基于数据融合的生物质加氢热解反应网络研究方法,Industrial & Engineering Chemistry Research

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基于数据融合的生物质加氢热解反应网络研究方法
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2023-03-07 , DOI: 10.1021/acs.iecr.2c04309
Fereshteh Sattari ₁ , Karthik Srinivasan ₁ , Anjana Puliyanda ₁ , Vinay Prasad ₁

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在这项工作中，我们提出并验证了一种使用数据驱动方法从光谱数据生成反应网络的方法，方法是将其应用于蒙特雷松生物质及其成分（即纤维素和木质素）的水热液化 (HTL)。这项工作是朝着水热液化过程的化学自动推理迈出的一步，从而限制了对人类专业知识的需求。光谱的贝叶斯层次聚类和自建模多变量光谱曲线分辨率用于生成化学相似物种组，其中的反应网络已使用贝叶斯网络开发。基于傅里叶变换红外光谱和质子核磁共振光谱的测量被用作输入数据。数据驱动的反应网络包括代表生物质成分分解、大分子水解和生成分子重组的途径，并且与文献一致。此外，对生物质及其组分（左旋葡聚糖，代表纤维素，2-苯氧基乙苯，代表木质素）生成的网络的比较揭示了生物质 HTL 反应网络与组分反应网络之间的关系。数据驱动的方法提供了一种诊断工具，可以识别复杂生物质原料最可能发生的化学反应，并可用于过程理解、设计和控制。生物质及其组分（左旋葡聚糖，代表纤维素，2-苯氧基乙苯，代表木质素）生成的网络的比较揭示了生物质HTL反应网络与组分反应网络之间的关系。数据驱动的方法提供了一种诊断工具，可以识别复杂生物质原料最可能发生的化学反应，并可用于过程理解、设计和控制。生物质及其组分（左旋葡聚糖，代表纤维素，2-苯氧基乙苯，代表木质素）生成的网络的比较揭示了生物质HTL反应网络与组分反应网络之间的关系。数据驱动的方法提供了一种诊断工具，可以识别复杂生物质原料最可能发生的化学反应，并可用于过程理解、设计和控制。

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Data Fusion-Based Approach for the Investigation of Reaction Networks in Hydrous Pyrolysis of Biomass

In this work, we present and validate a methodology for generating reaction networks from spectroscopic data using data-driven methods by applying it to the hydrothermal liquefaction (HTL) of Monterrey pine biomass and its constituents, viz., cellulose and lignin. This work is presented as a step toward automated inference of chemistry of the hydrothermal liquefaction process, thus limiting the need for human expertise. Bayesian hierarchical clustering of spectra and self-modeling multivariate spectral curve resolution are used to generate groups of chemically similar species, the reaction networks among which have been developed using Bayesian networks. Fourier transform infrared spectroscopy and proton nuclear magnetic resonance spectroscopy-based measurements are used as input data. The data-driven reaction network includes pathways representing decomposition of the biomass components, large molecule hydrolysis, and reformation of produced molecules and is consistent with the literature. Furthermore, the comparison of the networks generated for biomass and its components (levoglucosan, representing cellulose, and 2-phenoxy-ethyl benzene, representing lignin) reveals the relationship between the biomass HTL reaction network and the reaction networks of the components. The data-driven approach provides a diagnostic tool to identify the most probable reaction chemistry for complex biomass feedstocks and can be used for process understanding, design, and control.

更新日期：2023-03-07

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