Research Initiation: Nonlinear Predictive Process Control

点击翻译按钮获取中文摘要

The economic viability of a chemical manufacturing process is dependent on maintaining and regulating the system in some prescribed manner. The ability to maintain product specifications while minimizing energy or natural resource consumption is accomplished by the use of process control systems. The goal of this project is to develop control system techniques that use a nonlinear dynamic model of the chemical process as a basis for feedback control. A nonlinear parameter estimation procedure will be developed to estimate important process parameters such as effectiveness factors or heat transfer coefficients, as well as process disturbances such as feed stream composition. In addition to parameter estimation, a nonlinear optimization-based procedure will be used to maintain a desired output variable trajectory, similar to techniques that have been proven successful for linear systems. The process model, characterized by a set of nonlinear differential equations, will be transformed to algebraic equations using orthogonal collocation on finite elements. Sequential quadratic programming will be used to solve the nonlinear optimization subject to the algebraic equation equality constraints and manipulated and state variable inequality constraints. This nonlinear control strategy will result in tighter regulation of important process variables, allowing a more optimal operation of chemical processes. This research will also result in a more complete understanding of linear predictive control techniques, since many of the important effects proposed for study have not been previously addressed for the linear system case.