IJE TRANSACTIONS A: Basics Vol. 31, No. 4 (April 2018) 372-379    Article in Press

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R. Parvizi Moghadam
( Received: November 22, 2017 – Accepted: January 04, 2018 )

Abstract    A novel data-driven soft sensor is designed for online product quality prediction and control performance modification in industrial units. A combined approach of time variable parameter (TVP) model, dynamic auto regressive exogenous variable (DARX) algorithm, nonlinear correlation analysis and criterion-based elimination method is introduced in this work. The soft sensor performance validation is tested by data set of an industrial SRU. The comparative study indicated the result associated with more robust soft sensor and more appropriate performance index values compared to other methods for SRU soft sensor design in diverse achievements. Due to high prediction accuracy, low complication of the model and also time saving, this technique can be very noticeable in industrial processes control.


Keywords    Soft sensor, time varying parameter, SRU, Quality estimation, Identification, Data- based modeling


چکیده    یک حسگر نرم داده­محور جدید برای پیش­بینی آنلاین و بهبود عملکرد کنترل کیفیت محصول در واحدهای صنعتی طراحی شده است. رویکرد ترکیبی مدل پارامتری متغیر با زمان، الگوریتم دینامیکی متغیر خارجی خودرگرسیون، آنالیز همبستگی غیرخطی و روش حذفی با معیار ارزیابی عملکرد در این تحقیق معرفی شده است. اعتبار عملکرد حسگر نرم به وسیله بخشی از داده­های صنعتی واحد بازیافت گوگرد ارزیابی شد. نتایج دلالت بر مقاومت بیشتر و شاخص عملکردی مناسب­تر این حسگر در مقایسه با نتایج حاصل از دیگرروش­های به کار گرفته شده برای طراحی حسگر نرم واحد بازیافت گوگرد، در سایر تحقیقات داشت. به واسطه­ی دقت پیش­بینی بالا، عدم پیچیدگی مدل حاصل و صرفه­جویی در زمان، این تکنیک می­تواند در کنترل فرایندهای صنعتی بسیار مورد توجه قرار گیرد.

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