摘要
钢铁工业是中国国民经济的重要基础性产业,对于国家经济发展具有举足轻重的作用。然而其生产过程中能源消耗巨大并且碳排放问题日益严峻,这使得钢铁企业面临着环境保护和能源效率的双重压力。为了应对这一挑战,深入研究了钢铁企业的能源调度问题,特别是针对煤气、蒸汽、电力这3种主要能源介质的优化调度提出了1种基于多目标粒子群优化的能源调度算法,该算法目的在于实现能源经济成本和二氧化碳排放量这2个关键目标的综合优化。同时该优化算法结合了模糊数学方法,能够更精确地描述和处理能源调度中的不确定性和模糊性。通过不断迭代和寻优算法能够在多个目标之间找到最佳的平衡点进而有效平衡经济与环保之间的矛盾。以年产粗钢800万t的钢铁企业为例,对所提出的优化算法进行了实证研究。研究结果表明,该算法能够显著提升能源调度的效率,减少能源浪费,使得煤气、蒸汽和电力的利用更加合理和高效。同时,在减少二氧化碳排放量方面也取得了显著成效,为钢铁企业的绿色发展提供了有力支持。此外,该优化算法还能够提高Pareto解的质量和多样性,加快收敛速度,为决策者提供了更多样化的选择方案。这对于钢铁企业在面临复杂多变的市场环境和政策要求时,制定更加科学合理的能源调度策略具有重要意义。
The iron and steel industry is a fundamental sector of China's economy,playing a pivotal role in economic development.However,its production process involves substantial energy consumption,with carbon emissions becoming increasingly severe,placing enterprises under dual pressure from environmental protection and energy efficiency requirements.To address these challenges,an energy scheduling algorithm based on multi-objective particle swarm optimization is introduced,aiming to optimize the scheduling of gas,steam,and electricity while minimizing energy costs and carbon emissions.The algorithm incorporates fuzzy mathematics to address uncertainties and fuzziness in energy scheduling,enabling a balance between economic development and environmental protection through continuous optimization.A case study on an iron and steel enterprise with an annual crude steel output of 8 Mt demonstrates that the algorithm significantly improves energy scheduling efficiency,reduces energy waste,optimizes resource utilization,and lowers carbon emissions.Moreover,the algorithm enhances the quality and diversity of Pareto solutions,accelerates convergence,and provides decision-makers with more flexible options,supporting the formulation of scientific and rational energy strategies in complex market and policy environments.
作者
朱亚琴
ZHU Yaqin(College of Management,Jiangsu University,Zhenjiang 212000,Jiangsu,China)
出处
《钢铁》
北大核心
2025年第5期205-214,共10页
Iron and Steel
基金
国家自然科学基金资助项目(72243005)。
关键词
多目标粒子群优化
钢铁企业
模糊数学
能源调度
优化算法
碳排放
能源经济成本
PARETO解
multi-objective particle swarm optimization
iron and steel enterprises
fuzzy mathematics
energy scheduling
optimization algorithm
carbon emissions
energy economic cost
Pareto solutions
