Predicting the coagulant dosage is especially crucial to the purification process in water treatment plants, directly affecting the quality of the purified water. Nowadays, several mathematical methods have been adopted for the purification process, but their predictive precision and speed still need to be improved. This study applies a novel neural network called the extreme learning machine (ELM) to predict the coagulant dosage based on certain signification factors of the raw water. Performances are compared between ELM and back-propagation neural networks in this paper. The results show that both neural network algorithms perform well in this application and ELM can realize online prediction due to its short time consumption.

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