In this article, we propose a novel hybridization of regression trees (RTs) and radial basis function networks, namely, radial basis neural tree model, for waste recovery process (WRP) improvement in a 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 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 to perform prediction task to improve WRP efficiency. An idea of parameter optimization along with regularity conditions for the universal consistency of the proposed model is 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.

中文翻译：

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径向基神经树模型，用于改善造纸行业的废物回收过程

在本文中，我们提出了一种新型的回归树（RTs）与径向基函数网络的混合方法，即径向基神经树模型，用于造纸行业的废物回收过程（WRP）改进。作为造纸过程的副产品，造纸机会产生大量废物以及有价值的纤维和填料。WRP涉及将不需要的材料与有价值的材料分离，以便回收的纤维和填料可以在生产过程中进一步重复使用。这项工作由纤维填料回收设备（FFRE）完成。FFRE的效率取决于几个关键的工艺参数，而对其进行监控是一个困难的命题。所提出的模型可用于从可用数据集中找到基本参数，并执行预测任务以提高WRP效率。给出了参数优化以及针对所提出模型的通用一致性的规则性条件的想法。当应用于FFRE效率改进问题时，所提出的模型具有易于解释和性能优良的优点。改进的废物回收率不仅可以提高成本效益，还可以帮助该行业变得对环境友好，对生态的损害也较小。