摘要
A strategy of developing on-line optimization intelligent systems based on combiningflowsheeting simulation and optimization package with artificial neural networks(ANN)is presented inthis paper.A number of optimization cases for a certain chemical plant are obtained off-line byusing PROCESS-Ⅱ or other flowsheeting programming with optimization.Then,taking these cases astraining examples,we establish a neural network systems which can be used on-line as an optimizer toobtain setpoints from input data sampled from distributed control system through gross error detectionand data reconciliation procedures.Such an on-line optimizer possesses two advantages over nonlinearprogramming package:first of all,there is no convergence problem for the trained ANN to be usedonline;secondly,the frequency for setpoints updating is not limited because only algebraic calculationrather than optimization is required to be carried out on-line.Here two key problems ofimplementing ANN approaches to the on-line optimization
A strategy of developing on-line optimization intelligent systems based on combining flowsheeting simulation and optimization package with artificial neural networks (ANN) is presented in this paper. A number of optimization cases for a certain chemical plant are obtained off-line by using PROCESS - Ⅱ or other flowsheeting programming with optimization. Then, taking these cases as training examples, we establish a neural network systems which can be used on-line as an optimizer to obtain setpoints from input data sampled from distributed control system through gross error detection and data reconciliation procedures. Such an on-line optimizer possesses two advantages over nonlinear programming package: first of all, there is no convergence problem for the trained ANN to be used online; secondly, the frequency for setpoints updating is not limited because only algebraic calculation rather than optimization is required to be carried out on-line. Here two key problems of implementing ANN approaches to the on-line optimization are discussed: how to overcome local minimum often occured during the training of ANN and how to improve the prediction accuracy of ANNs models for meeting the optimization requirements. Results from an actual fractionation unit of a FCC plant in a refinery showed a 0.5% - 1.0% increase in the total recovery of light oil products. Details of the strategy used are described.
基金
Supported by the National Nature Science Foundation of China,the Research Foundation of General Corporation of China Petro-Chemical Industry and the Natural Science and Engineering Research Council of Canada.
