Publication

Abstract : Researchers, over the years, have performed copious experiments to determine the performance and the emission characteristics of SI and CI Diesel engines operated either by diesel or bio-diesel. After having obtained the data from experiments, the task at hand was to determine the optimal load, blend ratio, emissions, and other performance parameters. Innumerable optimization techniques were proposed to do the same. Each technique is sui generis. The employment of an optimization technique is contingent on the problem at hand. If the intention is to optimize a single objective, then the analogous single objective optimization technique is exercised; if the problem has many objectives to be optimized, viz., multi-objective, then a multi-objective optimization method is adapted and if we just have to improve the existing solution, then a suitable swarm optimization technique can be employed. This chapter focuses on the different optimization techniques – ranging from single-objective optimization methods such as Taguchi to multi-objective optimization such as Response surface methodology, Grey Relational Analysis, TOPSIS to swarm techniques such as Genetic algorithm used to optimize engine parameters. In addition to this, different prediction techniques such as Artificial Neural Network and Fuzzy algorithms are discussed.