Sparsity constrained wavelet neural networks for robust soft sensor design with application to the industrial KIVCET unit

Abstract

The main objective of this paper is to develop a soft sensor for predictions of the residue element concentrations of feed mixture in a KIVCET furnace, since such measurements are not available online and are crucial in achieving the objective of real-time optimization. To realize this, a wavelet neural network is considered owing to its high accuracy in approximating functions with a few functional components over networks with sigmoid/tanh activation functions. The idea of wavelet decompositions is used to address the challenges associated with random initialization in such networks. A one-norm penalty on the data-fit term is considered to make the approach robust to outliers, and a sparsity constraint on the parameter vector in terms of zero-norm is considered to handle the issues of over-fitting and input redundancy. The efficiency of the proposed method is demonstrated through an industrial KIVCET unit and a simulation study.