基于数据处理成组方法的硅直接氮化动力学高效简单预测模型

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2020-03-10 DOI:10.22068/IJMSE.17.1.77

E. Shahmohamadi, A. Mirhabibi, F. Golestani-Fard

{"title":"基于数据处理成组方法的硅直接氮化动力学高效简单预测模型","authors":"E. Shahmohamadi, A. Mirhabibi, F. Golestani-Fard","doi":"10.22068/IJMSE.17.1.77","DOIUrl":null,"url":null,"abstract":"In the present study, a soft computing method namely the group method of data handling (GMDH) is applied to develop a new and efficient predictive model for prediction of conversion percentage of silicon. A comprehensive database is obtained from experimental studies in literature. Several effective parameters like time, temperature, nitrogen percentage, pellet size and silicon particle size are considered. The performance of the model is evaluated through statistical analysis. Moreover, the silicon nitridation was performed in 1573 k and results were evaluated against model results for validation of the model. Furthermore, the performance and efficiency of the GMDH model is confirmed against the two most common analytical models. The most effective parameters in estimating the conversion percentage are determined through sensitivity analysis based on the Gamma Test. Finally, the robustness of the developed model is verified through parametric analysis.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2020-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Prediction of Silicon Direct Nitridation Kinetic By An Efficient and Simple Predictive Model Based on Group Method of Data Handling\",\"authors\":\"E. Shahmohamadi, A. Mirhabibi, F. Golestani-Fard\",\"doi\":\"10.22068/IJMSE.17.1.77\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the present study, a soft computing method namely the group method of data handling (GMDH) is applied to develop a new and efficient predictive model for prediction of conversion percentage of silicon. A comprehensive database is obtained from experimental studies in literature. Several effective parameters like time, temperature, nitrogen percentage, pellet size and silicon particle size are considered. The performance of the model is evaluated through statistical analysis. Moreover, the silicon nitridation was performed in 1573 k and results were evaluated against model results for validation of the model. Furthermore, the performance and efficiency of the GMDH model is confirmed against the two most common analytical models. The most effective parameters in estimating the conversion percentage are determined through sensitivity analysis based on the Gamma Test. Finally, the robustness of the developed model is verified through parametric analysis.\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2020-03-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.22068/IJMSE.17.1.77\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.22068/IJMSE.17.1.77","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

摘要

本研究采用软计算方法——数据处理分组法(GMDH)，建立了一种新的、高效的硅转化率预测模型。从文献的实验研究中获得了一个全面的数据库。考虑了时间、温度、氮含量、球团尺寸和硅粒度等几个有效参数。通过统计分析对模型的性能进行了评价。此外，在1573 k下进行了硅氮化，并将结果与模型结果进行了比较，以验证模型的有效性。此外，对比两种最常用的分析模型，验证了GMDH模型的性能和效率。通过基于伽玛测试的灵敏度分析，确定了估算转化率最有效的参数。最后，通过参数分析验证了所建模型的鲁棒性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Prediction of Silicon Direct Nitridation Kinetic By An Efficient and Simple Predictive Model Based on Group Method of Data Handling

In the present study, a soft computing method namely the group method of data handling (GMDH) is applied to develop a new and efficient predictive model for prediction of conversion percentage of silicon. A comprehensive database is obtained from experimental studies in literature. Several effective parameters like time, temperature, nitrogen percentage, pellet size and silicon particle size are considered. The performance of the model is evaluated through statistical analysis. Moreover, the silicon nitridation was performed in 1573 k and results were evaluated against model results for validation of the model. Furthermore, the performance and efficiency of the GMDH model is confirmed against the two most common analytical models. The most effective parameters in estimating the conversion percentage are determined through sensitivity analysis based on the Gamma Test. Finally, the robustness of the developed model is verified through parametric analysis.

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

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.