Statistical Process Control based Energy Monitoring of Chemical Processes

Advanced chemical process systems expected to maximize productivity and minimize cost and emission. Cost reduction needs Energy Monitoring and Targeting Systems that calculate actual energy usage, estimate energy needs at normal operation and highlight issues related to energy efficiency. Monitoring is based on continuous comparison of actual and estimated energy consumption. We developed Partial Least Squares (PLS) regression based targeting models that not only predict the expected value of energy consumption, but also visualize the operating regimes of the process. Soft-sensors working with PLS regression are widely used in chemical industry. The development of PLS models could be problematic because previous feature selection is needed. Since complex set of process variables determines Key Energy Indicators (KEIs) we applied Self-Organizing Map (SOM) models of that support visualization and feature selection of the process variables. Local linear target-models of different operating regions can be automatically determined based on the Voronoi diagram of the codebook of the SOM. We used Statistical Process Control (SPC) techniques to monitor the difference between the targeted and the measured energy consumption. We applied the concept of the resulted energy monitoring system at Heavy Naphtha Hydrotreater and CCR Reforming Units of MOL Hungarian Oil and Gas Company.