基于迁移学习工况划分的裂解炉收率PSO-LS-SVM建模

乙烯裂解炉收率的实时预测对于生产的先进控制及节能降耗具有重要意义。实际生产过程中,不同工况的收率具有较大差别,采用单一工况、单一模型无法满足生产需要。考虑到裂解炉不同运行过程中的相似性,同时为了减小建模过程中典型样本的采集成本,有效利用历史数据,辅以迁移学习算法实现工况的高精度划分。不同工况采用泛化能力强、训练速度高的最小二乘支持向量机建模,并利用粒子群算法对LS-SVM的参数寻优,进一步提高模型精度,从而实现了多工况、多模型的高精度收率预测。基于某乙烯厂现场数据的实验结果表明,多工况、多模型的预测效果更准确合理,PSO优化LS-SVM建立的裂解炉收率模型预测精度更高,趋势跟踪性能良好。

Modeling of cracking furnace yields with PSO-LS-SVM based on operating condition classification by transfer learning

The prediction of ethylene cracking furnace yields on line is significant in industrial production for advanced control and energy efficiency. Due to the great differences between different operating conditions, single condition and modeling may not satisfy the requirement of practical process. Considering the similarity of cracking furnace and the reduction of acquisition cost, the history data are utilized assisting transfer learning to improve the accuracy of operating condition classification. Least squares support vector machines (LS-SVM) is employed in modeling cracking furnace yields in different operating conditions, which enjoy stronger generalization ability and faster convergence speed compared with standard SVM. The accuracy is further improved by optimizing parameters of LS-SVM with particle swarm optimization (PSO), and thus establishing different operating condition models for yields prediction. The simulations and operating condition classifications are given based on the real industrial data to demonstrate that the operating condition classification is more reasonable. The prediction of LS-SVM optimized with PSO is more accuracy and behaves good trend tracking performance.