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.     

Oxidase-Peroxidase sequential polymerization for removal of a dye from contaminated water by horseradish peroxidase (HRP)/glucose oxidase (GOx)/polyurethane hybrid catalyst

Horseradish peroxidase (HRP) and glucose oxidase (GOx) were co-immobilized on polyurethane, and the resulting HRP/GOx/polyurethane biocatalyst was characterized using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDAX) mapping techniques. The prepared biocatalyst was used for removal of acid orange 7 as model azo dye. The required H₂O₂ for activation of HRP was in-situ produced using GOx to prevent deactivation of HRP in the presence of excess chemical H₂O₂. Central composite design (CCD) was applied for modeling and optimization of parameters affecting the activity of prepared biocatalyst. Under the optimum conditions, removal efficiency of the azo dye was predicted to be 87.47%, which was in good agreement with the experimental value (89.69%). In addition, the performance of the prepared biocatalyst for removal of two other dyes with different structure was investigated at the optimum conditions, and a removal efficiency of 91.56% and 95.25% was obtained for removal of methylene blue and malachite green, respectively. The results demonstrated that the resultant HRP/GOx/Polyurethane biocatalyst was able to decrease the chemical oxygen demand (COD) of a textile effluent from 740mg/L to 96mg/L, indicating that the prepared biocatalyst is an effective enzymatic system for treatment of real wastewater.     

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.     

Effects of cold plasma on the color parameters of Hyssop (Hyssopus officinalis L.) using color imaging instrumentation and spectrophotometer

This research was conducted to evaluate the effects of cold atmospheric plasma treatment on the color of Hyssop (Hyssopus officinalis L.) and also to compare the usage of the spectrophotometer vs the color imaging instrumentation for the evaluation of the treatment on the color parameters. The experiments were investigated at different treatment times of 1, 5, and 10 minutes and the voltage values of 17, 20, and 23 kV. Possible changes of color were evaluated by using CIE L*a*b* values obtained with HunterLab colorimeter and CIE L*a*b* values obtained with a digital still camera (DSC) using digital image processing (MATLAB software). The values of L*, a*, and b* of the samples were obtained using both the methods. The results revealed that the L*, a*, and b* values of the treated Hyssop samples changed with increasing the treatment time and the voltage applied. Evaluating the interaction effects revealed that there was a significant difference in the (−a^*/b^* ) ratio. In addition, the results showed that the effects of all variables on the color parameters were significantly different in the case of the DSC using digital image processing. However, these effects were not significantly different using HunterLab colorimeter except for time variable and interaction effects of a* and (−a^*/b^* ) ratio. The lightest green color and the maximum chlorophyll content loss were observed for 23 kV applied over 10 minutes. Based on the results, the digital image processing can be used as a practical tool to study the variations at the color of dried Hyssop leaves after cold plasma treatment.     

Experimental and numerical investigation of bridge pier scour estimation using ANFIS and teaching–learning-based optimization methods

Studies have shown that the major cause of the bridge failures is the local scour around the pier foundations or their abutments. The local scour around the bridge pier is occurred by changing the flow pattern and creating secondary vortices in the front and rear of the bridge piers. Until now, many researchers have proposed empirical equations to estimate the bridge pier scour based on laboratory and field datasets. However, scale impact, laboratory simplification, natural complexity of rivers and the personal judgement are among the main causes of inaccuracy in the empirical equations. Therefore, due to the deficiencies and disadvantages of existing equations and the complex nature of the local scour phenomenon, in this study, the adaptive network-based fuzzy inference system (ANFIS) and teaching–learning-based optimization (TLBO) method were combined and used. The parameters of the ANFIS were optimized by using TLBO optimization method. To develop the model and validate its performance, two datasets were used including laboratory dataset that consisted of experimental results from the current study and previous ones and the field dataset. In total, 27 scaled experiments of different types of pier groups with different cross sections and side slopes were carried out. To evaluate the model ability in prediction of scour depth, results were compared to the standard ANFIS and empirical equations using evaluation functions including Hec-18, Froehlich and Laursen and Toch equations. The results showed that adding TLBO to the standard ANFIS was efficient and can increase the model capability and reliability. Proposed model achieved better results than Laursen and Toch equation which had the best results among empirical relationships. For instance, proposed model in comparison with the Laursen and Toch equation, based on the RMSE function, yielded 50.4% and 71.8% better results in laboratory and field datasets, respectively.