Evaluation of the air-borne ultrasound on fluidized bed drying of shelled corn: Effectiveness,grain quality,and energy consumption

The objective of the study was to investigate the influence of high power ultrasound on a laboratory-scale fluidized bed shelled corn dryer. The drying time, moisture content variation, specific energy consumption, and quality parameters including ultimate compressive strength, toughness, shrinkage and color of corn kernels were investigated. Furthermore, artificial neural network (ANN) simulation models were developed for predicting the drying variables. Machine vision techniques were used to determine color and shrinkage as qualitative indices. Results showed that the lower frequencies had better penetrations at lower temperatures and cause a significant reduction in drying time. In addition, the ultrasound application led to reduction of ultimate compressive strength and toughness of the dried samples although ultrasound has nonthermal character as the subsidiary factor, it plays an important role in shrinkage and color specification. Based on error analysis results, the prediction capability of ANN model is found to be reasonable for the developed models. Application of ultrasound significantly decreased the specific energy consumption of drying process at the optimal drying condition.     

Artificial neural network approach for protection of the color of dried golden and pink oyster mushrooms with pretreatments

In this study, the possibilities of protecting the color of dried golden and pink mushrooms were investigated, and color parameters of dried mushrooms were modeled by artificial neural network (ANN). For this purpose, first, the golden oyster mushroom (Pleurotus citrinopileatus) and pink oyster mushroom (Pleurotus djamor) were cultivated. Then, pretreatments were applied using citric acid (CA) and potassium metabisulfite (KMS) with different rates (0.5%, 1.0%, and 1.5%) separately, excluding control group mushrooms. All mushrooms were dried for 330 minutes in a laboratory type oven at two different temperatures (40°C and 50°C) until completely dehydrated. Colorimetric values (L*, a*, and b*) were determined using Konica Minolta CM‐2600d spectrophotometer for 30 minute intervals during the drying process. The obtained data were modeled using the ANN technique. The results show that darkening of mushrooms increased as the drying temperature increased. CA and KMS showed better results for dried golden and pink mushrooms, respectively. Thanks to the pretreatment, the mushroom's original color was protected compared with control samples. All mean absolute percentage error values of models were determined, which were lower than 4.0%. It was concluded that ANN can be a good way to predict the color of dried golden and pink mushrooms (pretreated or not) with a high degree of accuracy.     

Prediction of Physicochemical Properties of Raspberry Dried by Microwave-Assisted Fluidized Bed Dryer Using Artificial Neural Network

The main target of this research is to dry raspberries in a microwave-assisted fluidized bed dryer. Artificial neural network (ANN) modeling was used in order to evaluate and predict the physicochemical properties of this fruit. In this research, the effects of five variables—microwave power (0, 300, and 600 W), temperature (55, 70, and 85°C), air flow rate (15, 20, and 25 m/s), starting time of microwave input (from the moment when the moisture content decreased until 334, 400, and 466 g water/g dry matter), and amount of loaded material (50, 100, and 150 g)—on nine outputs—drying time, rehydration capacity, density, porosity, hardness, water activity, phenolic compounds content, anthocyanins content, and the antioxidant activity of dried raspberries—were studied. A feed-forward multilayered perceptron trained by back-propagation algorithms for five independent variables was developed to predict these nine outputs. The optimal configuration of the neural network model had a hidden layer with nine neurons. The predictive ability of the ANN was compared using a separate data set of 52 unseen experiments based on root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R ²) for each output parameter. The optimum model was able to predict the nine output parameters with a coefficient of determination higher than 0.92. The results indicated that the experimental and ANN-predicted data sets were in good agreement, so it is feasible to use an ANN to predict the physicochemical properties of dried black raspberry.     

NEURAL NETWORK MODEL FOR FLUIDISED BED DRYERS

The application of an artificial neural network (ANN) to model a continuous fluidised bed dryer is explored. The ANN predicts the moisture and temperature of the output solid. A three-layer network with sigmoid transfer function is used. The ANN learning is made by using a set of data that were obtained by simulating the operation by a classical model of dryer. The number of hidden nodes, learning coefficient, size of learning data set and number of iterations in the learning of the ANN were optimised. The optimal ANN has five input nodes and six hidden nodes. It is able to predict, with an error less than 10%, the moisture and temperature of the output dried solid in a small pilot plant that can treat up to 5 kg/h of wet alpeorujo. This is a wet solid waste that is generated in the two-phase decanters used to obtain olive oil.     

Intelligent Prediction of CO2 Capture in Propyl Amine Methyl Imidazole Alanine Ionic Liquid: An Artificial Neural Network Model

The objective of this paper is to create a new artificial neural network (ANN) model to predict solubility of CO₂ in a new structure of task specific ionic liquids called propyl amine methyl imidazole alanine [pamim][Ala]. Equilibrium data of CO₂ solubility were measured at the temperatures of 25, 40, and 60°C and the pressures up to 50 bar. For the purpose of performance comparison, the two most common types of ANNs, multilayer perceptron (MLP) network and radial basis function (RBF) network were used. Water content, ionic liquid content, temperature, and pressure set as input variables to ANN, while CO₂ capture rate assigned as output. Based upon optimization process, MLP neural network with 14 neurons in the hidden layer, log-sigmoid transfer function in the hidden layer and linear transfer function in the output layer, exhibited much better performance in prediction task than RBF neural network with the same neuron numbers in the hidden layer. Results obtained demonstrated that there is a very little difference between the estimated results of ANN approach and experimental data of CO₂ capture rate for the training, validation, and test data sets. Furthermore, Henry’s law constants were obtained by fitting the equilibrium data.     

Prediction of Energy and Exergy of Carrot Cubes in a Fluidized Bed Dryer by Artificial Neural Networks

In this study both static and recurrent artificial neural networks (ANNs) were used to predict the energy and exergy of carrot cubes during fluidized bed drying. Drying experiments were conducted at air temperatures of 50, 60, and 70°C; bed depths of 3, 6, and 9 cm; and square-cubed carrot dimensions of 4, 7, and 10 mm. Five hundred eighteen patterns, obtained from experiments, were used to develop the ANN models. Initially, a static ANN was applied to correlate the outputs (energy and exergy of carrot cubes) to the four exogenous inputs (drying time, drying air temperature, carrot cube size, and bed depth). In the recurrent ANNs, in addition to the four exogenous inputs, two state inputs and outputs (energy and exergy of carrot cubes) were used. To find optimum ANN models, various numbers of hidden neurons were investigated. The energy and exergy of carrot cubes were predicted with R ² values of greater than 0.95 and 0.97 using static and recurrent ANNs, respectively. Accordingly, the optimal recurrent model could be utilized for determining the appropriate drying conditions of carrot cubes to reach the optimal energy efficiency in fluidized bed drying.     

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.     

Osmotic Dehydrated Raspberries: Changes in Physical Aspects and Bioactive Compounds

Raspberries are very labile fruits that have a short postharvest life; therefore, there is a need to develop alternative storage and processing methods for extending their shelf-life. The effect of wet (WI) and dry (DI) sucrose infusions (a_w = 0.85) on color and bioactive compounds of raspberries has been studied. Additives were included: citric acid, sodium bisulfite, or both. Moisture content decreased from 85% (w/w) for control fruit to approximately 51% (w/w) for infused samples. The shrinkage of fruits was approximately 27% and 46% after WI and DI, respectively. No major color changes occurred, except for WI-bisulfite treatment. Although the total polyphenols and anthocyanin content were significantly reduced in fruits during osmotic dehydration, mainly due to the dilution effect to the medium, 100 g serving of processed raspberries would supply, in some cases, over 50% of polyphenols provided by a glass of wine. The proposed infusion dehydration methods may be considered an alternative to produce shelf-stable products based on raspberries, with an improved quality in terms of appearance.     

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.     

Artificial neural network simulation of the condenser of seawater greenhouse in Oman

The prediction of freshwater production from the condenser of an agricultural seawater greenhouse is important for designing the greenhouse process. Two models, namely, Artificial Neural Network and multilinear regression (denoted as ANN and RA, respectively), were developed and tested to predict the freshwater production rate considering ambient solar intensity, condenser inlet moist-air temperature, humidity ratio and mass flowrate, and inlet coolant temperature. Statistical analysis indicated that all parameters significantly affected the prediction (p?<?0.05). The accuracy of the ANN and RA models was then compared to two models previously developed by Yetilmezsoy and Abdul-Wahab and Al-Ismaili et al. (denoted as Yetilmezsoy model and Al-Ismaili model, respectively). The ANN model showed the best prediction when seven statistical criteria were considered. The Pearson correlations for ANN, RA, Yetilmezsoy, and Al-Ismaili models were observed as 1.00, 0.98, 0.88, and 0.96, respectively, while mean absolute percentage errors (MAPE) were 17.84, 79.72, 63.24, and 80.50%, respectively. Hence it could be recommended to use ANN model for the prediction of freshwater production rate, however other three simple models could also be used with lower accuracy in the cases of unavailability of the ANN model.     

Drying of Chestnuts (Castanea sativa Mill.) after Osmotic Dehydration with Sucrose and Glucose Solutions

Chestnuts were dehydrated by using a combined method of osmotic dehydration followed by air drying. Samples were osmotically pretreated with sucrose (60% w/w) and glucose (56% w/w) for 8 h, air-dried at temperatures of 45, 55, and 65°C, at a relative humidity of 30% and at a velocity of 2.7 m·s^?1 and the experimental data of the drying kinetics were obtained. Whole samples were dried with different peelings: (a) removal of endocarp and pericarp (peeled) and (b) additionally the internal rough surface (cut). In all cases, cut chestnuts show greater drying rates than peeled samples, indicating that a significant mass transfer resistance in the layer nearest to the surface takes place. Peeled samples pretreated with sucrose solutions behave in a similar way to untreated samples. For the rest of the samples, the cut samples osmotically treated with sucrose solutions and all the samples treated with the glucose solution, the drying rates decrease during drying. Drying kinetics are successfully modeled by employing a diffusional model that takes the shrinkage into account. The effective coefficient of water diffusion was evaluated and correlated with temperature. The quality of the final product was monitored by color change. In spite of the fact that the total color difference is not modified by the osmotic treatment, the L?, a?, and b? color coordinates of cut samples treated with sucrose and glucose solutions do undergo changes; the L? and a? coordinates change less than the b?.     

A Polymath Approach for the Prediction of Optimized Transesterification Process Variables of Polanga Biodiesel

An attempt has been made to employ an artificial neural network (ANN) combined with a genetic algorithm (GA) in MATLAB 7.0 for predicting the optimized reaction variables for maximum biodiesel production of polanga oil by the transesterification process. The developed ANN is a multilayer feed-forward back-propagation network (5-10-1) with five input, ten hidden and one output layers. The input variables are the molar ratio of ethanol to oil (X ₁ in % v/v), the catalyst concentration (X ₂ in % w/v), the reaction temperature (X ₃ in °C), the reaction time (X ₄ in min), the agitation speed (X ₅ in rpm) and the output parameter is biodiesel yield (% by weight) of polanga oil. The experimental data used in the developed ANN were obtained from response surface methodology (RSM) based on a central composite design. The trained ANN was tested using different training functions from the MATLAB to predict the best correlation coefficients of training, testing and validation. The data generated by trained ANN is used by GA with regards to the best response (for predicting biodiesel yield greater than predicted by RSM) for different combinations of variables (X ₁, X ₂, X ₃, X ₄, and X ₅) to attain optimization. The average biodiesel yield (by performing experiments under optimized conditions) of 92 % by weight was produced against the proposed value of 91.08 % by weight.     

Artificial neural network and multiple linear regression for modeling sorption of Pb2+ ions from aqueous solutions onto modified walnut shell

ABSTRACT

In the present work, for the first time, a new carboxylate-functionalized walnut shell (CFWS) was prepared via esterification of walnut shell (WS) with isopropylidene malonate. The characterization of the CFWS by different techniques approved that carboxylic groups were introduced onto the surface of WS. The performance of the modified adsorbent was studied for the removal of Pb²⁺ ions from aqueous solutions in a batch adsorption system. The analysis data showed that the Langmuir isotherm could satisfactorily explain the equilibrium data, and the maximum adsorption capacity for Pb²⁺ ions was found to be 192.3 mg g^?1 at 0.8 g L^?1 of the adsorbent, pH 5.5, and a temprature of 298 K. Two models, namely artificial neural network (ANN) and multiple linear regression (MLR), were used to construct an empirical model for prediction of the removal percentage of Pb²⁺ ions under different experimental conditions. These models were validated using a test set of 20 data. A comparison between the developed models shows that the ANN model is able to predict the removal percentage of Pb²⁺ ions more accurately. Consequently, the ANN model could be applied for the design of an automated wastewater remediation plan. Also it has to be noted that the used CFWS was recovered using EDTA-2Na, and employed for the removal of Pb²⁺ ions from aqueous solutions.     

Modelling of adsorption of nickel (II) by blend hydrogels (cellulose nanocrystals and corn starch) from aqueous solution using adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural networks (ANN)

The current study looks at the effectiveness of the removal of nickel (II) from aqueous solution using an adsorption method in a laboratory-size reactor. An artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) were used in this study to predict blend hydrogels adsorption potential in the removal of nickel (II) from aqueous solution. Four operational variables, including initial Ni (II) concentration (mg/L), pH, contact duration (min), and adsorbent dose (mg/L), were used as an input with removal percentage (%) as the only output; they were studied to assess their impact on the adsorption study in the ANFIS model. In contrast, 70% of the data was used for training, while 15% of the data was used in testing and validation to build the ANN model. Feedforward propagation with the Levenberg–Marquardt algorithm was employed to train the network. The use of ANN and ANFIS models for experiments was used to optimize, construct, and develop prediction models for Ni (II) adsorption using blend hydrogels. The adsorption isotherm and kinetic models were also used to describe the process. The results show that ANN and ANFIS models are promising prediction approaches that can be applied to successfully predict metal ions adsorption. According to this finding, the root mean square errors (RMSE), absolute average relative errors (AARE), average relative errors (ARE), mean squared deviation (MSE), and R² for Ni (II) in the training dataset were 0.061, 0.078, 0.017, 0.019, and 0.986, respectively, for ANN. In the ANFIS model, the RMSE, AARE, ARE, MSE, and R² were 0.0129, 0.0119, 0.028, 0.030, and 0.995, respectively. The adsorption process was spontaneous and well explained by the Langmuir model, and chemisorption was the primary control. The morphology, functional groups, thermal characteristics, and crystallinity of blend hydrogels were all assessed.     

Optimization of acrylic dry spinning production line by using artificial neural network and genetic algorithm

Acrylic fibers are synthetic fibers with wide applications. A couple of methods can be utilized in their manufacture, one of which is the dry spinning process. The parameters in this method have nonlinear relationships, making the process very complex. To the best of the authors' knowledge, no comprehensive study has yet been conducted on the optimization of acrylic dry spinning production using computer algorithms. In this study, such parameters as extruder temperature in and around the head, solution viscosity, water content in the solution, formic acid content of the solution, and the retention time of the solution in the reactor were measured in an attempt to predict the behavior of the dry spinning process. The color index of the manufactured fibers was used as an indicator of production quality and statistical methods were employed to determine the parameters affecting the process. An artificial neural network (ANN) using the back propagation training algorithm was then designed to predict the color index. ANN parameters including the number of hidden layers, number of neurons in each layer, adaptive learning rate, activation functions, number of max fail epochs, validation and test data were optimized using a genetic algorithm (GA). The trial and error method was used to optimize the GA parameters like population size, number of generations, crossover or mutation rates, and various selection functions. Finally, an ANN with a high accuracy was designed to predict the behavior of the dry spinning process. This method is capable of preventing the manufacturing of undesired fibers. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Modeling of combined far-infrared radiation and air drying of a ring shaped-pineapple with/without shrinkage

A ring shape is commonly used for industrial process of pineapple. Unfortunately, there has been no study on modeling of pineapple rings. Therefore we developed the mathematical model of pineapple rings during combined far-infrared radiation and air convection drying to investigate the evolutions of moisture content and qualities. The drying model based on the solution of Fick's law was used to estimate moisture diffusion coefficient (D). The D values with and without taking into account shrinkage phenomenon of dried products were compared. The kinetics of dried pineapple qualities such as color, shear force ratio and shrinkage during drying also were studied. Pineapples were pretreated, cut into rings and dried at far-infrared intensities of 1–5 kW/m² combined with air temperatures of 40–60 °C and air velocities of 0.5–1.5 m/s. The D values were found to increase with increasing intensity and air temperature. The D values with shrinkage consideration were lower than the D values without shrinkage consideration for all drying conditions. The quartic model gave a better fit over the other three polynomial models for describing the color kinetics. The thin layer drying models such as Page, Henderson and Pabis, Logarithmic and Midilli–Kucuk were modified in order to describe shear force ratio (SFR) of dried pineapple. The statistically analyses from this present study indicated that modification of drying models can be used to describe the kinetics of SFR and Midilli–Kucuk's form gave a better fit over the other form. The quadratic model was better than the linear model to predict shrinkage kinetics for all four dimensions (outer radius, inner radius, thickness and volume) of pineapple rings.     

Artificial neural networks model for the prediction of steady state phenol biodegradation in a pulsed plate bioreactor

BACKGROUND: A recent innovation in fixed film bioreactors is the pulsed plate bioreactor (PPBR) with immobilized cells. The successful development of a theoretical model for this reactor relies on the knowledge of several parameters, which may vary with the process conditions. It may also be a time‐consuming and costly task because of their nonlinear nature. Artificial neural networks (ANN) offer the potential of a generic approach to the modeling of nonlinear systems. RESULTS: A feedforward ANN based model for the prediction of steady state percentage degradation of phenol in a PPBR by immobilized cells of Nocardia hydrocarbonoxydans (NCIM 2386) during continuous biodegradation has been developed to correlate the steady state percentage degradation with the flow rate, influent phenol concentration and vibrational velocity (amplitude × frequency). The model used two hidden layers and 53 parameters (weights and biases). The network model was then compared with a Multiple Regression Analysis (MRA) model, derived from the same training data. Further these two models were used to predict the percentage degradation of phenol for blind test data. CONCLUSIONS: The performance of the ANN model was superior to that of the MRA model and was found to be an efficient data‐driven tool to predict the performance of a PPBR for phenol biodegradation. Copyright © 2008 Society of Chemical Industry     

Modeling and optimization of electrospun PAN nanofiber diameter using response surface methodology and artificial neural networks

Response surface methodology (RSM) based on a three‐level, three‐variable Box‐Benkhen design (BBD), and artificial neural network (ANN) techniques were compared for modeling the average diameter of electrospun polyacrylonitrile (PAN) nanofibers. The multilayer perceptron (MLP) neural networks were trained by the sets of input–output patterns using a scaled conjugate gradient backpropagation algorithm. The three important electrospinning factors were studied including polymer concentration (w/v%), applied voltage (kV) and the nozzle‐collector distance (cm). The predicted fiber diameters were in agreement with the experimental results in both ANN and RSM techniques. High‐regression coefficient between the variables and the response (R² = 0.998) indicates excellent evaluation of experimental data by second‐order polynomial regression model. The R² value was 0.990, which indicates that the ANN model was shows good fitting with experimental data. Moreover, the RSM model shows much lower absolute percentage error than the ANN model. Therefore, the obtained results indicate that the performance of RSM was better than ANN. The RSM model predicted the 118 nm value of the finest nanofiber diameter at conditions of 10 w/v% polymer concentration, 12 cm of nozzle‐collector distance, and 12 kV of the applied voltage. The predicted value (118 nm) showed only 2.5%, difference with experimental results in which 121 nm at the same setting were observed. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks

In this research work, the thermal conductivity and density of alumina/silica(Al_2O_3/SiO_2) in water hybrid nanofluids at different temperatures and volume concentrations have been modeled using the artificial neural networks(ANN). The nanocolloid involved in the study was synthesized by the two-step method and characterized by XRD, TEM, SEM–EDX and zeta potential analysis. The properties of the synthesized nanofluid were measured at various volume concentrations(0.05%, 0.1% and 0.2%) and temperatures(20 to 60 °C). Established on the observational data and ANN, the optimum neural structure was suggested for predicting the thermal conductivity and density of the hybrid nanofluid as a function of temperature and solid volume concentrations. The results indicate that a neural network with 2 hidden layers and 10 neurons have the lowest error and a highest fitting coefficient o thermal conductivity, whereas in the case of density, the structure with 1 hidden layer consisting of 4 neurons proved to be the optimal structure.