Least squares support vector machine (LS-SVM) based on just-in-time (JIT) learning algorithm was used to build prediction model of leaching rate

when considered multi-variable multi-mode nonlinear characteristics of hydrometallurgical leaching process. Time order was introduced into selection rule of JIT learning set for the determination of modeling neighborhood of current operating point

so as to improve modelling accuracy. A cumulative similarity factor was adopted to improve real-time performance of the model and an adaptive similarity threshold was used to determine necessity of updating local model of the current operating point. The simulation results for hydrometallurgical leaching process show that the improved modeling method has high precision and good real-time performance in leaching rate prediction

which can be used in hydrometallurgical industrial production.