基于机器学习的预测,从纤维素废物中产生甲
Chao Song1, Fanfan Cai1, Shuang Yang1
1College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
Bioresource technology
|November 1, 2023
概括
机器学习预测了来自纤维素废物的甲产量,减少了对长时间的生物化学甲潜力测试的需求. 关键因素,如红素和纤维素含量影响生物气生产效率.
科学领域:
- 生物技术是生物技术.
- 可再生能源可再生能源是可再生能源.
- 环境科学 环境科学
背景情况:
- 生物化学甲潜力 (BMP) 试验是评估在无氧消化 (AD) 过程中纤维素废物生物降解性的标准.
- 优良生产规范测试耗时且昂贵,需要使用替代预测方法.
- 优化AD过程需要准确预测甲产量.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测从纤维纤维素废物 (LWs) 的累积甲产量 (CMY).
- 确定影响CMY的关键原料特性和消化参数.
- 为原料选择和AD工厂运行提供指导.
主要方法:
- 使用了157个LW的数据集,包括物理化学特性和消化条件.
- 开发并验证了一种ML模型来预测CMY.
- 进行模型解释性分析以确定重要的预测因素.
主要成果:
- 在CMY预测中获得0.869的确定系数 (R2).
- 确定了素含量,有机载荷和含量作为CMY的关键因素.
- 发现高纤维素含量 (>50%) 最初会降低CMY,但长时间的消化会增加它.
- 观察到15%以上的红素含量显著抑制了甲的产生.
结论:
- 开发的ML模型为传统的BMP测试提供了可靠和高效的替代方案.
- 原材料的组成,特别是纤维素和红素含量,极大地影响了甲产量.
- 优化原料选择和消化参数可以提高AD工厂的沼气生产.
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