人工神经网络和梯度增强机器用于回归评估气化过程:综述

Q4 Energy
Owen Sedej, E. Mbonimpa, Trevor W Sleight, J. Slagley
{"title":"人工神经网络和梯度增强机器用于回归评估气化过程:综述","authors":"Owen Sedej, E. Mbonimpa, Trevor W Sleight, J. Slagley","doi":"10.21926/jept.2203027","DOIUrl":null,"url":null,"abstract":"Waste-to-Energy technologies have the potential to dramatically improve both the natural and human environment. One type of waste-to-energy technology that has been successful is gasification. There are numerous types of gasification processes and in order to drive understanding and the optimization of these systems, traditional approaches like computational fluid dynamics software have been utilized to model these systems. The modern advent of machine learning models has allowed for accurate and computationally efficient predictions for gasification systems that are informed by numerous experimental and numerical solutions. Two types of machine learning models that have been widely used to solve for quantitative variables that are of predictive interest in gasification systems are gradient boosted machines and artificial neural networks. In this article, the reviewed literature used either gradient boosted machines or artificial neural networks to successfully model gasification systems. The review of such literature allows for a comparison in machine learning model architecture and resultant accuracy as well as an insight into what parameters are being used to inform the models and to make predictions.","PeriodicalId":53427,"journal":{"name":"Journal of Nuclear Energy Science and Power Generation Technology","volume":"26 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Neural Networks and Gradient Boosted Machines Used for Regression to Evaluate Gasification Processes: A Review\",\"authors\":\"Owen Sedej, E. Mbonimpa, Trevor W Sleight, J. Slagley\",\"doi\":\"10.21926/jept.2203027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Waste-to-Energy technologies have the potential to dramatically improve both the natural and human environment. One type of waste-to-energy technology that has been successful is gasification. There are numerous types of gasification processes and in order to drive understanding and the optimization of these systems, traditional approaches like computational fluid dynamics software have been utilized to model these systems. The modern advent of machine learning models has allowed for accurate and computationally efficient predictions for gasification systems that are informed by numerous experimental and numerical solutions. Two types of machine learning models that have been widely used to solve for quantitative variables that are of predictive interest in gasification systems are gradient boosted machines and artificial neural networks. In this article, the reviewed literature used either gradient boosted machines or artificial neural networks to successfully model gasification systems. The review of such literature allows for a comparison in machine learning model architecture and resultant accuracy as well as an insight into what parameters are being used to inform the models and to make predictions.\",\"PeriodicalId\":53427,\"journal\":{\"name\":\"Journal of Nuclear Energy Science and Power Generation Technology\",\"volume\":\"26 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Nuclear Energy Science and Power Generation Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21926/jept.2203027\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Energy\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nuclear Energy Science and Power Generation Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21926/jept.2203027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Energy","Score":null,"Total":0}
引用次数: 0

摘要

废物转化为能源的技术具有显著改善自然环境和人类环境的潜力。一种成功的废物转化为能源的技术是气化。有许多类型的气化过程,为了推动对这些系统的理解和优化,传统的方法,如计算流体动力学软件,已经被用来模拟这些系统。现代机器学习模型的出现使得通过大量实验和数值解决方案对气化系统进行准确和计算高效的预测成为可能。两种类型的机器学习模型已被广泛用于求解气化系统中具有预测意义的定量变量,即梯度增强机器和人工神经网络。在这篇文章中,综述的文献使用梯度推进机器或人工神经网络来成功地模拟气化系统。通过对这些文献的回顾,可以比较机器学习模型的架构和结果的准确性,并了解使用哪些参数来通知模型并进行预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Neural Networks and Gradient Boosted Machines Used for Regression to Evaluate Gasification Processes: A Review
Waste-to-Energy technologies have the potential to dramatically improve both the natural and human environment. One type of waste-to-energy technology that has been successful is gasification. There are numerous types of gasification processes and in order to drive understanding and the optimization of these systems, traditional approaches like computational fluid dynamics software have been utilized to model these systems. The modern advent of machine learning models has allowed for accurate and computationally efficient predictions for gasification systems that are informed by numerous experimental and numerical solutions. Two types of machine learning models that have been widely used to solve for quantitative variables that are of predictive interest in gasification systems are gradient boosted machines and artificial neural networks. In this article, the reviewed literature used either gradient boosted machines or artificial neural networks to successfully model gasification systems. The review of such literature allows for a comparison in machine learning model architecture and resultant accuracy as well as an insight into what parameters are being used to inform the models and to make predictions.
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
Journal of Nuclear Energy Science and Power Generation Technology
Journal of Nuclear Energy Science and Power Generation Technology Energy-Energy Engineering and Power Technology
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信