Adaptive on-line steady-state optimization of slow dynamic processes
Adaptive on-line steady-state optimization of slow dynamic processes
复制标题
DOI：
10.1016/0005-1098(78)90087-0
复制
发表时间：
1978-05
期刊：
影响因子：
--
通讯作者：
W. Bamberger;R. Isermann
中科院分区：
文献类型：
--
作者：
W. Bamberger;R. Isermann
开发了一个软件包OLIOPT，用于在相对较短的时间内对缓慢动态过程的稳态行为进行在线优化。在启动阶段，独立变量输入根据特定的测试信号改变。对非线性动态过程模型进行在线辨识。基于模型的静态部分和已知的输入，计算性能指标的梯度。优化算法将输入改变为它们的最优值。非线性模型的在线识别继续进行，最佳预测得到改善。在最后一个阶段，输入取其最佳值，过程遵循前馈控制，达到其最佳稳态。该方法适用于具有一个或多个可变输入的工业过程，其中效率的小幅提高会产生相对较大的财务回报。结果示出的热试验过程的在线优化。
A software package OLIOPT was developed for the on-line optimization of the steady-state behaviour of slow dynamic processes in a relatively short time period. In the starting phase, the independently variable inputs are changed according to a special test signal. A nonlinear dynamic process model is identified on-line. Based on the static part of the model and the known inputs, the gradients of the performance index are calculated. An optimization algorithm changes the inputs towards their optimal values. On-line identification of the nonlinear model continues and the prediction of the optimum improves. In the last phase, the inputs take their optimal values and the process follows, feedforward controlled, to its optimal steady-state. The method is suited for industrial processes with one or more variable inputs, where a small gain in efficiency turns out to give a relatively large financial return. Results are shown for the on-line optimization of a thermal pilot process.