ANN-assisted forecasting of adsorption efficiency to remove heavy metals

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Date

2019

Authors

Balku, Şaziye
Özalp Yaman, Şeniz

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Publisher

Tubitak Scientific & Technological Research Council Turkey

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Organizational Unit
Energy Systems Engineering
(2009)
The Department of Energy Systems Engineering admitted its first students and started education in the academic year of 2009-2010 under Atılım University School of Engineering. In this Department, all kinds of energy are presented in modules (conventional energy, renewable energy, hydrogen energy, bio-energy, nuclear energy, energy planning and management) from their detection, production and procession; to their transfer and distribution. A need is to arise for a surge of energy systems engineers to ensure energy supply security and solve environmental issues as the most important problems of the fifty years to come. In addition, Energy Systems Engineering is becoming among the most important professions required in our country and worldwide, especially within the framework of the European Union harmonization process, and within the free market economy.
Organizational Unit
Chemical Engineering
(2010)
Established in 2010, and aiming to train the students with the capacity to meet the demands of the 21st Century, the Chemical Engineering Department provides a sound chemistry background through intense coursework and laboratory practices, along with fundamental courses such as Physics and Mathematics within the freshman and sophomore years, following preparatory English courses.In the final two years of the program, engineering courses are offered with laboratory practice and state-of-the-art simulation programs, combining theory with practice.

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Abstract

In wastewater treatment, scientific and practical models utilizing numerical computational techniques suchas artificial neural networks (ANNs) can significantly help to improve the process as a whole through adsorption systems.In the modeling of the adsorption efficiency for heavy metals from wastewater, some kinetic models have been used such as pseudo first-order and second-order. The present work develops an ANN model to forecast the adsorption efficiency of heavy metals such as zinc, nickel, and copper by extracting experimental data from three case studies. To do this, we apply trial-and-error to find the most ideal ANN settings, the efficiency of which is determined by mean square error (MSE) and coefficient of determination (R2). According to the results, the model can forecast adsorption efficiency percent (AE%) with a tangent sigmoid transfer function (tansig) in the hidden layer with 10 neurons and a linear transferfunction (purelin) in the output layer. Furthermore, the Levenberg–Marquardt algorithm is seen to be most ideal for training the algorithm for the case studies, with the lowest MSE and high R2 . In addition, the experimental results and the results predicted by the model with the ANN were found to be highly compatible with each other.

Description

Buaisha, Dr.Magdi/0000-0001-9879-968X; Ozalp Yaman, Seniz/0000-0002-4166-0529

Keywords

Kimya, Analitik, Kimya, Uygulamalı, Kimya, Organik, Kimya, Tıbbi, Mühendislik, Kimya, Kimya, İnorganik ve Nükleer

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Citation

0

WoS Q

Q4

Scopus Q

Q3

Source

Turkish Journal of Chemistry

Volume

43

Issue

5

Start Page

1407

End Page

1424