scholarly journals Experimental Evaluation and Theoretical Optimization of an Indirect Solar Dryer with Forced Ventilation under Tropical Climate by an Inverse Artificial Neural Network

2021 ◽  
Vol 11 (16) ◽  
pp. 7616
Author(s):  
M. Moheno-Barrueta ◽  
O. May Tzuc ◽  
G. Martínez-Pereyra ◽  
V. Cardoso-Fernández ◽  
L. Rojas-Blanco ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Algorithms ◽  
Tropical Climate ◽  
Optimization Strategy ◽  
Drying Time ◽  
Solar Dryer ◽  
Artificial Neural ◽  
Metaheuristic Optimization Algorithms ◽  
Experimental Stage

In this theoretical–experimental study is presented a hybridization strategy based on the application of an inverse artificial neural network model (ANNi) coupled with metaheuristic optimization algorithms to optimize the drying velocity (vd) of an active indirect solar dryer for plantain and taro (Colocasia antiquorum). In the experimental stage, both fruits were evaluated in periods from 9:00 a.m. to 5:00 p.m. under a humid tropical climate region, varying the voltage of the air extractor fan (at 6 V, 9 V, and 12 V) to control the fan velocity. The experimental results showed that the maximum drying velocities were reached at 9 V, achieving a drying velocity of 1.5, 0.9, and 0.55 g/min, with a total drying time of 465 min for the taro, and 1.46, 1.46, and 0.36 g/min, with a total drying time of 495 min, for the plantain. As a result of the drying curves, it was observed that the drying velocity is higher in taro than in plantain. Subsequently, an artificial neural network (ANN) architecture was trained using the database generated from the solar dryer’s experimental stage. Six environmental variables and one operational variable were considered as the model’s inputs, feeding the ANN to estimate the drying velocity (vd), obtaining a linear regression coefficient R = 0.9822 between the experimental and simulated data. A sensitivity analysis was performed to determine the impact of all the input variables. A hybrid strategy based on ANNi was developed and evaluated with three metaheuristic optimization algorithms; the best result was obtained by genetic algorithms (ANNi-GA) with an error percentage of 0.83% and an average computational time of 4.3 s. The scope of this optimization strategy was to obtain a theoretical result that allows predicting the behavior of the dryer and improving its performance for its practical application in future work through the implementation in development boards. Lastly, the optimization strategy presented is not limited to indirect solar dryers and opens a research window for evaluating other solar drying technologies.

scholarly journals Modeling and Optimization for Konjac Vacuum Drying Based on Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1430
Author(s):  
Zhiheng Zeng ◽  
Ming Chen ◽  
Xiaoming Wang ◽  
Weibin Wu ◽  
Zefeng Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Vacuum Drying ◽  
Ann Model ◽  
Drying Process ◽  
Drying Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

To reveal quality change rules and establish the predicting model of konjac vacuum drying, a response surface methodology was adopted to optimize and analyze the vacuum drying process, while an artificial neural network (ANN) was applied to model the drying process and compare with the response surface methodology (RSM) model. The different material thickness (MT) of konjac samples (2, 4 and 6mm) were dehydrated at temperatures (DT) of 50, 60 and 70 °C with vacuum degrees (DV) of 0.04, 0.05 and 0.06 MPa, followed by Box–Behnken design. Dehydrated samples were analyzed for drying time (t), konjac glucomannan content (KGM) and whiteness index (WI). The results showed that the DT and MT should be, respectively, under 60 °C and 4 mm for quality and efficiency purposes. Optimal conditions were found to be: DT of 60.34 °C; DV of 0.06 MPa and MT of 2 mm, and the corresponding responses t, KGM and WI were 5 h, 61.96% and 82, respectively. Moreover, a 3-10-3 ANN model was established to compare with three second order polynomial models established by the RSM, the result showed that the RSM models were superior in predicting capacity (R2 > 0.928; MSE < 1.46; MAE < 1.04; RMSE < 1.21) than the ANN model. The main results may provide some theoretical and technical basis for the konjac vacuum drying and the designing of related equipment.

Artificial Neural Network Approach for Impingement Drying of Germinated Brown Rice Soaking with Turmeric Solution

2013 ◽  
Vol 372 ◽  
pp. 463-466
Author(s):  
Kiattisak Suntaro ◽  
Khwanruedi Sangchum ◽  
Supawan Tirawanichakul ◽  
Yutthana Tirawanichakul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Content ◽  
Brown Rice ◽  
Measured Data ◽  
Drying Kinetics ◽  
Drying Time ◽  
Rice Samples ◽  
Artificial Neural ◽  
Drying Conditions

The objectives of this research are to determine the evolution of moisture transfer for germinated Thai jasmine Khao Dawk Mali 105 (KDML105) brown rice variety using impingement drying by eight commonly empirical drying modeling and artificial neural network (ANN) method. The experiments were carried out with drying temperatures of 80-100°C, initial moisture content of KDML105 rice samples soaking with turmeric solution was of 54-55% dry-basis and the desired final moisture content for each drying conditions was fixed at 14-16% dry-basis. The air flow rate was fixed at 7.0 m/s. The measured data in each drying conditions were simulated for getting drying equation by non-linear regression analysis. The results showed that the rice soaking with herb turmeric solution had no effect to drying kinetics and the simulated data using empirical drying equation of Henderson model had the best fitting to all measured data (R2of 0.9978-0.9995 and RMSE of 0.0001441-0.000414). For applying ANN modeling approach, the drying temperature and drying time were considered as the input variables for the topology of neural network while the moisture ratio was the output layer. The simulation results concluded that the simulated values of the ANN model, which was not concerned with any complicated physical properties of grain rice kernels, could be used for prediction drying kinetics and was relatively high accuracy compared to those predicted results of empirical models. So the ANN method without any complicated properties related of rice samples can approach for good prediction their drying kinetics as well as the complicated drying simulations method.

Estimation of Moisture Ratio of a Mushroom Undergoing Microwave-vacuum Drying Using Artificial Neural Network and Regression Models

2007 ◽  
Vol 2 (3) ◽  
Author(s):  
Poonpat Poonnoy ◽  
Ampawan Tansakul ◽  
Manjeet Chinnan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Ratio ◽  
Vacuum Pressure ◽  
Ann Model ◽  
Drying Time ◽  
Feed Forward ◽  
Marquardt Algorithm ◽  
Ann Models ◽  
Artificial Neural

The drying rate of a mushroom undergoing microwave-vacuum (MV) drying (MVD) was controlled by moisture dissipation and was dependent on vacuum pressure levels. The main objective of this work was to develop artificial neural network (ANN) model to predict moisture ratio of MV-dried mushrooms. One-hidden-layer feed-forward ANN models were trained and validated with experimental data. The Levenberg-Marquardt algorithm was utilized in regulating the ANN model weights and biases. Inputs for ANN models were vacuum pressure and drying time. Output from ANN models was moisture ratio at a given drying time. Reduced chi-square (X 2) and root mean square error (RMSE), and residual sum of squares (RSS) of the results from ANN models were calculated and compared with those of a modified Page's model (an experimental-based mathematical model), which is commonly used in the literature. The X 2, RMSE, and RSS of the ANN model (2.272 x 10 -5, 4.023 x 10 -3, and 3.204 x 10 -3, respectively) were found to be lower than those of the modified Page's model (6.692 x 10 -4, 2.561 x 10 -2, and 12.98 x 10 -2, respectively). These results indicate that the feed-forward ANN model represented the drying characteristics of mushrooms better than the modified Page's model. Therefore, the ANN model could be considered as a better tool for estimation of the moisture content of mushrooms than by the modified Page's model.

Neuromodeling and Natural Optimization of Nonlinear Devices and Circuits

2011 ◽  
pp. 326-348 ◽  
Author(s):  
Paulo H. da F. Silva ◽  
Rossana M. S. Cruz ◽  
Adaildo G. D’Assunção
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Electromagnetic Modeling ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Nonlinear Devices

This chapter describes some/new artificial neural network (ANN) neuromodeling techniques and natural optimization algorithms for electromagnetic modeling and optimization of nonlinear devices and circuits. Neuromodeling techniques presented are based on single hidden layer feedforward neural network configurations, which are trained by the resilient back-propagation algorithm to solve the modeling learning tasks associated with device or circuit under analysis. Modular configurations of these feedforward networks and optimal neural networks are also presented considering new activation functions for artificial neurons. In addition, some natural optimization algorithms are described, such as continuous genetic algorithm (GA), a proposed improved-GA and particle swarm optimization (PSO). These natural optimization algorithms are blended with multilayer perceptrons (MLP) artificial neural network models for fast and accurate resolution of optimization problems. Some examples of applications are presented and include nonlinear RF/microwave devices and circuits, such as transistors, filters and antennas.

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