SUPERIOR PERFORMING ASSETS-ROLE OF INTELLIGENT PREDICTIONS BY MEGAVARIATE ANALYSIS AND NEURAL NETWORKS

In large process plants the process control computer systems are the depository of large amounts of operational information, data rich and information poor. The information obtained from such data can be used for a variety of purposes such as inferential measurements and model predictive control. However careful variable selection and data preprocessing is required for developing adequate models from this data. The objective of this paper is to examine in detail the methods to be adopted for developing successful empirical models from plant data. Three case studies have been presented from the hydrocarbon industry. The first case study deals with the development of a heat exchanger model by neural networks to be used in model predictive control. The second case study deals with the development of a soft sensor for predicting propane concentration in a depropaniser column. The third case study deals with development of a heat exchanger fouling model to be used as part of a preventive maintenance tool. In all the cases statistical model adequacy test showed that careful selection of variables and post modeling analysis helped in developing models which were adequate for the intended purposes.