scholarly journals Effect of temperature on the physical changes and drying kinetics in plum (Prunus domestica L.) Pozegaca variety

2011 ◽  
Vol 17 (3) ◽  
pp. 283-289 ◽  
Author(s):  
Milovan Zivkovic ◽  
Sveto Rakic ◽  
Radojka Maletic ◽  
Dragan Povrenovic ◽  
Milos Nikolic ◽  
...  
Keyword(s):  
Effective Diffusivity ◽  
Absolute Error ◽  
Drying Kinetics ◽  
Effect Of Temperature ◽  
Coefficient Of Determination ◽  
Maximum Absolute Error ◽  
Nonlinear Regression Models ◽  
Regression Error ◽  
Physical Changes ◽  
Model Correlation

In this study, drying kinetics of autochthonous variety Pozegaca plum was examined in a laboratory dryer at three temperatures. The whole plum fruits, together with the kernels were subjected to the drying process. The effect of drying has been examined at temperatures 55, 60 and 75?C, with a constant air velocity of 1.1 m s-1. The corresponding experimental results were tested using six nonlinear regression models. Coefficient of determination (R2), standard regression error (SSE), model correlation coeficient (Vy), as well as the maximum absolute error (?Y) showed that logaritmic model was in good agreement with the experimental data obtained. During drying of plums, the effective diffusivity was found to be between 5.6?10-9 for 55?C and 8.9?10-9 m2 s-1 at 75?C, respectively. The physical characteristics of the fresh (lenght 39.64 mm and width 29.15 mm) and dried (lenght 37.52 mm and width 22.85 mm) plum fruit were determined. Finally, by chemical analysis, the content of micro-and macro41 elements (Fe, Mn, Cu, B and N, F, K, Ca, Mg, S) in the skin and flesh of the dried product, prunes, has been established.

scholarly journals Drying kinetics of crushed mass of ‘jambu’: Effective diffusivity and activation energy

2018 ◽  
Vol 22 (7) ◽  
pp. 499-505 ◽  
Author(s):  
Francileni P. Gomes ◽  
Resende Osvaldo ◽  
Elisabete P. Sousa ◽  
Daneil E. C. de Oliveira ◽  
Francisco R. de Araújo Neto
Keyword(s):  
Experimental Data ◽  
Activation Energy ◽  
Agricultural Research ◽  
Effective Diffusion ◽  
Effective Diffusivity ◽  
Information Criterion ◽  
Drying Kinetics ◽  
Coefficient Of Determination ◽  
Chi Square ◽  
Chi Square Test

ABSTRACT The aim of this paper was to analyze the drying kinetics, test the Akaike information criterion (AIC) and Schwarz’s Bayesian information criterion (BIC) in the selection of models, determine the effective diffusivity and activation energy of the crushed mass of ‘jambu’ leaves for different conditions of temperature and layer thicknesses. The experiment was carried out at the Food Laboratory of the Brazilian Agricultural Research Corporation (Embrapa) in Macapá-AP. Drying was carried out in air circulation oven with speed of 1.0 m s-1 at various temperatures (60, 70 and 80 ºC) and layer thicknesses (0.005 and 0.010 m). The experimental data were fitted to 11 mathematical models. Coefficient of determination (R2), mean relative error (P), mean estimated error (SE), Chi-square test (χ2), AIC and BIC were the selection criteria for the models. For the effective diffusivity, the Fick’s diffusion model was used considering the flat plate geometry. It was found that Midilli and Logarithmic models showed the best fit to the experimental data of drying kinetics. Effective diffusion coefficient increases with increment in the thickness of the material and with the temperature elevation. Activation energy of the material was of 16.61 kJ mol-1 for the thickness of 0.005 m, and 16.97 kJ mol-1 for the thickness of 0.010 m. AIC and BIC can be additionally included to select models of drying.

Drying Kinetics, Desorption Isotherms and Apparent Diffusivity of Sage Leaves

2021 ◽  
Vol 406 ◽  
pp. 173-181
Author(s):  
Bensebia Ouahida
Keyword(s):  
Effective Diffusivity ◽  
Isosteric Heat ◽  
Moisture Diffusivity ◽  
Storage Conditions ◽  
Drying Kinetics ◽  
Effect Of Temperature ◽  
Medicinal And Aromatic Plants ◽  
Oven Drying ◽  
Kinetics Modelling ◽  
Desorption Isotherms

The use of fresh herb is limited in the food and pharmaceutical industry thats why the dry form of the herb is the one commonly used. To save the quality of medicinal and aromatic plants it is very important to provide optimum drying and storage conditions. The aim of this study is to determine and model the drying kinetics of sage leaves. Initially the desorption isotherms are determined for different temperatures (30, 45 and 60°C). The drying experiments were carried out in a convection oven at the same temperature range. For the desorption isotherms and the drying kinetics various models reported in the literature were used and from the statistical view, the Yanniotis and Blahovec and the Fick models fit well the results of desorption isotherms and the oven drying, respectively. The net isosteric heat of desorption of the sage leaves ranged from 6.86 to 63.45 kJ/mol. The total time of oven drying reduced substantially with an increase of the drying temperature. Effective moisture diffusivity values ranged from 1.1x10-12 to 3.7x10-12 m2/s and significantly affected by temperature. An Arrhenius relation with an activation energy value of 66.87 kJ/mol expressed effect of temperature on the diffusivity. Keywords: Desorption isotherms, Drying kinetics, Modelling, Effective diffusivity, Sage leaves

Validation of Adaptive Gaussian Process Regression Model Used for SIF Prediction

2018 ◽  
Author(s):  
Arvind Keprate ◽  
R. M. Chandima Ratnayake ◽  
Shankar Sankararaman
Keyword(s):  
Regression Model ◽  
Gaussian Process ◽  
Prediction Accuracy ◽  
Experimental Validation ◽  
Gaussian Process Regression ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Data Sets ◽  
Maximum Absolute Error ◽  
Data Points

The main aim of this paper is to perform the validation of the adaptive Gaussian process regression model (AGPRM) developed by the authors for the Stress Intensity Factor (SIF) prediction of a crack propagating in topside piping. For validation purposes, the values of SIF obtained from experiments available in the literature are used. Sixty-six data points (consisting of L, a, c and SIF values obtained by experiments) are used to train the AGPRM, while four independent data sets are used for validation purposes. The experimental validation of the AGPRM also consists of the comparison of the prediction accuracy of AGPRM and Finite Element Method (FEM) relative to the experimentally derived SIF values. Four metrics, namely, Root Mean Square Error (RMSE), Average Absolute Error (AAE), Maximum Absolute Error (MAE), and Coefficient of Determination (R2), are used to compare the accuracy. A case study illustrating the development and experimental validation of the AGPRM is presented. Results indicate that the prediction accuracy of the AGPRM is comparable with and even higher than that of the FEM, provided the training points of the AGPRM are aptly chosen.

scholarly journals Cost analysis of drying process by studying its kinetic parameters: A new study in Mexican chillies

2020 ◽  
Vol 38 (No. 6) ◽  
pp. 375-387
Author(s):  
José Carrera-Escobedo ◽  
Oscar Cruz-Domínguez ◽  
César Guzmán-Valdivia ◽  
Víctor Carrera-Escobedo ◽  
Mario García-Ruiz ◽  
...  
Keyword(s):  
Weibull Distribution ◽  
Kinetic Parameters ◽  
Effective Diffusivity ◽  
Drying Kinetics ◽  
New Method ◽  
Effect Of Temperature ◽  
Drying Process ◽  
Kinetics Parameters ◽  
Drying Time ◽  
The Cost

The drying process of vegetables is a widely used technique for food conservation. However, this process can be expensive, and the cost highly depends on the ventilation, drying temperature and drying characteristics of the chillies. The contribution of this new study was to obtain the drying kinetics parameters of two different types of Mexican Capsicum annuum (Puya and Mulato) and model it at different temperatures with two different ventilation levels. The aim of this study is to provide a method to analyse the cost of the drying process by studying its drying kinetics parameters. The experimental results were fitted to Weibull distribution and Newton’s model, obtaining an adequate numerical fit at different drying temperatures. The Weibull distribution demonstrates to be a better fit than Newton’s model. Drying kinetics parameters were also studied by a diffusive model with effective diffusivity. The effect of temperature on the diffusivity was described by the Arrhenius equation with activation energy of 49.7 kJ mol−1 for Puya and 24.1 kJ mol−1 for Mulato. The ventilation effect on chilli drying kinetics parameters was qualitatively assessed. As expected, the ventilation effect improved the drying rate and reduced the drying time, and consequently the cost of the drying process was reduced. In addition, a new method is presented to evaluate the cost of the drying process considering the kinetic parameters obtained. This new method allows evaluating the cost of the drying process in a simple way and with little experimental work. Consequently, it is possible to greatly reduce the cost of the drying process.

Comparing Different Metamodelling Approaches to Predict Stress Intensity Factor of a Semi-Elliptic Crack

2017 ◽  
Author(s):  
Arvind Keprate ◽  
R. M. Chandima Ratnayake ◽  
Shankar Sankararaman
Keyword(s):  
Stress Intensity Factor ◽  
Stress Intensity ◽  
Intensity Factor ◽  
Polynomial Regression ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Elliptic Crack ◽  
Maximum Absolute Error ◽  
Data Points

This paper examines the applicability of the different meta-models (MMs) to predict the Stress Intensity Factor (SIF) of a semi-elliptic crack propagating in topside piping, as an inexpensive alternative to the Finite Element Methods (FEM). Five different MMs, namely, multi-linear regression (MLR), second order polynomial regression (PR-2) (with interaction), Gaussian process regression (GPR), neural networks (NN) and support vector regression (SVR) have been tested. Seventy data points (SIF values obtained by FEM) are used to train the aforementioned MMs, while thirty data points are used as the testing points. In order to compare the accuracy of the MMs, four metrics, namely, Root Mean Square Error (RMSE), Average Absolute Error (AAE), Maximum Absolute Error (AAE), and Coefficient of Determination (R2) are used. Although PR-2 emerged as the best fit, GPR was selected as the best MM for SIF determination due to its capability of calculating the uncertainty related to the prediction values. The aforementioned uncertainty representation is quite valuable, as it is used to adaptively train the GPR model, which further improves its prediction accuracy.

scholarly journals Estimating Annual CO2Flux for Lutjewad Station Using Three Different Gap-Filling Techniques

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Carmelia M. Dragomir ◽  
Wim Klaassen ◽  
Mirela Voiculescu ◽  
Lucian P. Georgescu ◽  
Sander van der Laan
Keyword(s):  
Absolute Error ◽  
Empirical Method ◽  
Coefficient Of Determination ◽  
Ecosystem Change ◽  
Mean Square ◽  
Gap Filling ◽  
Maximum Absolute Error ◽  
The North ◽  
One Year

Long-term measurements of CO2flux can be obtained using the eddy covariance technique, but these datasets are affected by gaps which hinder the estimation of robust long-term means and annual ecosystem exchanges. We compare results obtained using three gap-fill techniques: multiple regression (MR), multiple imputation (MI), and artificial neural networks (ANNs), applied to a one-year dataset of hourly CO2flux measurements collected in Lutjewad, over a flat agriculture area near the Wadden Sea dike in the north of the Netherlands. The dataset was separated in two subsets: a learning and a validation set. The performances of gap-filling techniques were analysed by calculating statistical criteria: coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), maximum absolute error (MaxAE), and mean square bias (MSB). The gap-fill accuracy is seasonally dependent, with better results in cold seasons. The highest accuracy is obtained using ANN technique which is also less sensitive to environmental/seasonal conditions. We argue that filling gaps directly on measured CO2fluxes is more advantageous than the common method of filling gaps on calculated net ecosystem change, because ANN is an empirical method and smaller scatter is expected when gap filling is applied directly to measurements.

Quantitative Description of the Germination of Littleseed Canarygrass (Phalaris minor) in Response to Temperature

Weed Science ◽  
2014 ◽  
Vol 62 (2) ◽  
pp. 250-257 ◽  
Author(s):  
Abolfazl Derakhshan ◽  
Javid Gherekhloo ◽  
Ribas A. Vidal ◽  
Rafael De Prado
Keyword(s):  
Germination Rate ◽  
Quantitative Description ◽  
Absolute Error ◽  
Optimization Method ◽  
Coefficient Of Determination ◽  
Segmented Model ◽  
Nonlinear Regression Models ◽  
Time Required ◽  
Response To Temperature ◽  
Cardinal Temperatures

Littleseed canarygrass is a troublesome grass weed in wheat fields in Iran. Predicting weed emergence dynamics can help farmers more effectively control weeds. In this work, four nonlinear regression models (beta, three-piece segmented, two-piece segmented, and modified Malo's exponential sine) were compared to describe the cardinal temperatures for the germination of littleseed canarygrass. Two replicated experiments were performed with the same temperatures. An iterative optimization method was used to calibrate the models and different statistical indices (mean absolute error [MAE], coefficient of determination [R2], intercept and slope of the regression equation of predicted vs. observed hours to germination) were applied to compare their performance. The three-piece segmented model was the best model to predict the germination rate (R2= 0.99, MAE = 0.20 d, and coefficient of variation 1.01 to 4.06%). Based on the model outputs, the base, the lower optimum, the upper optimum, and the maximum temperatures for the germination of littleseed canarygrass were estimated to be 4.69, 22.60, 29.62, and 38.13 C, respectively. The thermal time required to reach 10, 50, and 90% germination was 31.98, 39.26 and 45.55 degree-days, respectively. The cardinal temperatures depended on the model used for their estimation. Overall, the three-piece segmented model was better suited than the other models to estimate the cardinal temperatures for the germination of littleseed canarygrass.

scholarly journals Drying kinetics of ‘gueroba’ (Syagrus oleracea) fruit pulp

2021 ◽  
Vol 25 (1) ◽  
pp. 23-29
Author(s):  
Ana P. P. Jorge ◽  
Weder N. Ferreira Junior ◽  
Lígia C. de M. Silva ◽  
Daniel E. C. de Oliveira ◽  
Osvaldo Resende
Keyword(s):  
Experimental Data ◽  
Activation Energy ◽  
Effective Diffusion ◽  
Drying Kinetics ◽  
Coefficient Of Determination ◽  
Chi Square ◽  
Adequate Model ◽  
Nonlinear Regression Models ◽  
Water Diffusivity ◽  
Chi Square Test

ABSTRACT The ‘Gueroba’ fruit can be used to produce flours with potential for the development of new products from the ‘Cerrado’ socio-biodiversity. The objective was to estimate the drying kinetics and determine the effective diffusion coefficient and activation energy for the pulp of ‘gueroba’ fruits subjected to different drying temperatures. ‘Gueroba’ fruits were manually pulped, removing the mesocarp with the epicarp, and this material was identified as the pulp. The material was subjected to oven drying at temperatures of 40, 50, 60 and 70 °C. Nonlinear regression models were fitted to the experimental data. The most adequate model was selected through the coefficient of determination, mean relative and estimated errors, Chi-square test, AIC and BIC. As the drying temperature increases, the processing time to achieve the same moisture content decreases, due to the increase in water diffusivity inside the product. The Midilli model showed the best fit to the experimental data obtained. The effective diffusion coefficients of the pulp of ‘gueroba’ fruits showed magnitudes between 3.11 x 10-9 to 5.84 x 10-9 m2 s-1 for temperatures from 40 to 70 °C. The activation energy of the process was 18.34 kJ mol-1.

scholarly journals An interpretable prediction method for university student academic crisis warning

2021 ◽  
Author(s):  
Zhai Mingyu ◽  
Wang Sutong ◽  
Wang Yanzhang ◽  
Wang Dujuan
Keyword(s):  
Academic Achievement ◽  
Visual Analysis ◽  
Prediction Method ◽  
Absolute Error ◽  
University Student ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Achievement Prediction ◽  
Academic Achievement Prediction ◽  
Education Administrators

AbstractData-driven techniques improve the quality of talent training comprehensively for university by discovering potential academic problems and proposing solutions. We propose an interpretable prediction method for university student academic crisis warning, which consists of K-prototype-based student portrait construction and Catboost–SHAP-based academic achievement prediction. The academic crisis warning experiment is carried out on desensitization multi-source student data of a university. The experimental results show that the proposed method has significant advantages over common machine learning algorithms. In terms of achievement prediction, mean square error (MSE) reaches 24.976, mean absolute error (MAE) reaches 3.551, coefficient of determination ($$R^{2}$$ R 2 ) reaches 80.3%. The student portrait and Catboost–SHAP method are used for visual analysis of the academic achievement factors, which provide intuitive decision support and guidance assistance for education administrators.

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