According to the requirement of energy consumption information representation and model application in aluminum hydroxide gas suspension calcinations process, a kind of energy consumption estimation model was established based on Least Squares Support Vector Machine (LS-SVM) method. By combining the energy consumption model with Genetic Algorithm (GA), a kind of parameters optimization and industry application strategy was presented. Input parameters of energy estimation model were analyzed through grey relational analysis method, and the main factors of input parameters consisted of main furnace temperature, oxygen content of exhaust gas and containing water of aluminum hydroxide. The sampled data of energy consumption parameters were regrouped and optimized through K-fold cross-validation method. By comparing prediction accuracy of energy consumption models with various kernel functions, Radial Basis Function (RBF) kernel function was adopted to express feature information of sampling data. A model switcher whose inputs were energy parameters and output was symbol parameter of energy estimation model was constructed by Support Vector Machines (SVM) method. Self-learning and dynamic optimization processes of energy estimation model were realized by sample data updating and clustering. Model selection and application were realized by using the model switcher according to various calcinations conditions. The experimental results show that the LS-SVM modeling and application strategy can improve the generalization capability and conditions adaptability of energy estimation model. The presented strategy of model application is a feasible method for energy parameter analysis and estimation.
