NDLI: Control of methylamine removal reactor using neural network based model predictive control

Content Provider	IEEE Xplore Digital Library
Author	Liu Zhi Long Yang Feng Zhou Ke Jun Xu Mei
Copyright Year	2014
Description	Author affiliation: Sch. of Autom., Chongqing Univ. of Post & Telecommun., Chongqing, China (Zhou Ke Jun) \|\| Sch. of Autom. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China (Liu Zhi Long; Yang Feng) \|\| Sch. of Energy Sci. & Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China (Xu Mei)
Abstract	Methylamine (MA) removal process using mixed bacteria strains depends highly on constant temperature (303 K), at which the mixed bacteria strains provide highest activity in removing MA. Controlling MA removal reactor is extremely difficult for its inherent process nonlinearities and complex reaction kinetics and other uncertain factors. In the designed approach, a network predicted model is trained as a nonlinear process to predict the future output of the controlled process according to current and previous input and output over the specified horizon. The advanced predictive control strategy is used to minimize the cost function in order to calculate the optimal output of the controller. In this work, a neural network based predictive control (NNMPC) algorithm was implemented to control the temperature of MA removal reactor and the controller performance in set-point tracking and disturbance rejection was investigated, and the performance results of NNMPC was compared with conventional PID controller. It is concluded that the NNMPC performance is superior to the conventional PID controller in the control of MA removal reactor.
Starting Page	374
Ending Page	381
File Size	3146966
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889463
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Inductors Predictive models Process control Artificial neural networks Wastewater Educational institutions
Content Type	Text
Resource Type	Article

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