Research on Energy-Saving Scheduling of a Forging Stock Charging Furnace Based on an Improved SPEA2 Algorithm

He, Fei; Shen, Kang; Guan, Li Ming; Jiang, Mingming

doi:10.3390/su9112154

Cited by 15 publications

(4 citation statements)

References 13 publications

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“…The authors concluded that the model could assist in decision-making to optimize the charge material mixture recipes, thereby decreasing electrical energy costs, which are significant operating expenses in an EAF. He et al [34] proposed fixing the furnace heating schedule issue to assist forging firms in achieving energy efficiency and reducing emissions. This study established a multi-objective furnace charging model to address the charging issue faced by continuous heating furnaces, aiming to minimize capacity difference and waiting time.…”

Section: Mathematical Modellingmentioning

confidence: 99%

Parametric Energy Efficiency Impact Analysis for Industrial Process Heating Furnaces Using the Manufacturing Energy Assessment Software for Utility Reduction

Bisht,

Gopalakrishnan,

Dahal

et al. 2024

Processes

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Industrial process heating furnace operations consume considerable energy in the U.S. manufacturing sector, making it crucial to identify energy efficient strategies due to the growing need to minimize energy usage and emissions. It is important to identify the potential impact of these factors to enable process engineers to operate process heating systems at the maximum possible efficiency. This study examines and identifies the key impact factors that influence the efficiency of process heating systems using MEASUR (v1.4.0), the DOE software tools such as the insulation effectiveness, the burner stoichiometry, cooling medium, thermal storage, and atmospheric gases. Data from a two-fuel-fired heat treatment furnace and an electric arc furnace (EAF) for steelmaking were employed to establish the baseline heat balance models in MEASUR. The fractional factorial design experiment was developed with two-level parameter values and energy efficiency strategies for the heat input into industrial furnaces. The three most significant parameters for the heat input for a fuel-fired industrial furnace, Industrial Furnace A, are excess air percentage or the oxygen percentage in flue gas (OF), average surface temperature (ST), and combustion air temperature (CT). Similarly, for an electric industrial furnace, Industrial Furnace B, the parameters are charge temperature (CHT), average surface temperature (ST), and time open (TO). A comparative analysis was carried out for the fuel-fired and equivalent electric resistance furnaces to identify the prospect of electrification of industrial furnaces relying upon fossil fuels. The study aims to assist industries and designers in making informed decisions regarding industrial furnace upgrades, process optimization, and maintenance investments, resulting in substantial energy and cost savings, and a reduced environmental impact.

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Section: Mathematical Modellingmentioning

confidence: 99%

Parametric Energy Efficiency Impact Analysis for Industrial Process Heating Furnaces Using the Manufacturing Energy Assessment Software for Utility Reduction

Bisht,

Gopalakrishnan,

Dahal

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…Consequently, the minimization of emission quantity with a constant emission-to-energy factor leads to a minimization of energy consumption. Energy consumption is also indirectly taken into account in approaches that maximize time in power-saving mode (such as in [22]), minimize unnecessary heating time (e.g., [23]), or minimize unnecessary waiting time on machines to reduce storage energy consumption (e.g., for holding the temperature of hot products, such as in [24]).…”

Section: Ecological Consideration Of Energy Consumptionmentioning

confidence: 99%

“…For example, the authors of [61,71,72] included "holding energy", which equals the energy needed for holding the temperature in induction furnaces until the finished job (i.e., melted metal) is removed. Comparable approaches can be found in [16,23,146]. Ref.…”

Section: Various Energy Utilization Factorsmentioning

confidence: 99%

Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme

2021

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Scarcity of resources, structural change during the further development of renewable energy sources, and their corresponding costs, such as increasing resource costs or penalties due to dirty production, lead industrial firms to adapt ecological actions. In this regard, research on energy utilization in production planning has received increased attention in the last years, resulting in a large number of research articles so far. With the paper at hand, we review the literature on energy-oriented production planning. The aim of this study is to derive similar core issues and related properties along energy-oriented models within hierarchical production planning. For this, we carry out a systematic literature review and analyze and synthesize 375 research articles. We classify the underlying literature with a novel two-dimensional classification scheme and identify three key topics and five frequently found characteristics, which are presented in detail throughout this article. Based on these results, we state several potentials for further research.

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“…Tis technique helps in generating diverse and uniformly distributed set of solutions. Subsequent studies have further enhanced the solution capability of population SPEA2-based optimization algorithms utilizing immunity algorithms [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Uniformity-Comprehensive Multiobjective Optimization Evolutionary Algorithm Based on Machine Learning

Luan,

He,

Yang

et al. 2023

International Journal of Intelligent Systems

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When solving real-world optimization problems, the uniformity of Pareto fronts is an essential strategy in multiobjective optimization problems (MOPs). However, it is a common challenge for many existing multiobjective optimization algorithms due to the skewed distribution of solutions and biases towards specific objective functions. This paper proposes a uniformity-comprehensive multiobjective optimization evolutionary algorithm based on machine learning to address this limitation. Our algorithm utilizes uniform initialization and self-organizing map (SOM) to enhance population diversity and uniformity. We track the IGD value and use K-means and CNN refinement with crossover and mutation techniques during evolutionary stages. Our algorithm’s uniformity and objective function balance superiority were verified through comparative analysis with 13 other algorithms, including eight traditional multiobjective optimization algorithms, three machine learning-based enhanced multiobjective optimization algorithms, and two algorithms with objective initialization improvements. Based on these comprehensive experiments, it has been proven that our algorithm outperforms other existing algorithms in these areas.

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Research on Energy-Saving Scheduling of a Forging Stock Charging Furnace Based on an Improved SPEA2 Algorithm

Cited by 15 publications

References 13 publications

Parametric Energy Efficiency Impact Analysis for Industrial Process Heating Furnaces Using the Manufacturing Energy Assessment Software for Utility Reduction

Parametric Energy Efficiency Impact Analysis for Industrial Process Heating Furnaces Using the Manufacturing Energy Assessment Software for Utility Reduction

Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme

Uniformity-Comprehensive Multiobjective Optimization Evolutionary Algorithm Based on Machine Learning

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