Prediction of Moisture Content of Bergamot Fruit during Thin-Layer Drying Using Artificial Neural Networks

2011 ◽  
pp. 71-80 ◽  
Author(s):  
Mohammad Sharifi ◽  
Shahin Rafiee ◽  
Hojjat Ahmadi ◽  
Masoud Rezaee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Moisture Content ◽  
Thin Layer ◽  
Thin Layer Drying ◽  
Artificial Neural

Estimation of the moisture content of tropical soils using colour images and artificial neural networks

CATENA ◽  
2015 ◽  
Vol 135 ◽  
pp. 100-106 ◽  
Author(s):  
Sidney Sara Zanetti ◽  
Roberto Avelino Cecílio ◽  
Estevão Giacomin Alves ◽  
Vitor Heringer Silva ◽  
Elias Fernandes Sousa
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Moisture Content ◽  
Tropical Soils ◽  
Artificial Neural

Ann Modelling for the Analysis of the Green Moulding Sands Properties

2013 ◽  
Vol 58 (3) ◽  
pp. 961-963 ◽  
Author(s):  
J. Jakubski ◽  
P. Malinowski ◽  
St.M. Dobosz ◽  
G. Major-Gabrýs
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Quality Control ◽  
Artificial Neural Networks ◽  
Moisture Content ◽  
Environmental Humidity ◽  
Sand Temperature ◽  
Artificial Neural ◽  
Moulding Sand

Abstract Application of modern technological solutions, as well as the economic and ecological solutions, is for foundries one of the main aspects of the competitiveness on the market for castings. IT solutions can significantly support technological processes. This article presents neural networks with different structures that have been used to determine the moisture content of the moulding sand based on the moulding sand selected properties research results. Neural networks were built using Matlab software. Moulding sand properties chosen for quality control processes were selected based on wide previous results. For the proposed moulding sand properties, neural networks can be a useful tool for predicting moisture content. The structure of artificial neural network do not have a significant influence on the obtained results. In subsequent studies on the use of neural networks as an application to support the green moulding sand rebonding process, it must be determined how factors such as environmental humidity and moulding sand temperature will affect the accuracy of data obtained with the use of artificial neural networks.

Three-Dimensional Modeling of Spatial Soil Properties via Artificial Neural Networks

2000 ◽  
Vol 1709 (1) ◽  
pp. 50-59 ◽  
Author(s):  
Omar M. Itani ◽  
Yacoub M. Najjar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Moisture Content ◽  
Soil Properties ◽  
Three Dimensional ◽  
Network Models ◽  
Standard Penetration Test ◽  
Computational Method ◽  
Contour Maps ◽  
Artificial Neural

Geotechnical engineers recognize the variability of the geological materials they work with, including uncertainties associated with subsurface characterization tasks. These uncertainties include data scattering, such as real spatial variation in soil properties, or random testing errors. Systematic errors, as can occur in bias measurement procedures, are also common. In almost all construction projects, penetration tests play a major role in subsoil characterization. Interpretation of test results is mostly empirical, and it is therefore prudent to find a suitable computational method to minimize the error in predicting values at points away from actual test locations. In this research, the capabilities of artificial neural networks (ANNs) are assessed as a computational method for predicting standard penetration test (SPT) results at any point ( x, y, z) in a field where a set of SPTs is performed. SPT and moisture content data for five bore holes are used to train and test the developed three-dimensional network models. To graphically visualize the underlying soil strata, select contour maps of blows and moisture content values at various locations are presented. The results obtained indicate the viability and flexibility of ANN methodology as an efficient tool for site characterization tasks.

scholarly journals ARTIFICIAL NEURAL NETWORKS AS A NEW TOOL FOR ASSESSING AND MONITORING WOOD MOISTURE CONTENT

2016 ◽  
Vol 40 (3) ◽  
pp. 543-549
Author(s):  
Antônio José Vinha Zanuncio ◽  
Amélia Guimarães Carvalho ◽  
Liniker Fernandes da Silva ◽  
Angélica de Cássia Oliveira Carneiro ◽  
Jorge Luiz Colodette
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Moisture Content ◽  
Equilibrium Moisture Content ◽  
Basic Density ◽  
High Accuracy ◽  
Wood Moisture Content ◽  
Wood Moisture ◽  
The Neural Network ◽  
Artificial Neural

ABSTRACT Drying of wood is necessary for its use and moisture control is important during this process. The aim of this study was to use artificial neural networks to evaluate and monitor the wood moisture content during drying. Wood samples of 2 × 2 × 4 cm were taken at 1.3 m above the ground, outside of radial direction, from seven 2-year-old materials and three 7-year-old materials. These samples were saturated and drying was evaluated until the equilibrium moisture content, then, the artificial neural networks were created. The materials with higher initial moisture reached equilibrium moisture content faster due to its higher drying rate. The basic density of all wood materials was inversely proportional at the beginning and directly proportional to the moisture at the end of drying. All artificial neural networks used in this work showed high accuracy to estimate the moisture, however, the neural network based on the basic density and drying days was the best. Therefore, artificial neural networks can be used to control the moisture content of wood during drying.

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