Radial basis neural tree model for improving waste recovery process in a paper industry

Abstract

In this article, we propose a novel hybridization of regression trees (RT) and radial basis function networks (RBFN), namely, radial basis neural tree (RBNT) model, for waste recovery process improvement in the paper industry. As a by-product of the paper manufacturing process, a lot of waste along with valuable fibers and fillers come out from the paper machine. The waste recovery process (WRP) involves separating the unwanted materials from the valuable ones so that the recovered fibers and fillers can be further reused in the production process. This job is done by fiber-filler recovery equipment (FFRE). The efficiency of FFRE depends on several crucial process parameters and monitoring them is a difficult proposition. The proposed model can be useful to find the essential parameters from the set of available data and perform prediction task to improve waste recovery process efficiency. An idea of parameter optimization along with regularity conditions for the universal consistency of the proposed model are given. The proposed model has the advantages of easy interpretability and excellent performance when applied to the FFRE efficiency improvement problem. Improved waste recovery will help the industry to become environmentally friendly with less ecological damage apart from being cost-effective.