人工神经模糊推理系统 (ANFIS) 与响应面法 (RSM) 模型在预测被动式处理系统水柱出口流量方面的比较

Ku Esyra Hani Ku Ishak, Ooi Wei Jie, Khairul Yusra Khairul Anuar, Suhaina Ismail, Mohd Syazwan Mohd Halim
{"title":"人工神经模糊推理系统 (ANFIS) 与响应面法 (RSM) 模型在预测被动式处理系统水柱出口流量方面的比较","authors":"Ku Esyra Hani Ku Ishak, Ooi Wei Jie, Khairul Yusra Khairul Anuar, Suhaina Ismail, Mohd Syazwan Mohd Halim","doi":"10.4028/p-4q7mqr","DOIUrl":null,"url":null,"abstract":"Acid mine drainage (AMD) is one of the major environmental problems the mining and mineral processing industries face. Treatment of AMD involves active and passive treatment. In the long term, passive treatment is the most effective way to treat acid mine drainage, but it can be expensive. if handled properly. Therefore, the study of flow rate in a passive treatment system is one of the important ways to identify optimum hydraulic retention time to ensure the maximum percentage of heavy metal removal can be achieved while keeping the cost to a minimum level. This study focused on developing and comparing the Response Surface Methodology (RSM) model and Artificial Neural Fuzzy Inference System (ANFIS) model to predict the outlet flow rate of the passive treatment system column based on three parameters inlet flow time, thickness of peat soil bed, and inlet flow rate. The RSM model was created by Design-Expert software whereas MATLAB created the ANFIS model with 80% of data used for the model training and 20% of the data for model testing. The models' performances were compared in terms of statistical errors (AAPE, RMSE, R2, STD, minimum error, and maximum error). It was found the ANFIS model has performed better in predicting the outlet flowrate with R2 value of 0.99 RSM model with the value of 0.97. The inlet flow rate was an insignificant parameter affecting the outlet flow rate of the passive treatment column. From the 3-D surface response plot, the highest outlet flow rate is predicted to be 524 mL/min.","PeriodicalId":8039,"journal":{"name":"Applied Mechanics and Materials","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comparison of Artificial Neural Fuzzy Inference System (ANFIS) and Response Surface Methodology (RSM) Model in Predicting the Outlet Flow Rate of Passive Treatment System Column\",\"authors\":\"Ku Esyra Hani Ku Ishak, Ooi Wei Jie, Khairul Yusra Khairul Anuar, Suhaina Ismail, Mohd Syazwan Mohd Halim\",\"doi\":\"10.4028/p-4q7mqr\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Acid mine drainage (AMD) is one of the major environmental problems the mining and mineral processing industries face. Treatment of AMD involves active and passive treatment. In the long term, passive treatment is the most effective way to treat acid mine drainage, but it can be expensive. if handled properly. Therefore, the study of flow rate in a passive treatment system is one of the important ways to identify optimum hydraulic retention time to ensure the maximum percentage of heavy metal removal can be achieved while keeping the cost to a minimum level. This study focused on developing and comparing the Response Surface Methodology (RSM) model and Artificial Neural Fuzzy Inference System (ANFIS) model to predict the outlet flow rate of the passive treatment system column based on three parameters inlet flow time, thickness of peat soil bed, and inlet flow rate. The RSM model was created by Design-Expert software whereas MATLAB created the ANFIS model with 80% of data used for the model training and 20% of the data for model testing. The models' performances were compared in terms of statistical errors (AAPE, RMSE, R2, STD, minimum error, and maximum error). It was found the ANFIS model has performed better in predicting the outlet flowrate with R2 value of 0.99 RSM model with the value of 0.97. The inlet flow rate was an insignificant parameter affecting the outlet flow rate of the passive treatment column. From the 3-D surface response plot, the highest outlet flow rate is predicted to be 524 mL/min.\",\"PeriodicalId\":8039,\"journal\":{\"name\":\"Applied Mechanics and Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Mechanics and Materials\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4028/p-4q7mqr\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Mechanics and Materials","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4028/p-4q7mqr","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

酸性矿井排水(AMD)是采矿和矿物加工业面临的主要环境问题之一。酸性矿井排水的处理包括主动处理和被动处理。从长远来看,被动处理是处理酸性矿井排水最有效的方法,但如果处理得当,其成本也会很高。因此,研究被动处理系统中的流速是确定最佳水力停留时间的重要方法之一,以确保在将成本保持在最低水平的同时,实现最大比例的重金属去除。本研究的重点是开发和比较响应面方法(RSM)模型和人工神经模糊推理系统(ANFIS)模型,以根据进水流速、泥炭土床厚度和进水流速三个参数预测被动处理系统柱的出水流速。RSM 模型由 Design-Expert 软件创建,而 ANFIS 模型则由 MATLAB 创建,其中 80% 的数据用于模型训练,20% 的数据用于模型测试。根据统计误差(AAPE、RMSE、R2、STD、最小误差和最大误差)对模型的性能进行了比较。结果发现,ANFIS 模型在预测出口流量方面表现更好,其 R2 值为 0.99,RSM 模型的 R2 值为 0.97。入口流量是影响被动处理塔出口流量的一个不重要参数。从三维表面响应图中可以预测最高出口流速为 524 毫升/分钟。
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
Comparison of Artificial Neural Fuzzy Inference System (ANFIS) and Response Surface Methodology (RSM) Model in Predicting the Outlet Flow Rate of Passive Treatment System Column
Acid mine drainage (AMD) is one of the major environmental problems the mining and mineral processing industries face. Treatment of AMD involves active and passive treatment. In the long term, passive treatment is the most effective way to treat acid mine drainage, but it can be expensive. if handled properly. Therefore, the study of flow rate in a passive treatment system is one of the important ways to identify optimum hydraulic retention time to ensure the maximum percentage of heavy metal removal can be achieved while keeping the cost to a minimum level. This study focused on developing and comparing the Response Surface Methodology (RSM) model and Artificial Neural Fuzzy Inference System (ANFIS) model to predict the outlet flow rate of the passive treatment system column based on three parameters inlet flow time, thickness of peat soil bed, and inlet flow rate. The RSM model was created by Design-Expert software whereas MATLAB created the ANFIS model with 80% of data used for the model training and 20% of the data for model testing. The models' performances were compared in terms of statistical errors (AAPE, RMSE, R2, STD, minimum error, and maximum error). It was found the ANFIS model has performed better in predicting the outlet flowrate with R2 value of 0.99 RSM model with the value of 0.97. The inlet flow rate was an insignificant parameter affecting the outlet flow rate of the passive treatment column. From the 3-D surface response plot, the highest outlet flow rate is predicted to be 524 mL/min.
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
自引率
0.00%
发文量
0
期刊最新文献
Analysis and Design of Steel Cement Storage Silo Study on Influence of Core Structure on Catalytic Converter Performance Using CFD Structural and Geochemistry of Air Piau Gold Mineralisation in Kelantan, North-East Peninsular Malaysia A Novel Powder Addition Method for Improving Tensile Strength of Polylactic-Acid Prepared by Using Fused Filament Fabrication (FFF) Monitoring of Building Services Using Artificial Intelligence
×
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