Assessing water quality provides a scientific foundation for the development and management of water resources. The objective of the research is to evaluate the impact treated effluent from North Rustumiyia wastewater treatment plant (WWTP) on the quality of Diyala river. The model of the artificial neural network (ANN) and factor analysis (FA) based on Nemerow pollution index (NPI). To define important water quality parameters for North Al-Rustumiyia for the line(F2), the Nemerow Pollution Index was introduced. The most important parameters of assessment of water variation quality of wastewater were the parameter used in the model: biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspension solids (SS), chloride, cl, hydrogen ion concentration, pH, sulfate, SO4-2, nitrate, NO3- and phosphate, PO4-3. Taking these criteria into account, samples of water from the sampling sites were graded as C, indicating the pollutant of the waste treatment. Then the water quality map using neural network model was based on the results of water quality assessment. The results showed that the model North Al-Rustumiyia for line F2 was more efficient and R2 was 0.965 with the impotence parameter was chloride (CL).

The dieless drawing process is an innovative methodemanated and appeared in coincidence with development of theconcept of metal superplasticity. It is utilized from the localheating of a wire or tube to a specified temperature and followedby a local cooling, so an additional deformation is inhibited. Inthis study, a special dieless drawing machine was designed tocarry out an experimental program on SUS304-stainless steel wirehaving diameter of (1.6-2) mm to investigate the main processparameters such as speeds, heat quantity, heating coil width andheating-cooling separation distance. Also, a numerical modelbased on thermo-mechanical analysis was developed and validatedwith experimental program. Furthermore, an artificial neuralnetwork ANN model based on current experimental data wasprepared to predict the dieless drawing behavior. A maximum areareduction of 40.7% was obtained in single pass. A 3.12mm/sfeeding velocity and 4.97mm/s drawing velocity were realizedthrough the experimental tests. The results showed that bothdrawing force and wire profile were effected by increasing offeeding speed, heating coil width and separation distance. Also, itis confirmed that strain rate was reduced by increasing the heatingcoil width and the reduction ratio was promoted. A maximumerror of 21% was recorded between ANN model and experimentalresults. The results showed a good agreement amongexperimental, numerical and ANN models.