Artificial Neural Network Modeling of Osmotic Dehydration Mass Transfer Kinetics of Fruits

Artificial neural network (ANN) models were developed for the prediction of transient moisture loss (ML) and solid gain (SG) in osmotic dehydration of fruits using process kinetics data from the literature. ANN models for ML and SG were developed based on data over a broad range of operating conditions and ten common processing variables: temperature and concentration of osmotic solution, immersion time, initial water and solid content of the fruit, porosity, surface area, characteristic length, solution-to–fruit mass ratio, and agitation level. The trained models were able to accurately predict the outputs with associated regression coefficients (r) of 0.96 and 0.93, respectively, for ML and SG. These ANN models performed much better than those obtained from linear multivariate regression analysis. The large number of process variables and their wide ranges considered along with their easy implementation in a spreadsheet make them very useful and practical for process design and control.     

ANN-Based Models for Moisture Diffusivity Coefficient and Moisture Loss at Equilibrium in Osmotic Dehydration Process

Equations were developed using artificial neural networks to predict water diffusivity coefficient (D _e ) and moisture loss at equilibrium point (ML _∞) in order to get the moisture loss (ML) at any time in osmotic dehydration of fruits. These models mathematically correlate nine processing variables (temperature and concentration of osmotic solution, water and solid composition of the fruit, porosity, surface area, characteristic length, solution-to-fruit mass ratio, and agitation level) with D _e and ML _∞. Models were developed using a wide variety of data from the literature and they were able to predict D _e (r = 0.98) and ML _∞(r = 0.94) in a wide range of variable conditions. With these two parameters known, ML can be calculated using Crank's solutions of Fick's second law. The wide range of processing variables considered for the formulation of these models, and their easy implementation in a spreadsheet, using a set of equations, makes them very useful and practical for process design and control.     

Experimental Validation of Thin‐layer Drying Models

The present work was aimed at investigating the applicability of the proposed correlation equations that link the coefficients of the thin‐layer equations. Materials of different properties were chosen and dried in four types of laboratory dryers. The experimental data were approximated applying the commonest thin‐layer equations: the Lewis equation, the modified Page's model, and Fick's second law. The best match was achieved with the modified Page's model. The applicability of the stated literature correlations was confirmed. Irrespective of the match between measuring data (X = f(t)) and Lewis equation, the evaluated drying constant K allowed evaluation of the parameters of the employed two‐parameter models.     

Effect of microwave-vacuum,ultrasonication, and freezing on mass transfer kinetics and diffusivity during osmotic dehydration of cranberries

The objectives of this study were (i) to evaluate moisture loss (ML), solids gain (SG), moisture loss rate (d(ML)/dt), solids gain rate (d(SG)/dt), effective moisture diffusivity (D_eM), and effective solids diffusivity (D_eS) during osmotic dehydration (OD), ultrasound-assisted osmotic dehydration (UA-OD), and osmotic dehydration of whole cranberries, preceded by microwave-vacuum pretreatment (MV?+?OD) or freezing/thawing treatment (F?+?OD ) and (ii) to use a mathematical model to predict moisture content ML_∞ and solids content SG_∞ in equilibrium state and the distribution of moisture and sucrose in whole cranberries during dehydration. Microwave-vacuum and OD treatments produced cranberries with the highest values of ML and SG (39.7 and 8.4?g 100?g?fresh?fruit^?1, respectively), d(ML)/dt and d(SG)/dt (76.9 and 8.5?h^?1, respectively), D_eM and D_eS (6.1 and 3.7?×?10^?10?m²?s^?1, respectively). Azuara’s and Peleg’s models adequately described the OD kinetics of whole cranberries in terms of ML and SG. The results indicate that the equilibrium values of ML_∞ and SG_∞ were not reached under the conditions specified in the present study.     

Studies on Dehydration of Sapota (Achras zapota)

Sapota (Achras zapota) is a tropical fruit found in several parts of India. It is one of the most popular fruits besides mango, custard apple, and several others. Once ripe, it needs to be consumed within a couple of days due to the highly perishable nature of this exquisite fruit variety. The best way to increase the shelf-life is through the process of dehydration of peeled sapota. In the present study, the convective air drying of sapota pulp was carried out and compared with low-temperature drying techniques such as heat pump–assisted drying and freeze drying. The sapota paste was dried in a convective dryer to study the effect of operating parameters such as air temperature and air velocity. In addition, the effect of additives such as oat and wheat fibers and the paste thickness on drying was also studied. The critical analysis of dehydrated sapota was carried out in terms of water activity, sugar content, color, and rehydration ratio. The drying data were analyzed using Page's model and Newton's model on the basis of root mean square error (RMSE), reduced χ square (χ²) and correlation coefficient (R²).     

A Comparison of Some Mathematical Models Used for the Prediction of Mass Transfer Kinetics in Osmotic Dehydration of Fruits

Mass transfer in osmotic dehydration of fruits at atmospheric pressure has been modeled either by a phenomenological approach using Crank's model (which is a Fick's law solution) or empirically, using models developed from polynomial equations, mass balances, or relations between process variables (i.e., Magee's, Azuara's, and Page's models). For all of these models, experimental data are required to determine the values of their adjustable parameters for specific processing conditions. From a wide variety of published data, the parameters for moisture loss (ML) and solid gain (SG) were calculated for the above-mentioned models, and a comparison of their correlation capability was made. Azuara's and Page's models yield better correlations (with mean absolute errors less than 1.26% for ML and 0.46% for SG) than Magee's and Crank's models (with mean absolute errors of up to 2.98 and 1.68% for ML and SG, respectively).     

Microwave Heat Treatment of Spinach: Drying Kinetics and Effective Moisture Diffusivity

The effect of microwave drying technique on moisture content, moisture ratio, drying rate, drying time, effective moisture diffusivity, and porosity of spinach (Spinacia oleracea L.) were investigated. By increasing the microwave output powers (180–900 W) and the sample amounts (25–100 g), the drying time decreased from 18 to 3.5 min and increased from 7.7 to 25 min, respectively. To determine the kinetic parameters, the drying data were fitted to various models based on the ratios of the differences between the initial and final moisture contents and equilibrium moisture content versus drying time. Among of the models proposed, Page's model gave a better fit for all drying conditions applied. The activation energy was calculated using an exponential expression based on Arrhenius equation. The relationship between the drying rate constant and effective moisture diffusivity was also estimated and gave a linear relationship.     

Determination of Drying Kinetics for Biomass by Thermogravimetric Analysis under Nonisothermal Condition

The nonisothermal drying kinetics of wheat straw and corn stalk has been studied by thermogravimetry. The experimental data have been obtained in order to fit to semitheoretical models widely used to describe drying behavior of agricultural products. Nonisothermal drying models—Newton, Henderson and Pabis, logarithmic, and Page—were evaluated based on the coefficient of determination (R ²), reduced chi-square (χ ²), and root means square error (RMSE). Page's model has been found to be the best for describing the nonisothermal drying characteristics of wheat straw and corn stalk. The activation energy values of wheat straw and corn stalk are determined to be 14.144 and 6.113 kJ mol^?1, respectively.     

Microwave Drying Kinetics of Okra

In this work, the effects of power level and sample mass on moisture content, moisture ratio, drying rate, and drying time of Turkey okra (Hibiscus esculenta L.) were investigated using microwave drying technique. Various microwave power levels ranging from to 180 to 900 W were used for drying of 100 g of okra. To investigate the effect of sample mass on drying, the samples in the range of 25 to 100 g were dried at microwave power level of 360 W. To determine the kinetic parameters, the drying data were fitted to various models based on the ratios of the differences between the initial and final moisture contents and equilibrium moisture content. Among of the models proposed, Page's model gave a better fit for all drying conditions used. The activation energy for microwave drying of okra was calculated using an exponential expression based on Arrhenius equation and was found to be 5.54 W/g.     

Impact of Sequential Ammonia Fiber Expansion (AFEX) Pretreatment and Pelletization on the Moisture Sorption Properties of Corn Stover

Combining ammonia fiber expansion (AFEX?) pretreatment with a depot processing facility is a promising option for delivering high-value densified biomass to the emerging bioenergy industry. However, because the pretreatment process results in a high moisture material unsuitable for pelleting or storage (40% wet basis), the biomass must be immediately dried. If AFEX pretreatment results in a material that is difficult to dry, the economics of this already costly operation would be at risk. This work tests the nature of moisture sorption isotherms and thin-layer drying behavior of corn (Zea mays L.) stover at 20°C to 60°C before and after sequential AFEX pretreatment and pelletization to determine whether any negative impacts to material drying or storage may result from the AFEX process. The equilibrium moisture content to equilibrium relative humidity relationship for each of the materials was determined using dynamic vapor sorption isotherms and modeled with modified Chung-Pfost, modified Halsey, and modified Henderson temperature-dependent models as well as the Double Log Polynomial (DLP), Peleg, and Guggenheim Anderson de Boer (GAB) temperature-independent models. Drying kinetics were quantified under thin-layer laboratory testing and modeled using the Modified Page's equation. Water activity isotherms for non-pelleted biomass were best modeled with the Peleg temperature-independent equation while isotherms for the pelleted biomass were best modeled with the Double Log Polynomial equation. Thin-layer drying results were accurately modeled with the Modified Page's equation. The results of this work indicate that AFEX pretreatment results in drying properties more favorable than or equal to that of raw corn stover, and pellets of superior physical stability in storage.     

An effective characterization procedure for petroleum reservoir fluids using molecular type methods and cubic equations of state

Two eminent molecular type composition methods (paraffins, naphthenes, and aromatics [PNA] and saturates, aromatics, and polynuclear aromatics [SAP]) are employed to construct new characterization procedures for predicting the phase behaviour of petroleum fluids using a modified Peng–Robinson equation of state. The PNA and SAP methods divide a petroleum fraction into (PNA) and (SAP) homologous groups, respectively. Two generalized models are developed to predict the physical properties ($\mathrm{Mw},\mathrm{SG},T_b$) and equation of state (EOS) parameters ($T_c,P_c,\omega$) for both PNA and SAP sub-fractions. Each generalized model covers 18 different correlations in a single mathematical form that enables the model to return 18 outputs for PNA and SAP sub-fraction parameters. A new lumping method is also developed to convert triple PNA or SAP pseudo-components into single characterized fractions. Accordingly, seven different characterization procedures are introduced and compared with one another. The first two procedures are completely constructed based on the proposed models, and the other procedures encompass the models already developed. The results obtained from the simulation of the differential liberation test for 12 diverse reservoir fluids and bubble pressure prediction for 40 oil samples revealed that the first two methods (1 and 2) could enhance the abilities of the traditional characterization procedures for reservoir fluid modelling. The mean value of average absolute relative deviations (AARDs) over a total of 52 oil samples is about 6.5% for the proposed methods and is about 13.2% for the best previously existing methods. Moreover, an efficient workflow is provided for the parameter tuning process, which is notably capable of reducing the level of prediction errors using only three adjustable parameters.     

Synergistic effect between amino sulfonate amphoteric surfactant and octylphenol polyoxyethylene ether (10) in aqueous solutions with different acidicities: consideration of different thermodynamic models

The micellization behavior of binary mixtures of sodium n‐dodecyl diamine sulfonate (C12AS) and octylphenol polyoxyethylene ether (10), OP‐10, in aqueous solutions at pH 3.5, 6.0, and 8.5 was investigated, and the values of mixed critical micelle concentration (C^M) were determined by both UV–vis spectroscopy using pyrene as a probe and by tensiometry. Within the framework of the pseudophase separation model, four thermodynamic models were adopted to describe the micellization parameters including the interaction parameters (β₁₂) between two surfactants, the components in real and ideal mixed micelles, the activity coefficients in mixed micelles, the thermodynamic parameters, and the thermodynamic stability. The experimental values of C^M, inferior to the ideal values, show nonideal mixing; the negative values of β₁₂ obtained from Rubingh's, Rodenas's, and Motomura's models mean the synergistic effect. Although there exist some divergences in the micellization parameters predicted by the three treatments, similar results were obtained, indicating the validity of these models in this investigation. Based on the calculation errors, Rubingh's model is best to describe the interacting behavior between the two surfactants in this investigation. Thermodynamic parameters predicted by the three treatments show a spontaneous process of micellization and an entropic contribution in the formation of mixed micelles. Different acidities of solutions result in different stabilities of micellization. The stability in feeble acidic solution is higher than in acidic or basic solution, and the stability in basic solution is slightly superior to that in acidic solution. All the above described phenomena can be explained rationally by the electrostatic effect of head groups of C12AS, the steric effect of head group for two surfactants, the molecular structures of surfactants, the influence of added counterion, and so on. © 2015 Society of Chemical Industry     

COMPREHENSIVE STUDY OF THE GAS HOLD UP AND BUBBLE SIZE DISTRIBUTION IN HIGHLY VISCOUS LIQUIDS II. CMC SOLUTIONS

Following properties of short bubble columns employing CMC solutions (1.0, 1.4 and 2.0%) and perforated plates (0.5, 1.0 and 3.0 mm hole diameters) were determined: relative mean gas hold up, E_G , bubble size distribution, Sauter bubble diameter, d_s , and the specific geometrical bubble surface areas, a′ and a″ due to the “intermediate to large bubbles.” The a″ values were compared with the corresponding volumetric mass transfer coefficients, k_La's and the mass transfer coefficients, k_L , were estimated. The properties of these systems were investigated as function of the superficial gas velocity, W_SG , CMC concentration and aerator type.     

DRYING OF CHOPPED SPRING ONION USING FLUIDIZATION TECHNIQUE

Abstract

This research investigates several parameters affecting the drying characteristics of chopped spring onion and develops an empirical model for predicting its drying behaviour in a fluidized bed dryer. Experimental results showed that at air temperature of 32 °C and relative humidity of 62 %, the minimum fluidization velocities were approximately 1.36, 1.20, 0.95 and 0.62 m/s at initial moisture contents of 95, 71, 56 and 5% w.b., respectively. Drying air temperature and specific air flow rate were parameters which affected drying rate. Page's model could predict the value closest to the experimental data. The air-product temperature should be kept lower than 53 °C to maintain the acceptable green color of the dried product.     

Sorption Isotherms and Drying Characteristics of Tomato Peel Isolated from Tomato Pomace

Tomato peel was separated from pomace by sedimentation and dried in cabinet and fluidized-bed dryer at 50–70°C using 4–12 kg/m²tray load. The drying of tomato peel took place under the falling rate period and the drying behavior was well described by Page's model with coefficient of determination greater than 0.99 and standard error of 0.003–0.016. A fluidized-bed dryer was much more efficient than a cabinet dryer to dry tomato peel. The moisture adsorption isotherms of tomato peel were obtained by equilibrating above saturated salt solutions of known a _w (0.113–0.92) at 20–60°C. The data were analyzed using fifteen sorption models based on coefficient of determination, standard error, and residual plots. Modified Henderson was the best model for tomato peel with coefficient of determination >0.99, standard error <0.210, and a scattered residual plot. The net isostearic heat of sorption, estimated using the Clausius-Clapeyron equation, was 0.74–23.23 kJ/mol at 2.0–2.5% moisture content (dry basis).     

DRYING CHARACTERISTICS AND PRODUCT QUALITY OF OKRA

ABSTRACT

Effects of size (whole and sliced), pre-treatment (blanching in water and 0.5% NaCl solution at 95° C) and temperature of drying air (40, 55 and 70° C) on the drying characteristics and quality of okra were studied. Estimation of the drying rate established that the drying of okra takes place under the falling rate period. Page's model was found to adequately describe the drying behavior of okra over a wide range of drying conditions used in the study. The coefficients of Page's model were correlated with air temperature and it was found that the dependence of the rate constant on the drying air temperature can be described using the Arrhenius law. The quality of the dried product was found to be best when okra was sliced and blanched at 95° C in 0.5% NaCl solution for 5 min and then dried at 55° C.     

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.     

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.