固体废物和煤的联合燃烧特征预测超临界CO2电源周期基于CFD模拟和机器学习算法
Ying Cui1, Xinwang Wang2, Shujun Jiang3
1School of Automotive and Transportation, Wuxi Institute of Technology, Binhu District, Wuxi, Jiangsu Province 214000, PR China; Key Laboratory of Energy Conversion and Process Measurement and Control Ministry of Education, School of Energy and Environment, Southeast University, Xuanwu District, Nanjing, Jiangsu Province 210096, PR China; ARC Research Hub for Computational Particle Technology, Department of Chemical Engineering, Monash University, Clayton, Vic 3800, Australia.
Waste management (New York, N.Y.)
|September 17, 2024
概括
在超临界二氧化碳循环流体化床中共同燃烧可燃固体废物和煤炭,有效地管理废物并减少排放. 一个先进的AI模型准确地预测了炉性能和污染物水平.
科学领域:
- 能源工程 能源工程
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 在超临界二氧化碳 (S-CO2) 循环流化床 (CFB) 中共同燃烧可燃固体废物 (CSW) 和煤炭,为废物管理和能源生产提供了可持续的解决方案.
- 这项技术可以提高煤炭转化效率,并大大减少污染物排放,从而有助于环境效益.
研究的目的:
- 在S-CO2 CFB炉中研究CSW和煤炭的联合燃烧特性.
- 分析运行参数,特别是燃料混合物比例和第一阶段固体测量对气体排放特征的影响.
- 开发和验证关键运行参数和排放的先进预测模型.
主要方法:
- 使用多相粒子在细胞 (MP-PIC) 方法与全面的化学反应模型相结合,用于在300MW S-CO2 CFB炉中的模拟.
- 对燃料混合物比率和第一阶段石化测量进行了参数研究,以评估对气体排放 (CO,NOx,SO2) 的影响.
- 开发了一种改进的鱼优化算法和双字典长短期记忆 (IWOA-BiLSTM) 模型,用于预测CFB温度,NOx和SO2度.
主要成果:
- 燃料混合物中煤炭质量比率的增加导致了CO和SO2排放量的减少,而NOx排放量增加.
- 较高的第一阶段静脉测量结果是增加了CO,减少了NOx,SO2排放量变化微不足道.
- IWOA-BiLSTM模型显示出卓越的预测准确性,平均相对误差为温度的0.032%,NOx的0.231%,SO2的0.157%.
结论:
- 该研究成功地描述了S-CO2 CFB中的CSW和煤炭联合燃烧,根据操作参数提供了关于排放趋势的见解.
- 开发的IWOA-BiLSTM模型提供了一个高度准确和可靠的工具,用于预测S-CO2 CFB炉的关键参数.
- 这项研究支持优化联合燃烧过程,以便在先进的发电系统中高效地利用废物和控制排放.
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