In Chapters 4 through 8, the development of various types of models was discussed. A large number of models used in chemical and biochemical processes are based on the application of laws of conservation of momentum, mass and energy. The models may be of a deterministic or stochastic nature. Simple models have analytical solutions. Rigorous models require numerical solutions of the model equations. Artificial neural network models are not based on any law but are developed using experimental data. The models developed are used for simulation after model validation. Simulation helps us in understanding the behaviour of the process. If the model predictions are similar to behaviour observed experimentally, then the predicted process behaviour can be accepted with more confidence. It is also possible to analyse the model and study the assumptions which are responsible for good predictions. This aspect is related to parametric sensitivity analysis.