Muti-objective optimization on energy consumption, CO2 emission and production cost for iron and steel industry

Yang, Yuhang; Zhang, Lei; Yuan, Yuxing; Sun, Jingchao; Che, Zichang; Qiu, Ziyang; Du, Tao; Na, Hongming; Shuai Che,

doi:10.1016/j.jenvman.2023.119102

Cited by 14 publications

(3 citation statements)

References 42 publications

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“…Energy consumption constitutes a fundamental component of contemporary economies, being involved in the matrix of residential needs [1,2], industrial functionality [3][4][5], and macroeconomic dynamics [6,7]. The significance of energy consumption exceeds its utilitarian value, emerging as a key factor influencing a nation's economic robustness [8], environmental protection [9], and general resident well-being, including functional [10], visual [11,12], and thermal comfort [13].…”

Section: Introductionmentioning

confidence: 99%

Energy Performance in Residential Buildings as a Property Market Efficiency Driver

Walacik,

Chmielewska

2024

Energies

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Energy consumption plays an important role in contemporary economies. Its significance extends beyond utilitarian value, impacting economic robustness, environmental protection, and residents’ well-being. The escalating global energy requisites necessitate efficient energy utilization and a shift towards renewable sources to address climate change and strengthen energy independence. Developing accurate predictive models to forecast long-term energy costs and savings remains a complex problem. This paper aims to provide a methodology to identify the influence of building energy performance on real estate market efficiency, focusing on property maintenance costs. Real estate plays a crucial role in human life, serving both as a fundamental need and as a vehicle for achieving personal aspirations and secure financial investments, particularly during times of economic and social instability. Through interdisciplinary methodological architecture, this study addresses three key issues: the impact of rising energy costs on market efficiency, the responsiveness of the real estate market to energy price fluctuations, and the significance of property maintenance costs on market value. The research approach includes creating and applying AI algorithms capable of evaluating extensive datasets pertaining to real estate features. Utilizing machine learning methods, the algorithm determines the importance of energy efficiency measures as well as various other inherent and external attributes of properties. The suggested methodology provides a novel approach to improve the effectiveness of market efficiency analysis.

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Section: Introductionmentioning

confidence: 99%

Energy Performance in Residential Buildings as a Property Market Efficiency Driver

Walacik,

Chmielewska

2024

Energies

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“…3 To maintain the normal supply and demand of steel, the energy-intensive iron and steel industries formulate a large amount of natural energy and resources consumption, greenhouse gas and pollutants emissions. 4 These phenomena are attributed to the lower energy and material efficiency level and industrial concentration, the lack universal application of energy-saving technologies, and insufficient production capacity ratio of electric arc furnace (EAF) steelmaking. 5 Hence, the Chinese government has proposed ambitious targets for carbon peak and carbon neutral to search for sustainable development.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modelling and techno-economic analysis of electric arc furnace steelmaking process with hot metal charging in China

Hu,

Yang,

Zou

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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Comprehensively considering the raw materials availability, production technology, and economy factor, it is unclear to choose the optimal electric arc furnace (EAF) steelmaking modes with charging scrap and hot metal (HM) in China. In this paper, the mathematical modelling and techno-economic analysis is adopted to quantitatively describe the consumption level of material and energy, as well as the economic performance in EAF steelmaking process. The calculation results reveal that considering the lowest smelting time, heat loss and total energy consumption, and better economic benefit and energy saving effect, the optimal EAF steelmaking process of 50% HM ratio with carbon powder and coal gas added is suggested, and the consumption of carbon powder and natural gas should be limited within 19.58 kg and 11.63 Nm3. This research is expected to provide some theoretical and operation guidance for low consumption and high efficiency lean production in EAF steelmaking process.

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“…optimization model for the blast furnace ironmaking process based on the conservation of energy and matter, with the energy consumption, cost, and CO 2 emission of blast furnace ironmaking as the objective function, and blast furnace batching, iron quality parameters, and production indexes as the decision variables. 14) Zhang Zongwang et al established a mathematical model for optimization of blast furnace ironmaking with energy consumption and cost as the objective function, and analyzed the effects of coke ratio, blast temperature, and coke oven gas injection on energy consumption and cost 15) . 4 non-dominated sorting genetic algorithm (NSGA) to improve the efficiency and economy of the blast furnace by searching and optimizing the dosage scheme; at present, the optimization models of the dosage process of blast furnace production are mainly classified into two categories: singleobjective optimization model and multi-objective optimization model.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Optimization Model of Blast Furnace Raw Materials and Operating Parameters Based on Intelligent Calculation

Liu,

Feng,

Zhao

et al. 2024

ISIJ Int.

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Aiming at the problem of coadjustment of blast furnace raw materials and operation parameters, this paper proposes a cooptimization model of blast furnace batching that integrates Random Forest and NSGA-Ⅲ (Non-dominated Sorting Genetic Algorithm III) algorithm. First, blast furnace field data were collected for a two-year time span, and a predictive model for CO 2 emissions and blast furnace permeability was constructed using the Random Forest algorithm; taking the goodness of fit (R 2 ), mean square error (MSE) and mean absolute error (MAE) as the evaluation indexes, the R 2 of the two prediction models obtained reached 0.93 and 0.96 respectively, and the MSE and MAE tended to be close to the zero value. Then, NSGA-Ⅲ was used to establish the blast furnace batching optimization model to optimally solve the batching scheme and the corresponding blast furnace operating parameters by taking the lowest batching cost, the lowest carbon dioxide emission and the maximum blast furnace permeability as the objective function, and the composition requirement of raw materials and the range limitation of operating parameters as the constraints; finally, the model was validated using the actual on-site data, and the application results showed that the output of the model conformed to the Finally, the results show that the model output meets the composition requirements and obtains a lower-cost dosage scheme than the original dosage ratio; moreover, this scheme corresponds to a blast furnace with less carbon dioxide emission, better blast furnace permeability and less slag.Therefore, the model can provide an effective reference for field operators to optimize blast furnace batching and operation.

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Muti-objective optimization on energy consumption, CO2 emission and production cost for iron and steel industry

Cited by 14 publications

References 42 publications

Energy Performance in Residential Buildings as a Property Market Efficiency Driver

Energy Performance in Residential Buildings as a Property Market Efficiency Driver

Mathematical modelling and techno-economic analysis of electric arc furnace steelmaking process with hot metal charging in China

Collaborative Optimization Model of Blast Furnace Raw Materials and Operating Parameters Based on Intelligent Calculation

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