An application of integrated EPC–SPC methodology for simultaneously monitoring multiple output characteristics

Abstract

Purpose The purpose of this paper is to develop an integrated engineering process control (EPC)–statistical process control (SPC) methodology for simultaneously monitoring and controlling autocorrelated multiple responses, namely, brightness and viscosity of the pulp bleaching process. Design/methodology/approach The pulp bleaching is a process of separating cellulose from impurities present in cooked wood chips through chemical treatment. More chemical dosage or process adjustments may result in better brightness but adversely affect viscosity. Hence, the optimum chemical dosage that would simultaneously minimize the deviation of pulp brightness and viscosity from their respective targets needs to be determined. Since the responses are autocorrelated, dynamic regression is used to model the responses. Then, the optimum chemical dosage that would simultaneously optimize the pulp brightness and viscosity is determined by fuzzy optimization methodology. Findings The suggested methodology is validated in 12 cases. The validation results showed that the optimum dosage simultaneously minimized the variation in brightness and viscosity around their respective targets. Moreover, suggested solution has been found to be superior to the one obtained by optimizing the responses independently. Practical implications This study provides valuable information on how to identify the optimum process adjustments to simultaneously ensure autocorrelated multiple responses on or close to their respective targets. Originality/value To the best of the authors’ knowledge, this paper is the first to provide application of the integrated EPC–SPC methodology for simultaneously monitoring multiple responses. The study also demonstrates the application of dynamic regression to model autocorrelated responses.

SPC-based model for evaluation of training processes in industrial context

“…According to Mirzaei et al (2016), Vicentin, Silva, Piccirillo, Bueno and Oprime (2018), John and Singhal (2019) and Prata, Chaves, Gomes & Passos (2020), SPC is an instrument made up of statistical procedures that aims to measure and analyze variations in processes. Together with diagnostic tools such as the Ishikawa diagram, cause and effect matrix, histograms, and others, SPC is able to identify problems with agility and reasonable assertiveness before the damage reaches a greater impact beyond those linked to its immediate cause.…”

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“…Because the volume not only suspected to have the serial correlation with past values but also correlated with external factors such as the type of day and day of the week, the model is developed using dynamic regression (Hwang, 2009; Martinussen and Scheike, 2007; John and Singhal, 2019). The time series model is developed using the R package (Crawley, 2007; Cryer and Chan, 2008).…”