Abstract
Artificial neural networks have been extensively used in modelling the coagulation process in water treatment. Multi-layer Perceptron neural networks (MLP) are commonly used for coagulation modelling. However, a major drawback of MLPs is the high computational effort required due to its iterative nature. The Extreme learning machine neural network has a prediction accuracy comparable to MLPs and consumes far less computational effort. In this study an ELM-single layer feedforward neural network (ELM-SLFN), ELM-radial basis neural network (ELM-RBF) and a MLP was developed to predict the optimum coagulant dosage. All neural networks performed well with correlation coefficients exceeding 0.97. However, the ELM-RBF network performed better than the MLP model with higher prediction accuracy. Thus ELM-RBF neural network is a more efficient model for prediction of coagulant dosage for water treatment.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.