In order to investigate the behavior of industrial processes for design, fault prevention, prediction, control, etc., a model of the process is necessary. Due to inherent nonlinearities proper to industrial processes, and/or nonlinearities due to the characteristics of the valves and pumps forming the entire industrial plant, nonlinear models are desired. Complete mathematical models of such plants proves to be time and efforts consuming, when not totally unrealizable. The fact that Artificial Neural Networks (ANNs) have been proven, by Cybenko, able to represent any nonlinear function, as well as their easy implementation, led to their widespread usage in the modeling community; often not at best and ending in controversial results. This paper proposes a methodology for designing and validating ANN models for modeling industrial plants, taking into consideration typical industrial constraints such as restricted data sets. The approach is applied to an industrial milk pasteurization plant.

In order to investigate the behavior of industrial processes for design, fault prevention, prediction, control, etc., a model of the process is necessary. Due to inherent nonlinearities proper to industrial processes, and/or nonlinearities due to the characteristics of the valves and pumps forming the entire industrial plant, nonlinear models are desired. Complete mathematical models of such plants proves to be time and efforts consuming, when not totally unrealizable. The fact that Artificial Neural Networks (ANNs) have been proven, by Cybenko, able to represent any nonlinear function, as well as their easy implementation, led to their widespread usage in the modeling community; often not at best and ending in controversial results. This paper proposes a methodology for designing and validating ANN models for modeling industrial plants, taking into consideration typical industrial constraints such as restricted data sets. The approach is applied to an industrial milk pasteurization plant.

In order to investigate the behavior of industrial processes for design, fault prevention, prediction, control, etc., a model of the process is necessary. Due to inherent nonlinearities proper to industrial processes, and/or nonlinearities due to the characteristics of the valves and pumps forming the entire industrial plant, nonlinear models are desired. Complete mathematical models of such plants proves to be time and efforts consuming, when not totally unrealizable. The fact that Artificial Neural Networks (ANNs) have been proven, by Cybenko, able to represent any nonlinear function, as well as their easy implementation, led to their widespread usage in the modeling community; often not at best and ending in controversial results. This paper proposes a methodology for designing and validating ANN models for modeling industrial plants, taking into consideration typical industrial constraints such as restricted data sets. The approach is applied to an industrial milk pasteurization plant.