A separation technique which has recently received a sharp increase in research activities is “ion flotation”. This technique has four important advantages for treating wastewaters: low energy consumption, small space requirements, small volume of sludge and acting selectively. The present study aims to optimize parameters of ion flotation for cadmium removal in simulated wastewater at laboratory scale. It was obtained on the reaction between Cd2+ and sodium dodecylesulfate (SDS) collector followed by flotation with ethanol as frother. Test solution was prepared by combining the required amount of cadmium ion, SDS and necessary frother or sodium sulfate solution. All experiments were carried out in a flotation column at laboratory temperature (27°C), adjusted pH = 4 and 120 minutes. The different parameters (namely: flow rate, cadmium, SDS and frother concentrations and ionic strength) influencing the flotation process were examined. The best removal efficiency obtained at a collector-metal ratio of 3:1 in 60 min with flow rate of 150 mL/min was 84%. The maximum cadmium removal was 92.1% where ethanol was introduced at a concentration 0.4% to flotation column with above conditions. The obtained results were promising, as both cadmium and collector were effectively removed from wastewater. Hence, the application of ion flotation for metal ions removal from effluents seems to be efficient.

ABSTRACT:Nowadays traffic data is obtained from multiple sources including GPS, Video Vehicle Detectors (VVD), Automatic Number Plate Recognition (ANPR), Floating Car Data (FCD), VANETs, etc. All such data can be used for route finding. This paper proposes a model for finding the optimum route based on the integration of traffic data from different sources. Ant Colony Optimization is applied in this paper because the concept of this method (movement of ants in a network) is similar to urban road network and movements of cars. The results indicate that this model is capable of incorporating data from different sources, which may even be inconsistent.

Abstract. Vehicle Routing Problem is one of the classic problems in GIS (Geospatial Information System) which had been studied for long times. An answer can be accepted as a good solution if it would be able to optimize the total length of the route or decrease the number of vehicles. A VRP defines finding the optimum route for some vehicles that serve to some customers and return to the service center. This problem is economically important because the cost and the time of serving to costumers are related to optimization of the problem’s answer. Furthermore, there are many problems like BUS management, Post pickup and delivery system and other servicing systems, which are technically similar to VRP. The aim of these problems is finding a composition of optimum routes between server and costumers. In addition, as the cost is related to time, finding shortest path means decreasing cost serving and decreasing time. In this article, a hybrid model using Artificial Bee Colony and Genetic Algorithm is proposed to solve VRP. In the first step, Artificial Bee Colony has been used to find a solution for five vehicles. The scout and the onlooker bees produced in 8 modes by two methods including the nearest neighborhood and the wide neighborhood. In the second step, the Genetic Algorithm helps to optimize the solutions. The results show that the production of the scout bees is the most effective factor in the answers to the problem and helps greatly converging the answers as soon as possible.

Commission VI, WG VI/4 ABSTRACT:Today, city management is one of the great challenges facing the world. The growth of population, industries, and services is in urgent need of transportation on a large scale. Meanwhile, transportation has great importance in urban management. Therefore, it is necessary to solve the traffic problem with scientific methods and reduce the traffic load of cities. An interesting way to reduce urban travels is using 2,3, or 4 people from one car that it is known as "Ride Sharing". In this research, the NetLogo software is used to simulate travel sharing scenarios. The three considered parameters are the number of passengers, the acceptable travel sharing radius, and the acceptable waiting time. The proposed algorithm uses a clustering method to find the best candidates to share a ride. Several scenarios were performed to evaluate numerical results. The number of passengers was 100, and 500, the radius of the trip was 1,000 and 2,000 meters, and the waiting time was 10 and 20 minutes. So, 8 experiments were carried out. The least amount of travel sharing was observed in the first scenario (100 passengers, 1000 m travel sharing radius and 10 minutes waiting time), in which 2% of single trips dropped out. The most sharing trips were in the final scenario (500 passengers, 2000 meters radius and 20 minutes waiting time), which saw a decrease of 36.4% of single trips. So, it can be said that sharing a trip can reduce traffic in cities and consequently reduce urban costs and either air pollution or noise pollution.