Multiple Shooting Based Research Of Dynamic Optimization Problem For Industrial Process

Strictly speaking, industrial process is a dynamic process, b ecause of th e state variables are changing with the advance of time and the transfer of space. In the past two decades, more and more process systems engineering researchers paid attention to the dynamic simulation and dynam ic optimization of industrial proc ess. Therefore, research of dynamic optimization not only b ecomes an important subject in chemical engineering design, optimal control and operation for the in dustry, but also becom es the frontier and hot in the field of interna tional process control. This paper analy zes the significance of dynam ic optimization, then, introduces several dif ferent dynamic optimization algorithm s, especially, focus on the fixed boundary dynam ic optimization problem, and improves some of the algorithms, and gives its application in industrial process. The main work and contributions are as follows:(1) The multiple shooting method is introduced to solve the two-point bou ndary value dynamic optimization problem in the petroleum and chemical process, which is used to solve the problem in the aerospace industry. The classic dynamic optimization examples of industrial process show that this algorithm c an ef fectively solve the above problem s. Meanwhile, this work lays a good foundation for the im proved algorithm.(2) Aiming at the fixed boundary dynam ic optimization problem, two ef fective improved algorithm s called based on the multiple shootin g m ethod with contro1parameterization and based on the multiple sho oting m ethod with iterative dynam ic programming are pres ented resp ectively. The specific steps and procedures of improved algorithms are also proposed. The classic dynam ic optimization examples of industrial process demonstrate that th e improved two algorithm s can ef fectively solve the fixed boundary dynam ic optim ization problem and the two im proved algorithm have a good optimal accuracy and solving effeciency.(3) Aiming at the fixed boundary dynam ic optim ization problem, an adaptive variable step Runge-Kutta method is used to solve differential equations. Based on the above, two ef fective improved algorithm called based on the m ultiple shooting method with adaptive variable step control parameterization and based on the multiple shooting method with adaptive variable st ep iterative dynam ic programm ing ar e presented. The specific steps and procedures of improved algorithm are also proposed. The classic dynamic optimization examples of industrial process demonstrate that the improved algorithms can ef fectively solve the fixed boundary dynam ic optimization problem and the two improved algorithm have a good stability, and im prove the optimal accuracy and solving efficiency.