Use of Artificial Neural Networks for Estimating Water Content of Natural Gases

2007 ◽  
Vol 46 (4) ◽  
pp. 1431-1438 ◽  
Author(s):  
Amir H. Mohammadi ◽  
Dominique Richon
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Content ◽  
Natural Gases ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Networks (ANN) Coupled with Near-InfraRed(NIR) Spectroscopy for Detection of Adulteration in Honey

2019 ◽  
Vol 3 ◽  
Author(s):  
Lucija Longin ◽  
Ana Jurinjak Tusek ◽  
Davor Valinger ◽  
Maja Benkovic ◽  
Tamara Jurina ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Content ◽  
Near Infrared ◽  
Technical Problem ◽  
Training Data ◽  
Corn Syrup ◽  
Halogen Light ◽  
Artificial Neural ◽  
Model Training

Honey is a naturally sweet and viscous product for which the addition of any substance is prohibited by international regulation. Detection of adulteration in honey is a technical problem: adulteration of honey with invert sugar and syrup may not be reliably detected by direct sugar analysis because its constituents are identical to the major natural components of the honey. Therefore, it is important to develop a rapid and reliable analytical method to detect such additions. We used near-infrared spectroscopy (NIR) combined with principle component analysis (PCA) and artificial neural networks (ANN) modelling to discriminate between honey and corn syrup in adulterated honey. Fifteen honey samples from north-west Croatia (Krapina-Zagorje County) were intentionally supplemented with differing proportions of corn syrup ranging from 10-90%. We collected a total of 460 NIR spectra using the Control Development NIR128L-1.7 spectrophotometer (Control Development, South Bend, Indiana, USA) with their software Spec32 software anda HL-2000 halogen light source. For each of the prepared samples, we measured water content by refractometer (Brouwland, Belgium), conductivity byconductometer (SevenCompact, MettlerToledo, Switzerland), and colour using a PCE-CSM3 colorimeter (PCE Instruments, Germany). Prior to ANN modelling, PCA was used to identify patterns and highlight similarities and differences in data of the individual set of the experiment. The goal of PCA is to extract important information from the data table and to express this information as a set of new orthogonal variables called principal components or factors (PCs or Fs). We conducted PCA of raw spectra using the Unscrambler® X 10.4 software (CAMO software, Norway). Data were divided into ANN model training, test, and validation datasets at a 70:15:15 ratio using the first five PCs. ANNs were calibrated using model training data, and evaluated using model test and model validation datasets for their ability to predict: i) the amount of added adultering substance in honey, ii) water content, iii) conductivity and iv) colour of the adulterated honey. Multiple layer perception (MLP) networks were developed in Statistica v.10.0 software (StatSoft, Tulsa, USA). Back error propagation algorithm available in Statistica v.10.0 was applied for the model training. Model performance was evaluated using R2 and root mean squared error (RMSE) values for model training, test, and validation datasets. Results show that network MLP 5-8-6 with five neurons in the input layer, 8 neurons in the hidden layer and 6 neurons in the output layer predicts the analysed output variables with high precision (R2validation,concentration = 0.995, R2validation,water content = 0.993, R2validation,conductivity = 0.992, R2validation,L = 0.939, R2validation,a = 0.895, R2validation,b = 0.924).

scholarly journals Application of Artificial Neural Networks in Modelling The Contact Area of Grain Seeds

2016 ◽  
Vol 20 (4) ◽  
pp. 27-37
Author(s):  
Jarosław Frączek ◽  
Sławomir Francik ◽  
Zbigniew Ślipek ◽  
Adrian Knapczyk
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Content ◽  
Contact Area ◽  
Neural Model ◽  
Dry Mass ◽  
Compression Force ◽  
Pressure Force ◽  
Empirical Formulas ◽  
Artificial Neural

Abstract The objective of the research was to create a model which defines the relation between a fundamental contact area of a seed and the pressure force, water content in a seed and its geometrical dimensions with application of artificial neural networks (SSN). Computer program Statistica Neural Networks v. 6.0. was used for formation of a neural model. Tests were carried out on Roma wheat seed and Dańkowskie Złote rye with six various water contents: 0.11 0.15 0.19 0.23 0.28 0.33 (kg·kg-1 dry mass). Caryopses were loaded with eight values of compression force - from 41 N to 230 N. Multiplicity of iterations was 5. Seed material was moistened to obtain a specific water content. Each seed was loaded with compression force with respectively growing values: 41N, 68N, 95N, 122N, 149N, 176N, 203N and 230N. A four-layer network of Perceptron type with 10 neurons in the first and 8 neurons in the second hidden layer was selected as a model which the best defines the contact area of grain seeds loaded with axial force at various moisture levels. This network has 4 inputs (water content, pressure force, thickness and length of caryopses) and one output (elementary contact area of rye and wheat seeds). Comparison of the neural model with empirical formulas obtained from nonlinear estimation proved a considerable higher precision of the first one.

Predicting the Dielectric Constant-Water Content Relationship Using Artificial Neural Networks

2002 ◽  
Vol 66 (5) ◽  
pp. 1424-1429 ◽  
Author(s):  
Magnus Persson ◽  
Bellie Sivakumar ◽  
Ronny Berndtsson ◽  
Ole H. Jacobsen ◽  
Per Schjønning
Keyword(s):  
Neural Networks ◽  
Dielectric Constant ◽  
Artificial Neural Networks ◽  
Water Content ◽  
Artificial Neural

Estimation of soil water content in watershed using artificial neural networks

2017 ◽  
Vol 62 (13) ◽  
pp. 2120-2138 ◽  
Author(s):  
Marquis Henrique Campos de Oliveira ◽  
Vanessa Sari ◽  
Nilza Maria dos Reis Castro ◽  
Olavo Correa Pedrollo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Artificial Neural
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