平板涂布机片剂涂布的人工神经网络建模

IF 2.3 4区 材料科学 Q2 Chemistry Journal of Coatings Technology and Research Pub Date : 2022-10-17 DOI:10.1007/s11998-022-00683-1
Assia Benayache, Lynda Lamoudi, Kamel Daoud
{"title":"平板涂布机片剂涂布的人工神经网络建模","authors":"Assia Benayache,&nbsp;Lynda Lamoudi,&nbsp;Kamel Daoud","doi":"10.1007/s11998-022-00683-1","DOIUrl":null,"url":null,"abstract":"<div><p>Our study decided to use the new and revolutionary approach in the field of pharmaceutical coating processes called the artificial neural network (ANN) by using the neural networks toolbox derived from the Matlab® software. The experiments were performed using tablets of Alfuzosin Chlorhydrate as a model filler, and an aqueous solution of Surelease as a polymer in different contents. The various parameters that can affect coating thickness, weight gain, and the coefficient of variation CV, such as spray rate, air pressure, solid content, speed of the drum, pan loading, and time of coating, were studied. The properties of the coated tablets were evaluated using the ANN, and both the parameters of the coating process and the properties of the coated tablets were used as a basis for optimization, as well as the choice of the optimal structure of the ANN model. It was found that the best neural network architecture had 7 neurons in the hidden layer, with a mean square error of 3.515 and a determination coefficient of nearly 1. The relative importance of each independent variable was quantified using the Garson equation. In this study, spray rate was found to have the highest impact on the properties of tablets<b>.</b></p></div>","PeriodicalId":48804,"journal":{"name":"Journal of Coatings Technology and Research","volume":"20 2","pages":"485 - 499"},"PeriodicalIF":2.3000,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Artificial neural network modeling of tablet coating in a pan coater\",\"authors\":\"Assia Benayache,&nbsp;Lynda Lamoudi,&nbsp;Kamel Daoud\",\"doi\":\"10.1007/s11998-022-00683-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Our study decided to use the new and revolutionary approach in the field of pharmaceutical coating processes called the artificial neural network (ANN) by using the neural networks toolbox derived from the Matlab® software. The experiments were performed using tablets of Alfuzosin Chlorhydrate as a model filler, and an aqueous solution of Surelease as a polymer in different contents. The various parameters that can affect coating thickness, weight gain, and the coefficient of variation CV, such as spray rate, air pressure, solid content, speed of the drum, pan loading, and time of coating, were studied. The properties of the coated tablets were evaluated using the ANN, and both the parameters of the coating process and the properties of the coated tablets were used as a basis for optimization, as well as the choice of the optimal structure of the ANN model. It was found that the best neural network architecture had 7 neurons in the hidden layer, with a mean square error of 3.515 and a determination coefficient of nearly 1. The relative importance of each independent variable was quantified using the Garson equation. In this study, spray rate was found to have the highest impact on the properties of tablets<b>.</b></p></div>\",\"PeriodicalId\":48804,\"journal\":{\"name\":\"Journal of Coatings Technology and Research\",\"volume\":\"20 2\",\"pages\":\"485 - 499\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2022-10-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Coatings Technology and Research\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s11998-022-00683-1\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Chemistry\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Coatings Technology and Research","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s11998-022-00683-1","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Chemistry","Score":null,"Total":0}
引用次数: 1

摘要

我们的研究决定通过使用Matlab®软件衍生的神经网络工具箱,在制药涂层工艺领域使用新的革命性方法,即人工神经网络(ANN)。实验以水合Alfuzosin片剂为模型填料,以不同含量的Surelease水溶液为聚合物。研究了喷涂速率、气压、固含量、转鼓速度、装盘速度和喷涂时间等参数对涂层厚度、增重和变异系数CV的影响。利用人工神经网络对包衣片剂的性能进行评价,并以包衣工艺参数和包衣片剂的性能作为优化的依据,选择人工神经网络模型的最优结构。结果表明,最优的神经网络结构为隐藏层7个神经元,均方误差为3.515,决定系数接近1。每个自变量的相对重要性使用Garson方程进行量化。在本研究中,发现喷雾速率对片剂性能的影响最大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

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
Artificial neural network modeling of tablet coating in a pan coater

Our study decided to use the new and revolutionary approach in the field of pharmaceutical coating processes called the artificial neural network (ANN) by using the neural networks toolbox derived from the Matlab® software. The experiments were performed using tablets of Alfuzosin Chlorhydrate as a model filler, and an aqueous solution of Surelease as a polymer in different contents. The various parameters that can affect coating thickness, weight gain, and the coefficient of variation CV, such as spray rate, air pressure, solid content, speed of the drum, pan loading, and time of coating, were studied. The properties of the coated tablets were evaluated using the ANN, and both the parameters of the coating process and the properties of the coated tablets were used as a basis for optimization, as well as the choice of the optimal structure of the ANN model. It was found that the best neural network architecture had 7 neurons in the hidden layer, with a mean square error of 3.515 and a determination coefficient of nearly 1. The relative importance of each independent variable was quantified using the Garson equation. In this study, spray rate was found to have the highest impact on the properties of tablets.

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 Coatings Technology and Research
Journal of Coatings Technology and Research CHEMISTRY, APPLIED-MATERIALS SCIENCE, COATINGS & FILMS
CiteScore
4.40
自引率
8.70%
发文量
0
期刊介绍: Journal of Coatings Technology and Research (JCTR) is a forum for the exchange of research, experience, knowledge and ideas among those with a professional interest in the science, technology and manufacture of functional, protective and decorative coatings including paints, inks and related coatings and their raw materials, and similar topics.
期刊最新文献
A parametric distribution model of electrostatic spray rotating bell and application for automobile painting Homogeneous dispersion of cellulose/graphite oxide nanofibers in water-based urushiol coatings with improved mechanical properties and corrosion resistance Temporal variations of surface roughness and thickness of polymer-coated quartz sand Effect of boron nitride modified by sodium tripolyphosphate on the corrosion resistance of waterborne epoxy coating Characterization of synthetic aluminum silicate-coated titanium dioxide photocatalysts as a functional filler
×
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