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Pulp and paper characterization by means of artificial neural networks for effluent solid waste minimization—A case study
Journal of Process Control ( IF 4.2 ) Pub Date : 2021-09-03 , DOI: 10.1016/j.jprocont.2021.08.012
Daniele Almonti 1 , Gabriele Baiocco 1 , Nadia Ucciardello 1
Affiliation  

Paper mills are among the most polluting industries, responsible for many organic and inorganic compounds emissions. The fibres electro-kinetic features strongly affect the ability to retain fillers since the fillers–fibres interactions are charge induced. The control and the prediction of these parameters would represent a precious aid for process management, allowing the fillers retention enhancement, a lower environmental impact and the paper sheet properties streamlining. The work presented deals with the implementation and training of four artificial neural networks (ANNs) for the prediction of the main electrochemical and physical features of cellulose pulp and paper. First, two ANNs predict the electrochemical parameters. Following, they were applied to predict the paper sheet properties and fillers retention. The neural models implemented showed outstanding prediction performance, with R2 in the order of 0.999 and a low mean error. The results demonstrate how Artificial Neural Networks may be a valuable instrument for paper mill pollutant reduction. However, they suggest a more inclusive investigation for a better fibres behaviour representation.



中文翻译:

利用人工神经网络对纸浆和纸张进行表征以最大限度地减少固体废物排放——案例研究

造纸厂是污染最严重的行业之一,造成许多有机和无机化合物的排放。由于填料-纤维相互作用是电荷诱导的,因此纤维的电动特性强烈影响保留填料的能力。这些参数的控制和预测对于过程管理来说是一种宝贵的帮助,可以提高填料的留着率、降低对环境的影响和简化纸张性能。所介绍的工作涉及四个人工神经网络 (ANN) 的实施和训练,用于预测纤维素纸浆和纸张的主要电化学和物理特征。首先,两个人工神经网络预测电化学参数。随后,它们被用于预测纸张性能和填料保留率。2以 0.999 的数量级和低平均误差。结果表明人工神经网络如何成为造纸厂污染物减少的宝贵工具。然而,他们建议对更好的纤维行为表示进行更具包容性的调查。

更新日期:2021-09-03
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