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Advances in statistical quality control chart techniques and their limitations to cement industry

COGENT ENGINEERING 2022年第1期9卷 Article: 2088463页

作者： Tegegne, Daniel Ashagrie Kitaw, Daniel Berhan, Eshetie Addis Ababa Univ Sch Mech & Ind Engn Addis Ababa Inst Technol Addis Ababa Ethiopia

Sustainability issues are challenging the cement industry due to its high emission of greenhouse gas, intensive energy consumption, and depletion of resources. One of the strategies to mitigate the problem is to improve process control techniques and optimize resources. The objective of this paper is to survey the approach and evolution of statistical process control chart techniques and study their significance and limitations in the case of optimization of cement production. The main research question this study address is "What are the significances and limitations of statistical process control chart methods to the optimization of cement process?" The methodology of the study followed the literature survey with meta-analysis and focused on identifying the statistical process control chart design techniques and their application to cement industries. The result of the survey indicated that statistical and mathematical algorithms are encapsulated by advanced soft computing methods;however, still, it is the foundation for advanced process control methods. Moreover, it is found that statistical process control has a theoretical and technical gap in the application of the cement industry. The theoretical gap identified in the literature is that in the case of a complex production system the techniques recognize the occurrence of the out-of-control case in the production process but are not able to identify the cause of variation. The technical gap in the statistical process control techniques is that there are several important theoretical control chart techniques, but they are not researched well on how to apply to the real world.

关键词： Univariate statistical process control multivariate statistical process control data mining-based process control machine learning-based process control process control in cement industry

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