Huge amount of sulfur production as a side product in petrochemical industries and its low application in country has been a challenging issue. In this paper, application of sulfur as a partial substitute for bitumen in process of asphalt mixture production is investigated. According to the results, the characteristics of the sulfur extended asphalt in comparison with the conventional asphalt made from pure bitumen, and the impact of sulfur addition on asphalt performance are expressed and the optimized formula for making the asphalt mixture is obtained. In this content, the modified asphalt was produced in mass production scale and its performance was studied. In this paper, the method of sulfur addition to pure bitumen and then its mixture with aggregate and finally optimized condition of sulfur modified asphalt production regarding pollution and environmental issues are also proposed.

Dam construction and surface runoff control is one of the most common approaches for water-needs supply of human societies. However, the increasing development of social activities and hence the subsequent increase in environmental pollutants leads to deterioration of water quality in dam reservoirs and eutrophication process could be intensified. So, the water quality of reservoirs is now one of the key factors in operation and water quality management of reservoirs. Hence, maintaining the quality of the stored water and identification and examination of changes along time has been a constant concern of humans that involves the water authorities. Traditionally, empirical trophic state indices of dam reservoirs often defined based on changes in concentration of effective factors (nutrients) and its consequences (increase in chlorophyll a), have been used as an efficient tool in the definition of dam reservoirs quality. In recent years, modeling techniques such as artificial neural networks have enhanced the prediction capability and the accuracy of these studies. In this study, artificial neural networks have been applied to analyze eutrophication process in the Dez Dam reservoir in Iran. In this paper, feed forward neural network with one input layer, one hidden layer and one output layer was applied using MATLAB neural network toolbox for trophic state index (TSI) analysis in the Dez Dam reservoir. The input data of this network are effective parameters in the eutrophication: nitrogen cycle parameters and phosphorous cycle parameters and parameters that will be changed by eutrophication: Chl a, SD, DO and the output data is TSI. Based on the results from estimation of modified Carlson trophic state index, Dez Dam reservoir is considered to be eutrophic in the early July to mid-November and would be mesotrophic with decrease in temperature. Therefore, a decrease in water quality of the dam reservoir during the warm seasons is expectable. The results indicated that artificial neural network (ANN) is a suitable tool for quality modeling of reservoir of dam and increment and decrement of nutrients in trend of eutrophication. Therefore, ANN is a suitable tool for quality modeling of reservoir of dam.

Resistance of the bare hull of the tourist submarine with spherical heads, moving in forward and transverse directions is analyzed in OpenFOAM using Computational Fluid Dynamics. The resistance coefficients of the submarine are estimated for different length-to-diameter ratios and Reynolds numbers. The Artificial Neural Network with the optimum number of neurons is then trained to predict the resistance coefficients. Two simplified Artificial Neural Network models and Nonlinear Least Squares Marquardt-Levenberg algorithm are employed to fit the results in the form of equations that may be used in the initial design of this type of submarines. The comparative analysis of different prediction models is performed and guidelines for their practical application are given.