An Artificial Neural Network (ANN) Modelling Approach for Evaluating Turbidity Properties of Paper Recycling Wastewater
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
North Carolina State Univ Dept Wood & Paper Sci
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
A pre-treatment process was evaluated in this work for wastewater from precipitation of contaminants through centrifugation. Artificial neural networks (ANNs) were used to analyze and optimize the turbidity values. Thirty experimental runs were utilized including microwave (MW) power, duration, centrifuge time, and centrifuge speed as input variables, generated by the Central Composite Full Design (CCFD) approach. The experimental turbidity ranged from 8.1 to 19.7 NTU, while predicted values ranged from 8.4 to 19.7 NTU by ANN. The ANN model showed a robust prediction performance with low mean squared error values during training and testing. Moreover, high R2 values showed a remarkable agreement between the experimental observations and ANN predictions. The results obtained from the input values (A:150.00, B:60.00, C:15.00, D:30.00) of sample 2, which gave the lowest turbidity value, showed the most removal of pollution. The results obtained from the input values (A:250.00, B:60.00, C:7.00, D:20.00) of sample 30, which gave the highest turbidity value, showed the least removal of pollution. The results showed that increasing RPM and time of the centrifugation process significantly affected the removal of pollution in wastewater.
Açıklama
Anahtar Kelimeler
Paper recycling, Wastewater, Turbidity, Artificial neural network, Microwave technology
Kaynak
Bioresources
WoS Q Değeri
Q2
Scopus Q Değeri
Q3
Cilt
19
Sayı
3
