@inproceedings{scholars2745,
         address = {Kuala Lumpur},
           title = {CO 2 emission model development employing particle swarm optimized Least squared SVR (PSO-LSSVR) hybrid algorithm},
             doi = {10.1109/ICIAS.2012.6306175},
            note = {cited By 3; Conference of 2012 4th International Conference on Intelligent and Advanced Systems, ICIAS 2012 ; Conference Date: 12 June 2012 Through 14 June 2012; Conference Code:93534},
          volume = {1},
           pages = {137--142},
         journal = {ICIAS 2012 - 2012 4th International Conference on Intelligent and Advanced Systems: A Conference of World Engineering, Science and Technology Congress (ESTCON) - Conference Proceedings},
            year = {2012},
            isbn = {9781457719677},
          author = {Pathmanathan, E. and Ibrahim, R. and Asirvadam, V. S.},
        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. {\^A}{\copyright} 2012 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867944531&doi=10.1109\%2fICIAS.2012.6306175&partnerID=40&md5=5830be61c146c4f0422744bb689888e9},
        keywords = {Acid gas incinerators; Analytical Instrumentation; Clean air regulations; Continuous emission monitoring system; Emission level; Emission model; Feasible alternatives; Hybrid algorithms; Hyperparameters; Industrial pollution; Least squares support vector regression; Malaysia; Particle swarm; Predictive algorithms; Predictive techniques, Algorithms; Carbon dioxide; Gas emissions; Neural networks; Regulatory compliance; Support vector machines, Particle swarm optimization (PSO)}
}