A sludge compost quality evaluation method based on chaos genetic support vector machine

Because of the complicated interaction of the sludge compost components, it makes the compost quality evaluation system appear the non-linearity and uncertainty. According to the physical circumstances of sludge compost, a compost quality evaluation method based on chaos genetic support vector machine (SVM) is proposed in this paper. The fitness function, genetic operator and encoded mode of chaos genetic algorithm are improved. The SVM network structure that used for compost quality evaluation is constructed, and we use the chaos genetic algorithm to optimize SVM parameters, which can gain optimized SVM classification model, thereby enhancing the convergence rate and the classification accuracy. We select the index of sludge compost quality and take the high temperature duration, degradation rate, nitrogen content, average oxygen concentration and maturity degree as the evaluation parameters. With the ability of strong pattern classification and self-learning and well generalization of SVM, the compost quality evaluation method can truly evaluate the sludge compost quality. The actual evaluation results show that this method is feasible and effective.