CBR-ANN hybrid model to optimize the sequence of wastewater treatments

This paper refers to a way of proposing the optimal sequence of treatments that should be applied to wastewater by using a hybrid model. It combines cases-based reasoning and artificial neural networks, getting the best of both approaches. Preliminary results demonstrate that it is a feasible model. 1. Domain Description The hydric resources of our country have been affected for the industrial and domestic wastewater emissions, some of them with deficient treatments. Wastewaters have contaminant properties that can be transmitted to the final receptor. In order to know the contaminant power of wastewaters it is necessary to study theirs physical, chemical and biological properties. Besides, designing, control and good operation of the wastewater treatment plants is important to determine such parameters. Among the common parameters for an adequate characterization, we can find the following: (Díaz, 1986) TemperatureIt is important due its effect on the aquatic life, on the chemical reactions and on the microorganism’s development with the perturbation in the treatment process. pH This is the measure of the water's acidity once it leaves the plant. Ideally, the water's pH would match the pH of the river or lake that receives the output of the plant. BOD5 (biological oxygen demand) – It is a measure of the strength of the wastewater. BOD5 is a measure of how much oxygen in the water will be required to finish digesting the organic material left in the effluent.