Feasibility Study of Microwave-Assisted Biodiesel Production from Vegetable Oil Refinery Waste

Vegetable oil refinery waste containing acid oil is used as an inexpensive feedstock for producing biodiesel by microwave-assisted esterification (MAE) method. Effects of some main variables such as free fatty acid:methanol molar ratio (1:1, 1:5, and 1:10), reaction time (5, 30, and 60 min), and catalyst concentration (1%, 2%, and 3%) on physicochemical properties of produced biodiesel are investigated. Optimum reaction conditions of MAE are free fatty acid:methanol molar ratio of 1:10, reaction time of 60 min, and a catalyst concentration of 3%, while having 95.79% conversion yield. By increasing the conversion yield of the biodiesel, density and color brightness increase, while viscosity and refractive index decrease. There are no significant differences between physicochemical and heating properties of biodiesel produced by MAE and magnetic stirrer esterification (MSE) methods. Meanwhile, energy consumption of MAE method is almost four times lower than that of MSE. MAE as a promising alternative to the conventional esterification method can be considered as an energy-efficient method for producing biodiesel from inexpensive vegetable oil refinery waste. Practical applications : Acid oil is an inexpensive by-product of alkali refining in vegetable oil plants that would pollute the environment if not rendered safely. In this study, MAE is used to convert acid oil to biodiesel as a practical process for bringing alkali refining waste into production cycle. Acid oil can provide a reduction in the cost of biodiesel production. In addition, application of energy-efficient MAE method can facilitate the economical production of biodiesel.     

Thermal antioxidative kinetics of sesamol in triacylglycerols and fatty acid methyl esters of sesame,olive, and canola oils

Oxidation kinetics of the triacylglycerols (TAGs) and fatty acid methyl esters (FAMEs) of sesame, olive, and canola oils were studied in the presence of the different concentrations of sesamol (0.1%–0.16%) at 60, 80, and 100°C. Sesamol increased the temperature coefficient, T_C, and Q₁₀ number of the TAGs more significantly compared to the FAMEs. All the sesamol-added TAG and FAME systems, respectively, of the olive, canola, and sesame oils, respectively, exerted increased values of the Arrhenius (activation energy, E_a, and frequency factor, A) and Eyring (enthalpy, ΔH⁺⁺, and entropy, ΔS⁺⁺) equation parameters. Sesamol improved the Gibbs free energy (ΔG⁺⁺) of the activated complex formation in the canola, sesame, and olive systems, respectively, and the effect was greater in the FAMEs.     

Design and optimization of LNA topologies with image rejection filters

An important problem in designing RFIC in CMOS technology is the parasitic elements of passive and active devices that complicate design calculations. This article presents three LNA topologies including cascode, folded cascade, and differential cascode and then introduces image rejection filters for low‐side and high‐side injection. Then, a new method for design and optimization of the circuits based on a Pareto‐based multiobjective genetic algorithm is proposed. A set of optimum device values and dimensions that best match design specifications are obtained. The optimization method is layout aware, parasitic aware, and simulation based. Circuit simulations are carried out based on TSMC 0.18 μm CMOS technology by using Hspice. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2010.     

Optimal non-periodic inspection scheme for a multi-component repairable system using A search algorithm

This paper proposes an approach for finding an optimal non-periodic inspection scheme on a finite time horizon for a multi-component repairable system. The system consists of several components, each of which is subjected to soft failure. Soft failures of each component do not cause the system to stop functioning, but increase the system operating costs and are detected only if inspection is performed. Thus, the system is inspected at the scheduled inspection instances and if any of its components is found to have failed, the failed component is minimally repaired. The system’s expected total cost associated with a given inspection scheme includes inspection costs, repair costs, and the penalty costs that are incurred due to the time delay between the actual occurrence of a soft failure of the components and its detection at an inspection. The objective is to determine the optimal inspection scheme which minimizes system’s expected total cost.     

A Modified LAPSUS Model to Enhance the Effective Rainfall Estimation by SCS-CN Method

In this study, LAPSUS model is modified to enhance the effective rainfall estimation by SCS curve number method. The LAPSUS model calculates discharge based on effective rainfall and routs it towards lower neighbouring grid cells following the multiple flow direction principle. Then, the sediment transport capacity and sediment transport rate are calculated in each grid cell. Finally, erosion or sedimentation is calculated by comparing the sediment transport rate with the sediment already in the transport of each grid cell. The amount of rainfall, curve number, convergence factor, discharge exponent, slope exponent, erodibility factor, and sedimentation ability factor are inputted to the application page of the modified model that was created in the C⁺⁺ programming. The outputs of the model are runoff and erosion maps in ASCII format. Evaluating performance of the modified model showed a high accuracy of its results. The value of the coefficient of determination (R²) calculated 0.99 for runoff and 0.97 for erosion. The Nash-Sutcliffe efficiency was 0.96 for runoff and 0.97 for erosion. The value of the precision index calculated 0.81 for both runoff and erosion. Also, the nRMSE calculated 3% for both runoff and erosion. The result showed that the modified model capable to estimate the runoff and erosion on a landscape in a micro sub-catchment scale.     

An artificial immune algorithm for multiple-route job shop scheduling problem

This paper addresses the multiple-route job shop scheduling problem to minimize makespan. The problem is recognized to be extremely difficult because of its combinatorial nature of integer optimization and the large size of the real problem. The goal is, thus, to obtain near-optimal schedules in a computationally efficient manner. Mathematical formulation of the problem is first presented. Then, an approach based on artificial immune algorithm is proposed. In order to evaluate the effectiveness of the proposed approach, 30 problems in small, medium, and large size are designed and solved using the proposed approach. Problems are also solved using Lingo software and the results are compared. The computational results show that the proposed approach generates high-quality schedules in a timely fashion.