Support Vector Regression and Ant Colony Optimization for Combustion Performance of Boilers

Support vector regression (SVR) is a powerful tool for modeling the inputs and output(s) of complex and nonlinear systems. However, the control parameters are critical to the performance of SVR and also difficult to be selected. For actual applications in most cases, self-modeling of studied systems without any manual operation was needed. In this work, ant colony (ACO) optimization was developed to search the optimal control parameters so as to achieve this purpose. ACO is a meta-heuristic optimization algorithm for solving both discrete and continuous optimization problems. As a case study to demonstrate the applicability of the proposed method, SVR model is constructed for correlating historic data comprising values of operating and output variables of a boiler. Parameters selection was performed with the help of ACO. Next, model inputs describing process operating variables are also optimized using ACO with a view to maximize the combustion efficiency of the boiler. The results showed that the proposed approach, by comparing with neural network model, was an efficient way to model boiler in automation style with good predictive accuracy. ACO and SVR provide a useful tool for maximizing the combustion efficiency of boiler. Also, the method can be easily extended to other applications.