Fuzzy Modeling to Evaluate the Effect of Temperature on Batch Transesterification of Jatropha Curcas for Biodiesel Production

Biodiesel is an alternative source of fuel that can be synthesized from edible, non-edible and waste oils through transesterification. Firstly Transesterification reaction of Jatropha Curcas oil with butanol in the ratio of 1:25 investigated by using of sodium hydroxide catalyst with mixing intensity of 250 rpm in isothermal batch reactor. Secondly the fuzzy model of the temperature is developed. Performance was evaluated by comparing fuzzy model with the batch kinetic data. Fuzzy models were developed using adaptive neuro-fuzzy inference system (ANFIS

Optimization of alkali catalyst for transesterification of jatropha curcus using adaptive neuro-fuzzy modeling

Transesterification of Jatropha curcus for biodiesel production is a kinetic control process, which is complex in nature and controlled by temperature, the molar ratio, mixing intensity and catalyst process parameters. A precise choice of catalyst is required to improve the rate of transesterification and to simulate the kinetic study in a batch reactor. The present paper uses an Adaptive Neuro-Fuzzy Inference System (ANFIS) approach to model and simulate the butyl ester production using alkaline catalyst (NaOH). The amounts of catalyst and time for reaction have been used as the model’s input parameters. The model is a combination of fuzzy inference and artificial neural network, including a set of fuzzy rules which have been developed directly from experimental data. The proposed modeling approach has been verified by comparing the expected results with the practical results which were observed and obtained through a batch reactor operation. The application of the ANFIS test shows which amount of catalyst predicted by the proposed model is suitable and in compliance with the experimental values at 0.5% level of significance

Optimization of Substitution Matrix for Sequence Alignment of Major Capsid Proteins of Human Herpes Simplex Virus

Protein sequence alignment has become an informative tool in modern molecular biology research. A number of substitution matrices have been readily available for sequence alignments, but it is challenging task to compute optimal matrices for alignment accuracy. Here, we used the parameter optimization procedure to select the optimal Q of substitution matrices for major viral capsid protein of human herpes simplex virus. Results predict that Blosum matrix is most accurate on alignment benchmarks, and Blosum 60 provides the optimal Q in all substitution matrices. PAM 200 matrices results slightly below than Blosum 60, while VTML matrices are intermediate of PAM and VT matrices under dynamic programming