Integration of Statistical and Engineering Process Control in a Batch Processes Monitoring: Case of Alkyd Polymerization Reactor

The objective of this paper is to develop a framework that integrates two important concepts: Statistical process control (SPC) and engineering process control (EPC). Most of the literature researches on integrated SPC/EPC systems are focused into continuous process mainly with Algorithmic SPC. The integrated SPC/EPC systems in batch process control have not received the same degree of attention. In particular, there is an only Run-to-Run (RTR) control methodology application, which is mostly focused in semiconductor industry. This paper is a first of its kind in integrated SPC/EPC systems that applied in batch process and it based on a data-driven quality improvement tools. The proposed SPC/EPC integration is performed continually in two successive phases: (1) Active SPC for the batch making advance, and (2) RTR control action between batches. Control limits for critical variables are developed using information from the historical reference distribution of past successful batches. EPC application is based on the development of progressive knowledge-based rules. For a validation purpose, the proposed approach is applied to data collected from an industrial batch alkyd polymerization reactor, which evolution is monitored by measuring the overflow water weight, the acidity index and the viscosity of samples withdrawn from the reactor. This industrial process is poorly automated, subject to several disturbances, and the batches have uneven lengths. The synthesis is stopped at the maximum yield allowed by the gelation point of the cold product. Through this case study application, process engineers at the company are now able to use a valuable decision making tool when the production process is affected by certain disruptions, with obvious consequences on product quality, productivity and competitiveness.