CO2 emission model development employing particle swarm optimized - Least squared SVR (PSO-LSSVR) hybrid algorithm
This paper aims to develop a CO 2 emission model of acid gas incinerator using a hybrid of particle swarm optimization (PSO) and least squares support vector regression (LSSVR). Malaysia DOE is actively imposing the Clean Air Regulation to mandate the installation of analytical instrumentation known...
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| creator | Pathmanathan, E. Ibrahim, R. Asirvadam, V. S. |
| description | This paper aims to develop a CO 2 emission model of acid gas incinerator using a hybrid of particle swarm optimization (PSO) and least squares support vector regression (LSSVR). Malaysia DOE is actively imposing the Clean Air Regulation to mandate the installation of analytical instrumentation known as Continuous Emission Monitoring System (CEMS). CEMS is used to report emission level online to DOE office. As hardware based analyzer, CEMS is expensive, maintenance intensive and often unreliable. Therefore, software predictive techniques is often preferred and considered as a feasible alternative to replace the CEMS for regulatory compliance. The LSSVR model is developed based on the emissions data from an acid gas incinerator that operates in a LNG Complex. PSO technique is used to optimize the hyperparameters used in training the LSSVR model. Overall, the LSSVR models have shown good performance in certain key areas in comparison with the BPNN model. |
| doi_str_mv | 10.1109/ICIAS.2012.6306175 |
| format | Conference Proceeding |
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S.</creator><creatorcontrib>Pathmanathan, E. ; Ibrahim, R. ; Asirvadam, V. S.</creatorcontrib><description>This paper aims to develop a CO 2 emission model of acid gas incinerator using a hybrid of particle swarm optimization (PSO) and least squares support vector regression (LSSVR). Malaysia DOE is actively imposing the Clean Air Regulation to mandate the installation of analytical instrumentation known as Continuous Emission Monitoring System (CEMS). CEMS is used to report emission level online to DOE office. As hardware based analyzer, CEMS is expensive, maintenance intensive and often unreliable. Therefore, software predictive techniques is often preferred and considered as a feasible alternative to replace the CEMS for regulatory compliance. The LSSVR model is developed based on the emissions data from an acid gas incinerator that operates in a LNG Complex. PSO technique is used to optimize the hyperparameters used in training the LSSVR model. 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S.</creatorcontrib><title>CO2 emission model development employing particle swarm optimized - Least squared SVR (PSO-LSSVR) hybrid algorithm</title><title>2012 4th International Conference on Intelligent and Advanced Systems (ICIAS2012)</title><addtitle>ICIAS</addtitle><description>This paper aims to develop a CO 2 emission model of acid gas incinerator using a hybrid of particle swarm optimization (PSO) and least squares support vector regression (LSSVR). Malaysia DOE is actively imposing the Clean Air Regulation to mandate the installation of analytical instrumentation known as Continuous Emission Monitoring System (CEMS). CEMS is used to report emission level online to DOE office. As hardware based analyzer, CEMS is expensive, maintenance intensive and often unreliable. Therefore, software predictive techniques is often preferred and considered as a feasible alternative to replace the CEMS for regulatory compliance. The LSSVR model is developed based on the emissions data from an acid gas incinerator that operates in a LNG Complex. PSO technique is used to optimize the hyperparameters used in training the LSSVR model. Overall, the LSSVR models have shown good performance in certain key areas in comparison with the BPNN model.</description><subject>Accuracy</subject><subject>artificial neural networks</subject><subject>Computational modeling</subject><subject>Data models</subject><subject>Incineration</subject><subject>industrial pollution</subject><subject>Monitoring</subject><subject>Optimization</subject><subject>particle swarm optimization</subject><subject>predictive algorithms</subject><subject>support vector machines</subject><subject>Training</subject><isbn>9781457719684</isbn><isbn>1457719681</isbn><isbn>9781457719660</isbn><isbn>9781457719677</isbn><isbn>1457719673</isbn><isbn>1457719665</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp9j7FOwzAURY0QEojmB2B5IwxJ7TRNnBFFICpVKiKoa-WSR_uQHRvbgMLXk6FLl97l3qMzXcZuBM-E4PV00Swe2iznIs_KGS9FNT9jSV1JUcyrStRlyc-PWBaXLAnhk4-RQhZCXjHfrHJAQyGQ7cHYDjV0-IPaOoN9HJXTdqB-B075SO8aIfwqb8C6SIb-sIMUlqhChPD1rfzI7foV7l7aVbpsx3kP-2HrqQOld9ZT3JsJu_hQOmBy6Gt2-_T41jynhIgb58koP2wOj2an7T8-dU3c</recordid><startdate>201206</startdate><enddate>201206</enddate><creator>Pathmanathan, E.</creator><creator>Ibrahim, R.</creator><creator>Asirvadam, V. S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201206</creationdate><title>CO2 emission model development employing particle swarm optimized - Least squared SVR (PSO-LSSVR) hybrid algorithm</title><author>Pathmanathan, E. ; Ibrahim, R. ; Asirvadam, V. S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_63061753</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Accuracy</topic><topic>artificial neural networks</topic><topic>Computational modeling</topic><topic>Data models</topic><topic>Incineration</topic><topic>industrial pollution</topic><topic>Monitoring</topic><topic>Optimization</topic><topic>particle swarm optimization</topic><topic>predictive algorithms</topic><topic>support vector machines</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Pathmanathan, E.</creatorcontrib><creatorcontrib>Ibrahim, R.</creatorcontrib><creatorcontrib>Asirvadam, V. S.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pathmanathan, E.</au><au>Ibrahim, R.</au><au>Asirvadam, V. S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>CO2 emission model development employing particle swarm optimized - Least squared SVR (PSO-LSSVR) hybrid algorithm</atitle><btitle>2012 4th International Conference on Intelligent and Advanced Systems (ICIAS2012)</btitle><stitle>ICIAS</stitle><date>2012-06</date><risdate>2012</risdate><volume>1</volume><spage>137</spage><epage>142</epage><pages>137-142</pages><isbn>9781457719684</isbn><isbn>1457719681</isbn><eisbn>9781457719660</eisbn><eisbn>9781457719677</eisbn><eisbn>1457719673</eisbn><eisbn>1457719665</eisbn><abstract>This paper aims to develop a CO 2 emission model of acid gas incinerator using a hybrid of particle swarm optimization (PSO) and least squares support vector regression (LSSVR). Malaysia DOE is actively imposing the Clean Air Regulation to mandate the installation of analytical instrumentation known as Continuous Emission Monitoring System (CEMS). CEMS is used to report emission level online to DOE office. As hardware based analyzer, CEMS is expensive, maintenance intensive and often unreliable. Therefore, software predictive techniques is often preferred and considered as a feasible alternative to replace the CEMS for regulatory compliance. The LSSVR model is developed based on the emissions data from an acid gas incinerator that operates in a LNG Complex. PSO technique is used to optimize the hyperparameters used in training the LSSVR model. Overall, the LSSVR models have shown good performance in certain key areas in comparison with the BPNN model.</abstract><pub>IEEE</pub><doi>10.1109/ICIAS.2012.6306175</doi></addata></record> |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy artificial neural networks Computational modeling Data models Incineration industrial pollution Monitoring Optimization particle swarm optimization predictive algorithms support vector machines Training |
| title | CO2 emission model development employing particle swarm optimized - Least squared SVR (PSO-LSSVR) hybrid algorithm |
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