Optimization procedures using a variety of input parameters have gotten a lot of attention, but using three non-edible seed oils of Jatropha (Jatropha curcas), Sesame (Sesamum indicum), and Sweet Almond (Prunusamygdalus dulcis) has a few advantages, including availability and non-food competitiveness. Optimizing a two-stage trans-esterification process using a sodium hydroxide-based catalyst at a fixed catalyst (1.0wt %) and temperature (60 oC) while varying molar ratio (1:3, 1:6, 1:12), time (20–60 min), and mixing speed (500–1000 rpm), to produce optimal responses of yields were studied using response surface methodology (RSM). The optimization solution of molar ratio (1:3), time (40.9 min.), and speed (500 rpm) resulted in an 86.9 % for refined jatropha biodiesel (RJB), the optimization for refined sesame biodiesel (RJB) with molar ratio (1:6), time (41.7 min.), and speed (619 rpm) resulted in an 88.5 %, and the optimization for refined sweet almond biodiesel (RSAB) with the molar ratio (1:3), time (49.359 min.), and speed (500 rpm) resulted in an 88.7 % at the conditions. RJO, RJB, and RSAB had predicted biodiesel yields of 86.9 %, 88.5 %, and 88.7 %, with less than 0.2 % variation, respectively. The characteristics of biodiesel were studied, and the results were determined to meet both ASTM D6751 and EN14214 criteria. The effects of molar ratio, and time on biodiesel yield from their respective oils were important parameters that greatly influenced the yields, but speed only changed the yields marginally. This work has addressed important difficulties influencing mass production of biodiesel such as the utilization of low-cost feedstock such as non-edible vegetable oils, boosting production efficiency through variable optimization of process parameters, and lowering catalyst dosages through catalyst regeneration.
This study investigates the implementation of Taguchi design in the estimation of minimum corrosion rate of mild-steel in cooling tower that uses saline solution of different concentration. The experiments were set on the basis of Taguchi’s L16 orthogonal array. The runs were carried out under different condition such as inlet concentration of saline solution, temperature, and flowrate. The Signal-to- Noise ratio and ANOVA analysis were used to define the impact of cooling tower working conditions on the corrosion rate. A regression had been modelled and optimized to identify the optimum level for the working parameters that had been founded to be 13%NaCl, 35ᴼC, and 1 l/min. Also a confirmation run to establish the p
... Show MoreBasrah crude oil Vacuum residue 773+ K with specific gravity 1.107 and 4.87wt. % sulfur, was treated with hexane commercial fraction provided from Al-Taji Gas Company for preparing deasphaltened oil(DAO)suitable for hydrotreating process. Deasphaltening was carried out with 1h mixing time, 10ml:1g solvent to oil ratio and at room temperature. Hexane deasphaltened oil was hydrotreated on presulfied commercial Co-Mo/γ-Al2O3 catalyst in a trickle bed reactor. The hydrotreating process was carried out at temperature 660 K,LHSV 1.3 h^ –1, H2/oil ratio 300 l/l and constant pressure of 4MPa. The hydrotreated product was distillated under vacuum distillation unit. It is found that the mixture of 75% of vacuum residue with 25% anthracene satisfie
... Show MoreThe experiment was carried out in the green house of, Biology Department of ,College of
Education , Ibn Al-Haitham , Baghdad University. For growth season 2008-2009 via planting
seeds of three genotypes of barley cultivars Baraka , Becson and Baraka x Forest ,to study the
effect of different concentrations of NaCl (o , 50 ,100 , 150 , 200 ) mM.L on some
growth parameters such as fresh and dry weight of shoot and elements plants the experiment
carried out by using completely randomized design applied with four replicates . Data were
statistically analyzed by using least significant differenceat0.05probabilitylevel.
The results showed that an increase in NaCl salt concentrations from 0 to 200mM.L
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The aim of this research is to investigation the optimization of the machining parameters (spindle speed, feed rate, depth of cut, diameter of cutter and number of flutes of cutter) of surface roughness for free-form surface of composite material (Aluminum 6061 reinforced boron carbide) by using HSS uncoated flat end mill cutters which are rare use of the free-form surface. Side milling (profile) is the method used in this study by CNC vertical milling machine. The purpose of using ANFIS to obtain the better prediction of surface roughness values and decreased of the error prediction value and get optimum machining parameters by using Taguchi method for the best surface roughness at spindle speed 4500 r.p.m, 920mm/rev feed rate, 0.6mm de
... Show MorePhenol is one of the worst-damaging organic pollutants, and it produces a variety of very poisonous organic intermediates, thus it is important to find efficient ways to eliminate it. One of the promising techniques is sonoelectrochemical processing. However, the type of electrodes, removal efficiency, and process cost are the biggest challenges. The main goal of the present study is to investigate the removal of phenol by a sonoelectrochemical process with different anodes, such as graphite, stainless steel, and titanium. The best anode performance was optimized by using the Taguchi approach with an L16 orthogonal array. the degradation of phenol sonoelectrochemically was investigated with three process parameters: current de
... Show MoreSeveral correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability
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