کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6595543 | 458533 | 2014 | 47 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A probabilistic self-validating soft-sensor with application to wastewater treatment
ترجمه فارسی عنوان
یک سنسور نرم با قابلیت اطمینان احتمالی خود را با استفاده از تصفیه فاضلاب
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کلمات کلیدی
سنسور نرم فاضلاب، خود تایید، تجزیه و تحلیل اجزای اصلی متغیر بایس، ماشین بردار مربوطه،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
مهندسی شیمی (عمومی)
چکیده انگلیسی
In the wastewater treatment plants (WWTPs), soft sensors are viewed as a simple signal estimator for hard-to-measure quantities. However, the presence of unreliable data, coupled with increasing demands for measurement quality assurance, has rendered inadequate such a simplistic view. In this paper, a probabilistic self-validating soft-sensor is proposed with the capability of performing self-diagnostics, self-reconstruction and online uncertainty measurement. In this framework, data collecting for soft-sensor modeling (easy-to-measure data) is validated by a Variational Bayesian Principal Component Analysis (VBPCA) model before performing a soft-sensor model construction. By integrating Relevant Vector Machine (RVM) as a predictive model, not only prediction values are obtained, but also the credibility of information for easy-to-measure and hard-to-measure quantities can be generated. The performance of the proposed soft-sensor is validated through two simulation studies of WWTPs with different process characteristics. The results suggest that the proposed strategy significantly improves the prediction performance.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Chemical Engineering - Volume 71, 4 December 2014, Pages 263-280
Journal: Computers & Chemical Engineering - Volume 71, 4 December 2014, Pages 263-280
نویسندگان
Yiqi Liu, Jingdong Chen, Zonghai Sun, Yan Li, Daoping Huang,