دانلود رایگان مقاله مدل اقتصادی کنترل پیش بینی فرآیندهای شیمیایی با عدم قطعیت پارامتر

Abstract

This work proposes an EMPC (Economic Model Predictive Control) algorithm that integrates RTO (Real Time Optimization) and EMPC objectives within a single optimization calculation. Robust stability conditions are enforced on line through a set of constraints within the optimization problem. A particular feature of this algorithm is that it constantly calculates a set point with respect to which stability is ensured by the aforementioned constraints while searching for economic optimality over the horizon. In contrast to other algorithms reported in the literature, the proposed algorithm does not require terminal constraints or penalty terms on deviations from fixed set points that may lead to conservatism. Changes in model parameters over time are also compensated for through parameter updating. The latter is accomplished by including the parameters’ values as additional decision variables within the optimization problem. Several case studies are presented to demonstrate the algorithm’s performance.

4. Conclusions

An Economic Model Predictive Control online algorithm was proposed that is robust to model error. The algorithm uses the set point as an additional decision variable for economic optimization and it also updates the model parameters to account for parametric errors. The proposed EMPC algorithm was successfully applied to three nonlinear reaction engineering applications and it was compared to a previously reported technique. It was shown that the proposed EMPC algorithm may result in improvement in profit as compared to previously reported formulations that use terminal constraints and/or modified costs to satisfy dissipativity. Also, the algorithm provides robust stability gence of the parameter to its true value in case of initial parameter error. Smaller values of were shown to increase the speed of convergence. Also, it was shown that regardless of the choice of Q and the cost ultimately converged to the best economic optimum. On the otherhand,the algorithmhashighcomputational requirements since it enforces the robust stability conditions on-line. These limitations may be reduced in the future by the use of more effective software and hardware or by the use of a distributed computation strategy.