INVESTIGATION OF INDUSTRIAL WASTEWATER MANAGEMENT CONDITION BASED ON WASTEWATER ORGANIC POLLUTION LOADS AND TREATMENT FACILITIES IN THE INDUSTRIAL DISTRICT OF WEST TEHRAN

Industrial wastewater characteristics were investigated using questionnaire of Environmental Protection Organization of Iran, visiting industrial factories and interviewing. Industries and mining organization data base of all industries were classified in ten categories. Number of industries with greater than 50 staff in industry in this region was 283; fifty industries were selected with classified weighted sampling method for this study. The average wastewater flow rate generated by these 50 industries was 9422 m3/day, with an average volume of wastewater generated per capita found to be about e 437 liters per day. Minimum organic loading was related to non-metallic mineral industries with COD of 205 mg/L and BOD of 85 mg/L. maximum COD was related to paper industries with a rate of 8800 mg/L and maximum BOD was recorded at food and drug industries with rate of 1536 mg/L. All of these 10 categories of industries were scored based on generated COD, BOD loading and wastewater load per capita from 1 (minimum) to10 (maximum) and then were ranked from most polluting to least polluting industry. In this study, paper industries with overall score of 28 in three aforementioned parameters were major source of pollution in the industrial district of west Tehran. On the other hand non metallic industry with the overall score of 5 was found to be the least polluting industry. &nbsp

Feed Forward Artificial Neural Network Model to Estimate the TPH Removal Efficiency in Soil Washing Process

Background & Aims of the Study: A feed forward artificial neural network (FFANN) was developed to predict the efficiency of total petroleum hydrocarbon (TPH) removal from a contaminated soil, using soil washing process with Tween 80. The main objective of this study was to assess the performance of developed FFANN model for the estimation of   TPH removal. Materials and Methods: Several independent repressors including pH, shaking speed, surfactant concentration and contact time were used to describe the removal of TPH as a dependent variable in a FFANN model. 85% of data set observations were used for training the model and remaining 15% were used for model testing, approximately. The performance of the model was compared with linear regression and assessed, using Root of Mean Square Error (RMSE) as goodness-of-fit measure Results: For the prediction of TPH removal efficiency, a FANN model with a three-hidden-layer structure of 4-3-1 and a learning rate of 0.01 showed the best predictive results. The RMSE and R2 for the training and testing steps of the model were obtained to be 2.596, 0.966, 10.70 and 0.78, respectively. Conclusion: For about 80% of the TPH removal efficiency can be described by the assessed regressors the developed model. Thus, focusing on the optimization of soil washing process regarding to shaking speed, contact time, surfactant concentration and pH can improve the TPH removal performance from polluted soils. The results of this study could be the basis for the application of FANN for the assessment of soil washing process and the control of petroleum hydrocarbon emission into the environments