Optimization of Alkyldiethanolamides Synthesis from Terminalia catappa L. Kernel Oil through Enzymatic Reaction.

Alkyldiethanolamides (fatty acid diethanolamides) synthesis from Terminalia catappa L. kernel oil was optimized using lypozyme as a catalyst. The result showed that the optimal reaction conditions were 2 hours reaction time, with a ratio of oil mass (g) to diethanolamine (mmol) of 1:5, a ratio of oil mass to enzyme (g) of 1: 0.075, and a temperature of 40°C. The percentage of alkyldiethanolamides at optimum condition was 56-60%. The synthesis results were also analyzed by FTIR. FTIR spectra revealed specific absorption at several wave numbers (3434 cm-1, 1655 cm-1, 1280 cm-1), indicating that amide and alcohol bonds (C=O, C-N, and O-H) were formed. GC-MS was employed to identify the types of fatty acid diethanolamides that were successfully synthesized. The fatty acid diethanolamides formed were palmitoyldiethanolamide (Rt = 32.96 min) and oleyldiethanolamide (Rt = 35.57 min). The total nitrogen content of alkyldietanolamides was 0.26%, or 0.19 mmol of the amide group in 1 g of sample.

Optimization of the Enzymatic Synthesis of Biodiesel from Terminalia cattapa L. Kernel Oil Using Response Surface Methodology.

The synthesis of biodiesel using Terminalia cattapa L. (local language: Ketapang) kernel oil and ethanol has been conducted. The effect of variable parameters of synthesis includes reaction time (3-7 h), temperature (35-60°C), amount of enzyme (0.1-0.3 g) and substrate molar ratio (ketapang kernel oil to ethanol, 1:1-1:3) were studied. Response surface methodology based on a five-level and four-variable central composite rotatable design were used to evaluate the interactive effects of the sinthesis parameters on the percentage yield of biodiedsel. The analysis of variance shows that the optimum conditions of the synthesis reaction were at 4.03 h of reaction time, 0.25 g of weight of enzyme, 45.04°C of temperature and 1.50 of substrate molar ratio. The actual experimental yield was 83.9% under optimum condition, which compared well to the maximum predicted value of 83.71%.

Comparison of estimation capabilities of response surface methodology (RSM) with artificial neural network (ANN) in lipase-catalyzed synthesis of palm-based wax ester.

BACKGROUND: Wax esters are important ingredients in cosmetics, pharmaceuticals, lubricants and other chemical industries due to their excellent wetting property. Since the naturally occurring wax esters are expensive and scarce, these esters can be produced by enzymatic alcoholysis of vegetable oils. In an enzymatic reaction, study on modeling and optimization of the reaction system to increase the efficiency of the process is very important. The classical method of optimization involves varying one parameter at a time that ignores the combined interactions between physicochemical parameters. RSM is one of the most popular techniques used for optimization of chemical and biochemical processes and ANNs are powerful and flexible tools that are well suited to modeling biochemical processes. RESULTS: The coefficient of determination (R2) and absolute average deviation (AAD) values between the actual and estimated responses were determined as 1 and 0.002844 for ANN training set, 0.994122 and 1.289405 for ANN test set, and 0.999619 and 0.0256 for RSM training set respectively. The predicted optimum condition was: reaction time 7.38 h, temperature 53.9 degrees C, amount of enzyme 0.149 g, and substrate molar ratio 1:3.41. The actual experimental percentage yield was 84.6% at optimum condition, which compared well to the maximum predicted value by ANN (83.9%) and RSM (85.4%). The order of effective parameters on wax ester percentage yield were; respectively, time with 33.69%, temperature with 30.68%, amount of enzyme with 18.78% and substrate molar ratio with 16.85%, whereas R2 and AAD were determined as 0.99998696 and 1.377 for ANN, and 0.99991515 and 3.131 for RSM respectively. CONCLUSION: Though both models provided good quality predictions in this study, yet the ANN showed a clear superiority over RSM for both data fitting and estimation capabilities.