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Statistical Process Control in Automotive Industry

characteristics of phosphate conversion coating. Ruse : PB at Angel Kanchev University of Ruse, vol. 40, pp. 221-224. KELLER, P. 2011. Statistical process control. 1st ed. The McGraw-Hill Companies. 320 pp. ISBN 978-0-07-174250-4. Krastev , G. - Kangalov , P. 2004. Physico-mechanical and operational characteristics of restoration electrolytic coatings. Ruse : PB at Angel Kanchev University of Ruse, vol. 41, pp. 142-144. Nikolov , m. - Kangalov , P. 2012. Benefits from maintenance and repair in utilization of resources. In

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Implementation of Statistical Process Control (SPC) in the Sewing Section of Garment Industry for Quality Improvement

References [1] Mason, B. & Antony, J., Is SPC Just about Control Charts. Unpublished work. [2] Montgomery, D. C., Introduction to Statistical Quality Control, 6th Edition. New York: John Wiley and Sons, 2009. [3] Shewhart, W. A, “Statistical Method from the Viewpoint of Quality Control”, Washington DC: The Graduate School of the US Department of Agriculture, 1939. [4] Sultana, F., Islam, N.R., Azeem, A. Implementation of Statistical Process Control (SPC) For Manufacturing Performance Improvement

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Grinding in Ball Mills: Modeling and Process Control

References 1. Ajaal, T., R. W. Smith, W. T. Yen. The Development and Characterization of a Ball Mill for Mechanical Alloying. - Canadian Metallurgical Quarterly, Vol. 41 , 2002, No 1, 7-14. 2. Bauer, M., I. K. Craig. Economic Assessment of Advanced Process Control - A Survey and Framework. - Journal of Process Control, Vol. 18 , 2008, 2-18. 3. Berthiaux, H., C. Varinot, J. Dodds. Approximate Calculation of Breakage Parameters from Batch Grinding Tests. - Chemical Engineering Science, Vol. 51 , 1996

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Statistical Process Control as a Failure Removal Improvement Tool

. Oakland, J. S. 2007. Statistical Process Control. Oxford: Butterworth-Heinemann, 458 pp. ISBN 9780750669627. Romao, X. - Delgado, R. - Costa, A. 2010. An empirical power comparison of univariate goodness-of-fit tests for normality. In Journal of Statistical Computation and Simulation, vol. 80, pp. 545-591. Šibalija, T. 2004. Attaining process robustness through design of experiment and statistical process control. In Proceedings of the 11th CIRP International Conference on Life Cycle Engineering - LCE 2004 - Quality Management Issues, pp

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Teaching process control in food engineering: dynamic simulation of a fermentation control process

Abstract

Students usually have difficulties to understand abstract concepts of process control. Implementing in teaching process the inquiry-based learning helps students to follow methods and practices similar to those of professional scientists in order to construct knowledge. The paper describes the steps reached in simulation-based learning: from experimental data obtained by the students in their practical method (study and measurement of variables to some fermentation processes) to the simulated the behaviour of the process under a feedback control system. By providing opportunities for students to check their understanding and reflect on their learning process performance is enhanced over a traditional lecture course.

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Intelligent methods for process control and diagnostics of a mill fan system

Abstract

The intelligent methods for process control and diagnostics of the mill fan system is an established field of scientific and applied investigations. In the present paper several types of process control approaches with different structures are considered. In order to choose the most efficient one, comparative analysis is carried out. The mill fans are a basic element of the dust-preparing systems of steam generators with direct breathing of the coal dust in the furnace chamber. Such generators in Bulgaria are the ones in Maritsa East 2 Thermal Power Plant, in Maritsa East 3 Thermal Power Plant and also in Bobov Dol Thermal Power Plant. The subject of this research is a device from Maritsa East 2 Thermal Power Plant. This is the largest thermal power plant on the Balkan Peninsula. Standard statistical and probabilistic (Bayesian) approaches for diagnostics are inapplicable to estimate the mill fan technical state due to non-stationarity, non-ergodicity and the significant noise level. The possibility to predict eventual damages or wearing out without switching off the device is significant for providing faultless and reliable work, avoiding the losses caused by planned maintenance.

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BUSINESS INTELLIGENCE IN PROCESS CONTROL

-89066-60-7 9. KEBÍSEK, M. 2010. Využitie dolovania dát vo výrobných procesoch (The utilization of data mining in the industrial process control). Dissertation thesis. Trnava: MTF STU, 116 p.

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Neural network based explicit MPC for chemical reactor control

, Monnigmann M (2019) Mimicking predictive control with neural networks in domestic heating systems. In Fikar M and Kvasnica M, editors, Proceedings of the 22 nd International Conference on Process Control, pages 19—24, Šrbské Pleso, Slovakia. Slovak University of Technology in Bratislava, Slovak Chemical Library. Maciejowski JM (2002) Predictive Control with Constraints. PEARSON Prentice-Hall. Mayne DQ, Rawlings JB, Rao CV, Scokaert POM (2000) Constrained model predictive control: Stability and optimality. Automatica, 36(6): 789—814. Pourdehi S

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Statistical Control Charts: Performances of Short Term Stock Trading in Croatia

and Quantitative Analysis, Vol. 43 No. 1, pp. 191-212. 4. Box, G. E. P., Luceno, A., Paniagua-Quinones, M. D. C., (2009), “Statistical Control by Monitoring and Adjustment”, 2nd ed., New Jersey, John Wiley & Sons. 5. Caporale, G. M., Howells, P. G. A, Soliman, A. M. (2004), “Stock market development and economic growth: The causal linkage”, Journal of Economic Development, Vol. 29 No. 1, pp. 33-50. 6. Chen, T. (2010), “On reducing false alarms in multivariate statistical process control”, Chemical Engineering Research

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Performance comparison of different control strategies for heat exchanger networks

Francisco, CA, USA. 10. Bao, J., Lee, P.L., Wan, F.Y. & Zhou, W.B. (2000). A New Approach to Decentralized Process Control Using Passivity and Sector Stability Conditions. Chem. Eng. Commun . 182(1), 213–237. DOI: 10.1080/00986440008912835. 11. Bao, J., Zhang, W.Z. & Lee, P.L. (2002). Passivity-Based Decentralized Failure-Tolerant Control. Ind. Eng. Chem. Res. 41(23), 5702–5715. DOI: 10.1021/ie0201314. 12. Zhang, W.Z., Bao, J. & Lee, P.L. (2002). Decentralized Unconditional Stability Conditions Based on the Passivity Theorem for Multi-loop Control

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