Numerical Simulation of Distributed Dynamic Systems using Hybrid Tools of Intelligent Computing

Background

An input-output process model is a set of equations to predict the future outputs of a system based on input data. The model should closely represent the relationships between inputs, outputs, and system variables to reduce the error caused by plant-model mismatch. Distributed dynamic systems are often represented by partial differential equations. For example, a large number of processes in different industries are distributed in nature, thus a significant number of modeling, control, and optimization applications arise for these types of systems. The state variables depend on at least two independent variables (i.e. time and space). For example, a tubular reactor may be modeled by a second order partial differential equation describing the change in axial position in the reactor and in time. Partial differential equations can be discretized so the system is represented by ordinary differential equations at separate discrete spatial points, which can then be used to synthesize a model-based control.