Investigation of models of the yarn-bobbin drying process by determination of their parameters using genetic algorithm

Abstract

In the first part of this study, the drying behavior of wool-acrylic yarn bobbins was investigated by a theoretical model and genetic algorithm method. Each candidate solution for Do, D1 and D2 was presented on a single chromosome. The values of Do, D1 and D2 yielding the best fit between the experimental and predicted moisture contents were obtained using the genetic algorithm. In the second part of this study, the suitability of various empirical and semiempirical models in the modeling of the drying process was investigated by the genetic algorithm. The population number was taken as 30 and the tournament selection method was used. The calculations were performed until the 20th generation for the theoretical model and 100th generation for the empirical and semiempirical models. The results show that the genetic algorithm can be successfully used in the modeling of the drying process of yarn bobbins. The results also show that the Verma et al. and Diffusion Approach models yield the best fit with experimental data.

Genetic Algorithm Optimisation of a TNT Solidification Model

“…The genetic algorithm mimics the natural selection of individuals inside a population to build an optimisation problem that maximises a fitness function [14][15] .…”

“…(10) and genetic algorithm settings are as presented in Table 2. The initial population is usually randomly generated and each candidate solution for T D is represented by a single individual (or chromosome) [14][15] . The chromosome is an array of binary numbers (or genes) and is as represented in Fig.…”

“…The best individuals are selected for the next generation, according to fitness function [14][15] . Tournament selection method has been performed for two randomly selected individuals and the tournament selection parameter is set as 1 to guarantee that the best individual will be in the next generation.…”

“…In order to recall lost information and ensuring the search space large enough, mutation is applied sweeping population and changing value on the gene from 0 to 1 or vice versa due to mutation probability for a binary representation [14][15] .…”