智能脱黑:利用可解释的人工智能进行臭氧运输建模和优化
Muhammad Rizwan1, Muhammad Ahmad Khan1, Sharifullah Khan1
1School of Computing Sciences, Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology, Haripur, 22650, Pakistan.
Scientific reports
|December 29, 2025
概括
机器学习模型准确地预测了环保臭氧化预处理期间的素去除效率. 这种方法通过了解关键的脱灰化因素来优化生物质加工以实现可持续的生物燃料生产.
科学领域:
- 生物质价值化和可持续能源生产.
- 化学工程和过程优化.
- 人工智能在绿色化学中的应用.
背景情况:
- 作为生物质的主要组成部分的素,由于其反抗性结构,阻碍了纤维素和半纤维素的可访问性.
- 有效的素去除对于生物燃料生产至关重要,因为素阻碍了水解,并非生产性地结合了酶.
- 臭氧化是一种新兴的,环保的脱硫技术,有可能改善生物质预处理.
研究的目的:
- 调查机器学习 (ML) 技术的应用,以预测在臭氧化预处理过程中木质素去除效率.
- 评估各种ML回归模型在捕捉delignification中的复杂关系方面的性能.
- 通过特征重要性分析来确定影响红素去除的关键过程变量.
主要方法:
- 使用PyCaret框架与实验性臭氧化数据训练和评估了19个回归模型.
- 使用了额外的树木回归器,显示了最高的预测准确度.
- 采用了SHapley添加式解释 (SHAP) 来解释特征重要性和量化变量贡献.
主要成果:
- 额外树木回归模型实现了对脱结果的最高预测准确度.
- 机器学习模型有效地捕捉了控制基于臭氧化的脱的复杂关系.
- SHAP分析提供了对每个工艺变量对素去除的贡献的定量见解.
结论:
- 机器学习作为一种强大的预测和解释工具,用于优化基于臭氧化的脱.
- 使用ML的数据驱动方法可以提高生物质利用效率,以实现可持续的生物燃料生产.
- 这项研究表明了机器学习在推进化学工程过程以获得更绿色的能源解决方案方面的潜力.
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