Design and application of Soft Sensor using Ensemble Methods

Industries are faced with the choice of suitable process control policies to improve costs, quality and raw material consumption. In the paper pulp industry, it is important to estimate quickly the Chemical Oxygen Demand (COD), a parameter that is highly correlated to product quality. Soft Sensors (SSs) have been established as alternative to hardware sensors and laboratory measurements for monitoring and control purposes. However, in real setups it is often difficult to get sufficient data for SS development. This work proposes Ensemble Methods (EMs) as a way to improve the SS performance for small datasets. EMs use a set of models to obtain better prediction. Their success is usually attributed to the diversity. Bootstrap and noise injection are used to produce diverse models. Several combinations of EMs are compared. The SS is successfully applied to estimate COD in a pulp process.