精确的生物炭产量预测使用随机森林和XGBoost与泰勒图可视化
Sudhakar Uppalapati1, Prabhu Paramasivam2, Naveen Kilari3
1Department of Mechanical Engineering, Marri Laxman Reddy Institute of Technology and Management, Hyderabad, 500043, India.
Scientific reports
|February 27, 2025
概括
机器学习模型预测生物质热解的生物碳产量. XGBoost表现出卓越的准确性,识别了灰,水分和含量等关键因素,以优化生物炭生产.
科学领域:
- *生物质转化和可再生能源技术.
- * 机器学习在化学工程和材料科学中的应用.
背景情况:
- * 热解是一种关键的废物转化为能源的过程,产生有价值的产品,如生物炭.
- *生物炭产量受原料特性和热解条件的影响,需要准确的预测方法.
- * 传统的产量预测实验方法耗时且资源密集.
研究的目的:
- *使用机器学习开发和比较生物炭产量的数据驱动预测模型.
- * 确定影响生物炭产量的最有影响力的原料参数.
- *通过先进的计算方法克服经验建模的局限性.
主要方法:
- *采用了五种机器学习算法:拉索回归,Tweedie回归,随机森林,XGBoost和梯度增强回归.
- *利用历史实验数据进行训练和测试预测模型.
- * 基于合作游戏理论的应用SHAP (夏普利添加式解释) 来解释模型预测并确定特征的重要性.
主要成果:
- *XGBoost的表现优于其他模型,其预测准确度高,R平方值为0.9739 (训练) 和0.8875 (测试).
- *XGBoost模型的误差很低:平均绝对百分比误差为2.14% (训练) 和3.8% (测试).
- *SHAP分析显示,原料特性如灰和水分含量会对生物炭产量产生负面影响,而固定碳 (FPT),和碳含量会对其产生积极影响.
结论:
- * 数据驱动的机器学习模型,特别是XGBoost,为预测生物炭产量提供了强大而准确的实证方法替代方案.
- *了解原料特性的影响对于优化生物质转化过程和最大限度地提高生物炭生产至关重要.
- *精确预测技术提高了生物质物流,转换效率和生物质作为可再生能源的整体利用.
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