INTERNATIONAL JOURNAL OF ENGINEERING
Year:2013 | Volume:26 | Issue:6 (TRANSACTIONS C: ASPECTS)|Papers Count:7

Waste cooking oil (WCO) was used as a potential feedstock for biodiesel production. High level of free fatty acids (9.85% w/w) in WCO makes it an undesirable substrate for direct transesterification reaction. To solve this issue, a two-step process was implemented in this research. Firstly, esterification reaction was performed in the presence of sulfuric acid as a common acid catalyst to reduce the amount of free fatty acids (FFA) to less than 1.5% w/w. The triglycerides (TGs) in WCO were transesterified with methanol catalyzed by potassium hydroxide in the second step of reaction. Several effective parameters were evaluated in terms of reaction temperature (323-363 K), reaction time (0.5-3 h) and catalyst concentration (0-5.52% w/w). The optimum reaction temperature, reaction time and catalyst concentration were 355 K, 3 h and 3.68~4% w/w, respectively. In fact, maximum FFA conversion of 87 wt% was obtained. The biodiesel synthesis condition in the second stage of reaction was defined as the reaction temperature (338 K), methanol/ WCO molar ratio (6: 1), KOH concentration (1% w/w) and reaction time (90 min). Under the above conditions, maximum TG conversion of 96.66 wt% was obtained.

The CFD simulation of heat transfer characteristics of a nanofluid in a circular pipe under convective heat transfer was considered using the fluent software (version 6.3.26) in the laminar flow. Al2O3 nano-particles in water with concentrations of 0.5, 1.0, 1.5, 2 and 2.5% were studied in the simulation. All thermo-physical properties of nanofluids were temperature independent. It was concluded that heat transfer coefficient increased with the Peclet number. Furthermore, the effect of nano-particles concentration on the convective heat transfer coefficient was theoretically investigated and the results were compared with the experimental data obtained from the literature. The maximum convective heat transfer coefficient was observed at the highest concentration of nano-particles in water (2.5%). The simulated data were in good agreement with the literature (with the discrepancy of less than 10%).

Adders and multipliers are two main parts of arithmetic units of computer hardware and play important role in reversible computations. This paper introduces a novel reversible 4x4 multiplier circuit that is based on an advanced “Partial Product Generation Circuits” (PPGC) with Peres gates only without duplicating gates. Again, an optimized Peres full adder reversible gate is used in “Reversible Parallel Adder” (RPA) part with accompaniment with the carry save adder technique. Comparison of the proposed design with previous ones shows that the proposed reversible multiplier improves the quantum parameters. The proposed design shows lower quantum cost and depth with the help of a novel design in PPGC. The circuit cost of the proposed design is a little higher than the best compared design, but the proposed design shows the lowest total cost which is defined as sum of quantum cost and circuit cost. Moreover, the number of gates, garbage input and output has no change regarding to the best compared design. The proposed multiplier can be generalized as an nxn bit multiplication.

This work proposes a novel method to find the phase error and oscillation amplitude in multiphase LC oscillators. A mathematical approach is used to find the relationship between every stage's output phase and its coupling factor. To much more general analysis, every stage assumed to have a different coupling factor. The mismatches in LC tanks are considered as the main source of phase errors and accordingly the phase deviation of each stage is calculated in a distinct equation. Then, the amplitude of oscillation for each stage is individually derived using phase error results. Not only considering different coupling factors increases the generality of this work but also the results are in better conformance with simulations compared to previous works. The theoretical results are evaluated and confirmed through extensive simulations using ADS software.

Cluster analysis is a useful technique in multivariate statistical analysis. Different types of hierarchical cluster analysis and K-means have been used for data analysis in previous studies. However, the K-means algorithm can be improved using some metaheuristics algorithms. In this study, we propose simulated annealing based algorithm for K-means in the clustering analysis which we refer it as SA K-means. In this algorithm, an evaluation criterion is used in the clustering stage to have accurate clusters. Then, another cost based criterion has been introduced to have efficient and accurate clusters.The proposed approach has been presented for solving the location allocation problem. To show the effectiveness of the proposed approach, some numerical examples of location allocation problems have been tested by the proposed approach. Comparing the results of the proposed approach with exact solution and another developed GA algorithm for numerical examples of the location allocation problem show that the performance of the proposed SA K-means approach is satisfactory.

This paper presents a novel multi-objective mathematical model for a capacitated single-allocation hub location problem. There is a vehicle capacity constraint considered in this model. Additionally, our model balances the amount of the incoming flow to the hubs. Moreover, there is a set of available capacities for each potential hub, among which one can be chosen. The multiple objectives are to minimize the total cost of the networks regarding minimizing the maximum travel time between nodes. Due to the NP-hard property of this problem, the model is solved by a multi-objective imperialist competitive algorithm (MOICA). To prove its efficiency, the related results are compared with the results obtained by the well-known multi-objective evolutionary algorithm, namely NSGA-II. The results confirm the efficiency and the effectiveness of our proposed MOICA to provide good solutions, especially for medium and large-sized problems. Finally, we conclude that the proposed MOICA finds quality solutions rather than the solutions obtained by the NSGA-II algorithm.

Control charts are the most important tools of statistical process control used to discriminate between assignable and common causes of variation and to improve the quality of a process. To design a control chart, three parameters including sample size, sampling interval, and control limits should be determined. The objectives are hourly expected cost, in-control average run length, power of the control chart, and average time to signal. Different approaches such as statistical design, economic design, and economic-statistical design of control charts have been considered by many researchers. Recently, multi-objective design of control chart has been investigated in the literature. In this paper we propose a multi-objective economic-statistical design of np control chart (np-MOESD).To solve the multi-objective model, a method is used to find the Pareto optimal solution and then a combined method based on Data Envelopment Analysis (DEA) is proposed to determine the most efficient design parameters. A numerical example of Duncan [1] illustrates the proposed approach. Sensitivity analysis is performed to evaluate the proposed model. In addition, the proposed model is compared with pure economic design (Duncan’s model) as well as another model in the literature. Results show that the proposed np-MOESD model improves statistical properties of np control charts.