基于物理的动态混合建模,用于在炼油厂共加工中实时可再生CO2跟踪.
Liang Cao1, Yang Liu2, Jing Liu3
1Department of Chemical Engineering, Massachusetts Institute of Technology, Boston, 02139, United States.
ISA transactions
|November 7, 2025
概括
精确追踪炼油厂共同加工产生的可再生二氧化碳排放,对于减缓气候变化至关重要. 这项研究引入了一种适应式建模框架,以准确地实时量化这些排放,帮助脱碳努力.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 炼油厂的共同加工对于整合可再生生物料库存至关重要.
- 由于工艺复杂性,对这些原料的二氧化碳排放的准确归因具有挑战性.
- 现有的方法无法解决动态原料变化和非线性问题.
研究的目的:
- 开发适应性建模框架,用于在炼油厂共加工过程中实时归因二氧化碳排放量.
- 提高可再生能源二氧化碳排放估计的准确性和可解释性.
- 为透明的炼油厂脱碳提供实用解决方案.
主要方法:
- 一个混合适应型建模框架,结合了受约束的递归最小平方 (RLS) 和稀疏的通用增量模型 (GAM).
- 合规预测的整合,以进行可靠的不确定性量化.
- 应用非负性约束的物理解释性.
主要成果:
- 拟议的框架在工业数据集 (>86,000个样本) 上实现了高预测准确度 (RMSE: 345.4,R2: 0.950).
- 在估计二氧化碳排放方面表现优于15种基线方法.
- 证明可再生原料贡献的实时,可解释和不确定性意识的跟踪.
结论:
- 混合适应模型为炼油厂共加工中的二氧化碳排放归因提供了优质的解决方案.
- 这种方法促进了透明和数据驱动的脱碳战略.
- 能够准确监测可再生生物原料对炼油厂排放的影响.
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