间歇过程实时优化

Real-time optimization for batch process

由于生产过程中参数的不确定性和各种扰动作用,间歇生产过程的最优操作条件需要随时作出调整以保证生产的正常进行和满足生产产品质量的要求。针对间歇生产过程的操作条件的变化和不确定性因素的影响提出一种应用于间歇生产过程中的实时优化策略,其主要构成步骤包括动态模型建立、模型降阶、动态优化、在线监测、模型更新和在线调整,并以一个典型可逆酯化反应为研究对象进行方法和理论框架的运用,结果表明本文提出的基于在线测量的实时优化方法能够很好地针对生产过程中的操作条件变化和不确定性因素的影响,提高生产效率和产品质量。

In batch process, with uncertainty stemming from model mismatch and process disturbances, it is not sufficient to determine numerically an optimal solution on the basis of a nominal model and apply it to the process to implement an optimization-based control system. In this paper, a new integrated framework for real-time optimization of batch process with the change of operating condition and parameter uncertainty is presented. It mainly constructs with dynamic modeling, model reduction, dynamic optimization, on-line measurement, model updating and nonlinear control. And at last, the optimization strategy has been applied to a typical scale reversible reaction. The results show the feasibility of the proposed strategy to deal with the change of operating condition and the effect of parameter uncertainty and a significant increase in efficient and quality of the process.