Please use this identifier to cite or link to this item:
Title: Modeling error PDF shape based data-driven model for batch processes
Authors: Jia, L.
Cao, L.
Chiu, M. 
Keywords: Batch process
Data-driven model
Output probability density function (PDF) control
Issue Date: Jul-2012
Source: Jia, L.,Cao, L.,Chiu, M. (2012-07). Modeling error PDF shape based data-driven model for batch processes. Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument 33 (7) : 1505-1512. ScholarBank@NUS Repository.
Abstract: The key of optimal control of batch processes depends on obtaining an accurate model. The data-driven modeling method based on process input-output data points is a hot spot in the study of batch process modeling. This paper breaks through the idea that mean squared error (MSE) is employed as the index function in traditional data-driven modeling method, and a novel data-driven modeling approach for batch process is proposed. The conception of probability density function (PDF) control is firstly introduced and then a model error control system of batch process is built. The adjustable parameters of the data-driven model are taken as the control system input, and the shape of the PDF as the corresponding output. As a result, the open-loop model parameter identification problem is transferred into a closed-loop control problem of the shape of PDF. The adjustable parameters dominate the spatial distribution of the PDF of the model error, which not only guarantees the accuracy of the model but also eliminates the colored noise. Simulation results demonstrate that the proposed data-driven modeling approach based on the shape of PDF has better modeling precision, robustness and generalization ability; and also provides a new way for the data-driven modeling of batch processes.
Source Title: Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument
ISSN: 02543087
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Feb 16, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.