Artificial neural Network−Genetic algorithm modeling for moisture content prediction of savory leaves drying process in different drying conditions

2018 ◽  
Vol 11 (4) ◽  
pp. 232-238 ◽  
Author(s):  
Amin Taheri-Garavand ◽  
Venkatesh Meda ◽  
Leila Naderloo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Moisture Content ◽  
Drying Process ◽  
Artificial Neural ◽  
Drying Conditions

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.

scholarly journals Application of Artificial Neural Network for Prediction of Key Indexes of Corn Industrial Drying by Considering the Ambient Conditions

2020 ◽  
Vol 10 (16) ◽  
pp. 5659 ◽  
Author(s):  
Bin Li ◽  
Chengjie Li ◽  
Junying Huang ◽  
Changyou Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Exchanger ◽  
Moisture Content ◽  
Radiant Energy ◽  
Prediction Performance ◽  
Drying Process ◽  
Ambient Conditions ◽  
Drying System ◽  
Artificial Neural

Uncontrollable ambient conditions are the main factors limiting the self-adaption control of an industrial drying system. To achieve the goal of accurate control of the drying process, the influence of the ambient conditions on the drying behavior should be taken into consideration when modeling the drying process. Present work introduced an industrial drying system with a loading capacity of 50 t, two artificial neural network prediction models with (IANN) and without (OANN) considering the ambient conditions were established using artificial neural network modeling approach. The ambient conditions on the moisture content (MC), exergy efficiency of the heat exchanger (ηex,h) and specific recovered radiant energy (Er) of the drying process were also investigated. The results showed that the ηex,h and Er increase with the increase of ambient temperature while the drying time decrease with the increase of the ambient temperature. The IANN model has a better prediction performance that that of OANN model. An optimal architecture of 9-2-12-3 artificial neuron network model was developed and the best prediction performance of the artificial neural network (ANN) model were found at a training epoch number of 30, and a momentum coefficient of 0.4, where the coefficient of determination of moisture content, exergy efficiency of heat exchanger, and the specific recovered radiant energy, respectively are 0.998, 0.992, and 0.980, indicating that the model has an excellent prediction performance and can be used in engineering practice.

Modelling and analysis of compressive strength properties of parboiled paddy and milled rice

2014 ◽  
Vol 28 (1) ◽  
pp. 73-83 ◽  
Author(s):  
Abozar Nasirahmadi ◽  
Mohammad H. Abbaspour-Fard ◽  
Bagher Emadi ◽  
Nasser Behroozi Khazaei
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Moisture Content ◽  
Mean Squared Error ◽  
Strength Properties ◽  
Milled Rice ◽  
Artificial Neural ◽  
Hidden Layer

Abstract The present investigation deals with analyzing the compressive strength properties of two varieties (Tarom and Fajr) of parboiled paddy and milled rice including: ultimate stress, modulus of elasticity, rupture force and rupture energy. Combined artificial neural network and genetic algorithm were also applied to model these properties. The parboiled samples were prepared with three soaking temperatures (25, 50 and 75°C) and three steaming times (10, 15 and 20 min). The samples were then dried to final moisture contents of 8, 10 and 12% (w.b.). In general, Tarom variety had higher compressive strength properties for paddy and milled rice than Fajr variety. With increase in steaming time from 10 to 20 min, all mentioned properties increased significantly, whereas these properties were decreased with increasing moisture content from 8 to 12% (w.b.). Coupled artificial neural network and genetic algorithm model with one hidden layer, three inputs (soaking temperature, steaming time and moisture content), was developed to predict the compressive strength properties as model outputs. Results indicated that this model could predict these properties with high correlation and low mean squared error.

scholarly journals Drying Klunzinger's mullet fish Planiliza klunzingeri using Halogen Dryer and modeling the moisture content with artificial neural network

2020 ◽  
Vol 33 (1) ◽  
pp. 231-261
Author(s):  
Hassan H. Al-Rubaiy ◽  
, Sabah M. Al-Shatty ◽  
Asaad R. Al-Hilphy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chemical Composition ◽  
Moisture Content ◽  
Ph Value ◽  
Ann Model ◽  
Drying Process ◽  
Drying Time ◽  
Dried Fish ◽  
Artificial Neural

Salted and unsalted Klunzinger's mullet Planiliza klunzingeri were dried using infrared halogen dryer with different temperatures (60, 65, 70, 75 and 80)°C and  different storage periods (0, 7, 14, 21, 28 and 35) days and studying their qualitative characteristics. The results showed that the moisture content decreased as drying time increased. The drying efficiency of the halogen dryer was 70.36 % at 60 °C and decreased as the drying temperature increased. Chemical composition of dried fish (salted and unsalted) showed that the moisture percentage was decreased, but the percentage of protein, fat and ash was increased after drying process. The percentage of moisture increased during the storage periods (0, 7, 14, 21, 28 and 35) days, unlike the other chemical composition percentages were decreased with increasing storage periods. The results showed that the rehydration was decreased with the increase of drying temperatures for salted and unsalted dried fish. Moreover, the results showed that there was an increase in TBA after the drying process and during the storage periods. In addition, the results revealed that the microbial content of dried salted and unsalted fish was decreased. The results illustrated that the first order model can be used to predict pH value during storage periods. Artificial neural network   (ANN) model had a good result of predicted moisture content.

Sign in / Sign up

Export Citation Format

Share Document