Machine learning predicts and optimizes hydrothermal liquefaction of biomass

水热液化 生物量(生态学) 生物炼制 液化 热液循环 产量(工程) 环境科学 工艺工程 相关系数 碳纤维 制浆造纸工业 计算机科学 化学 机器学习 废物管理 化学工程 工程类 生物燃料 材料科学 算法 地质学 有机化学 冶金 复合数 海洋学
作者
Alireza Shafizadeh,Hossein Shahbeig,Mohammad Hossein Nadian,Hossein Mobli,Majid Dowlati,Vijai Kumar Gupta,Wanxi Peng,Su Shiung Lam,Meisam Tabatabaei,Mortaza Aghbashlo
出处
期刊:Chemical Engineering Journal [Elsevier BV]
卷期号:445: 136579-136579 被引量:118
标识
DOI:10.1016/j.cej.2022.136579
摘要

The hydrothermal liquefaction process has recently attracted more attention in biorefinery design and implementation because of its capability of handling various wet biomass feedstocks. However, measuring the quantitative and qualitative characteristics of hydrothermal liquefaction (by)products is challenging because of the need for time-consuming and cost-intensive experiments. Machine learning technology can cope with this issue thanks to its ability to learn from past datasets and mechanisms. Hence, machine learning was applied herein to quantitatively and qualitatively characterize hydrothermal liquefaction (by)products based on biomass composition and reaction conditions. The data patterns compiled from the published literature were used to develop a universal machine learning model applicable to a wide range of biomass feedstocks and reaction conditions. The collected data were statistically analyzed and mechanistically discussed. Among the four machine learning models considered, Gaussian process regression could provide the highest accuracy, with a correlation coefficient higher than 0.926 and a mean absolute error lower than 0.031. An effort was also made to maximize biocrude oil quantity and quality and minimize byproducts quantity using the objective functions developed by the selected model. The optimal biocrude oil yield (48.7–53.5%) was obtained when the carbon, hydrogen, nitrogen, oxygen, sulfur, and ash contents of biomass were in the range of 40.9–48.3%, 9.72–9.80%, 11.9–13.6%, 15.2–15.6%, 0.0–0.94%, and 0.0–2.92%, respectively. The optimal operating conditions were: operating dry matter = 31.4–33.0%, temperature = 394–400 °C, reaction time = 5–9 min, and pressure = 30.0–35.6 MPa. An easy-to-use software package was developed based on the selected machine learning model to pave the way for bypassing unnecessary lengthy and costly experiments without requiring extensive machine learning knowledge. The present study highlights the vast potential of machine learning for modeling biomass hydrothermal liquefaction.
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