Ensemble just-in-time learning framework through evolutionary multi-objective optimization for soft sensor development of nonlinear industrial processes

Abstract Just-in-time learning (JIT) has recently gained growing popularity for soft sensor development of nonlinear processes. However, traditional JIT methods aim to pursue a globally optimal learning configuration while ignoring the diversity of JIT learning. Since strong JIT learners are desirable yet difficult to get while weak JIT learners are easy to obtain in real practice, a novel soft sensor modeling framework, referred to as evolutional multi-objective optimization (EMO) based ensemble JIT learning (EMO-EJIT), is proposed. Within this framework, the diversity of JIT learning is created by perturbing the weighted Euclidean distance (WED) based similarity measure. Then diverse and accurate JIT learners equipped with diverse WED similarity measures are generated using an EMO approach. Finally, a stacking strategy is used to achieve ensemble prediction. By employing the proposed modeling framework, EMO based ensemble locally weighted partial least squares (EMO-ELWPLS) and EMO based ensemble JIT extreme learning machine (EMO-EJITELM) soft sensors are developed. The application results from a simulated fed-batch penicillin fermentation process and an industrial fed-batch chlortetracycline fermentation process have demonstrated the effectiveness and superiority of the proposed method.