Title: A novel distribution-free hybrid regression model for manufacturing process efficiency improvement,
Abstract: This work is motivated by a particular problem of a modern paper
manufacturing industry, in which maximum efficiency of the fiber-filler
recovery process is desired. A lot of unwanted materials along with valuable
fibers and fillers come out as a by-product of the paper manufacturing process
and mostly goes as waste. The job of an efficient Krofta supracell is to
separate the unwanted materials from the valuable ones so that fibers and
fillers can be collected from the waste materials and reused in the
manufacturing process. The efficiency of Krofta depends on several crucial
process parameters and monitoring them is a difficult proposition. To solve
this problem, we propose a novel hybridization of regression trees (RT) and
artificial neural networks (ANN), hybrid RT-ANN model, to solve the problem of
low recovery percentage of the supracell. This model is used to achieve the
goal of improving supracell efficiency, viz., gain in percentage recovery. In
addition, theoretical results for the universal consistency of the proposed
model are given with the optimal value of a vital model parameter. Experimental
findings show that the proposed hybrid RT-ANN model achieves higher accuracy in
predicting Krofta recovery percentage than other conventional regression models
for solving the Krofta efficiency problem. This work will help the paper
manufacturing company to become environmentally friendly with minimal
ecological damage and improved waste recovery.