Traditional support vector regression(SVR) algorithm based on batch training can’t satisfy the real-time
requirement of online modeling for leaching process. Therefore, with the analysis of a support vector regression algorithm
based on the vector base learning, an online modeling method for leaching process is proposed. Bayesian evidence framework
is used to optimize the model parameters. By calculating the angle between the new sample vector and the vector space, the
criteria for determining whether the measurement vector is one of the BVS is derived. The method is adopted in the prediction
of leaching rate, and tested by experiment. The experiment result shows that the proposed method can track the trend of the
leaching rate, and reduce the operation time effectively.