在高温烧结矿石生产过程中,对能源效率和碳减排的智能优化和影响分析
Yuxing Yuan1,2,3, Jingchao Sun1,2,3, Lei Zhang1,2,3
1SEP Key Laboratory of Eco-Industry, Northeastern University, Shenyang 110819, China.
Materials (Basel, Switzerland)
|November 27, 2024
概括
这项研究利用NSGA-III算法优化了烧结生产以节省能源和减少污染. 关键策略包括调整混合物中的水含量和燃料成分,从而大幅降低成本和排放.
科学领域:
- 金工程 金工程 金工程
- 环境科学 环境科学
- 工业过程优化 工业过程优化
背景情况:
- 烧结生产需要平衡节能,效率和减少污染.
- 现有的方法在多目标优化和量化烧结系统中的污染物影响方面扎.
研究的目的:
- 开发一个综合系统优化模型,用于烧结矿石生产和废热回收.
- 解决多目标协作优化和污染物影响量化方面的缺陷.
主要方法:
- 将非主导排序基因算法III (NSGA-III) 与系统优化模型集成.
- 分析最佳配置参数和关键因素的定量影响.
主要成果:
- 能源利用效率提高了0.67%,能源消耗减少了17.3MJ/t.
- 生产成本下降了11.45元人民币/,同时减少了CO2,SO2和NOx排放.
- 确定了最佳参数,并证实了减少混合物中的水含量,减少中间煤和增加材料层厚度是有效的策略.
结论:
- 拟议的多目标优化方法显著提高了烧结过程的效率和可持续性.
- 优化为钢铁行业带来了巨大的经济和环境效益.
- 调整运行参数为减少能源消耗和污染物排放提供了可行的途径.
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