PREDICTION OF QUALITY CHANGES DURING OSMO-CONVECTIVE DRYING OF BLUEBERRIES USING NEURAL NETWORK MODELS FOR PROCESS OPTIMIZATION

Artificial neural network (ANN) models were used for predicting quality changes during osmo-convective drying of blueberries for process optimization. Osmotic drying usually involves treatment of fruits in an osmotic solution of predetermined concentration, temperature and time, and generally affects several associated quality factors such as color, texture, rehydration ratio as well as the finish drying time in a subsequent drier (usually air drying). Multi-layer neural network models with 3 inputs (concentration, osmotic temperature and contact time) were developed to predict 5 outputs: air drying time, color, texture, and rehydration ratio as well as a defined comprehensive index. The optimal configuration of neural network model was obtained by varying the main parameters of ANN: transfer function, learning rule, number of neurons and layers, and learning runs. The predictability of ANN models was compared with that of multiple regression models, confirming that ANN models had much better performance than conventional mathematical models. The prediction matrices and corresponding response curves for main processing properties under various osmotic dehydration conditions were used for searching the optimal processing conditions. The results indicated that it is feasible to use ANN for prediction and optimization of osmo-convective drying for blueberries.     

An Artificial Neural Network Model for Prediction of Drying Rates

Abstract

Drying rate data were generated for training of an ANN model using a liquid diffusion model for potato slices of different thicknesses using air at different velocities, humidities and temperatures. Moisture content and temperature dependence of the liquid diffusivity as well as the heat of wetting for bound moisture were included in the diffusion model making it a highly nonlinear system. An ANN model was developed for rapid prediction of the drying rates using the Page equation fitted to the drying rate curves. The ANN model is verified to provide accurate interpolation of the drying rates and times within the ranges of parameters investigated.     

PREDICTION OF QUALITY CHANGES DURING OSMO-CONVECTIVE DRYING OF BLUEBERRIES USING NEURAL NETWORK MODELS FOR PROCESS OPTIMIZATION

Artificial neural network (ANN) models were used for predicting quality changes during osmo-convective drying of blueberries for process optimization. Osmotic drying usually involves treatment of fruits in an osmotic solution of predetermined concentration, temperature and time, and generally affects several associated quality factors such as color, texture, rehydration ratio as well as the finish drying time in a subsequent drier (usually air drying). Multi-layer neural network models with 3 inputs (concentration, osmotic temperature and contact time) were developed to predict 5 outputs: air drying time, color, texture, and rehydration ratio as well as a defined comprehensive index. The optimal configuration of neural network model was obtained by varying the main parameters of ANN: transfer function, learning rule, number of neurons and layers, and learning runs. The predictability of ANN models was compared with that of multiple regression models, confirming that ANN models had much better performance than conventional mathematical models. The prediction matrices and corresponding response curves for main processing properties under various osmotic dehydration conditions were used for searching the optimal processing conditions. The results indicated that it is feasible to use ANN for prediction and optimization of osmo-convective drying for blueberries.     

Modelling of A Fluidized Bed Drier Using Artificial Neural Network

ABSTRACT

Proper modelling of a fluidized bed drier (FBD) is important to design model based control strategies. A FBD is a non-linear multivariable system with non-minimum phase characteristics. Due to the complexities in FBD conventional modelling techniques are cumbersome. Artificial neural network (ANN) with its inherent ability to “learn” and “absorb” non-linearities, presents itself as a convenient tool for modelling such systems.

In this work, an ANN model for continuous drying FBD is presented. A three layer fully connected feedfordward network with three inputs and two outputs is used. Backpropagation learning algorithm is employed to train the network. The training data is obtained from computer simulation of a FBD model from published literature. The trained network is evaluated using randomly generated data as input and observed to predict the behaviour of FBD adequately.     

Optimization of an Artificial Neural Network Topology for Predicting Drying Kinetics of Carrot Cubes Using Combined Response Surface and Genetic Algorithm

In this study, the advantages of integrated response surface methodology (RSM) and genetic algorithm (GA) for optimizing artificial neural network (ANN) topology of convective drying kinetic of carrot cubes were investigated. A multilayer feed-forward ANN trained by back-propagation algorithms was developed to correlate output (moisture ratio) to the four exogenous input variables (drying time, drying air temperature, air velocity, and cube size). A predictive response surface model for ANN topologies was created using RSM. The response surface model was interfaced with an effective GA to find the optimum topology of ANN. The factors considered for building a relationship of ANN topology were the number of neurons, momentum coefficient, step size, number of training epochs, and number of training runs. A second-order polynomial model was developed from training results for mean square error (MSE) of 50 developed ANNs to generate 3D response surfaces and contour plots. The optimum ANN had minimum MSE when the number of neurons, step size, momentum coefficient, number of epochs, and number of training runs were 23, 0.37, 0.68, 2,482, and 2, respectively. The results confirmed that the optimal ANN topology was more precise for predicting convective drying kinetics of carrot cubes.     

An Investigation of Drying Process of Shelled Pistachios in a Newly Designed Fixed Bed Dryer System by Using Artificial Neural Network

In this paper, the drying of Siirt pistachios (SSPs) in a newly designed fixed bed dryer system and the prediction of drying characteristics using artificial neural network (ANN) are presented. Drying characteristics of SSPs with initial moisture content (MC) of 42.3% dry basis (db) were studied at different air temperatures (60, 80, and 100 °C) and air velocities (0.065, 0.1, and 0.13 m/s) in a newly designed fixed bed dryer system. Obtained results of experiments were used for ANN modeling and compared with experimental data. Falling rate period was observed during all the drying experiments; constant rate period was not observed. Furthermore, in the presented study, the application of ANN for predicting the drying time (DT) for a good quality product (output parameter for ANN modeling) was investigated. In order to train the ANN, experimental measurements were used as training data and test data. The back propagation learning algorithm with two different variants, so-called Levenberg–Marguardt (LM) and scaled conjugate gradient (SCG), and tangent sigmoid transfer function were used in the network so that the best approach can be determined. The most suitable algorithm and neuron number in the hidden layer are found out as LM with 15 neurons. For this number level, after the training, it is found that Root-mean squared (RMS) value is 0.3692, and absolute fraction of variance (R²) value is 99.99%. It is concluded that ANNs can be used for prediction of drying SSPs as an accurate method in similar systems.     

DYNAMIC AND HYBRID NEURAL MODEL OF THERMAL DRYING IN A FLUTDIZED BED

ABSTRACT

A preliminary study aimed at comparing Classical Dynamic Neural Modelling (CDNM) and Hybrid Neural Modelling (HNM) to describe thermal dewatering process in a fluidized bed is presented Two schemes of HN modelling were developed to find the most efficient way of combining a classical mathematical model of the process and Artificial Neural Network (ANN). CDN model was developed using “moving window” technique. In the first scheme of HNM a feed-forward ANN was trained to predict evaporation rate and heat flux in the drying process. In the second scheme of the HN model, ANN was used to determine heat transfer coefficient only. Excellent prediction of drying process by HNM is proved.     

Continuous real-time monitoring and neural network modeling of apple slices color changes during hot air drying

A hot air drying system equipped with real-time computer vision system was used to investigate the effects of drying variables on apple slices color changes. Drying experiments were conducted at drying air temperatures of 50–70 °C, drying air velocities of 1–2 m/s, and samples thicknesses of 2–6 mm. A multilayer perceptron (MLP) artificial neural network (ANN) was also used to correlate color parameters and moisture content of apple slices with drying variables and drying time. The effects of drying air temperature and sample thickness on color changes were dominated over the effect of drying air velocity. However, non-linear and somewhat complex trends were obtained for all color parameters as function of moisture content. The MLP ANN satisfactorily approximated the color and moisture variations of apple slices with correlation coefficient higher than 0.92. Therefore, the computer vision system supplemented with ANN can be used as a non-invasive, low cost, and easy method for fast and in-line assessing and controlling of foodstuffs color and moisture changes during drying.     

Prediction of Timber Kiln Drying Rates by Neural Networks

The purpose of this exploratory work was to apply artificial neural network (ANN) modeling to the prediction of timber kiln drying rates based on species and basic density information for the hem-fir mix that grows along the local coastal areas. The ANN models with three inputs (initial moisture content, basic density, and drying time) were developed to predict one output, namely, average final moisture content. The back-propagation algorithm, the most common neural network learning method, was implemented for testing, training, and validation. Optimal configuration of the network model was obtained by varying its main parameters, such as transfer function, learning rule, number of neurons and layers, and learning runs. Accurate prediction of the experimental drying rate data by the ANN model was achieved with a mean absolute relative error less than 2%, thus supporting the powerful predictive capacity of this modeling method.     

Prediction of Timber Kiln Drying Rates by Neural Networks

The purpose of this exploratory work was to apply artificial neural network (ANN) modeling to the prediction of timber kiln drying rates based on species and basic density information for the hem-fir mix that grows along the local coastal areas. The ANN models with three inputs (initial moisture content, basic density, and drying time) were developed to predict one output, namely, average final moisture content. The back-propagation algorithm, the most common neural network learning method, was implemented for testing, training, and validation. Optimal configuration of the network model was obtained by varying its main parameters, such as transfer function, learning rule, number of neurons and layers, and learning runs. Accurate prediction of the experimental drying rate data by the ANN model was achieved with a mean absolute relative error less than 2%, thus supporting the powerful predictive capacity of this modeling method.     

ARTIFICIAL NEURAL NETWORKS: PRINCIPLE AND APPLICATION TO MODEL BASED CONTROL OF DRYING SYSTEMS - A REVIEW

Abstract

This paper reviews the developments in the model based control of drying systems using Artificial Neural Networks (ANNs). Survey of current research works reveals the growing interest in the application of ANN in modeling and control of non-linear, dynamic and time-variant systems. Over 115 articles published in this area are reviewed. All landmark papers are systematically classified in chronological order, in three distinct categories; namely, conventional feedback controllers, model based controllers using conventional methods and model based controllers using ANN for drying process. The principles of ANN are presented in detail. The problems and issues of the drying system and the features of various ANN models are dealt with upto-date. ANN based controllers lead to smoother controller outputs, which would increase actuator life. The paper concludes with suggestions for improving the existing modeling techniques as applied to predicting the performance characteristics of dryers. The hybridization techniques, namely, neural with fuzzy logic and genetic algorithms, presented, provide, directions for pursuing further research for the implementation of appropriate control strategies. The authors opine that the information presented here would be highly beneficial for pursuing research in modeling and control of drying process using ANN     

Experimental and theoretical investigation of shelled corn drying in a microwave-assisted fluidized bed dryer using Artificial Neural Network

Drying characteristics of shelled corn (Zea mays L) with an initial moisture content of 26% dry basis (db) was studied in a fluidized bed dryer assisted by microwave heating. Four air temperatures (30, 40, 50 and 60 °C) and five microwave powers (180, 360, 540, 720 and 900 W) were studied. Several experiments were conducted to obtain data for sample moisture content versus drying time. The results showed that increasing the drying air temperature resulted in up to 5% decrease in drying time while in the microwave-assisted fluidized bed system, the drying time decreased dramatically up to 50% at a given and corresponding drying air temperature at each microwave energy level. As a result, addition of microwave energy to the fluidized bed drying is recommended to enhance the drying rate of shelled corn. Furthermore, in the present study, the application of Artificial Neural Network (ANN) for predicting the drying time (output parameter for ANN modeling) was investigated. Microwave power, drying air temperature and grain moisture content were considered as input parameters for the model. An ANN model with 170 neurons was selected for studying the influence of transfer functions and training algorithms. The results revealed that a network with the Tansig (hyperbolic tangent sigmoid) transfer function and trainrp (Resilient back propagation) back propagation algorithm made the most accurate predictions for the shelled corn drying system. The effects of uncertainties in output experimental data and ANN prediction values on root mean square error (RMSE) were studied by introducing small random errors within a range of ±5%.     

Color prediction of mushroom slices during drying using Bayesian extreme learning machine

Abstract

Color is an important appearance attribute of fruits and vegetables during drying processing, as it influences consumer’s preference and acceptability. Establishing color change kinetics model is an effective way for better understanding the quality changes and optimization of drying process. However, it is difficult to quickly and accurately predict color change kinetics during drying as it is highly nonlinear, complex, dynamic, and multivariable. To alleviate this problem, a new model based on extreme learning machine integrated Bayesian methods (BELM) has been developed for the prediction of color changes of mushroom slices during drying process. The effects of drying temperature (55, 60, 65, 70, and 75?°C) and air velocity (3, 6, 9, and 12?m/s) on color change kinetics of mushroom slices during hot air impingement drying were firstly explored and the experimental results indicated that both drying temperature and air velocity significantly affected the color attributes. Then, to validate the robustness and effectiveness of BELM, the basic extreme learning machine (ELM) and traditional back-propagation neural network (BPNN) models have also been employed to predict the color quality. In terms of prediction accuracy and execution time, BELM could achieve least similar or even better performance than ELM and BPNN. It overcame the overfitting problems of ELM. The test results of optimal BELM model by two new cases revealed that the lowest R² and highest RMSE of BELM model were 0.9725 and 0.0563, respectively. The absolute values of relative errors between the actual and predicted values were lower than 8.5%.     

Artificial neural network-based modeling of pressure drop coefficient for cyclone separators

In order to build the complex relationships between cyclone pressure drop coefficient (PDC) and geometrical dimensions, representative artificial neural networks (ANNs), including back propagation neural network (BPNN), radial basic functions neural network (RBFNN) and generalized regression neural network (GRNN), are developed and employed to model PDC for cyclone separators. The optimal parameters for ANNs are configured by a dynamically optimized search technique with cross-validation. According to predicted accuracy of PDC, performance of configured ANN models is compared and evaluated. It is found that, all ANN models can successfully produce the approximate results for training sample. Further, the RBFNN provides the higher generalization performance than the BPNN and GRNN as well as the conventional PDC models, with the mean squared error of 5.84 × 10^?4 and CPU time of 120.15 s. The result also demonstrates that ANN can offer an alternative technique to model cyclone pressure drop.     

Baking of Flat Bread in an Impingement Oven: Modeling and Optimization

An artificial neural network (ANN) was developed to model the effect of baking parameters on the quality attributes of flat bread; i.e., crumb temperature, moisture content, surface color change and bread volume increase during baking process. As the hot air impinging jets were employed for baking, the baking control parameters were the jet temperature, the jet velocity, and the time elapsed from the beginning of the baking. The data used in the training of the network were acquired experimentally. In addition, using the data provided by ANN, a multi-objective optimization algorithm was employed to achieve the baking condition that provides the quality of the bread in all aspects simultaneously.     

EXTENSION OF THE NEURAL NETWORKS OPERATING RANGE BY THE APPLICATION OF DIMENSIONLESS NUMBERS IN PREDICTION OF HEAT TRANSFER COEFFICIENTS

ABSTRACT

The paper presents a study aimed at extending the neural network mapping ability. In traditional modelling, operational process parameters (gas/material temperature, air velocity, etc.) are the inputs and outputs to and from the network. In this approach dimensionless numbers (Re, Ar, H/d) were used as inputs to predict the heat transfer coefficient in a fluidised bed drying process. To produce the data set necessary to train the networks, drying trials of different materials in a fluidised bed were carried out.

A series of simulations were performed and several neural networks structures were tested to find an optimal topology of the network. Training data set contained information only about two materials. The networks were tested using data obtained for the third product.

Performance of the network was satisfactory, however further improvement of mapping ability may be expected after filtration of the testing data.     

Modeling Intermittent Drying Using an Adaptive Neuro-Fuzzy Inference System

ABSTRACT

Artificial intelligence systems such as artificial neural networks (ANN) and fuzzy inference systems (FIS) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. The advantages of a combination of ANN and FIS are obvious. This article presents the application of a hybrid neuro-fuzzy system called adaptive-network-based fuzzy inference system (ANFIS) to time dependent drying processes and is illustrated by an application to model intermittent drying of grains in a spouted bed. An introduction to the ANFIS modeling approach is also presented. The model showed good performance in terms of various statistical indices.     

Use of Artificial Neural Network and Image Analysis to Predict Physical Properties of Osmotically Dehydrated Pumpkin

The objectives of this research were to predict, using neural networks, the color intensity (ΔE), percentage of shrinkage as well as the Heywood shape factor, which is the representative of deformation, of osmotically dehydrated and air dried pumpkin pieces. Several osmotic solutions were used including 50% (w/w) sorbitol solution, 50% (w/w) glucose solution, and 50% (w/w) sucrose solution. Optimum artificial neural network (ANN) models were developed based on one to two hidden layers and 10–20 neurons per hidden layer. The ANN models were then tested against an independent data set. The measured values of the color intensity, percentage of shrinkage, and the Heywood shape factor were predicted with R² > 0.90 in all cases, except when all the drying methods were combined in one data set.     

Artificial Neural Network Modeling of the Deposition Rate of Lactose Powder in Spray Dryers

A spray dryer is the ideal equipment for the production of food powders because it can easily impart well-defined end product characteristics such as moisture content, particle size, porosity, and bulk density. Wall deposition of particles in spray dryers is a key processing problem and an understanding of wall deposition can guide the selection of operating conditions to minimize this problem. The stickiness of powders causes the deposition of particles on the wall. Operating parameters such as inlet air temperature and feed flow rate affect the air temperature and humidity inside the dryer, which together with the addition of drying aids can affect the stickiness and moisture content of the product and hence its deposition on the wall. In this article, an artificial neural network (ANN) method was used to model the effects of inlet air temperature, feed flow rate, and maltodextrin ratio on wall deposition flux and moisture content of lactose-rich products. An ANN trained by back-propagation algorithms was developed to predict two performance indices based on the three input variables. The results showed good agreement between predicted results using the ANN and the measured data taken under the same conditions. The optimum condition found by the ANN for minimum moisture content and minimum wall deposition rate for lactose-rich feed was inlet air temperature of 140°C, feed rate of 23 mL/min, and maltodextrin ratio of 45%. The ANN technology has been shown to be an excellent investigative and predictive tool for spray drying of lactose-rich products.