scholarly journals Artificial neural network model of pork meat cubes osmotic dehydratation

2013 ◽  
Vol 67 (3) ◽  
pp. 465-475 ◽  
Author(s):  
Lato Pezo ◽  
Biljana Curcic ◽  
Vladimir Filipovic ◽  
Milica Nicetin ◽  
Gordana Koprivica ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Osmotic Dehydration ◽  
Coefficient Of Determination ◽  
Dry Matter Content ◽  
Pork Meat ◽  
Triceps Brachii ◽  
Solid Gain ◽  
Wide Range ◽  
Artificial Neural

Mass transfer of pork meat cubes (M. triceps brachii), shaped as 1x1x1 cm, during osmotic dehydration (OD) and under atmospheric pressure was investigated in this paper. The effects of different parameters, such as concentration of sugar beet molasses (60-80%, w/w), temperature (20-50?C), and immersion time (1-5 h) in terms of water loss (WL), solid gain (SG), final dry matter content (DM), and water activity (aw), were investigated using experimental results. Five artificial neural network (ANN) models were developed for the prediction of WL, SG, DM, and aw in OD of pork meat cubes. These models were able to predict process outputs with coefficient of determination, r2, of 0.990 for SG, 0.985 for WL, 0.986 for aw, and 0.992 for DM compared to experimental measurements. The wide range of processing variables considered for the formulation of these models, and their easy implementation in a spreadsheet calculus make it very useful and practical for process design and control.

scholarly journals The Effect of a New Coating on the Drying Performance of Fruit and Vegetables Products: Experimental Investigation and Artificial Neural Network Modeling

Foods ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 308
Author(s):  
S. M. Atiqure Rahman ◽  
Ahmed M. Nassef ◽  
Mujahed Al-Dhaifallah ◽  
Mohammad Ali Abdelkareem ◽  
Hegazy Rezk
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Loss ◽  
Alginic Acid ◽  
Osmotic Dehydration ◽  
Neural Network Modeling ◽  
Polygalacturonic Acid ◽  
Coating Materials ◽  
Solid Gain ◽  
Artificial Neural

A study on mass transfer using new coating materials (namely alginic acid and polygalacturonic acid) during osmotic dehydration—and hence in a laboratory-scale convective dryer to evaluate drying performance—was carried out. Potato and apple samples were examined as model heat-sensitive products in this study. Results indicate that the coating material containing both alginic acid and polygalacturonic acid causes higher water loss of about 17% and 7.5% and lower solid gain of about 4% and 8%, respectively, compared to uncoated potato sample after a typical 90 min osmotic dehydration process. Investigation of drying performance using both coating materials showed a higher reduction in the moisture content of about 22% and 18%, respectively, compared with uncoated samples after the 3 h drying period. Comparisons between the two proposed coating materials were also carried out. Samples (potato) coated with alginic acid demonstrated better performance in terms of higher water loss (WL), lower solid gain (SG), and notable enhancement of drying performance of about 7.5%, 8%, and 8%, respectively, compared to polygalacturonic acid. Similar outcomes were observed using apple samples. Additionally, an accurate model of the drying process based on the experimental dataset was created using an artificial neural network (ANN). The obtained mean square errors (MSEs) for the predicted water loss and solid gain outputs of the potato model were 4.0948e−5 and 3.924e−6, respectively. However, these values for the same parameters were 3.164e−5 and 4.4915e−6 for the apple model. The coefficient of determination (r2) values for the two outputs of the potato model were found to be 0.99969 and 0.99895, respectively, while they were 0.99982 and 0.99913 for the apple model, which reinforces the modeling phase.

scholarly journals Osmotic dehydration of pork meat cubes: Response surface method analysis

2013 ◽  
pp. 11-19 ◽  
Author(s):  
Biljana Curcic ◽  
Lato Pezo ◽  
Ljubinko Levic ◽  
Violeta Knezevic ◽  
Milica Nicetin ◽  
...  
Keyword(s):  
Response Surface ◽  
Osmotic Dehydration ◽  
Process Variable ◽  
Solution Concentration ◽  
Matter Content ◽  
Solution Temperature ◽  
Dry Matter Content ◽  
Pork Meat ◽  
Triceps Brachii ◽  
Solid Gain

The main objective was to examine the influence of different osmotic parameters on the mass transfer kinetics during osmotic treatment of pork meat (M. triceps brachii). The system?s response parameters observed were: water loss (WL), solid gain (SG), final dry matter content (DM) and water activity (aw). The optimum osmotic parameters seem to be: osmotic time of 4 h, molasses solution concentration of 72% and solution temperature of 45oC. These conditions were determined using Response Surface Methodology (RSM), and by superimposing the contour plots of each process variable. The predicted responses for the optimum drying conditions were: DM of 64.5%, WL in the close vicinity of 0.53, SG about 0.15 and aw in the range of 0.83 to 0.84.

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Artificial Neural Network and the Taguchi Method Application for Robust Wheatstone Bridge Design

1994 ◽  
Author(s):  
Jung-eui Hong ◽  
Cihan H. Dagli ◽  
Kenneth M. Ragsdell
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wheatstone Bridge ◽  
Parameter Design ◽  
System Response ◽  
Measurement Circuit ◽  
Wide Range ◽  
Reduce Measurement Error ◽  
On Line ◽  
Artificial Neural

Abstract The primary function of the Wheatstone bridge is to measure an unknown resistance. The elements of this well-known measurement circuit will take on different values depending upon the range and accuracy required for a particular application. The Taguchi approach to parameter design is used to select values for the measurement circuit elements so as to reduce measurement error. Next we introduce the use of an artificial neural network to extrapolate limited experimental results to predict system response over a wide range of applications. This approach can be employed for on-line quality control of the manufacture of such device.

Predicting the ground vibration induced by mine blasting using imperialist competitive algorithm

2018 ◽  
Vol 35 (4) ◽  
pp. 1774-1787 ◽  
Author(s):  
Katayoun Behzadafshar ◽  
Fahimeh Mohebbi ◽  
Mehran Soltani Tehrani ◽  
Mahdi Hasanipanah ◽  
Omid Tabrizi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ground Vibration ◽  
Imperialist Competitive Algorithm ◽  
Empirical Models ◽  
Coefficient Of Determination ◽  
Content Type ◽  
Competitive Algorithm ◽  
Spacing Ratio ◽  
Artificial Neural

PurposeThe purpose of this paper is to propose three imperialist competitive algorithm (ICA)-based models for predicting the blast-induced ground vibrations in Shur River dam region, Iran.Design/methodology/approachFor this aim, 76 data sets were used to establish the ICA-linear, ICA-power and ICA-quadratic models. For comparison aims, artificial neural network and empirical models were also developed. Burden to spacing ratio, distance between shot points and installed seismograph, stemming, powder factor and max charge per delay were used as the models’ input, and the peak particle velocity (PPV) parameter was used as the models’ output.FindingsAfter modeling, the various statistical evaluation criteria such as coefficient of determination (R2) were applied to choose the most precise model in predicting the PPV. The results indicate the ICA-based models proposed in the present study were more acceptable and reliable than the artificial neural network and empirical models. Moreover, ICA linear model with theR2 of 0.939 was the most precise model for predicting the PPV in the present study.Originality/valueIn the present paper, the authors have proposed three novel prediction methods based on ICA to predict the PPV. In the next step, we compared the performance of the proposed ICA-based models with the artificial neural network and empirical models. The results indicated that the ICA-based models proposed in the present paper were superior in terms of high accuracy and have the capacity to generalize.

Burst Pressure Prediction of Cylindrical Vessels Using Artificial Neural Network

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Abolfazl Zolfaghari ◽  
Moein Izadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pressure Vessel ◽  
Pressure Vessels ◽  
Machine Learning Techniques ◽  
Burst Pressure ◽  
Outer Diameter ◽  
Wide Range ◽  
Artificial Neural ◽  
Predictor Model

Abstract Pressure vessel plays an important role in wide range of applications to store gas or liquid substances. In order to design a pressure vessel safely, one of the main factors which has to be considered is selection of proper burst pressure perdition criterion. Due to large range of available materials in manufacturing of the vessels under different working conditions, several criteria to forecast burst pressure of the vessels have been developed and used by designers. Choosing the most proper criterion based on working condition and the material is a vital task to meet design requirements because inappropriate criterion may lead to unsafe vessel or over design. This issue makes not only pressure vessel design more complex but also maintenance planning, especially for designers who do not have enough experience, is a challenging task. Therefore, lack of a burst pressure predictor model, which is able to determine the pressure more accurately for wide range of materials and applications, has been remained unsolved. To evaluate machine learning techniques in prediction of burst pressure of pressure vessels, in this paper, a new model based on artificial neural network (ANN) has been proposed and developed. Input parameters of the model include internal and outer diameter, thickness, ultimate and yield strength; output is burst pressure. The obtained results showed that the constructed model has a good potential to be used as more applicable model compared to current models in design of pressure vessels.

CFD-based blade shape optimization of MGT-70(3)axial flow compressor

2019 ◽  
Vol 30 (6) ◽  
pp. 3307-3321 ◽  
Author(s):  
Mohammad Reza Pakatchian ◽  
Hossein Saeidi ◽  
Alireza Ziamolki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shape Optimization ◽  
Fluid Dynamic ◽  
Axial Compressor ◽  
Axial Flow ◽  
Design Parameters ◽  
Content Type ◽  
Wide Range ◽  
Artificial Neural

Purpose This study aims at enhancing the performance of a 16-stage axial compressor and improving the operating stability. The adopted approaches for upgrading the compressor are artificial neural network, optimization algorithms and computational fluid dynamics. Design/methodology/approach The process starts with developing several data sets for certain 2D sections by means of training several artificial neural networks (ANNs) as surrogate models. Afterward, the trained ANNs are applied to the 3D shape optimization along with parametrization of the blade stacking line. Specifying the significant design parameters, a wide range of geometrical variations are considered by implementation of appropriate number of design variables. The optimized shapes are analyzed by applying computational fluid dynamic to obtain the best geometry. Findings 3D optimal results show improvements, especially in the case of decreasing or elimination of near walls corner separations. In addition, in comparison with the base geometry, numerical optimization shows an increase of 1.15 per cent in total isentropic efficiency in the first four stages, which results in 0.6 per cent improvement for the whole compressor, even while keeping the rest of the stages unchanged. To evaluate the numerical results, experimental data are compared with obtained data from simulation. Based on the results, the highest absolute relative deviation between experimental and numerical static pressure is approximately 7.5 per cent. Originality/value The blades geometry of an axial compressor used in a heavy-duty gas turbine is optimized by applying artificial neural network, and the results are compared with the base geometry numerically and experimentally.

Application of Artificial Neural Network Over Nickel-Based Catalyst for Combined Steam-Carbon Dioxide of Methane Reforming (CSDRM)

2020 ◽  
Vol 20 (9) ◽  
pp. 5716-5719 ◽  
Author(s):  
Cho Hwe Kim ◽  
Young Chul Kim
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Coefficient Of Determination ◽  
Feed Ratio ◽  
Squared Error ◽  
Highly Nonlinear ◽  
Feed Forward Network ◽  
Artificial Neural

The application of artificial neural network (ANN) for modeling, combined steam-carbon dioxide reforming of methane over nickel-based catalysts, was investigated. The artificial neural network model consisted of a 3-layer feed forward network, with hyperbolic tangent function. The number of hidden neurons is optimized by minimization of mean square error and maximization of R2 (R square, coefficient of determination) and set of 8 neurons. With feed ratio, flow rate, and temperature as independent variables, methane, carbon dioxide conversion, and H2/CO ratio, were measured using artificial neural network. Coefficient of determination (R2) values of 0.9997, 0.9962, and 0.9985 obtained, and MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and MAPE (Mean Absolute Percentage Error) showed low value. This study indicates ANN can successfully model a highly nonlinear process and function.

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