Development of Process Inverse Neural Network Model to Determine the Required Alum Dosage at Segama Water Treatment Plant Sabah, Malaysia

Abstract

Abstract The determination of the optimal coagulant dosage in the coagulation process of a water treatment plant (WTP) is very essential to produce satisfactory treated water quality and to maintain economic plant operation such as reducing manpower and expensive chemical costs. Failing to do this will also reduce the efficiency in sedimentation and filtration process in the treatment plant. Traditionally, jar test is used to determine the optimum coagulant dosage. However, this method is expensive, time-consuming and does not enable responses to changes in raw water quality in real time. Modeling such as neural network can be used to overcome these limitations. In this work, an inverse neural network model is developed to predict the optimum coagulant dosage in Segama WTP in Lahad Datu, Sabah, Malaysia. Real data from the WTP was obtained along with extensive data analysis and preparation, significant input-output selection and consideration of important raw and treated water lag parameters were carried out. The modeling results shown that the prediction capabilities are improving with the consideration of appropriate input parameters. Neural network models with different network architectures, including single and two hidden layers were developed and the optimum network architecture obtained was [11-27-9-1]. This model performed very well over the range of data used for training, with r-value of 0.95, mean square error (MSE) of 0.0019 and mean absolute error (MAE) of 0.024 mg/l when applied on the testing data set. Hence, the proposed techniques can significantly improve and have a great potential of replacing the conventional method of jar test due to its advantages; quick responsive tools, economical operating cost and its ability to be applied in real time process.