Application of support vector machine and ant colony algorithm in optimization of coal ash fusion temperature

The model of coal ash fusion temperature was made by support vector machine. An improved ant colony algorithm for solving continuous space optimization problems was proposed. The parameters of support vector machine were optimized by the improved ant colony algorithm. And it was also used to make a global optimization to find the suitable chemical compositions of coal ash corresponding to the maximum and minimum ash fusion temperature. The results indicate that the maximum and average relative predicting errors of the model are 2.02% and 0.56% respectively. The optimization results show that the chemical compositions of the coal ash are consistent with that in practice. And not only the convergence rate but also the convergence performance was improved.