基于动态模型的在线反馈优化

针对化工过程动态波动明显、优化模型存在较多的不确定性等特点，提出了一种考虑过程不确定性、基于过程动态模型的在线反馈优化策略。将过程动态模型按一定周期离散化为差分方程，基于差分方程进行动态优化，优化目标函数为优化时域的终端时刻的经济指标，优化变量为过程的操作变量，采用非线性规划作为优化算法；优化结果在实施后根据可测输出进行在线反馈，在优化模型的差分方程中引入误差修正项，将对应时刻的状态变量和相关变量的实际值代入可求出误差修正项，从而实现在线反馈优化。仿真结果表明，与传统的稳态操作优化相比，基于动态模型的反馈优化同样可将过程运行于最优操作点，同时具有很强的实时性，在外界干扰出现时可以立即作出反应，将过程推向最优操作点。

On-line feedback optimization based on dynamic model

Chemical processes are always of great dynamic fluctuations and uncertainty.An on-line feedback optimization with model uncertainty was presented to resolve the problem.The process dynamic model was discretized as difference equations according to a specific period,and dynamic optimization was carried out based on the difference equations.The profit at the end of optimization horizon was used as optimization objective,manipulation variables were used as optimization variables,a non-linear program was used as optimization algorithm.On-line feedback was implemented according to measured variables after the optimization results were adopted.In the difference equations correction terms were introduced,which could be calculated when the actual values of process variables were imported into the difference equations.Simulations showed that compared with steady optimization,feedback optimization not only could also make the process run at the optimum operating point but also could have high real-time quality.