Cost and energy consumption collaborative optimization for sintering burdening in iron and steel enterprise

Wang, Junkai; Qiao, Fei

doi:10.1109/coase.2014.6899370

Cited by 4 publications

(3 citation statements)

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“…Such energy-conservation oriented optimization of sintering raw materials could be given more attention, which could result in achieving energy efficient burdening solutions. Some initial work has been done on this issue [4] in which energy consumption was described as a linear model. It is believed that the proposed nonlinear prediction models could result in better solutions for such optimization problems.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…The top ten attributes and the weights are listed in Tables 3 and 4. For SEC, the five most related attributes turned out to be: (1) amount of coke, (2) amount of dolomite, (3) amount of ore blends, (4) ignition temperature #3, and (5) material flow rate on conveyor 1S-1 (ascending to raw mix hopper). For GEC, they were: (1) material flow rate on conveyor PD-1 (carrying hearth-layer material), (2) ignition temperature #3, (3) cooling water flow rate, (4) #1 pipe gas flow rate, and (5) ignition temperature #2.…”

Section: Case Studymentioning

confidence: 99%

“…The sintering process typically accounts for 8-10% of the energy use of a steelmaking enterprise. With this in mind, efforts are being made to reduce energy consumption and emissions associated with the sintering process, such as sintering burdening [4]. However, it has been reported that the energy efficiency of Chinese sintering operations lags that of other steel-producing nations, and sintering energy consumption varies significantly from plant to plant.…”

Section: Introductionmentioning

confidence: 99%

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A Data-Driven Model for Energy Consumption in the Sintering Process

Wang

Qiao

Zhao

et al. 2016

Journal of Manufacturing Science and Engineering

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As environmental performance becomes increasingly important, the sintering process is receiving more attention since it consumes large amounts of energy. This paper proposes a data-driven model for sintering energy consumption, which considers both model accuracy and time efficiency. The proposed model begins with removing data anomalies using a local outlier factor (LOF) algorithm and an attribute selection module using the RReliefF method. Then, to accurately predict sintering energy consumption, an integrated predictive model is employed that uses bagging-enhanced extreme learning machine (ELM) and support vector regression (SVR) machine, combined with an entropy weight method. A case study is used to demonstrate the effectiveness of the proposed model using actual production data for a year. Results show that the proposed model outperforms other models and is computationally efficient. Optimal parameters of the LOF (1.3) and number of attributes (30) were identified. It was found that coke powder has the most significant impact on the solid energy consumption (SEC), while cooling water flow rate provides the most significant impact on the gas energy consumption (GEC) within each recorded attribute variation. Parametric analysis further revealed the relationships between energy consumption and the significant attributes mentioned above. It is suggested that the proposed model could effectively reduce the energy consumption by attaining more efficient attribute settings.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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