Multivariable Control System Design and Application for Energy Saving of Temperature and Humidity Chamber

Obika, Masanobu; Oyama, Masaki; Sakane, Koki; Watabe, Katsuhiko; Shimada, Tetsuya; Yamamoto, Toru

doi:10.1541/ieejias.133.436

Cited by 3 publications

(3 citation statements)

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“…In this paper, estimation accuracy is improved by applying PLS to estimate the difference between oxygen amount estimated by the physical model and its actual amount. PLS is widely used for purity prediction in distillation columns and other facilities, and its effectiveness was verified . Here we use dynamic PLS proven by Lakshminarayanan et al., Kano et al., and other researchers.…”

Section: Estimation Modelmentioning

confidence: 99%

“…PLS is widely used for purity prediction in distillation columns and other facilities, and its effectiveness was verified. 1 Here we use dynamic PLS proven by Lakshminarayanan et al, 5 Kano et al, 6 and other researchers. Specifically, we use explanatory variables not only at a moment t but also at t -1, …, tn.…”

Section: Estimation With Plsmentioning

confidence: 99%

“…In the recent years, energy saving has been greatly promoted worldwide from the standpoint of business competitiveness and corporate social responsibility, in addition to preventing global warming. Energy conservation measures can be divided into those based on high efficiency of manufacturing equipment, and those based on control methods and other software (intangible) improvements.…”

Section: Introductionmentioning

confidence: 99%

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Energy‐saving inferential control of an industrial furnace

Obika

Yamamoto

2018

Elect Comm in Japan

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In process industries, there is a growing trend toward low carbon society. Especially in oil refinement and petrochemical industries, furnaces consume a lot of energy. In order to reduce the energy consumption, excess O2 has to be reduced safely. However, high gain O2 control cannot be done due to L/T > 1. Therefore, conventional O2 controllers are operated with low gains, that is, high stable control performance for disturbances has not been able to obtain, then minimum excess O2 control cannot be achieved. The proposed method that utilizes the estimated O2 variable can derive much better control performance. Moreover, since the proposed estimation technique consists of a physical model part and a stochastic soft‐sensor, both estimation accuracy and reliability for extrapolation can be obtained. The validity of the proposed scheme is verified through actual application results.

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Section: Estimation Modelmentioning

confidence: 99%