. O.
Publication year - 2002
Publication title -
journal of food processing and preservation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.511
H-Index - 48
eISSN - 1745-4549
pISSN - 0145-8892
DOI - 10.1111/j.1745-4549.2002.tb00855.x
Subject(s) - artificial neural network , retort , computer science , backpropagation , thermal diffusivity , generalization , transfer function , artificial intelligence , algorithm , mathematics , engineering , mathematical analysis , physics , electrical engineering , quantum mechanics , waste management
Two neural network approaches — a moving‐window and hybrid neural network — which combine neural network with polynomial regression models, were used for modeling F(t) and Qv(t) dynamic functions under constant retort temperature processing. The dynamic functions involved six variables: retort temperature (116–132C), thermal diffusivity (1.5–2.3 × 10 −7 m 2 /s), can radius (40–61 mm), can height (40–61 mm), and quality kinetic parameters z (15–39C) and D (150–250 min). A computer simulation designed for process calculations of food thermal processing systems was used to provide the fundamental data for training and generalization of ANN models. Training data and testing data were constructed by both second order central composite design and orthogonal array, respectively. The optimal configurations of ANN models were obtained by varying the number of hidden layers, number of neurons in hidden layer and learning runs, and a combination of learning rules and transfer function. Results demonstrated that both neural network models well described the F(t) and Qv(t) dynamic functions, but moving‐window network had better modeling performance than the hybrid ANN models. By comparison of the configuration parameters, moving‐window ANN models required more neurons in the hidden layer and more learning runs for training than the hybrid ANN models.