Amirkabir University of Technology

About: Amirkabir University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Fuzzy logic. The organization has 15254 authors who have published 31165 publications receiving 487551 citations. The organization is also known as: Tehran Polytechnic & Tehran Polytechnic University.

Binary system dye removal by electrocoagulation from synthetic and real colored wastewaters

Abstract: In this study, binary system dye removal by electrocoagulation (EC) process using aluminum electrode was studied in a batch electrochemical reactor. Acid Black 52 and Acid Yellow 220 were used as model dyes. The effect of operating parameters such as conductivity, current density, initial dye concentration and pH on the electrocoagulation process was studied and the electrical energy consumption was calculated. Also the wool dyeing process has been performed and the dye removal from real colored wastewater by the electrocoagulation process has been studied. It was found that the increasing of the current density up to 40 A/m 2 had increased the dye removal efficiency and the optimum pH for EC process was 5. The increasing of electrolyte concentration from 0 to 8 g/L had a negligible effect on the color removal but it has decreased the electrical energy consumption. Data for single and binary systems of dye removal and the results for the synthetic solutions and the real colored wastewater were too close and it can be concluded that the electrocoagulation process is an effective method to remove dyes from colored wastewaters.

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Abstract: Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)–artificial neural network (ANN), genetic algorithm (GA)–ANN and particle swarm optimization (PSO)–ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R2). The obtained results showed that although all predictive models are able to approximate flyrock, PSO–ANN predictive model can perform better compared to others. Based on R2, values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA–ANN, PSO–ANN and GA–ANN predictive models, respectively. These results show higher efficiency of the PSO–ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others.

Synthesis and Characterization of Poly(vinyl alcohol)/Sulfonated Graphene Oxide Nanocomposite Membranes for Use in Proton Exchange Membrane Fuel Cells (PEMFCs)

Abstract: In this study, novel cross-linked nanocomposite membranes have been prepared from poly(vinyl alcohol) (PVA) and aryl sulfonated graphene oxide (SGO) and a way of cross-linking to improve the chemical, thermal, and mechanical stabilities of the nanocomposite was adopted. The surface of the graphene oxide nanoparticles was modified by aryl diazonium salt of sulfanilic acid. It was revealed that addition of SGO (5 wt %) into the PVA matrix improves the thermal stability (melting temperature, Tm = 223 °C), mechanical stability (tensile strength, TS = 67.8 MPa) and proton conductivity (σ = 0.050 S cm–1) of the nanocomposite proton exchange membranes. A proton exchange membrane fuel cell (PEMFC) fabricated with the PVA/SGO membrane showed a maximum power density of 16.15 mW cm–2 at 30 °C. As a result, the investigated PVA/SGO nanocomposite membranes have good potential for further studies and applications in PEMFCs.