International Journal of Engineering
Volume:25 Issue: 2, May 2012

In this research, the amount of Iron removal by bioleaching of a kaolin sample with high iron impurity with Aspergillus niger was optimized. In order to study the effect of initial pH, sucrose and spore concentration on iron, oxalic acid and citric acid concentration, more than twenty experiments were performed. The resulted data were utilized to train, validate and test the two layer artificial neural network (ANN). In order to minimize the overfitting, Bayesian regularization and early stopping methods with back propagation algorithm were utilized as training algorithm of ANN. Good validation for prediction of iron removal percent was resulted due to the inhibition of over-fitting problems with selection of appropriate ANN topology and training algorithm. The results showed that optimized condition of initial pH, sucrose and spore concentration to achieve high Iron removal should be 6, 60 g/l and 3.5×107, respectively.

The boundary element technique is a practical problem-solving tool and useful in comparison to other currently popular alternatives. In this paper, a circular tunnel of a diameter 3.4 m is to be excavated through a clayey soil and surrounded by concrete lining of a thickness 0.2 m, so the clear internal diameter is 3.0 m. A plane strain analysis is adopted for such problem and the soil is assumed to be homogeneous, isotropic and linearly elastic medium. The program MRBEM (multi-region BEM) is adopted to analyze a 2D circular lined tunnel in a soil medium of an infinite extent. The existence of a lined tunnel, as in the case of any structure, through the soil medium is affected by and affects the surrounding soil. This may be understood throughout the study of many parameters that influence the tunnel\''s behavior. The parametric study for the lined tunnel covers the influence of the following factors: Depth of the tunnel Undrained shear strength of soil. It was found that Ground surface settlement, due to the effect of surcharge load, shows its maximum value at both sides of the lined tunnel and not above the center of the tunnel. The maximum positive stresses at the lining interface occur at the crown and invert regions. These stresses tend to decrease gradually until they reach their minimum negative values at the springline region.

Handwriting recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. It has numerous applications that includes, reading aid for blind, bank cheques and conversion of any hand written document into structural text form. Neural Network (NN) with its inherent learning ability offers promising solutions for handwritten character recognition. This paper identifies the most suitable NN for the design of hand written English character recognition system. Different Neural Network (NN) topologies namely, back propagation neural network, nearest neighbour network and radial basis function network are built to classify the characters. All the NN based Recognition systems use the same training data set and are trained for the same target mean square error. Two hundred different character data sets for each of the 26 English characters are used to train the networks. The performance of the recognition systems is compared extensively using test data to draw the major conclusions of this paper.

Economic growth in developing countries has resulted in increasing demand for infrastructure projects like power plants. To meet these development needs, the government of Iran has engaged companies to carry out power plant projects. While many papers have been written on the subject of project risk management, little information exists on the actual use of risk management in practice. The primary objective of this paper is to identify and rank the risks in these power plant projects. The proposed model allows risks to be ranked based on management priorities using a combined fuzzy analytic network process (fuzzy-ANP) and fuzzy Technique for Order Preference by Similarity to Ideal Solution (fuzzy-TOPSIS) method. Probability and impact are two commonly used criteria in project risk management. However, these criteria do not sufficiently address all aspects of project risk. Moreover, there may be relations and dependencies among the various criteria. Therefore, we proposed a hierarchical structure for ranking risk in power-plant projects. The proposed structure can consider dependence among the different criteria. We use fuzzy-ANP for calculating weights. The outputs of fuzzy-ANP calculations are used in a fuzzy-TOPSIS procedure for the evaluation of important risks. A case study of a power plant project is presented to demonstrate the applicability and performance of the proposed model. More than 100 risks were identified and categorized according to their source and to their relative impact on the project. We evaluated important risks based on the fuzzy-ANP and fuzzy-TOPSIS method. In addition, we used a sensitivity analysis to discuss and explain the results of the method. The proposed method is a suitable approach when performance ratings and weights are vague and imprecise.

The risk response development is one of the main phases in the project risk management that has major impacts on a large-scale project’s success. Since projects are unique, and risks are dynamic through the life of the projects, it is necessary to formulate responses of the important risks. Conventional approaches tend to be less effective in dealing with the imprecise of the risk response development. This paper presents a new decision making methodology in a fuzzy environment to evaluate and select the appropriate responses for the project risks. For this purpose, two fuzzy well-known techniques, namely decision tree and TOPSIS, are extended based on multiple selected criteria, simplifying parameterized metric distance and fuzzy similarity measure. Finally, a case study in an oil and gas project in Iran is provided to show the capability and suitability of the proposed fuzzy methodology in the large-scale practical situation.

In this paper, we focused on the effects of initial solution’s pH on the composition, structure and morphology of hydrothermally synthesized titanium oxide nanoparticles. TiCl4 and NaOH were used as titanium source and precipitant, respectively. Phase and structure determination of samples were obtained by X-ray diffraction (XRD) analysis. Scanning electron microscopy (SEM) images were used for size and morphology evaluation of the obtained particles. Also, simultaneous thermal analysis (STA) was used for phase transformation evaluation during calcination process. Results indicated that increase in initial pH of the solution could led to increase in crystallite size of hydrothermally obtained nanopowder which is because of enhanced growth condition by increasing hydroxide groups in the solution. Particle sizes of the synthesized nanopowders were smaller than 100 nm and the mean particle sizes decreased from 60 to 25 nm by increasing in initial pH from 3 to 7, respectively. The results also showed that at initial pH=9 the obtained synthesized nanopowders were in amorphous state and calcination in 700 ۫C was led to sodium titanate phase.

In this paper, the effects of container size on the porosity of random loose packing of mono size particles have been investigated using an Event Dynamics (ED) based model. Simultaneous effects of square container walls on particles packing and their order are also investigated. Our simulation results indicate higher container size will increase the total packing factor and high density regions which can be attributed to decrease of low density regions near the walls and also the order imposed by them. By growing packing container size, reproducibility of packing factor values increases which is another indication of more packing randomness.

A model for heat-recirculating micro combustor is developed. It investigates the structure of laminar, one-dimensional and steady state flame propagation in uniformly premixed wood particles with considering the effects of heat recirculation caused by configuration of micro combustor. The flame structure is divided into three regions: a preheat-devolatilization zone where the rate of the gas-phase chemical reaction is small; a narrow reaction zone where convection and the rate of devolatilization of the fuel particles are small; and finally a convection zone where diffusive terms in the conservation equation are small. In this model, it is assumed that the fuel particles vaporize first to yield a gaseous fuel of known chemical structure. The analysis is investigated in the asymptotic limit. The overall investigation of this study leads to a novel non-linear burning velocity correlation that heat recirculation term is considered to calculate it. The results shows that heat recirculation affect the flame structure by different parameters.