In case of complex and changeable working conditions

traditional multi-model soft-sensor techniques lacked an online update mechanism and decreased accuracy upon updating. A new soft-sensor method based on just-in-time algorithm (JIT) and multi-model online support regression (MOSVR) was proposed. In offline phase

fuzzy C-mean clustering (FCM) was employed to classify training data and SVR was used to build initial model set. In online phase

main output was multi-model SVR works

which would be switched to JIT model by online strategy of model updating (OSMU) and the current model set was updated online simultaneously when new working condition was encountered. The new method not only possessed rapidity and accuracy of multi-model outputs

but also guaranteed continuity

stability and accuracy of JIT outputs at model updating. Method effectiveness was demonstrated by numerical simulation and application in soft-sensor measurement of ethane concentration in ethylene production.