Online Modelling and Forecasting of the Production of Isopropyl Myristate using TD- HMLP Neural Network

Abstract

The objective of this study is to measure modelling performance using anonlinemodelling and forecasting for the fabrication of Isopropyl Myristate in Semibatch Reactive Distillation. A network which called Trend Data Hybrid Multilayered Perceptron Networkwas applied to compare with conventional Hybrid Multilayered Perceptron Network. These two networks were coupled with an online learning algorithm as a nonlinear model. The input-output data for data training were determined from simulation of Isopropyl Myristate production using Aspen Plus. An online model was usedto predict the percentage of Isopropyl Myristate fabricationforthe determination and direction of future trends. The results of the both networks performance are based on the one step ahead forecasting, multi-step ahead forecasting and adjusted R square. The results of the multi step ahead forecasting indicated that Trend Data-Hybrid Multilayered Perceptron Network is preferable than the conventional Hybrid Multilayered Perceptron Network. Trend Data-Hybrid Multilayered Perceptron Networkhasimproved the online forecasting performance in the generated of more promising steps in multi-step ahead forecasting.