利用微过滤回收乳清蛋白:基于人工神经网络的建模和不同操作参数的影响

IF 2.7 3区 农林科学 Q3 ENGINEERING, CHEMICAL Journal of Food Process Engineering Pub Date : 2024-10-12 DOI:10.1111/jfpe.14756
Prafful Kumar Meena, Jai Gopal Sharma, Manish Jain
{"title":"利用微过滤回收乳清蛋白:基于人工神经网络的建模和不同操作参数的影响","authors":"Prafful Kumar Meena,&nbsp;Jai Gopal Sharma,&nbsp;Manish Jain","doi":"10.1111/jfpe.14756","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>Microfiltration is one of the most suitable processes for protein recovery from whey due to its low energy consumption and lack of use of heat and chemicals. However, membrane fouling is one of the limiting factors in the microfiltration process, preventing its commercial use. In this study, an artificial neural network (ANN) based model was employed to study the effects of different operating parameters on membrane fouling in whey concentration. Trans-membrane pressure, Reynolds number, and feed temperature were selected as the input parameters. Experimental data from the available studies were used to train the ANN. The ANN with 23 neurons gave a minimum mean squared error (MSE) for trans-membrane pressure and Reynolds number. The ANN with seven neurons gave the minimum MSE for feed temperature. The predicted values from both ANNs well fitted with the experimental results with <i>R</i><sup>2</sup> &lt; 0.99. Simulations showed that membrane fouling increased as flux reduction increased from 36.3% to 76.39% when trans-membrane pressure increased from 0.5 to 2 bar. In contrast, a 19.96% reduction in flux was observed by increasing the Reynolds number from 750 to 2500. An increment of 77.37% of flux reduction was observed with increasing feed temperature from 30°C to 40°C. Simulations confirmed that transmembrane pressure, Reynolds number, and feed temperature strongly influence membrane fouling. An ANN-based approach was the most accurate method to model membrane fouling for whey protein separation.</p>\n </div>","PeriodicalId":15932,"journal":{"name":"Journal of Food Process Engineering","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Recovery of Whey Protein by Using Microfiltration: Artificial Neural Network–Based Modeling and Effects of Different Operating Parameters\",\"authors\":\"Prafful Kumar Meena,&nbsp;Jai Gopal Sharma,&nbsp;Manish Jain\",\"doi\":\"10.1111/jfpe.14756\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>Microfiltration is one of the most suitable processes for protein recovery from whey due to its low energy consumption and lack of use of heat and chemicals. However, membrane fouling is one of the limiting factors in the microfiltration process, preventing its commercial use. In this study, an artificial neural network (ANN) based model was employed to study the effects of different operating parameters on membrane fouling in whey concentration. Trans-membrane pressure, Reynolds number, and feed temperature were selected as the input parameters. Experimental data from the available studies were used to train the ANN. The ANN with 23 neurons gave a minimum mean squared error (MSE) for trans-membrane pressure and Reynolds number. The ANN with seven neurons gave the minimum MSE for feed temperature. The predicted values from both ANNs well fitted with the experimental results with <i>R</i><sup>2</sup> &lt; 0.99. Simulations showed that membrane fouling increased as flux reduction increased from 36.3% to 76.39% when trans-membrane pressure increased from 0.5 to 2 bar. In contrast, a 19.96% reduction in flux was observed by increasing the Reynolds number from 750 to 2500. An increment of 77.37% of flux reduction was observed with increasing feed temperature from 30°C to 40°C. Simulations confirmed that transmembrane pressure, Reynolds number, and feed temperature strongly influence membrane fouling. An ANN-based approach was the most accurate method to model membrane fouling for whey protein separation.</p>\\n </div>\",\"PeriodicalId\":15932,\"journal\":{\"name\":\"Journal of Food Process Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-10-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Food Process Engineering\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/jfpe.14756\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Process Engineering","FirstCategoryId":"97","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jfpe.14756","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0

摘要

微过滤能耗低,无需使用热量和化学品,是最适合从乳清中回收蛋白质的工艺之一。然而,膜堵塞是微过滤工艺的限制因素之一,阻碍了其商业应用。在这项研究中,采用了基于人工神经网络(ANN)的模型来研究不同操作参数对乳清浓缩膜污垢的影响。输入参数包括跨膜压力、雷诺数和进料温度。现有研究的实验数据被用于训练 ANN。在跨膜压力和雷诺数方面,23 个神经元的 ANN 的均方误差(MSE)最小。在进料温度方面,有 7 个神经元的 ANN 的平均平方误差最小。两个方差网络的预测值与实验结果非常吻合,R2 为 0.99。模拟结果表明,当跨膜压力从 0.5 巴增加到 2 巴时,膜污垢会随着通量减少从 36.3% 增加到 76.39%。相反,当雷诺数从 750 增加到 2500 时,通量减少了 19.96%。进料温度从 30°C 提高到 40°C,流量减少了 77.37%。模拟证实,跨膜压力、雷诺数和进料温度对膜污垢有很大影响。基于 ANN 的方法是为乳清蛋白分离建立膜污垢模型的最准确方法。
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
Recovery of Whey Protein by Using Microfiltration: Artificial Neural Network–Based Modeling and Effects of Different Operating Parameters

Microfiltration is one of the most suitable processes for protein recovery from whey due to its low energy consumption and lack of use of heat and chemicals. However, membrane fouling is one of the limiting factors in the microfiltration process, preventing its commercial use. In this study, an artificial neural network (ANN) based model was employed to study the effects of different operating parameters on membrane fouling in whey concentration. Trans-membrane pressure, Reynolds number, and feed temperature were selected as the input parameters. Experimental data from the available studies were used to train the ANN. The ANN with 23 neurons gave a minimum mean squared error (MSE) for trans-membrane pressure and Reynolds number. The ANN with seven neurons gave the minimum MSE for feed temperature. The predicted values from both ANNs well fitted with the experimental results with R2 < 0.99. Simulations showed that membrane fouling increased as flux reduction increased from 36.3% to 76.39% when trans-membrane pressure increased from 0.5 to 2 bar. In contrast, a 19.96% reduction in flux was observed by increasing the Reynolds number from 750 to 2500. An increment of 77.37% of flux reduction was observed with increasing feed temperature from 30°C to 40°C. Simulations confirmed that transmembrane pressure, Reynolds number, and feed temperature strongly influence membrane fouling. An ANN-based approach was the most accurate method to model membrane fouling for whey protein separation.

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 Food Process Engineering
Journal of Food Process Engineering 工程技术-工程:化工
CiteScore
5.70
自引率
10.00%
发文量
259
审稿时长
2 months
期刊介绍: This international research journal focuses on the engineering aspects of post-production handling, storage, processing, packaging, and distribution of food. Read by researchers, food and chemical engineers, and industry experts, this is the only international journal specifically devoted to the engineering aspects of food processing. Co-Editors M. Elena Castell-Perez and Rosana Moreira, both of Texas A&M University, welcome papers covering the best original research on applications of engineering principles and concepts to food and food processes.
期刊最新文献
Simulation Study on Heat and Moisture Transfer in Cross-Flow Drying Aeration of Bulk Corn in a Cone-Bottom Silo Using Natural and Low-Temperature Air Optimizing the Precipitation of Bioactive Compounds From Extracted Curcuma longa Linn. Using Gas Anti-Solvent Process Issue Information The Impact of Drying Techniques on Stabilizing Microencapsulated Astaxanthin From Shrimp Shells: A Comparative Study of Spray Drying Versus Freeze Drying Improvement in uniformity of co-linear pulsed electric field treatment chamber by sub-electrodes and its optimization
×
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