机器学习与现象学模型混合建模用于预测超临界水中生物质气化制氢过程

Julles Mitoura dos Santos Junior, Ícaro Augusto Maccari Zelioli, A. Mariano
{"title":"机器学习与现象学模型混合建模用于预测超临界水中生物质气化制氢过程","authors":"Julles Mitoura dos Santos Junior, Ícaro Augusto Maccari Zelioli, A. Mariano","doi":"10.3390/eng4020086","DOIUrl":null,"url":null,"abstract":"Process monitoring and forecasting are essential to ensure the efficiency of industrial processes. Although it is possible to model processes using phenomenological approaches, these are not always easy to apply and generalize due to the complexity of the processes and the high number of unknown parameters. This work aims to present a hybrid modeling architecture that combines a phenomenological model with machine learning models. The proposal is to enable the use of simplified phenomenological models to explain the basic principles behind a phenomenon. Next, the data-oriented model corrects deviations from the simplified model predictions. The research hypothesis consists of showing the benefits of integrating prior knowledge of chemical engineering in simplifying data-based models, enhancing their generalization and improving their interpretability. The gasification process of lignin biomass with supercritical water was used as a case study for this methodology and the variable to be observed was the production of hydrogen. The real experimental data of this process were augmented using Gibbs energy minimization with the Peng–Robinson equation of state, thus generating a more voluminous database that was considered as real process data. The ideal gas model was used as a simplified model, producing significant deviations in predictions (relative deviations greater than 20%). Deviations (∆H2 = H2real−H2predict) were used as the target variable for the machine learning model. Linear regression models (LASSO and simple linear regression) were used to predict ∆H2 and this variable was added to the simplified forecast model. This consisted of the hybrid prediction of the resulting hydrogen formation (H2predict). Among the verified models, the simple linear regression adjusted better to the values of ∆H2 (R2 = 0.985) and MAE smaller than 0.1. Thus, the proposed hybrid architecture allowed for the prediction of the formation of hydrogen during the gasification process of lignin biomass, despite the thermodynamic limitations of the ideal gas model. Hybridization proved to be robust as a process monitoring tool, providing the abstraction of non-idealities of industrial processes through simple, data-oriented models, without losing predictive power. The objective of the work was fulfilled, presenting a new possibility for the monitoring of real industrial processes.","PeriodicalId":10630,"journal":{"name":"Comput. Chem. Eng.","volume":"4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hybrid Modeling of Machine Learning and Phenomenological Model for Predicting the Biomass Gasification Process in Supercritical Water for Hydrogen Production\",\"authors\":\"Julles Mitoura dos Santos Junior, Ícaro Augusto Maccari Zelioli, A. Mariano\",\"doi\":\"10.3390/eng4020086\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Process monitoring and forecasting are essential to ensure the efficiency of industrial processes. Although it is possible to model processes using phenomenological approaches, these are not always easy to apply and generalize due to the complexity of the processes and the high number of unknown parameters. This work aims to present a hybrid modeling architecture that combines a phenomenological model with machine learning models. The proposal is to enable the use of simplified phenomenological models to explain the basic principles behind a phenomenon. Next, the data-oriented model corrects deviations from the simplified model predictions. The research hypothesis consists of showing the benefits of integrating prior knowledge of chemical engineering in simplifying data-based models, enhancing their generalization and improving their interpretability. The gasification process of lignin biomass with supercritical water was used as a case study for this methodology and the variable to be observed was the production of hydrogen. The real experimental data of this process were augmented using Gibbs energy minimization with the Peng–Robinson equation of state, thus generating a more voluminous database that was considered as real process data. The ideal gas model was used as a simplified model, producing significant deviations in predictions (relative deviations greater than 20%). Deviations (∆H2 = H2real−H2predict) were used as the target variable for the machine learning model. Linear regression models (LASSO and simple linear regression) were used to predict ∆H2 and this variable was added to the simplified forecast model. This consisted of the hybrid prediction of the resulting hydrogen formation (H2predict). Among the verified models, the simple linear regression adjusted better to the values of ∆H2 (R2 = 0.985) and MAE smaller than 0.1. Thus, the proposed hybrid architecture allowed for the prediction of the formation of hydrogen during the gasification process of lignin biomass, despite the thermodynamic limitations of the ideal gas model. Hybridization proved to be robust as a process monitoring tool, providing the abstraction of non-idealities of industrial processes through simple, data-oriented models, without losing predictive power. The objective of the work was fulfilled, presenting a new possibility for the monitoring of real industrial processes.\",\"PeriodicalId\":10630,\"journal\":{\"name\":\"Comput. Chem. Eng.\",\"volume\":\"4 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Comput. Chem. Eng.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/eng4020086\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Comput. Chem. Eng.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/eng4020086","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

过程监测和预测是保证工业过程效率的必要条件。虽然可以使用现象学方法对过程进行建模,但由于过程的复杂性和大量未知参数,这些方法并不总是容易应用和推广。这项工作旨在提出一种混合建模架构,将现象学模型与机器学习模型相结合。这个建议是为了使用简化的现象学模型来解释现象背后的基本原理。接下来,面向数据的模型修正了与简化模型预测的偏差。研究假设包括展示整合化学工程先验知识在简化基于数据的模型、增强其泛化和提高其可解释性方面的好处。以木质素生物质在超临界水条件下的气化过程为例,对该方法进行了研究,观察到的变量是氢气的产生。利用Gibbs能量最小化和Peng-Robinson状态方程对该过程的实际实验数据进行了扩充,从而产生了一个更大的数据库,被认为是真实的过程数据。理想气体模型作为简化模型,预测偏差较大(相对偏差大于20%)。偏差(∆H2 = H2real−H2predict)作为机器学习模型的目标变量。采用线性回归模型(LASSO和简单线性回归)预测∆H2,并将该变量加入简化预测模型。这包括对生成氢的混合预测(H2predict)。经验证的模型中,简单线性回归对∆H2 (R2 = 0.985)和MAE < 0.1的调整效果较好。因此,尽管理想气体模型存在热力学限制,但所提出的混合结构允许在木质素生物质气化过程中预测氢的形成。杂交被证明是一种强大的过程监控工具,通过简单的、面向数据的模型提供工业过程非理想性的抽象,而不会失去预测能力。这项工作的目标已经实现,为监测实际工业过程提供了一种新的可能性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Hybrid Modeling of Machine Learning and Phenomenological Model for Predicting the Biomass Gasification Process in Supercritical Water for Hydrogen Production
Process monitoring and forecasting are essential to ensure the efficiency of industrial processes. Although it is possible to model processes using phenomenological approaches, these are not always easy to apply and generalize due to the complexity of the processes and the high number of unknown parameters. This work aims to present a hybrid modeling architecture that combines a phenomenological model with machine learning models. The proposal is to enable the use of simplified phenomenological models to explain the basic principles behind a phenomenon. Next, the data-oriented model corrects deviations from the simplified model predictions. The research hypothesis consists of showing the benefits of integrating prior knowledge of chemical engineering in simplifying data-based models, enhancing their generalization and improving their interpretability. The gasification process of lignin biomass with supercritical water was used as a case study for this methodology and the variable to be observed was the production of hydrogen. The real experimental data of this process were augmented using Gibbs energy minimization with the Peng–Robinson equation of state, thus generating a more voluminous database that was considered as real process data. The ideal gas model was used as a simplified model, producing significant deviations in predictions (relative deviations greater than 20%). Deviations (∆H2 = H2real−H2predict) were used as the target variable for the machine learning model. Linear regression models (LASSO and simple linear regression) were used to predict ∆H2 and this variable was added to the simplified forecast model. This consisted of the hybrid prediction of the resulting hydrogen formation (H2predict). Among the verified models, the simple linear regression adjusted better to the values of ∆H2 (R2 = 0.985) and MAE smaller than 0.1. Thus, the proposed hybrid architecture allowed for the prediction of the formation of hydrogen during the gasification process of lignin biomass, despite the thermodynamic limitations of the ideal gas model. Hybridization proved to be robust as a process monitoring tool, providing the abstraction of non-idealities of industrial processes through simple, data-oriented models, without losing predictive power. The objective of the work was fulfilled, presenting a new possibility for the monitoring of real industrial processes.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Study of the Synthesis Variables in the Preparation of CoAl2O4 Pigment Using Microwaves to Reduce Energetic Consumption Remarks on Constitutive Modeling of Granular Materials Analysis and Design Methodology of Radial Flux Surface-Mounted Permanent Magnet Synchronous Motors The Effect of High-Energy Ball Milling of Montmorillonite for Adsorptive Removal of Cesium, Strontium, and Uranium Ions from Aqueous Solution Review of Graphene-Based Materials for Tribological Engineering Applications
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1