Abstract
Support vector regression (SVR) models have been designed to predict the concentration of chemical oxygen demand in sequential batch reactors under high temperatures. The complex internal interaction between the sludge characteristics and their influent were used to develop the models. The prediction becomes harder when dealing with a limited dataset due to the limitation of the experimental works. A radial basis function algorithm with selected kernel parameters of cost and gamma was used to developed SVR models. The kernel parameters were selected by using a grid search method and were further optimized by using particle swarm optimization and genetic algorithm. The SVR models were then compared with an artificial neural network. The prediction results R2 were within >90% for all predicted concentration of COD. The results showed the potential of SVR for simulating the complex aerobic granulation process and providing an excellent tool to help predict the behaviour in aerobic granular reactors of wastewater treatment.
Highlights
IntroductionOne of the well-known wastewater treatment technologies is aerobic granular sludge (AGS), which is able to overcome the limitations of conventional activated sludge (CAS) [1]
The hyperparameters in this study were tuned using the grid search method, which will yield the best value of kernel function cost C and gamma γ, paired with the highest performance
Two models were developed in this work, which are Support Vector Regression (SVR) and feed-forward neural network (FFNN), to predict the concentration of chemical oxygen demand (COD) in aerobic granular sludge at three different temperatures (30 ◦ C, 40 ◦ C, and 50 ◦ C)
Summary
One of the well-known wastewater treatment technologies is aerobic granular sludge (AGS), which is able to overcome the limitations of conventional activated sludge (CAS) [1]. AGS has a compact and dense biomass characteristic, and it relies on the growth of granules. When AGS was inoculated with a bubble column through a sequential batch reactor (SBR), it has been observed to have a short settling time for the formation of AGS [2,3,4]. AGS technology has attracted the interest of the builders for its advantages, especially in reducing footprints besides increasing the efficiency of the wastewater treatment plant. Aside from that, SBR is a preferred choice for improving AGS technology, because it offers operational flexibility with the reliable and rapid cultivation of AGS
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