Jun Yan, Shuang Du, Jinhua Hu, Kazuhiro Tamura, Hong Li
{"title":"基于反向传播神经网络的 CI 分散红 4 和 CI 分散红 15 在超临界二氧化碳中的溶解度预测","authors":"Jun Yan, Shuang Du, Jinhua Hu, Kazuhiro Tamura, Hong Li","doi":"10.1111/cote.12706","DOIUrl":null,"url":null,"abstract":"<p>The solubility of 1-amino-2-hydroxy-4-methoxy-anthraquinone (CI Disperse Red 4) and 1-amino-2-hydroxy-anthraquinone (CI Disperse Red 15) in supercritical carbon dioxide was measured using a dynamic method over a temperature range from 343.15 to 373.15 K and a pressure range from 14 to 22 MPa. The experimental data are analysed by using the back propagation neural network constructed by MATLAB. In the back propagation neural network, the input layer consisted of two inputs, which are temperature and pressure, the output layer consisted of the solubility of dyes, and the hidden layer function was composed of a non-linear function. The results of the analysis showed that a good fitting level of 0.99 was obtained, which means that the back propagation neural network can accurately estimate the solubility data in supercritical carbon dioxide.</p>","PeriodicalId":10502,"journal":{"name":"Coloration Technology","volume":"140 2","pages":"230-238"},"PeriodicalIF":2.0000,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Solubility prediction of CI Disperse Red 4 and CI Disperse Red 15 in supercritical carbon dioxide based on the back propagation neural network\",\"authors\":\"Jun Yan, Shuang Du, Jinhua Hu, Kazuhiro Tamura, Hong Li\",\"doi\":\"10.1111/cote.12706\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The solubility of 1-amino-2-hydroxy-4-methoxy-anthraquinone (CI Disperse Red 4) and 1-amino-2-hydroxy-anthraquinone (CI Disperse Red 15) in supercritical carbon dioxide was measured using a dynamic method over a temperature range from 343.15 to 373.15 K and a pressure range from 14 to 22 MPa. The experimental data are analysed by using the back propagation neural network constructed by MATLAB. In the back propagation neural network, the input layer consisted of two inputs, which are temperature and pressure, the output layer consisted of the solubility of dyes, and the hidden layer function was composed of a non-linear function. The results of the analysis showed that a good fitting level of 0.99 was obtained, which means that the back propagation neural network can accurately estimate the solubility data in supercritical carbon dioxide.</p>\",\"PeriodicalId\":10502,\"journal\":{\"name\":\"Coloration Technology\",\"volume\":\"140 2\",\"pages\":\"230-238\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2023-05-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Coloration Technology\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/cote.12706\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, APPLIED\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Coloration Technology","FirstCategoryId":"88","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/cote.12706","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, APPLIED","Score":null,"Total":0}
Solubility prediction of CI Disperse Red 4 and CI Disperse Red 15 in supercritical carbon dioxide based on the back propagation neural network
The solubility of 1-amino-2-hydroxy-4-methoxy-anthraquinone (CI Disperse Red 4) and 1-amino-2-hydroxy-anthraquinone (CI Disperse Red 15) in supercritical carbon dioxide was measured using a dynamic method over a temperature range from 343.15 to 373.15 K and a pressure range from 14 to 22 MPa. The experimental data are analysed by using the back propagation neural network constructed by MATLAB. In the back propagation neural network, the input layer consisted of two inputs, which are temperature and pressure, the output layer consisted of the solubility of dyes, and the hidden layer function was composed of a non-linear function. The results of the analysis showed that a good fitting level of 0.99 was obtained, which means that the back propagation neural network can accurately estimate the solubility data in supercritical carbon dioxide.
期刊介绍:
The primary mission of Coloration Technology is to promote innovation and fundamental understanding in the science and technology of coloured materials by providing a medium for communication of peer-reviewed research papers of the highest quality. It is internationally recognised as a vehicle for the publication of theoretical and technological papers on the subjects allied to all aspects of coloration. Regular sections in the journal include reviews, original research and reports, feature articles, short communications and book reviews.