A Hybrid Prediction Model of Energy Consumption Per Ton for Fused Magnesia

Wu, Zhiwei; Chai, Tianyou; Wu, Yong-Jian

doi:10.3724/sp.j.1004.2013.02002

Cited by 12 publications

(3 citation statements)

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“…There are many types of research on the active power balance control of EFMF load, including component model construction [4] , furnace condition recognition [5] , control strategies [6] , etc. As mentioned above, the existing research works mainly focus on energy consumption per ton and condition identification, and the research goal is energy consumption stability and optimal control.…”

Section: Introductionmentioning

confidence: 99%

The research of declared primary frequency capacity and control strategy for electrical fused magnesium furnace load

Jia,

Hao,

Guo

et al. 2023

J. Phys.: Conf. Ser.

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As a potential control resource of the load side, fused magnesium load can effectively make up for the loss of the primary frequency modulation capacity of the system, in which the conventional units are replaced by new energy sources. However, the capacity of fused magnesium load is closely related to the operating condition and changes dynamically. It is necessary to accurately characterize its capacity and determine the optimal declared capacity of participating in frequency modulation service. According to the technical regulations of primary frequency modulation service, the basic principle is presented, which the electric molten magnesium load participates in primary frequency modulation control in the case of lack of large power. Considering the risk of reward and punishment, the optimal reporting capacity optimization model is constructed. Taking the year as the time scale, a stochastic production simulation method involving primary frequency modulation was proposed. According to its operating characteristics, a real-time control strategy involving primary frequency modulation is designed. Simulation examples show that the proposed method can accurately simulate the production process of molten magnesium load, reasonably realize the operation control of molten magnesium load, and optimize the declared capacity considering the risk.

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Section: Introductionmentioning

confidence: 99%

The research of declared primary frequency capacity and control strategy for electrical fused magnesium furnace load

Jia,

Hao,

Guo

et al. 2023

J. Phys.: Conf. Ser.

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“…Pourya Azadi et al [31] developed a hybrid dynamic model for the prediction of iron silica content and slag alkalinity in the blast furnace process by analyzing the principles of the blast furnace operation process. Wu Zhiwei et al [32] proposed an energy consumption prediction model for electrofused magnesia products, consisting of a single tonne energy consumption master model for mechanistic analysis and a neural-network-based compensation model. Jie Yang et al [33] combined a mechanistic model with a data-driven approach to achieve power demand forecasting for the electrofusion magnesium smelting process.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Output Regression Model for Energy Consumption Prediction Based on Optimized Multi-Kernel Learning: A Case Study of Tin Smelting Process

Wang,

Feng,

et al. 2023

Processes

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Energy consumption forecasting plays an important role in energy management, conservation, and optimization in manufacturing companies. Aiming at the tin smelting process with multiple types of energy consumption and a strong coupling with energy consumption, the traditional prediction model cannot be applied to the multi-output problem. Moreover, the data collection frequency of different processes is inconsistent, resulting in few effective data samples and strong nonlinearity. In this paper, we propose a multi-kernel multi-output support vector regression model optimized based on a differential evolutionary algorithm for the prediction of multiple types of energy consumption in tin smelting. Redundant feature variables are eliminated using the distance correlation coefficient method, multi-kernel learning is introduced to improve the multi-output support vector regression model, and a differential evolutionary algorithm is used to optimize the model hyperparameters. The validity and superiority of the model was verified using the energy consumption data of a non-ferrous metal producer in Southwest China. The experimental results show that the proposed model outperformed multi-output Gaussian process regression (MGPR) and a multi-layer perceptron neural network (MLPNN) in terms of measurement capability. Finally, this paper uses a grey correlation analysis model to discuss the influencing factors on the integrated energy consumption of the tin smelting process and gives corresponding energy-saving suggestions.

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“…A model of fused magnesia product energy consumption per ton was proposed by using the law of conservation of energy and neural network based compensation, the effectiveness was verified by industrial applications (Yang et al, 2009). Similarly, the optimal setting of alumina blending process was solved by an integrated model with the same structure as above (Wu et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Integrated modeling of coking flue gas indices based on mechanism model and improved neural network

Wang

Tan

2018

Transactions of the Institute of Measurement and Control

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Focusing on the first domestic coking flue gas desulfurization and denitration integrated unit in China, the current condition of inlet flue gas indices cannot be determined timely owing to the large detection lag and complex upstream coking process, which is extremely unfavorable for the optimal control of desulfurization and denitration process. In order to solve this problem, an intelligent integrated modeling method of flue gas SO2 concentration, O2 content and NOx concentration is proposed. Firstly, the gas flow diagram in combustion process is built, the mechanism models of SO2, NOx concentration and O2 content are established according to the principle of material balance and reaction kinetics, respectively. Then the RBF neural network is adopted to compensate the prediction error, an improved training algorithm combining optimal stopping principle and dual momentum adaptive learning rate is proposed to improve the training speed and generalization ability of neural network. Based on the practical data of two 55-hole and 6-meter top charging coke ovens in the coking group, the effectiveness and superiority of proposed model and method are verified by simulation via comparison of various methods.

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A Hybrid Prediction Model of Energy Consumption Per Ton for Fused Magnesia

Cited by 12 publications

References 0 publications

The research of declared primary frequency capacity and control strategy for electrical fused magnesium furnace load

The research of declared primary frequency capacity and control strategy for electrical fused magnesium furnace load

A Multi-Output Regression Model for Energy Consumption Prediction Based on Optimized Multi-Kernel Learning: A Case Study of Tin Smelting Process

Integrated modeling of coking flue gas indices based on mechanism model and improved neural network

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