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首页> 外文会议>IEEE International Conference on System Engineering and Technology >Improvement of Process Quality via Integration of Statistical Process Control and Engineering Process Control in Batch Process
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Improvement of Process Quality via Integration of Statistical Process Control and Engineering Process Control in Batch Process

机译：通过在批处理过程中集成统计过程控制和工程过程控制来改进流程质量

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Batch process is one of a main type of process in the chemical industry alongside continuous process. However, it is difficult for production and process engineers to complete the task of achieving optimal performance of industrial batch processes. Batch processes have many major issues such as they are big batch-to-batch variations, highly non-linear dynamics, and difficulty in real-time measurement. It is significant to control the process in real-time, in order to avoid many issues such as off-spec product. While the study of EPC and SPC is garnering attraction in controlling strategy, the implementing of the integration is still rare compare to continuous process. The objective of this study is to develop a methodology that integrates two important techniques of Statistical Process Control (SPC) and Engineering Process Control (EPC) for quality improvement. Integrating SPC/EPC is a very effective way since the features from both SPC and EPC could give a complementary performance to improve product quality in industrial process by on-line monitoring, regulating and correcting actions. The approach is applied to data collected from experimentation process of carboxylmethyl carbon, as a case study where it is a batch process. SPC method used is EWMA control chart meanwhile EPC method used Integral (I) controller of feedforward with bounded chart. Based on result, the implementation of integration of SPC and EPC managed to reduce the process variation by 32.8% and reduced the standard deviation by 18%. λ of 0.5 was the best option with target = 0.89209, stand deviation = 0.05983, variance = 0.00358 and PM = 0.00367. Therefore, EWMA of bounded chart produced new average that is closer to target, variance that is smaller, standard deviation that is improved and performance measure that has the smallest value.
机译：批处理是化学工业的主要过程之一，连续过程。然而，生产和过程工程师难以完成实现工业批处理过程最佳性能的任务。批处理具有许多重大问题，例如它们是大批量到批量变化，高度非线性动力学，以及实时测量的难度。实时控制过程很重要，以避免许多诸如非规范产品的问题。虽然EPC和SPC的研究是控制策略的吸引力，但整合的实施仍然与连续过程进行比较。本研究的目的是开发一种方法，该方法集成了两个统计过程控制（SPC）和工程过程控制（EPC）的重要技术的重要性。整合SPC / EPC是一种非常有效的方式，因为SPC和EPC的功能可以通过在线监测，调节和纠正行动来提高工业过程中的产品质量来提供互补性能。该方法应用于从羧基甲基碳的实验过程中收集的数据，如批量生学的情况。使用的SPC方法是EWMA控制图表，同时EPC方法使用有界图的Feedforward的Integral（I）控制器。基于结果，实施SPC和EPC集成的实施将过程变化降低了32.8％，并将标准差减少18％。 λ为0.5是目标= 0.89209的最佳选择，站偏差= 0.05983，方差= 0.00358和PM = 0.00367。因此，有界图表的EWMA产生了更接近目标的新平均值，方差较小，标准偏差，其具有最小值的性能和性能测量。

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