Artificial neural network employment for element determination in Mugil cephalus by ICP OES in Pontal Bay, Brazil

2020 ◽  
Vol 12 (29) ◽  
pp. 3713-3721
Author(s):  
Milana Aboboreira Simões Batista ◽  
Luana Novaes Santos ◽  
Bruna Cirineu Chagas ◽  
Ivon Pinheiro Lôbo ◽  
Cleber Galvão Novaes ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sample Preparation ◽  
Chemical Elements ◽  
Mixture Design ◽  
Mugil Cephalus ◽  
Self Organizing Map ◽  
Icp Oes ◽  
Element Determination ◽  
Artificial Neural

Mixture design applied to sample preparation of Mugil cephalus and exploratory evaluation of the concentrations of chemical elements using Kohonen Self-Organizing Map (KSOM) combined with Artificial Neural Network (ANNs).

scholarly journals Model for Predicting the Machinability of Continuously Cast and Subsequently Rolled Steel Using the Artificial Neural Network

2021 ◽  
Vol 15 (3) ◽  
pp. 381-386
Author(s):  
Miha Kovačič ◽  
Shpetim Salihu ◽  
Uroš Župerl
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
Chemical Elements ◽  
Cast Steel ◽  
Model Development ◽  
Cutting Speed ◽  
Steel Production ◽  
Continuously Cast ◽  
Artificial Neural

The paper presents a model for predicting the machinability of steels using the method of artificial neural networks. The model includes all indicators from the entire steel production process that best predict the machinability of continuously cast steel. Data for model development were obtained from two years of serial production of 26 steel grades from 255 batches and include seven parameters from secondary metallurgy, four parameters from the casting process, and the content of ten chemical elements. The machinability was determined based on ISO 3685, which defines the machinability of a batch as the cutting speed with a cutting tool life of 15 minutes. An artificial neural network is used to predict this cutting speed. Based on the modelling results, the steel production process was optimised. Over a 5-month period, an additional 39 batches of 20MnV6 steel were produced to verify the developed model.

scholarly journals Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network

Nanomaterials ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 697 ◽  
Author(s):  
Milad Sadeghzadeh ◽  
Heydar Maddah ◽  
Mohammad Hossein Ahmadi ◽  
Amirhosein Khadang ◽  
Mahyar Ghazvini ◽  
...  
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Sol Gel ◽  
Back Propagation ◽  
Thermal Conductivity Ratio ◽  
Self Organizing Map ◽  
Artificial Neural ◽  
The Heat Transfer Coefficient

In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable.

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