Multilayer feedforward networks are universal approximators
Neural Networks
Neural Networks in Bioprocessing and Chemical Engineering: With Disk
Neural Networks in Bioprocessing and Chemical Engineering: With Disk
Modelling of Turkey's net energy consumption using artificial neural network
International Journal of Computer Applications in Technology
International Journal of Computer Applications in Technology
Simulation of a paper mill wastewater treatment using a fuzzy neural network
Expert Systems with Applications: An International Journal
International Journal of Computer Applications in Technology
International Journal of Computer Applications in Technology
Analysis of prediction performance of training-based models using real network traffic
International Journal of Computer Applications in Technology
A 3D shape classifier with neural network supervision
International Journal of Computer Applications in Technology
Recognising 2.5D manufacturing feature using neural network
International Journal of Computer Applications in Technology
Fault diagnosis of nuclear power plant based on genetic-RBF neural network
International Journal of Computer Applications in Technology
New improved FLANN approach for dynamic modelling of sensors
International Journal of Computer Applications in Technology
Paper: Model predictive heuristic control
Automatica (Journal of IFAC)
International Journal of Computer Applications in Technology
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Neural networks (NNs) have been widely used for complex processes that are poorly described by first principle models, such as wastewater biological treatment systems. In this paper, we propose an Artificial Neural Network (ANN) based predictive model for assessing the performance of paper and pulp effluent treatment plants. Mathematical models were created for the thickener area of the clarifier by correlating process control parameters such as mean cell residence time (θc), initial suspended solid concentration (Co), underflow concentration (Cu) and recycling ratio (R). For any values of Cu, Co, R and θc, area of the secondary clarifier can be determined using the model developed based on ANN. The predicted models give a rational approach to the design of secondary clarifier. The developed models prove consistently well in the face of varying accuracy and size of input data phase.