Feasibility of Artificial Neural Networks and Fuzzy Logic Models for Prediction of NO Concentrations in Nonthermal Plasma-Treated Diesel Exhaust
High-voltage discharge-based nonthermal plasma (NTP) treatment for diesel exhaust is a laboratorial proven efficient technique. A prophecy of the treatment results based on the knowledge of its parameters would be a step forward toward bringing it into real-time applications of pollution control. In...
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Veröffentlicht in: | IEEE transactions on plasma science 2019-05, Vol.47 (5), p.2637-2644 |
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description | High-voltage discharge-based nonthermal plasma (NTP) treatment for diesel exhaust is a laboratorial proven efficient technique. A prophecy of the treatment results based on the knowledge of its parameters would be a step forward toward bringing it into real-time applications of pollution control. In this paper, artificial neural networks (ANNs) and fuzzy logic model (FLM) have been used to model the NO _{ X } (sum of NO and NO 2 ) concentrations as a function of parameters of the NTP process. A data set of 4032 input-output pairs has been collected by conducting experiments, in which 70% of the data are used for the training of the models derived. The performances of all the considered models have been evaluated by testing them for the remaining 30% of the data, which is novel for the models. Furthermore, a comparison of the models has been made based on the root-mean-square error (RMSE) and mean relative error (MRE), where the FLM has been found to be the better compared to the ANN-based models, i.e., ANN, multilayer perceptrons (MLP), and functional link ANN (FLANN). The RMSE of FLM is 2.53 ppm for a test data of 1210 sets. It can be said from these results that the NO _{ X } concentrations can be predicted using FLM with a good accuracy. |
doi_str_mv | 10.1109/TPS.2019.2907313 |
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A prophecy of the treatment results based on the knowledge of its parameters would be a step forward toward bringing it into real-time applications of pollution control. In this paper, artificial neural networks (ANNs) and fuzzy logic model (FLM) have been used to model the NO<inline-formula> <tex-math notation="LaTeX">_{ X } </tex-math></inline-formula> (sum of NO and NO 2 ) concentrations as a function of parameters of the NTP process. A data set of 4032 input-output pairs has been collected by conducting experiments, in which 70% of the data are used for the training of the models derived. The performances of all the considered models have been evaluated by testing them for the remaining 30% of the data, which is novel for the models. Furthermore, a comparison of the models has been made based on the root-mean-square error (RMSE) and mean relative error (MRE), where the FLM has been found to be the better compared to the ANN-based models, i.e., ANN, multilayer perceptrons (MLP), and functional link ANN (FLANN). The RMSE of FLM is 2.53 ppm for a test data of 1210 sets. It can be said from these results that the NO<inline-formula> <tex-math notation="LaTeX">_{ X } </tex-math></inline-formula> concentrations can be predicted using FLM with a good accuracy.]]></description><identifier>ISSN: 0093-3813</identifier><identifier>EISSN: 1939-9375</identifier><identifier>DOI: 10.1109/TPS.2019.2907313</identifier><identifier>CODEN: ITPSBD</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Artificial neural networks ; Artificial neural networks (ANNs) ; Data models ; Diesel engines ; diesel exhaust ; Discharges (electric) ; electric discharge ; Electrodes ; Feasibility studies ; Fuzzy logic ; High voltages ; Inductors ; Mathematical model ; Mathematical models ; Multilayer perceptrons ; Neural networks ; Nitrogen dioxide ; NO<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₓ removal ; Plasma ; Plasmas ; Pollution control ; prediction of NO<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₓ ; Predictions ; Process parameters ; Root-mean-square errors</subject><ispartof>IEEE transactions on plasma science, 2019-05, Vol.47 (5), p.2637-2644</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1365-a06f3df08a97ca1460ded857010c7b870db4871a19cdae7e97306cb6e23a407d3</citedby><cites>FETCH-LOGICAL-c1365-a06f3df08a97ca1460ded857010c7b870db4871a19cdae7e97306cb6e23a407d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8686341$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27924,27925,54758</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8686341$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Allamsetty, Srikanth</creatorcontrib><creatorcontrib>Mohapatro, Sankarsan</creatorcontrib><title>Feasibility of Artificial Neural Networks and Fuzzy Logic Models for Prediction of NO Concentrations in Nonthermal Plasma-Treated Diesel Exhaust</title><title>IEEE transactions on plasma science</title><addtitle>TPS</addtitle><description><![CDATA[High-voltage discharge-based nonthermal plasma (NTP) treatment for diesel exhaust is a laboratorial proven efficient technique. A prophecy of the treatment results based on the knowledge of its parameters would be a step forward toward bringing it into real-time applications of pollution control. In this paper, artificial neural networks (ANNs) and fuzzy logic model (FLM) have been used to model the NO<inline-formula> <tex-math notation="LaTeX">_{ X } </tex-math></inline-formula> (sum of NO and NO 2 ) concentrations as a function of parameters of the NTP process. A data set of 4032 input-output pairs has been collected by conducting experiments, in which 70% of the data are used for the training of the models derived. The performances of all the considered models have been evaluated by testing them for the remaining 30% of the data, which is novel for the models. Furthermore, a comparison of the models has been made based on the root-mean-square error (RMSE) and mean relative error (MRE), where the FLM has been found to be the better compared to the ANN-based models, i.e., ANN, multilayer perceptrons (MLP), and functional link ANN (FLANN). The RMSE of FLM is 2.53 ppm for a test data of 1210 sets. It can be said from these results that the NO<inline-formula> <tex-math notation="LaTeX">_{ X } </tex-math></inline-formula> concentrations can be predicted using FLM with a good accuracy.]]></description><subject>Artificial neural networks</subject><subject>Artificial neural networks (ANNs)</subject><subject>Data models</subject><subject>Diesel engines</subject><subject>diesel exhaust</subject><subject>Discharges (electric)</subject><subject>electric discharge</subject><subject>Electrodes</subject><subject>Feasibility studies</subject><subject>Fuzzy logic</subject><subject>High voltages</subject><subject>Inductors</subject><subject>Mathematical model</subject><subject>Mathematical models</subject><subject>Multilayer perceptrons</subject><subject>Neural networks</subject><subject>Nitrogen dioxide</subject><subject>NO<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₓ removal</subject><subject>Plasma</subject><subject>Plasmas</subject><subject>Pollution control</subject><subject>prediction of NO<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₓ</subject><subject>Predictions</subject><subject>Process parameters</subject><subject>Root-mean-square errors</subject><issn>0093-3813</issn><issn>1939-9375</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kF1LwzAUhoMoOKf3gjcBrztPmq5pLmU6FeYcOK9LlpxqtGs0SdHtV_iTbZ14bl44vB_wEHLKYMQYyIvl4nGUApOjVILgjO-RAZNcJpKL8T4ZAEie8ILxQ3IUwisAy8aQDsj3FFWwK1vbuKGuopc-2spqq2o6x9b_Svx0_i1Q1Rg6bbfbDZ25Z6vpvTNYB1o5TxcejdXRuqbvmD_QiWs0NtGr_heobejcNfEF_bprXNQqrFWy9KgiGnplMWBNr79eVBviMTmoVB3w5E-H5Gl6vZzcJrOHm7vJ5SzRjOfjREFecVNBoaTQimU5GDTFWAADLVaFALPKCsEUk9ooFCgFh1yvcky5ykAYPiTnu9537z5aDLF8da1vusky7Q_6mc4FO5f2LgSPVfnu7Vr5Tcmg7LmXHfey517-ce8iZ7uIRcR_e5EXOc8Y_wEbDYAz</recordid><startdate>201905</startdate><enddate>201905</enddate><creator>Allamsetty, Srikanth</creator><creator>Mohapatro, Sankarsan</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope></search><sort><creationdate>201905</creationdate><title>Feasibility of Artificial Neural Networks and Fuzzy Logic Models for Prediction of NO Concentrations in Nonthermal Plasma-Treated Diesel Exhaust</title><author>Allamsetty, Srikanth ; Mohapatro, Sankarsan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1365-a06f3df08a97ca1460ded857010c7b870db4871a19cdae7e97306cb6e23a407d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Artificial neural networks</topic><topic>Artificial neural networks (ANNs)</topic><topic>Data models</topic><topic>Diesel engines</topic><topic>diesel exhaust</topic><topic>Discharges (electric)</topic><topic>electric discharge</topic><topic>Electrodes</topic><topic>Feasibility studies</topic><topic>Fuzzy logic</topic><topic>High voltages</topic><topic>Inductors</topic><topic>Mathematical model</topic><topic>Mathematical models</topic><topic>Multilayer perceptrons</topic><topic>Neural networks</topic><topic>Nitrogen dioxide</topic><topic>NO<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₓ removal</topic><topic>Plasma</topic><topic>Plasmas</topic><topic>Pollution control</topic><topic>prediction of NO<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₓ</topic><topic>Predictions</topic><topic>Process parameters</topic><topic>Root-mean-square errors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Allamsetty, Srikanth</creatorcontrib><creatorcontrib>Mohapatro, Sankarsan</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>IEEE transactions on plasma science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Allamsetty, Srikanth</au><au>Mohapatro, Sankarsan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Feasibility of Artificial Neural Networks and Fuzzy Logic Models for Prediction of NO Concentrations in Nonthermal Plasma-Treated Diesel Exhaust</atitle><jtitle>IEEE transactions on plasma science</jtitle><stitle>TPS</stitle><date>2019-05</date><risdate>2019</risdate><volume>47</volume><issue>5</issue><spage>2637</spage><epage>2644</epage><pages>2637-2644</pages><issn>0093-3813</issn><eissn>1939-9375</eissn><coden>ITPSBD</coden><abstract><![CDATA[High-voltage discharge-based nonthermal plasma (NTP) treatment for diesel exhaust is a laboratorial proven efficient technique. A prophecy of the treatment results based on the knowledge of its parameters would be a step forward toward bringing it into real-time applications of pollution control. In this paper, artificial neural networks (ANNs) and fuzzy logic model (FLM) have been used to model the NO<inline-formula> <tex-math notation="LaTeX">_{ X } </tex-math></inline-formula> (sum of NO and NO 2 ) concentrations as a function of parameters of the NTP process. A data set of 4032 input-output pairs has been collected by conducting experiments, in which 70% of the data are used for the training of the models derived. The performances of all the considered models have been evaluated by testing them for the remaining 30% of the data, which is novel for the models. Furthermore, a comparison of the models has been made based on the root-mean-square error (RMSE) and mean relative error (MRE), where the FLM has been found to be the better compared to the ANN-based models, i.e., ANN, multilayer perceptrons (MLP), and functional link ANN (FLANN). The RMSE of FLM is 2.53 ppm for a test data of 1210 sets. It can be said from these results that the NO<inline-formula> <tex-math notation="LaTeX">_{ X } </tex-math></inline-formula> concentrations can be predicted using FLM with a good accuracy.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TPS.2019.2907313</doi><tpages>8</tpages></addata></record> |
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subjects | Artificial neural networks Artificial neural networks (ANNs) Data models Diesel engines diesel exhaust Discharges (electric) electric discharge Electrodes Feasibility studies Fuzzy logic High voltages Inductors Mathematical model Mathematical models Multilayer perceptrons Neural networks Nitrogen dioxide NO<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₓ removal Plasma Plasmas Pollution control prediction of NO<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">ₓ Predictions Process parameters Root-mean-square errors |
title | Feasibility of Artificial Neural Networks and Fuzzy Logic Models for Prediction of NO Concentrations in Nonthermal Plasma-Treated Diesel Exhaust |
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