利用机器学习理解和优化生物质废物的气化

IF 9.1 Q1 ENGINEERING, CHEMICAL
Jie Li , Lanyu Li , Yen Wah Tong , Xiaonan Wang
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引用次数: 23

摘要

气化是同时生产可燃H2合成气的生物质废物处理的可持续方法。然而,这种热化学过程相当复杂,产生了多相产物。产物的分布和组成也高度依赖于原料信息和气化条件。目前,充分理解和优化这一过程仍然具有挑战性。在此背景下,应用四种数据驱动的机器学习(ML)方法对生物质废物气化过程进行建模,以进行产品预测、过程解释和优化。结果表明,梯度助推回归(GBR)模型在预测三相产物和合成气组成方面表现出良好的性能,测试R2为0.82–0.96。基于GBR模型的解释表明,进料和气化条件(包括进料灰分、碳、氮、氧的含量和气化温度)是影响焦炭、焦油和合成气分布的重要因素。此外,研究发现,在800°C以上的温度下,具有较高碳(>;48%)、较低氮(<;0.5%)和灰分(1%-5%)含量的原料可以获得更高的富H2合成气产率。结果表明,该模型提出的最佳条件可以实现含60%–62%合成气的产量,并实现44.34 mol/kg的H2产量。基于模型的解释提供的这些有价值的见解可以帮助理解和优化生物质气化,以指导富H2合成气的生产。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Understanding and optimizing the gasification of biomass waste with machine learning

Understanding and optimizing the gasification of biomass waste with machine learning

Gasification is a sustainable approach for biomass waste treatment with simultaneous combustible H2-syngas production. However, this thermochemical process was quite complicated with multi-phase products generated. The product distribution and composition also highly depend on the feedstock information and gasification condition. At present, it is still challenging to fully understand and optimize this process. In this context, four data-driven machine learning (ML) methods were applied to model the biomass waste gasification process for product prediction and process interpretation and optimization. The results indicated that the Gradient Boosting Regression (GBR) model showed good performance for predicting three-phase products and syngas compositions with test R2 of 0.82–0.96. The GBR model-based interpretation suggested that both feed and gasification condition (including the contents of feedstock ash, carbon, nitrogen, oxygen, and gasification temperature) were important factors influencing the distribution of char, tar, and syngas. Furthermore, it was found that a feedstock with higher carbon (> 48%), lower nitrogen (< 0.5%), and ash (1%–5%) contents under a temperature over 800 °C could achieve a higher yield of H2-rich syngas. It was shown that the optimal conditions suggested by the model could achieve an output containing 60%–62% syngas and achieve an H2 yield of 44.34 mol/kg. These valuable insights provided from the model-based interpretation could aid the understanding and optimization of biomass gasification to guide the production of H2-rich syngas.

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来源期刊
Green Chemical Engineering
Green Chemical Engineering Process Chemistry and Technology, Catalysis, Filtration and Separation
CiteScore
11.60
自引率
0.00%
发文量
58
审稿时长
51 days
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