A Methodology for Data Based Root-cause Analysis for Process Performance Deviations in Continuous Processes

The surge of computational power and the increasing availability of data in the process industry result in a growing interest in data based methods for process modelling and control. In this contribution a concept is described that uses statistical methods to analyse the root-causes for deviations from baselines that are used for the monitoring of the resource efficiency of the production in dashboards. This is done by comparing historical data during resource efficient operation under similar process conditions with data during inefficient operation. Statistically significant deviations are identified, sorted by the likelihood of causing the performance deviation. The concept is applied to a reference model called Best Demonstrated Practice which is in use at INEOS in Cologne. It represents the most resource efficient process performance under given conditions. Deviations from efficient plant performance are analysed using the described concept and the results are given to the operators as a decision support tool, including reference values for the degrees of freedom under the control of the operators. This concept is already used in a root-cause analysis tool at INEOS in Cologne and detected energy savings of over 20% for specific cases.