Performance Evaluation of Al-Karkh Water Treatment Plant Using Model-driven and Data-Driven Models

Abstract

There is a great operational risk to control the day-to-day management in water treatment plants, so water companies are looking for solutions to predict how the treatment processes may be improved due to the increased pressure to remain competitive. This study focused on the mathematical modeling of water treatment processes with the primary motivation to provide tools that can be used to predict the performance of the treatment to enable better control of uncertainty and risk. This research included choosing the most important variables affecting quality standards using the correlation test. According to this test, it was found that the important parameters of raw water: Total Hardness, Calcium, Magnesium, Total Solids, Nitrite, Nitrates, Ammonia, and Silica are to be used to construct the specific model, while pH, Fluoride, Aluminium, Nitrite, Nitrate, Ammonia, Silica, and Orthophosphate of the treated water were eliminated from the analysis. For modeling the coagulation and flocculation process temperature, Alkalinity and pH of raw water were the depended variables of the model. As for the modeling process turbidity of the treated water was used as the output variable. In general, the linear models including model-driven type, (Multivariate multiple regression, MMR and Multiple linear regression, MLR) have slightly higher prediction efficiencies than the, data-driven type (artificial neural network, ANNM). The coefficients of determination (R2) reached 66 to 85% for the MMR and MLR models and 65 to 81% for the ANN models.

Physico-chemical Characterization and Assessment of the Risk of Pollution: A Case of Wastewater Generated by the Regional Hospital and the Central Prison of the City of Ngaoundere, Cameroon

“…Two indices were used to assess the wastewater from the three study sites. The wastewater quality index (WWQI) was used for parameters other than heavy metal according to the method recommended by various authors [9][10][11][12]. This index is used to assess the overall quality of the water.…”

“…This index is used to assess the overall quality of the water. When the index is greater than 100, the water quality is very poor [9][10][11]. The WWQI is obtained from the following equation:…”