e-journal

A Bayesian Networks Structure Learning and Reasoning-Based Byproduct Gas Scheduling in Steel Industry

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It is very crucial for the byproduct gas system in steel industry to perform an accurate and timely scheduling, which enables to reasonably utilize the energy resources and effectively reduce the production cost of enterprises. In this study, a novel datadriven-based dynamic scheduling thought is proposed for the realtime gas scheduling, in which a probability relationship described by a Bayesian network is modeled to determine the adjustable gas users that impact on the gas tanks level, and to give their scheduling amounts online by maximizing the posterior probability of the users’ operational statuses. For the practical applicability, the obtained scheduling solution can be further verified by a recurrent neural network reported in literature. To indicate the effectiveness of the proposed data-driven scheduling method, the real gas flow data coming from a steel plant in China are employed, and the experimental results indicate that the proposed method can provide real-time and scientific gas scheduling solution for the energy system of steel industry.
Note to Practitioners—Given that the byproduct gas system in steel industry can hardly be described by a physics or mechanism based model, its balance scheduling is widely realized by the experience based manual measure at present. A large number of realtime energy data have been accumulated by the SCADA system implemented in most of steel plants, hence a novel data-driven real-time scheduling is proposed, which takes the experienced operations as the sample data for depicting the dynamics among the energy units. The proposedmethod aims at the short term dynamic scheduling, which can provide the optimized solution via monitoring the circumstances of the gas system. Therefore, it is required for the plant in advance to implement the SCADA system for the energy data management, and the sampling interval should be less than or equal to 1 minute. And, it is necessary for the sample data to complete the preliminary processing such as eliminating the industrial noises or perform the data imputation if needed. Because
such preliminary processing for the sample data belongs to a class of generic methods, this work avoids the redundant technical introduction.
Index Terms—Byproduct gas system, Bayesian network, dynamic scheduling, structural learning and reasoning.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING

No. Panggil

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Penerbit

New York : IEEE., 2014

Deskripsi Fisik

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, VOL. 11, NO. 4, OCTOBER 2014

Bahasa

English

ISBN/ISSN

1545-5955

Klasifikasi

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Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

VOL. 11, NO. 4, OCTOBER 2014

Subjek

TEKNIK

Info Detail Spesifik

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Pernyataan Tanggungjawab

Yuli/Agus

Versi lain/terkait

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