Applying Artificial Neural Networks in the Modelling of Copper Recovery Process by Ionic Exchange in Aqueous Solutions

2008 ◽  
Vol 59 (7) ◽  
Author(s):  
Leonard Mihaly Cozmuta ◽  
Camelia Varga ◽  
Monica Marian ◽  
Anca Mihaly Cozmuta ◽  
Tutu Hlanganani
Keyword(s):  
Mathematical Modeling ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Aqueous Solutions ◽  
Exchange Process ◽  
Theoretical Data ◽  
Copper Recovery ◽  
Ionic Exchange ◽  
Artificial Neural

Artificial neuronal networks are widely used in different fields such as finance, medicine, engineering, geology, chemistry, physics in order to predict, classify and control the development of different processes. The paper presents experimental data on the adsorption process of copper from aqueous solutions and compares it with theoretical data obtained by mathematical modeling using artificial neural networks (ANN). The aim of the paper is to demonstrate that ANN ensure a high accuracy in the mathematical modeling of the process. We have collected experimental data by using synthetic solutions with different pH and cooper ions concentrations, which were retained on a PUROLITE S930 cationic resin. Both experimental and theoretical data obtained using ANN show the correlation between factors which influence the ionic exchange process (pH, temperature, initial copper concentration, activation energy).

Technique of scientific and technical forecasting using neural networks

2018 ◽  
pp. 103-110
Author(s):  
V. V. Nefedev
Keyword(s):  
Mathematical Modeling ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Original Method ◽  
Forecasting Methods ◽  
Expert Assessments ◽  
Artificial Neural

For the definition and implementation of breakthrough technologies the most important is the role of scientific and technical forecasting. Well-known forecasting methods based on extrapolation, expert assessments and mathematical modeling are not universal and have a number of significant disadvantages. The article proposes an original method of scientific and technical forecasting based on the use of the methodology of artificial neural networks. 

scholarly journals Identifying nonclassicality from experimental data using artificial neural networks

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Valentin Gebhart ◽  
Martin Bohmann ◽  
Karsten Weiher ◽  
Nicola Biagi ◽  
Alessandro Zavatta ◽  
...  
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural

scholarly journals Artificial Neural Networks Application in Modal Analysis of Tires

2013 ◽  
Vol 13 (5) ◽  
pp. 273-278 ◽  
Author(s):  
P. Koštial ◽  
Z. Jančíková ◽  
D. Bakošová ◽  
J. Valíček ◽  
M. Harničárová ◽  
...  
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Modal Analysis ◽  
Speckle Pattern ◽  
Point Of View ◽  
Electronic Speckle Pattern Interferometry ◽  
Data Sets ◽  
Artificial Neural

Abstract The paper deals with the application of artificial neural networks (ANN) to tires’ own frequency (OF) prediction depending on a tire construction. Experimental data of OF were obtained by electronic speckle pattern interferometry (ESPI). A very good conformity of both experimental and predicted data sets is presented here. The presented ANN method applied to ESPI experimental data can effectively help designers to optimize dimensions of tires from the point of view of their noise.

scholarly journals Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

2017 ◽  
Vol 43 (4) ◽  
pp. 26-32 ◽  
Author(s):  
Sinan Mehmet Turp
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Data Sets ◽  
Adsorption Efficiency ◽  
Back Propagation Algorithm ◽  
Artificial Neural

AbstractThis study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

Ultraviolet-activated persulfate oxidation of methyl orange: a comparison between artificial neural networks and factorial design for process modelling

2015 ◽  
Vol 14 (3) ◽  
pp. 528-535 ◽  
Author(s):  
Zacharias Frontistis ◽  
Evroula Hapeshi ◽  
Despo Fatta-Kassinos ◽  
Dionissios Mantzavinos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Methyl Orange ◽  
Aqueous Solutions ◽  
Factorial Design ◽  
Azo Dye ◽  
Process Modelling ◽  
Persulfate Oxidation ◽  
Artificial Neural ◽  
Activated Persulfate

In this work, the degradation of the azo dye methyl orange in model aqueous solutions by UVC light-induced persulfate oxidation was studied.

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