Process models may be developed by using either first principles such as material and energy balances, or process input and output information (data). First principles (fundamental) models describe the internal dynamics of the process based on physical, chemical or biological laws, and explain the behavior of the process. But the cost of model development is high. They may be biased by the views and speculations of the model developer, and are limited by the lack of information about specific model parameters. Often, some physical, chemical or transport parameters are computed using empirical relations, or they are derived from experimental data. In either case, there is some uncertainty about their accuracy. As details are added to the model, it may become too complex and too large to run model computations on the computer within an acceptable amount of time. Fundamental models developed may be too large for computations that are fast enough to be used in process monitoring and control activities. These activities require fast update of model predictions so that regulation of process operation can be made in a timely manner.