Optimum coagulant forecasting by modeling jar test experiments using ANNs

Currently, the proper utilization of water treatment plants and optimizing their use is of particular importance. Coagulation and flocculation in water treatment are the common ways through which the use of coagulants leads to instability of particles and the formation of larger and heavier particles, resulting in improvement of sedimentation and filtration processes. Determination of the optimum dose of such a coagulant is of particular significance. A high dose, in addition to adding costs, can cause the sediment to remain in the filtrate, a dangerous condition according to the standards, while a sub-adequate dose of coagulants can result in the reducing the required quality and acceptable performance of the coagulation process. Although jar tests are used for testing coagulants, such experiments face many constraints with respect to evaluating the results produced by sudden changes in input water because of their significant costs, long time requirements, and complex relationships among the many factors (turbidity, temperature, pH, alkalinity, etc.) that can influence the efficiency of coagulant and test results. Modeling can be used to overcome these limitations; in this research study, an artificial neural network (ANN) multi-layer perceptron (MLP) with one hidden layer has been used for modeling the jar test to determine the dosage level of used coagulant in water treatment processes. The data contained in this research have been obtained from the drinking water treatment plant located in Ardabil province in Iran. To evaluate the performance of the model, the mean squared error (MSE) and correlation coefficient (R2) parameters have been used. The obtained values are within an acceptable range that demonstrates the high accuracy of the models with respect to the estimation of water-quality characteristics and the optimal dosages of coagulants; so using these models will allow operators to not only reduce costs and time taken to perform experimental jar tests but also to predict a proper dosage for coagulant amounts and to project the quality of the output water under real conditions

The Impact of On-The-Job Pilates Exercise on Job Satisfaction Among the Female Employees of Urmia Electricity Distribution Company

This study intends to investigate the effect of on-the-job pilates exercise on job satisfaction. The study follows a quasi-experimental design with a pre-test and post-test method with a focus on control and experimental groups. The statistical population comprises of all female employees working at Urmia Electricity Distribution Company. The experimental group received 8 weeks of exercise doing sessions (3 sessions per week, 30 minutes per session) in Electricity Distribution Company gym while the control group did not receive any treatment.  According to the results obtained, one can conclude that on-the-job Pilates exercise as sports technology had an effect on the job satisfaction of female employees at Urmia Electricity Distribution Company