chapter  5
20 Pages

Modeling Food Thermal Processes Using Artiƒcial Neural Networks

Articial neural networks (ANNs) are being successfully applied for a wide range of problem domains in diverse areas including engineering, physics, ƒnance, medicine, and others related to purposes of prediction, classiƒcation, or control. This extensive success can be attributed to many factors: (1) Power of modeling: Neural networks (NNs) are very sophisticated techniques capable of modeling extremely complex functions. A priori knowledge of the system is not needed for constructing the ANN because the ANN will learn its internal representation from the input/output data of its environment and response. (2) Ease of use: NNs learn by example. The user of NNs gathers representative data and then invokes training algorithms to automatically learn the structure of the data. Although the user does need to have some heuristic knowledge of how to select and prepare

5.1 Introduction .......................................................................................................................... 111 5.2 Inspiration from Biological Neurons .................................................................................... 112 5.3 Principles of a Basic Artiƒcial Model .................................................................................. 113

5.3.1 NN Architecture ....................................................................................................... 113 5.3.2 Artiƒcial Neurons ..................................................................................................... 114 5.3.3 Learning Rules ......................................................................................................... 115

5.4 Developing NNs .................................................................................................................... 116 5.5 Applications in the Food Thermal Processing ..................................................................... 117

5.5.1 NN Modeling of Heat Transfer to Liquid Particle Mixtures in Cans Subjected to End-over-End Processing ..................................................................................... 119

5.5.2 Neuro-Computing Approach for Modeling of RTD of Carrot Cubes in a Vertical SSHE ........................................................................................................... 119

5.5.3 Modeling and Optimization of Constant Retort Temperature Thermal Processing Using Coupled NNs and Genetic Algorithms ........................................ 121

5.5.4 ANN Model-Based Multiple Ramp-Variable Retort Temperature Control for Optimization of Thermal Processing ....................................................................... 123

5.5.5 Analysis of Critical Control Points in Deviant Thermal Processes Using ANNs..... 123 5.6 Conclusions ........................................................................................................................... 125 Nomenclature ................................................................................................................................. 127 Abbreviations ................................................................................................................................. 128 References ...................................................................................................................................... 128

data, how to select an appropriate NN, and how to interpret the results, the level of user knowledge needed to successfully apply NNs is much lower than to use some more traditional nonlinear statistical methods. (3) High computational speed: The ANN is an inherently parallel architecture. The result comes from the collective behavior of a large number of simple parallel processing units. Therefore, once trained, ANN can calculate results from a given input very quickly. Because of this feature, ANNs have a greater potential to be used for the online control system than conventional modeling methods.