This article addresses methods to adjust operating requirements in water treatment plants (WTPs) in order to increase the efficiency of water treatment plants based on the nature of the water inflows into the systems. In the past, various studies have suggested that the quality of water inflow into the WTP has an impact on the efficiency and economic viability of operating treatment plants. Among all other quality parameters, the concentration of dissolved oxygen (DO) is one of the basic indicators about the overall quality of the water. Identification of a temporal pattern can help the engineers to adapt the WTP operations and can save the unnecessary wasting of plant resources. That is why the present article has proposed a new model that can predict the temporal patterns of various chemical parameters with the help of an analytic neuronal network. The model was applied to the case of a WTP that responds to a peri-urban catchment, leading to regular variations in the DO of water inflow. According to the performance metrics utilized the model was able to predict the temporal pattern at a lag of 1 hour.
a b s t r a c tThe WHO estimates that, on average, dehydration caused by water borne illnesses claims up to 1.5 million lives a year, with a disproportionate number of casualties located in developing nations. In order to mitigate risks to public health, previous studies have helped to gain extensive insights and create management techniques to ensure that water quality standards are maintained. The present study will investigate a relatively neglected field, which is the need for the prioritization of monitoring the quality of a water treatment plant's inflow, which may vary significantly in quantity and quality throughout the day. The technique proposed for this investigation is a novel application of the multiple criteria decision making (MCDM) method, adapted particularly for the purposes of decision making for optimal scenarios, called the multi variable temporal decision making method for the selection of optimal solutions (MVTDMSOS). By cascading self-selecting neural network algorithms, which are implicit in such systems, this method is designed to eliminate human bias and aims to identify the priority parameters based on optimal, rather than normal, scenarios. The introduction of polynomial neural networks ensures adaptability and alacrity of the modeling framework. Test results encourage further application of the proposed technique.
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