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Artificial Neural Network Approach for Modelling of Mercury Ions Removal from Water Using Functionalized CNTs with Deep Eutectic Solvent

التفاصيل البيبلوغرافية
العنوان: Artificial Neural Network Approach for Modelling of Mercury Ions Removal from Water Using Functionalized CNTs with Deep Eutectic Solvent
المؤلفون: Seef Saadi Fiyadh, Mohamed Khalid AlOmar, Wan Zurina Binti Jaafar, Mohammed Abdulhakim AlSaadi, Sabah Saadi Fayaed, Suhana Binti Koting, Sai Hin Lai, Ming Fai Chow, Ali Najah Ahmed, Ahmed El-Shafie
المصدر: International Journal of Molecular Sciences, Vol 20, Iss 17, p 4206 (2019)
بيانات النشر: MDPI AG, 2019.
سنة النشر: 2019
المجموعة: LCC:Biology (General)
LCC:Chemistry
مصطلحات موضوعية: adsorption, environmental modelling, mercury ions removal, deep eutectic solvents, carbon nanotubes, artificial neural network, Biology (General), QH301-705.5, Chemistry, QD1-999
الوصف: Multi-walled carbon nanotubes (CNTs) functionalized with a deep eutectic solvent (DES) were utilized to remove mercury ions from water. An artificial neural network (ANN) technique was used for modelling the functionalized CNTs adsorption capacity. The amount of adsorbent dosage, contact time, mercury ions concentration and pH were varied, and the effect of parameters on the functionalized CNT adsorption capacity is observed. The (NARX) network, (FFNN) network and layer recurrent (LR) neural network were used. The model performance was compared using different indicators, including the root mean square error (RMSE), relative root mean square error (RRMSE), mean absolute percentage error (MAPE), mean square error (MSE), correlation coefficient (R2) and relative error (RE). Three kinetic models were applied to the experimental and predicted data; the pseudo second-order model was the best at describing the data. The maximum RE, R2 and MSE were 9.79%, 0.9701 and 1.15 × 10−3, respectively, for the NARX model; 15.02%, 0.9304 and 2.2 × 10−3 for the LR model; and 16.4%, 0.9313 and 2.27 × 10−3 for the FFNN model. The NARX model accurately predicted the adsorption capacity with better performance than the FFNN and LR models.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1422-0067
Relation: https://www.mdpi.com/1422-0067/20/17/4206; https://doaj.org/toc/1422-0067
DOI: 10.3390/ijms20174206
URL الوصول: https://doaj.org/article/7653af146b2548eab29fc2c48a4f2308
رقم الأكسشن: edsdoj.7653af146b2548eab29fc2c48a4f2308
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:14220067
DOI:10.3390/ijms20174206