The evaluation of engine performance and emission of biodiesel produced from African pear seed oil (APO) and its prediction using multi‐input‐multi‐output (MIMO) artificial neural network (ANN) technique was studied. A standard diesel test bed was used to carry out the evaluation using petrol‐diesel, biodiesel, and its blends, operating at various engine revolutions per minute. Sensitivity analysis used the connection weight method. The performance results showed that petro‐diesel blended with a small amount of APO methyl ester (B20) improved the engine efficiency and emissions better than only petrol‐diesel. The engine performance was predicted well, with the MIMO model having better correlation coefficients of 0.99779, 0.99993, and 0.99774 on training, validation, and testing, respectively, than the Multiple input Single Output (MISO) model. The desired outputs compared between their measured and simulated values exhibited a very low mean square error of 0.0000014488. The blend had the highest relative impact (62.08%) on the output variables. The MIMO model has therefore shown the potential to adopt two input variables equally in predicting the engine performance of biodiesel rather than only complex variables. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd

In this research the engine performance of biodiesel made with castor oil through homogeneous alkali catalyzed transesterification was analyzed. The input variables for the performance analysis were biodiesel blend and engine speed while the response variables were break power (BP), basic specific fuel consumption (BSFC), break thermal efficiency (BTE), torque and unit cost. The engine performance was modeled using artificial neural network (ANN) and the ANN was subsequently used as the objective function for a non dominated sorting genetic algorithm (NSGA-II) for multi objective optimization of the engine performance. The ANN was equally coupled with a desirability function whose outputs were optimized using simulated annealing for multi objective optimization of the engine performance. Subsequent comparison of the two optimization models was done. The results show that biodiesel from castor oil could be a good replacement for biodiesels from fossil fuels. The ANN model predicted engine performance very well with the lowest value of the correlation coefficient between the experimental responses and ANN predictions being 0.9733. The multi objective optimization using desirability function performed excellently well with the optimum blend and speed being 78.7% and 1754.48 rpm respectively. The Pareto front from the NSGA-II algorithm generally has high desirability values. The Pareto front solution which is more flexible than the desirability function solution would serve as an excellent guide for engine designers. Finally, castor oil based biodiesel cost was for the first time integrated into engine performance optimization studies.

This study focuses on the kinetic modeling of homogeneous catalyzed transesterification of neem seed oil and the determination of the rate‐limiting step (RLS). The oil was extracted from the seeds by cold solvent extraction and it was characterized to determine its physicochemical properties. The effect of factors such as time, amount of catalyst, temperature, methanol / oil molar ratio, and agitation speed on the biodiesel yield were investigated. The kinetic experiments for the transesterification reaction were carried out by batch process, assuming a pseudo second‐order reaction. Rate equations for the arrays of reversible reactions of triglyceride to glycerol and biodiesel were developed to determine the RLS. The oil yield extracted was 34%. The extracted oil contained 60.2% monounsaturated fatty acid and the dominant monosaturated fatty acid is oleic acid having 40.6% of total composition of monosaturated fatty acid in the oil. The biodiesel yield produced by transesterification of pretreated neem oil was 90% and biodiesel properties were within the limits of American Society for Testing and Materials (ASTM) (D6751‐02) standards. The kinetic modeling of the reaction revealed that the rate constant values for forward reactions (transesterification) (k1, k3, and k5) were higher than those of backward reactions (k2, k4 and k6) at temperatures of 45, 55, and 65 °C. The rate constant (k1) for the conversion of triglyceride to diglyceride was the least among the rate constants of forward reactions at all the studied temperatures showing the RLS. The rate constants for the RLS are 0.0174, 0.0208, and 0.0303 min−1 at temperatures of 45, 55, and 65 °C respectively and the activation energy is 24.9 kJ mol−1. © 2020 Society of Industrial Chemistry and John Wiley & Sons Ltd

The heterogeneous catalysis of transesterification of gmelina seed oil to biodiesel is evaluated. The oil was extracted from the seeds with n‐hexane by solvent extraction and characterized to determine its physiochemical properties. Response surface methodology was applied to optimize the effect of process variables on the biodiesel yield. The base‐activated clay catalyst performed as montmorillonite clay with the characteristic property of a Brønsted acid. It has an improved surface area after activation that enhanced its catalytic activity on transesterification reaction. Under optimal conditions, the biodiesel yield was 70.1 %, thus demonstrating that the model predicted well the biodiesel production.

No abstract