Integration of Statistical and Engineering Process Control for Quality Improvement (A Case Study: Chemical Industry - National Chlorine Industries)

The quality of product in industrial process can be improved by on-line monitoring, regulating and correcting actions. There are two approaches addressing this problem: (1) Statistical Process Control (SPC), which focuses on identifying assignable causes that can be removed, leads to permanent process improvement or reduction in variability; and (2) Engineering Process Control (EPC), based on adjusting the process variables to get less deviation from target (often called feedback adjustment). Feedback adjustment regulates the process to account for sources of variability that cannot be removed by the SPC approach.Integrating SPC/EPC is a very effective way in quality control, since features from both SPC and EPC could give a complementary performance. This work introduces a framework that integrates SPC and EPC in one methodology; SPC control limits for critical key variables are developed depending on information from the historical reference of past successful processes. While the EPC algorithm is derived of a progressive set of knowledge-based rules. The approach is applied to data collected from chlorine industry, as a case study. This is poorly automated, subject to several disturbances, monitored by measuring the feed brine solution concentration and the acidity index of samples from the ferric brine treatment tank. The experimental results proved that, when the production process is affected by certain disruptions, the process engineers have a decision making tool, by on-line monitoring and regulating the process key variables. Furthermore, by implementing proper adjustment strategies, the stability of the process can be better maintained, and significant economic benefits will be achieved.