ABSTRACT
Process models may be developed by using either first principles such as material and energy balances, or process input and output information. The advantages of first principle models include the ability to incorporate the scientist’s view of the process into the model, describe the internal dynamics of the process, and explain the behavior of the process. Their disadvantages are the high cost of model development, the bias that they may have because of the model developer’s decisions, and the limitations on including the details due to 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 the actual value of the parameter. 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. However, this constraint has a moving upper limit, since new developments in computer hardware and software technologies permit faster execution. Fundamental models developed may be too large for faster execution to be used in process monitoring and control activities. These activities require fast execution of the models so that regulation of process operation can be made in a timely manner. The alternative model development paradigm is based on developing relations based on process data.
