NEURAL NETWORK MODELING OF END-OVER-END THERMAL PROCESSING OF PARTICULATES IN VISCOUS FLUIDS YANG MENG and HOSAHALLI S. RAMASWAMY 1 Department of Food Science McGill University Macdonald Campus, 21111 Lakeshore Ste-Anne-de-Bellevue, Quebec, Canada H9X 3V9 Accepted for Publication March 27, 2008 ABSTRACT Modeling of the heat transfer process in thermal processing is important for the process design and control. Artificial neural networks (ANNs) have been used in recent years in heat transfer modeling as a potential alternative to conventional dimensionless correlation approach and shown to be even better performers. In this study, ANN models were developed for apparent heat transfer coefficients associated with canned particulates in high viscous New- tonian and non-Newtonian fluids during end-over-end thermal processing in a pilot-scale rotary retort. A portion of experimental data obtained for the associated heat transfer coefficients were used for training while the rest were used for testing. The principal configuration parameters were the combination of learning rules and transfer functions, number of hidden layers, number of neurons in each hidden layer and number of learning runs. For the Newtonian fluids, the optimal conditions were two hidden layers, five neurons in each hidden layer, the delta learning rule, a sine transfer function and 40,000 learning runs, while for the non-Newtonian fluids, the optimal conditions were one hidden layer, six neurons in each hidden layer, the delta learning rule, a hyperbolic tangent transfer function and 50,000 learning runs. The prediction accuracies for the ANN models were much better compared with those from the dimensionless correlations. The trained network was found to predict responses with a mean relative error of 2.9–3.9% for the Newtonian fluids and 4.7–5.9% for the non-Newtonian fluids, which were 27–62% lower than those associated with the dimensionless correlations. Algebraic solutions were included, which could be used to predict the heat transfer coefficients without requiring an ANN. 1 Corresponding author. TEL: 514-398-7919; FAX: 514-398-7977; EMAIL: Hosahalli.Ramaswamy@ McGill.ca Journal of Food Process Engineering 33 (2010) 23–47. All Rights Reserved. © Copyright the Authors Journal Compilation © 2009 Wiley Periodicals, Inc. DOI: 10.1111/j.1745-4530.2008.00272.x 23