Development of Predictive Models for Activated Carbon Synthesis from Different Biomass for CO2 Adsorption Using Artificial Neural Networks

2021 ◽  
Vol 60 (38) ◽  
pp. 13950-13966
Author(s):  
Hossein Mashhadimoslem ◽  
Milad Vafaeinia ◽  
Mobin Safarzadeh ◽  
Ahad Ghaemi ◽  
Farnoush Fathalian ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Activated Carbon ◽  
Predictive Models ◽  
Co2 Adsorption ◽  
Artificial Neural

Künstliche neuronale Netze für das Feinschneiden*/Artificial neural networks for fine blanking - A methodology for the development of predictive models for fine blanking

2017 ◽  
Vol 107 (10) ◽  
pp. 719-724
Author(s):  
F. Prof. Klocke ◽  
J. Stanke ◽  
D. Trauth ◽  
P. Mattfeld ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Models ◽  
Neuronale Netze ◽  
Fine Blanking ◽  
Current Trends ◽  
New Challenges ◽  
Artificial Neural ◽  
Künstliche Neuronale Netze ◽  
Blanking Process

Höhere Anforderungen an die Produktivität und aktuelle Trends stellen den Feinschneidprozess vor neue Herausforderungen. Infolgedessen ist eine optimierte Prozessführung notwendig. Ein Modell, welches großes Potenzial für eine Optimierung der Prozessführung bietet, sind künstliche neuronale Netze. Dieser Fachbeitrag stellt eine Methodik zur Nutzung von künstlichen neuronalen Netzen für die Optimierung des Feinschneidprozesses vor.   Higher demands on productivity and current trends pose new challenges to the fine blanking process. They require an optimization of the process realization. One model that offers great potential for the optimization of the process realization are artificial neural networks. This article provides a methodology for the use of artificial neural networks for the optimization of the fine blanking process.

Prediction of short and medium term PM10 concentration using artificial neural networks

2019 ◽  
Vol 30 (2) ◽  
pp. 414-436 ◽  
Author(s):  
Elaine Schornobay-Lui ◽  
Eduardo Carlos Alexandrina ◽  
Mônica Lopes Aguiar ◽  
Werner Siegfried Hanisch ◽  
Edinalda Moreira Corrêa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Models ◽  
Urban Areas ◽  
Meteorological Data ◽  
High Volume ◽  
Data Series ◽  
Content Type ◽  
Artificial Neural ◽  
Narx Model

Purpose There has been a growing concern about air quality because in recent years, industrial and vehicle emissions have resulted in unsatisfactory human health conditions. There is an urgent need for the measurements and estimations of particulate pollutants levels, especially in urban areas. As a contribution to this issue, the purpose of this paper is to use data from measured concentrations of particulate matter and meteorological conditions for the predictions of PM10. Design/methodology/approach The procedure included daily data collection of current PM10 concentrations for the city of São Carlos-SP, Brazil. These data series enabled to use an estimator based on artificial neural networks. Data sets were collected using the high-volume sampler equipment (VFA-MP10) in the period ranging from 1997 to 2006 and from 2014 to 2015. The predictive models were created using statistics from meteorological data. The models were developed using two neural network architectures, namely, perceptron multilayer (MLP) and non-linear autoregressive exogenous (NARX) inputs network. Findings It was observed that, over time, there was a decrease in the PM10 concentration rates. This is due to the implementation of more strict environmental laws and the development of less polluting technologies. The model NARX that used as input layer the climatic variables and the PM10 of the previous day presented the highest average absolute error. However, the NARX model presented the fastest convergence compared with the MLP network. Originality/value The presentation of a given PM10 concentration of the previous day improved the performance of the predictive models. This paper brings contributions with the NARX model applications.

scholarly journals Development of Simple-to-Use Predictive Models to Determine Thermal Properties of Fe2O3/Water-Ethylene Glycol Nanofluid

Computation ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 18 ◽  
Author(s):  
Mohammad Hossein Ahmadi ◽  
Ali Ghahremannezhad ◽  
Kwok-Wing Chau ◽  
Parinaz Seifaddini ◽  
Mohammad Ramezannezhad ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Thermophysical Properties ◽  
Predictive Models ◽  
Dynamic Viscosity ◽  
Least Square ◽  
Support Vector ◽  
Mass Ratio ◽  
Artificial Neural

Thermophysical properties of nanofluids play a key role in their heat transfer capability and can be significantly affected by several factors, such as temperature and concentration of nanoparticles. Developing practical and simple-to-use predictive models to accurately determine these properties can be advantageous when numerous dependent variables are involved in controlling the thermal behavior of nanofluids. Artificial neural networks are reliable approaches which recently have gained increasing prominence and are widely used in different applications for predicting and modeling various systems. In the present study, two novel approaches, Genetic Algorithm-Least Square Support Vector Machine (GA-LSSVM) and Particle Swarm Optimization- artificial neural networks (PSO-ANN), are applied to model the thermal conductivity and dynamic viscosity of Fe2O3/EG-water by considering concentration, temperature, and the mass ratio of EG/water as the input variables. Obtained results from the models indicate that GA-LSSVM approach is more accurate in predicting the thermophysical properties. The maximum relative deviation by applying GA-LSSVM was found to be approximately ±5% for the thermal conductivity and dynamic viscosity of the nanofluid. In addition, it was observed that the mass ratio of EG/water has the most significant impact on these properties.

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