Adaptive on-line prediction soft sensing without historical data

Current soft sensing algorithms assume the availability of a large amount of training data. The collection of the historical data often takes a lot of time and can be expensive. At the same time not being able to provide sufficient amount of training data can result in sacrificing the performance of the soft sensor. This can be problematic in situations, where a soft sensor is urgently required and, at the same time, there is not enough training data available. This situation can occur, for example, when a new plant is taken into operation or, more critically, when there is a significant change in some parameters (e.g. operating point or the input materials) in a running plant. To deal with such a situation, we propose an algorithm, called Recursive Soft Sensing Algorithm (ReSSA), which delivers predictions without any explicit training phase. The proposed algorithm is based on the recursive functionality of the RPLS technique, which is embedded into local learning framework. More than that, during the run-time of the algorithm, it is not necessary to store any past data as the algorithm requires only the latest data point for its operation and recursive adaptation. In order to demonstrate the performance of the proposed method, it is applied to the prediction of a catalyst activity in a multi-tube reactor.