Optimization of non‐steady‐state operation of reactors

Abstract

Non‐steady‐state operation of reactors is approached from the optimal control point of view. To find the best type of input, dynamic programming is used. The results show that for some systems, the optimal input is oscillatory in nature as has been found experimentally by others. A modification to conventional dynamic programming is used to avoid the need for interpolation and thus allow high‐dimensional systems to be handled more readily. Three chemical engineering systems of different complexity were considered to test the computational aspect of the approach. In all cases, dynamic programming gives the optimum policy with respect to the period, cycle‐split and amplitude.

Economic Optimization of an Industrial Semibatch Reactor Applying Dynamic Programming

“…In our study the task is to determine the variation of the feed rate F ( t ) with time to minimize the objective function defined above. For this purpose we used the modified dynamic programming algorithm described by ref . Usually with dynamic programming stages of equal time length are used for modeling the chemical process.…”

Simulation and optimization of a horizontal ammonia synthesis reactor using genetic algorithm

“…[2][3][4][5][6][7] Many of these studies have shown that mathematical modeling is a convenient way to analyze and optimization of the reactor. [6][7][8][9][10][11] In practice, a small improvement in the conversion degree considerably affects the overall economic balance of the ammonia production plant. Therefore, accurate modeling is essential for the analysis and design.…”

Optimization and Control of Semibatch Reactors

“…Nevertheless, the calculated reference trajectory depends on the model and is unpredictable. A similar set-point policy is obtainable directly by the technique of dynamic programming (de Tremblay and Luus, 1989;Luus, 1990a;Luus, 1990b;Luss and Rosen, 1991). The appealing feature is that we need do no prior analyses of characteristics of reaction modes to find a suitable operating path.…”

“…This optimization technique preserves the following attractive features in yielding a global optimal operating trajectory for batch or semibatch processes facing constraints of hardware: (a) the optimization routine is executed easily without excessive mathematical manipulation; (b) the computational load is moderate; (c) when the states of the system over the course of operation are chosen as optimal reference trajectories, one can devise a suitable control system to achieve properly the control objective. The way to formulate an optimal control problem and to solve it by dynamic programming appears in Appendix A (Luus, 1989).…”

“…34, No. 2, 1995 555 For a detailed computational algorithm to execute dynamic programming, the reader is referred to the literature (Luus, 1989).…”