Computational modeling and multi-objective optimization of engine performance of biodiesel made with castor oil

Abstract

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.

The Effect of Different Mixing Proportions and Different Operating Conditions of Biodiesel Blended Fuel on Emissions and Performance of Compression Ignition Engines

“…As the rotational speed increases, the power generated by each fuel unit decreases, and the BSFC of all five experimental fuels shows an increasing trend. Muteeb ul Haq et al [40] and Jonah Chukwudi Umeuzuegbu et al [41] also found the same trend in BSFC variation when studying castor biodiesel blends.…”

Modeling and Optimization of a Compression Ignition Engine Fueled with Biodiesel Blends for Performance Improvement

“…To avoid financial constraints and time-consuming experiments, scholars have employed different modeling techniques, including artificial intelligence (AI), in terms of an artificial neural network (ANN) tool, and fuzzy modeling in modeling engine performances and exhaust emissions [41][42][43][44]. Accordingly, the correlation coefficient between the experimental data and ANN predictions can accurately forecast the engine efficiency and exhaust emissions powered with biodiesel mixes [45].…”

Preface to the Special Issue on Computational Performance Analysis based on Novel Intelligent Methods: Exploration and Future Directions in Production and Logistics

“…In the literature, numerous research studies can be found which try to address the application of CPA in developing computational methods for production and logistics systems [13,6,7,4,12]. Furthermore, most studies have been addressing sustainable development as a necessity for real-life problems to be achieved through intelligent methods [3].…”