Estimation of pH and MLSS using Neural Network
Abstract: The main challenges
to achieving a reliable model which can predict well the process are the nonlinearities
associated with many biological and biochemical processes in the system.
Artificial intelligent approaches revolved as better alternative in predicting
the system. Typical measured variables for effluent quality of wastewater
treatment plant are pH, and mixed liquor suspended solids (MLSS). This paper presents
an adaptive neuro-fuzzy inference system (ANFIS) and feed-forward neural
network (FFNN) modeling applied to the domestic plant of the Bunus regional
sewage treatment plant. ANFIS and feedforward neural network techniques as
nonlinear function approximators have demonstrated the capability of predicting
nonlinear behaviour of the system. The data for the period of two years and
nine months sampled weekly (140 week samples) were collected and used for this
study. Simulation studies showed that the prediction capability of the ANFIS
model is somehow better than that of the FFNN model. The ANFIS model may serves
as a valuable prediction tool for the plant.
Author: Nur Sakinah Ahmad
Yasmin
Journal Code: jptkomputergg170065
