Improved support vector machine using optimization techniques for an aerobic granular sludge
| Autor: | Norhaliza Abdul Wahab, Aznah Nor Anuar, Nur Sakinah Ahmad Yasmin |
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| Rok vydání: | 2020 |
| Předmět: |
Support vector machine
Control and Optimization Computer Networks and Communications Computer science business.industry Particle swarm optimization Experimental data Sequencing batch reactor SVM-PSO Modelling Data set Hardware and Architecture Control and Systems Engineering Aerobic granular sludge Genetic algorithm Hyperparameter optimization Computer Science (miscellaneous) Sewage treatment Electrical and Electronic Engineering Process engineering business Instrumentation SVM-GA Information Systems |
| Zdroj: | Bulletin of Electrical Engineering and Informatics. 9:1835-1843 |
| ISSN: | 2302-9285 2089-3191 |
| Popis: | Aerobic granular sludge (AGS) is one of the treatment methods often used in wastewater systems. The dynamic behavior of AGS is complex and hard to predict especially when it comes to a limited data set. Theoretically, support vector machine (SVM) is a good prediction tool in handling limited data set. In this paper, an improved SVM using optimization approaches for better predictions is proposed. Two different types of optimization are built which are particle swarm optimization (PSO) and genetic algorithm (GA). The prediction of the models using SVM-PSO, SVM-GA and SVM-Grid Search are developed and compared prior to several feature analysis for verification purposes. The experimental data under hot temperature of 50˚C obtained from sequencing batch reactor is used. From simulation results, the proposed SVM with optimizations improve the prediction of chemical oxygen demand compared to the conventional grid search method and hence provide better prediction of effluent quality using AGS wastewater treatment systems. |
| Databáze: | OpenAIRE |
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