Influent flow rate effect on sewage pump station performance based on organic and sediment loading

Abstract EN

The performance of sewage pumps stations affected by many factors through its work time which produce undesired transportation efficiency.

This paper is focus on the use of artificial neural network and multiple linear regression (MLR) models for prediction the major sewage pump station in Baghdad city.

The data used in this work were obtained from Al-Habibia sewage pump station during specified records- three years in Rusafa district, Baghdad.

Pumping capability of the stations was recognized by considering the influent input importance of discharge, total suspended solids (TSS) and biological oxygen demand (BOD).

In addition, the chemical oxygen demands (COD), pH and chloride (Cl).

The proposed model performance has compared with the correlation coefficient (r).

The suitable structure design of neural network model is examined through many trials, error, preparations and evaluation steps.

Two prediction models of organic and sediment loading are presented.

Results found that the estimating of the organic and sediment loading by ANN model could be successful.

Moreover, results showed that influent discharge rate have more effect on organic and sediment loading predicting to other parameters.