Intelligent modeling and response behavior of process of producing hydrogen-rich hydrothermal bio-oil from wastes with high-moisture and high-lipid

LIU Xiangjie; YUAN Qiaoxia; CAO Hongliang

doi:10.13300/j.cnki.hnlkxb.2025.05.027

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Intelligent modeling and response behavior of process of producing hydrogen-rich hydrothermal bio-oil from wastes with high-moisture and high-lipid

Food science and agricultural engineering | 更新时间：2026-11-05

- Intelligent modeling and response behavior of process of producing hydrogen-rich hydrothermal bio-oil from wastes with high-moisture and high-lipid
- Journal of Huazhong Agricultural University Vol. 44, Issue 5, Pages: 270-279(2025)
- 作者机构：
  
  华中农业大学工学院，武汉430070
- 作者简介：
- 基金信息：
- DOI：10.13300/j.cnki.hnlkxb.2025.05.027
  CLC： S216.2;X705
- Received：04 July 2024，
  
  Published：15 September 2025
- 稿件说明：
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刘项杰，袁巧霞，曹红亮.高湿高脂废弃物产富氢水热油过程的智能建模与响应行为解析［J］.华中农业大学学报，2025，44（5）：270‐279.

LIU Xiangjie,YUAN Qiaoxia,CAO Hongliang.Intelligent modeling and response behavior of process of producing hydrogen-rich hydrothermal bio-oil from wastes with high-moisture and high-lipid[J].Journal of Huazhong Agricultural University,2025,44(05):270-279.
刘项杰，袁巧霞，曹红亮.高湿高脂废弃物产富氢水热油过程的智能建模与响应行为解析［J］.华中农业大学学报，2025，44（5）：270‐279. DOI： 10.13300/j.cnki.hnlkxb.2025.05.027.

LIU Xiangjie,YUAN Qiaoxia,CAO Hongliang.Intelligent modeling and response behavior of process of producing hydrogen-rich hydrothermal bio-oil from wastes with high-moisture and high-lipid[J].Journal of Huazhong Agricultural University,2025,44(05):270-279. DOI： 10.13300/j.cnki.hnlkxb.2025.05.027.

摘要

为构建水热生物油氢含量高预测精度、强泛化能力的预测模型，深入探讨生物质水热转化规律和机制，以文献中收集的243组病死畜禽、藻类等高湿高脂废弃物水热制备富氢生物油试验数据为基础，采用随机森林和极端梯度提升树2类高适配性机器学习算法，建立高精宽域的水热生物油氢含量预测模型（R2>0.93）；基于数学预测模型，采用可解释技术（Shapley additive explanations，SHAP）及局部依赖性（partial dependence plot，PDP）分析方法，解析水热成油条件的贡献度、局部依赖性响应行为及其互作耦合规律。结果表明：高湿高脂废弃物中的脂质含量与氢含量是制备富氢生物油的决定性因素，二者对油相中氢的富集贡献度排名位居前二，能显著影响油相中氢的积累；随着原料氢含量的增加，油相中氢含量得到提升，表明富氢原料为制备富氢生物油提供了便利条件，提升效果最高可达4%，而原料高位热值是生物油富氢行为的主要抑制因素，抑制作用高达4%。此外，从特征类别间的互作关系来看，元素信息、工业信息及生物质组分信息间的耦合作用强烈，但原料特性与操作条件间的局部耦合作用较小。

Abstract

Two types of high-fitness machine learning algorithms including random forest and extreme gradient enhancement Tree were used to establish a prediction model of the content of hydrogen in hydrothermal bio-oil with high-precision and wide-range（

0.93） based on 243 sets of experimental data collected from literature on the hydrothermal preparation of hydrogen-rich bio-oil from wastes with high-moisture and high-lipid including dead livestock and algae to construct a model for predicting the content of hydrogen in hydrothermal bio-oil with high accuracy of prediction and strong ability of generalization， and to study in depth the laws and mechanisms of hydrothermal conversion of biomass. SHAP interpretable technology and local dependence analysis method were used to analyze the contribution， local dependence response behavior and interactive coupling law of conditions for producing hydrothermal bio-oil based on the mathematical prediction model. The results showed that the content of lipid and hydrogen in the wastes with

high-moisture and high-lipid were the determining factors for the preparation of hydrogen-rich bio-oil， ranked the top two in terms of its contribution to the enrichment of hydrogen in the oil phase and significantly affected the accumulation of hydrogen in the oil phase. The content of hydrogen in the oil phase was increased with the increase of the content of hydrogen in the raw material， indicating that hydrogen-rich raw materials provide convenient conditions for the preparation of hydrogen-rich bio-oil， with an improvement effect of up to 4%. The high calorific value of raw materials was the main inhibitory factor for the hydrogen-enriched behavior of bio-oil， with an inhibitory effect of up to 4%. The coupling effect between the information of element， industry， and components of biomass was strong from the interactions among the characterized categories， but the effect of local coupling between the characteristics of raw material and operating conditions was relatively small.

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references

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