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  1. Ahmad Yasmin NS, Abdul Wahab N, Ismail FS, Musa MJ, Halim MHA, Anuar AN
    Membranes (Basel), 2021 Jul 22;11(8).
    PMID: 34436317 DOI: 10.3390/membranes11080554
    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.
  2. Basri HF, Anuar AN, Halim MHA, Yuzir MA, Muda K, Omoregie AI, et al.
    Environ Monit Assess, 2023 Feb 21;195(3):420.
    PMID: 36809517 DOI: 10.1007/s10661-023-11028-9
    This paper presents an assessment of the start-up performance of aerobic granular sludge (AGS) for the treatment of low-strength (chemical oxygen demand, COD 
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