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Pulp and paper characterization by means of artificial neural networks for effluent solid waste minimization—A case study
- Publication Year :
- 2021
- Publisher :
- ELSEVIER SCI LTD, 2021.
-
Abstract
- 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.
- Subjects :
- Artificial neural network
Optimization
Municipal solid waste
Computer science
engineering.material
Industrial and Manufacturing Engineering
Process engineering
Representation (mathematics)
Effluent
Pollutant
business.industry
Pulp (paper)
Paper mill
Zeta potential
Settore ING-IND/16
Computer Science Applications
Charge demand
Manufacturing
Sustainability
Control and Systems Engineering
Modeling and Simulation
engineering
Minification
business
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....fe83c09c66073c6bf06b98fd665a90d6