In this study, the optimum operating conditions for sesame oil extraction were studied. N-hexane was used as a solvent. Different variables were investigated; sesame seeds particle sizes, ratio of solvent to seeds mass, contact time, stirring effect, roasting impact and extraction temperatures. Results obtained showed that higher rates of extraction were obtained when roasting sesame seed to 150˚C. The ratio of solvent to seeds found to be 6:1 gave higher extraction. Moreover, stirring speed was tested and had been optimized to 600 rpm. Finally, the extraction under heating was studied and results showed that increasing operating temperature to more than 40˚C did not increase extraction efficiency.

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Reverse osmosis technique was applied in removing hexavalent chromium ions from artificial wastewater. Different operating conditions were studied to monitor the separation process using commercial Reverse Osmosis BW30XFR membrane. Different concentrations of hexavalent chromium; 5, 30, and 100 ppm were tested. Samples were subjected to incrementally increasing operating pressure; 10, 30, and 45 bar and flow rates; 2.2, 3.4, and 4.5 L/min under various temperatures; 25, 35, 45, and 55 °C. Collected permeate and concentrations were measured after each experiment using a UV spectrophotometer. Results obtained presented a higher rejection percentage at lower feed concentrations with a value up to 99.8% for 5 ppm in comparison to 94.3% for 30 ppm and 77.2% for 100 ppm concentration due to concentration polarization; however, it showed no effect of increasing operating flow rates. Moreover, the increase in feed temperature from 25 to 55 °C had positively increased permeate flux to more than 300 times. The permeate flux at 25 °C is recorded for all tested samples in the range of 30 to 158 kg/h·m2, this range has risen at 55 °C under the same conditions to the range of 70 to 226 kg/h·m2, indicating alteration within the membrane pore size due to temperature increase and high applied pressure concluding high sensitivity of polymeric membranes towards changing permeate flow rate with increasing temperatures.

The efficiency of Azadirachta indica (neem leaves) on the removal of Pb(II) ions by adsorption from aqueous solution was investigated in this study. The efficiency of these leaves (without chemical or thermal treatment) for the adsorption of Pb(II) ions has not previously been reported. Batch experiments were performed to study the effect of the particle size, pH, adsorbent dose, contact time, initial Pb(II) ion concentration, and temperature. The maximum removal of 93.5% was achieved from an original Pb(II) ion solution concentration of 50 mg/L after 40 min, at pH 7, with 0.60 g of an adsorbent dose. The maximum adsorption capacity recorded was 39.7 mg/g. The adsorption process was also studied by examining Langmuir, Freundlich, Temkin isotherm, and Dubinin–Radushkevich (D-R) isotherm models. The results revealed that the adsorption system follows the pseudo-second-order model and fitted the Freundlich model. Several thermodynamic factors, namely, the standard free energy (∆G°), enthalpy (∆H°), and entropy (∆S°) changes, were also calculated. The results demonstrated that the adsorption is a spontaneous, physical, and exothermic process. The surface area, pore size, and volume of adsorbent particles were measured and presented using a surface area analyzer (BET); the morphology was scanned and presented with the scanning electron microscope technique (SEM); and the functional groups were investigated using μ-FTIR.

This paper compares four different modeling techniques: Response Surface Method (RSM), Linear Radial Basis Functions (LRBF), Quadratic Radial Basis Functions (QRBF), and Artificial Neural Network (ANN). The models were tested by monitoring their performance in predicting the optimum operating conditions for Sesame seed oil extraction yields. Experimental data using three different solvents—hexane, chloroform, and acetone—with varying ratios of solvents to seeds, all under different temperatures, rotational speeds, and mixing times, were modeled by the three proposed techniques. Efficiency for model predictions was examined by monitoring error value performance indicators (R2, R2adj, and RMSE). Results showed that the applied modeling techniques gave good agreements with experimental data regardless of the efficiency of the solvents in oil extraction. On the other hand, the ANN model consistently performed more accurate predictions with all tested solvents under all different operating conditions. This consistency is demonstrated by the higher values of R2 and R2adj ratio equals to one and the very low value of error of RMSE (2.23 × 10−3 to 3.70 × 10−7), thus concluding that ANN possesses a universal ability to approximate nonlinear systems in comparison to other models.