Predicted Moisture Ratios Research Articles

Electrohydrodynamic, oven and natural drying of mint leaves and effects on the physiochemical indices of the leaves

Electrohydrodynamics (EHD) enhanced the thin-layer drying of mint (Mentha Spicata Huds) leaves using multiple point-to-plate electrodes. Its configurations were carried out under DC high voltage and its effects were compared to oven drying at 40 °C and open-air-drying with natural convection at 25 °C. The EHD was run in an optimum electric field of 3.2 kV·cm<sup>–1 </sup>using positive ionising conical needles to a plate electrode covered by a steel screen grid to prevent the leaves drifting. The samples subjected to the EHD and oven exhibited faster drying kinetics than natural convection. Compared to the oven drying, the EHD electrical power consumption was negligible. The EHD method developed fewer undesirable changes in the colour features and the leaves’ total chlorophyll, whereas the oven-dried sample colour underwent a significant change in colour. The samples dried by EHD had lower active microorganisms. The empirical modelling, based on the maximum value of R<sup>2</sup> and the minimum value of RMSE and SSE between the experimental and predicted moisture ratios, showed that the diffusion and logarithmic models were the best models for describing the EHD and oven drying behaviour of the mint leaves.

Modelling to Predict Moisture Ratio in Infrared Drying of Machine Plaster by Particle Swarm Optimization

Background: Gypsum plaster is one of the most important building materials. The use of gypsum plasters is very common due to their many advantages. The drying process is an important stage in the production of gypsum materials and applications. Modelling of drying phenomenon can benefit drying technology. Recently, Particle Swarm Optimization (PSO) technique has been used to obtain optimum model equations for drying processes. Objective: The aim of this study was to determine a new modeling approach to infrared drying of machine plaster by using PSO. Methods: Experimental studies supplied by previous work in the literature have been performed by a laboratory scale infrared dryer in the temperature range of 50-70°C and at atmospheric conditions. Experimental moisture ratio values were compared with various mathematical model equations developed for the drying process by using Particle Swarm Optimization (PSO) technique. Results: Fitting tests indicate that the results obtained from the PSO technique are better than those of the previous study because of lower χ2, RMSE, and RSS values. The best model equation was the model equation based on the Newton drying equation existing in the previous study. However, the model equation derived by Modified Page has been determined as the most compatible model with the experimental data. Conclusion: It can be said that PSO is successively and reliably used to predict or optimize the experimental data of drying phenomena.

Modelling approaches for predicting moisture transfer during baking of chhana podo (milk cake) incorporated with tikhur (Curcuma angustifolia) starch

Chhana podo, a popular dairy product of India, is prepared by baking the dough of chhana (heat-acid coagulated milk solids), semolina and sugar. Heat induced moisture loss during baking influences the chain of physico-chemical changes, which in turn determine the quality of the final product. Thus, in this work, modelling approaches for predicting moisture transfer during baking of chhana podo prepared by replacing semolina with tikhur (Curcuma angustifolia) starch were developed. Moisture loss, moisture ratio, specific volume, oven spring, volumetric expansion and other physico-chemical quality changes of the dough were determined during baking, and were used in the development of moisture transfer models. The effective moisture diffusivity of chhana podo increased from 8.10 × 10–9 to 37.05 × 10–9 m2/s as baking temperature increased from 110 to 150 °C. The artificial neural networks (ANN) model based on multilayer feed-forward (MLFF) algorithm and radial basis function (RBF) and adaptive neuro-fuzzy inference system (ANFIS) models based on Sugeno-type fuzzy inference system were developed to predict moisture ratio, and their predictive performance was compared with classical multiple linear regression and empirical models. Amongst the four prediction approaches, the MLFF neural network with baking temperature and time as input factors, and with 12 neurons in the hidden layer, produced the best performance in predicting moisture ratio with R2 as high as 0.9946 (99.46% accuracy) and RMSE of 0.0188 for the entire range of baking temperature studied. Sensitivity analysis showed that baking temperature was the most influential parameter (strength ratio was 0.89) deciding moisture transfer. MLFF neural network was observed to be a simple and efficient technique for predicting the complex moisture transfer phenomenon during baking, which could be used by the industry to optimize the baking conditions.

A novel thin‐layer model for drying of Indian dark red onion slices at high velocity

AbstractThin‐layer equations for drying have been used to estimate drying duration for numerous agricultural produces. In the present work, a novel thin‐layer model is proposed based on drying duration, velocity, and temperature. Model results are compared with experimental and 11 models to simulate drying at four high air velocity (2, 3.6, 5.7, and 7.7 m/s) for Indian dark red onion slices. The experimental effective moisture diffusivity rises with the rise in velocity and it is observed between 1.096646 × 10−11 and 1.295467 × 10−11. Predicted moisture ratio demonstrates good agreement to experimental moisture ratio with the adjusted R2 0.9916 and R2 0.9921.Practical applicationsIn the Food Industry, mass production is an indispensable objective. To achieve targeted production in limited time, hot air at higher velocity plays a vital role. This paper is written to focus the results of proposed mathematical modeling of Indian dark red onion slices at high air velocity (2, 3.6, 5.7, and 7.7 m/s). The proposed mathematical model simulates drying behavior considering the combined effect of drying duration, velocity, and temperature.

Refractance Window Drying of Apple Slices: Mass Transfer Phenomena and Quality Parameters

The present study investigates the effect of Refractance Window (RW) drying at different temperatures (60 °C, 70 °C, 80 °C, and 90 °C) on drying characteristics and quality of apple slices and its comparison with hot air (HA) drying. Results showed that RW drying requires a shorter time (~ 25–37.5%) as compared to HA drying under similar conditions of drying. Also, RW drying at 90 °C resulted in higher retention of ascorbic acid (96%), without any significant change in color (ΔE = 5.5), compared to freeze drying. The microstructure analysis showed porous structure in RW-dried slices as compared to HA-dried ones. The influence of drying conditions on moisture diffusion was estimated using Fick’s, anomalous diffusion, and Dincer and Dost models. The values of moisture diffusivity (from 2.75 × 10−9 to 1.14 × 10−8 m2 s−1) obtained with the Dincer and Dost model were higher for RW as compared to HA and also were higher with respect to other models employed. Anomalous diffusion and Dincer and Dost models showed excellent agreement (R2 > 0.989) between experimental and predicted moisture ratios. This study showed that RW drying could effectively be used to dry thin layers of heat-sensitive fruits such as apple in a shorter time with better product quality as compared to HA drying.

Kinetics and mathematical modeling of thin layer drying of osmo‐treated Aloe vera (Aloe barbadensis) gel slices

AbstractIn the present work, the drying kinetics of osmo‐pretreated Aloe vera gel slices (AVGS) was investigated. AVGS with an initial moisture content of 98.29% (wet basis) were subjected to 5 hr of osmosis process (10% NaCl solution, at 40°C, NaCl solution to A. vera ratio = 5:1), prior to atmospheric thin layer drying at air temperatures of 55, 70, and 85°C and air volumetric air flow rates of 0.015, 0.036, and 0.054 m3/s for the duration of 160 min. Results showed the mean values of the moisture ratio in air temperatures of 55, 70, and 85°C were categorized into three levels (p < .01) of 0.551, 0.429, and 0.336, respectively, while the corresponding values for the osmo‐convective method were 0.561, 0.418, and 0.321, respectively. Similarly, the mean values of the moisture ratio in air flow rates of 0.015, 0.036, and 0.054 m3/s for the convective and osmo‐convective method assigned to different levels (p < .01) of 0.511, 0.417, 0.385 and 0.510, 0.416, 0.372, respectively. Superposition technique was utilized to propose a new model for each drying method. The predicted moisture ratios evaluated by the superposition technique were in a good agreement with experimental values of moisture ratio.Practical ApplicationsIn the current investigation, a superposition technique was utilized to propose a new model for two drying methods (osmo‐convective and convective). Subsequently, an equation describing the drying process for each method was derived. This technique while being a simple model, delivers high accuracy data for all influencing parameters in the drying process.

Experimental Study of the Drying Kinetics of Mango (<i>mangifera indica L.</i>) during Airflow Drying Licking Countercurrent

This work was conducted to study the thin-film drying kinetics of mango (mangifera indica L.) using a modular electrical dryer licking countercurrent and forced convection. Drying experiments were carried out in the Laboratory of Energetics and Applied Thermal (LETA) of the National School of Agro-Industrial Sciences of the University of Ngaoundere under different conditions of drying air temperature (40, 50 and 60°C) for a speed 0.6 m/s. Four trays were used to spread the products in the dryer. To estimate and select the appropriate drying model, ten different models were applied to the experimental data and compared. The performances of these models were compared using the R², the χ² and the RMSE between the observed and predicted moisture ratios values, which ranged from 0.925 to 0.999; 4.10-6 - 0.00540 and 0.0021 - 0.0736 respectively. Among the models used, the Midilli et al., model was found to best explain the thin-film drying airflow drying licking countercurrent of mango.

Mathematical and Kinetic Modelling for Convective Hot Air Drying of Sweet Potatoes (<i>Ipomoea batatas L</i>)

The kinetic modelling of thin layer drying is performed with various agricultural products such as seeds, grains, fruits and some plant species with economic importance. Sweet Potato (Ipomoea batatas L) is a widely consumed, and its industrialization is on the increase. The thin layer drying kinetics of sweet potato were experimentally investigated in a convective hot air dryer. Comparison was made between the experimental and model predicted moisture ratio by nonlinear regression analysis. Furthermore, the effect of drying temperature and slice thickness on the selected model constants was evaluated. From the experimental data obtained, it was observed that moisture ratio of the potato sample was directly proportional to the mass of the sample while the time of drying was inversely proportional and these two parameters (mass and time) were used to develop a mathematical model for the thin layer drying of sweet potatoes using a convective hot air dryer. The mathematical model developed was validated. The experimental data fitted the developed mathematical model and gave the Sum of Squares (SSE) value of 0.0001296 and coefficient of determination (R2) as unity. The parameters obtained from the kinetic modelling of the selected models studied could be used in designing low cost dryers for optimum drying conditions.

MATHEMATICAL MODELS TO DETERMINE OF THIN LAYER DRYING KINETIC OF GINGER SLICES

In the present work, the effects of some parametric values on the thin layer drying process of ginger slices were investigated. Drying was done in the laboratory by using cyclone type convective dryer. The drying air temperature was varied as 40, 50, 60 and 70 ˚C and the air velocity is 0.8, 1.5 and 3 m/s. All drying experiments had only falling rate period. The drying data were fitted to the twelve mathematical models and performance of these models was investigated by comparing the determination of coefficient (R2), reduced chi-square (χ2) and root mean square error (RMSE) between the observed and predicted moisture ratios. Among these models, drying model developed by Midilli and Kucuk model showed good agreement with the data obtained from the experiments.

Mathematical modeling on the thin layer drying kinetics of cassava chips in a multipurpose convective-type tray dryer heated by a gas burner

The drying kinetics of cassava chips in a newly developed multipurpose convective-type tray dryer heated by a gas burner (consisting of four drying compartments) was studied. The temperature of the drying air was 50 °C, and drying loads were 8 kg and 12 kg. Drying of cassava chips occurred in both heating and falling periods. It was found that cassava chips dried faster in the drying compartments that were closer to the heat source, that is, the first to the fourth drying compartments, consecutively. Twelve thin layer drying models were placed to the experimental moisture ratio data. The models were compared based on their coefficient of determination (R2), reduced mean square of deviation (χ2), and root means square error (RMSE) between experimental and predicted moisture ratios. From all the mathematical models investigated, the Page and Midilli et al. models satisfactorily described the drying behavior of cassava chips in all drying compartments with drying loads of 8 kg and 12 kg, respectively.

Selection and Verification of a Drying Model for Maize ( Zea mays L .) in Forced Convection Solar Grain Dryer

Various researchers have fitted experimental drying curves for various products to existing drying models. In this study, an experimental forced convection solar grain dryer was used to select the best fitting drying model for shelled maize. 0.04 m thick grain layer of shelled maize was dried an air velocity of 0.408 m/s and a 40°C drying air temperature. Using Root Mean Square Error (RMSE), Coefficient of Determination (R2) and Chi Square (2) the selected drying model was the one by Midilli et al. (2002), with R2, 2 and RMSE values of 0.9487, 0.4278 and 0.1723 respectively. The model coefficients were determined for drying air temperatures of 40, 45, 50 and 55°C. It was found that the predicted and experimental data agreed satisfactorily with R2 and RMSE values of 0.9225-0.9786 and 0.0325-0.0750 respectively. A computer simulation model was developed to predict moisture ratio at a given drying time.

Bioactive compounds’ contents, drying kinetics and mathematical modelling of tomato slices influenced by drying temperatures and time

This study investigated the influence of drying temperature, and time on antioxidant activity, phenolic, flavonoid, lycopene and β – carotene contents of tomato slices. It also evaluated the influence of drying process on drying kinetics, moisture diffusivity and activation energy. Oven processed tomato slices had temperature-dependent significant increase in antioxidant activity at 30 and 60 min, phenolic from 30 to 120 min and lycopene contents from 120 to 300 min. Significantly decreased contents of flavonoid and β – carotene were obtained for oven processed tomato slices with increasing drying temperature and time. Page model accurately predicted the drying process of tomato slices. Similarity between experimentally determined moisture ratio and Page predicted moisture ratio was obtained with high correlation (R2 = 0.9986). Effective moisture diffusivities indicated that drying process of tomato slices was temperature dependent while Arrhenius equation explained the relationship between activation energy and temperature.

Analyzing drying characteristics and modeling of thin layers of peppermint leaves under hot-air and infrared treatments

The drying kinetics of peppermint leaves was studied to determine the best drying method for them. Two drying methods include hot-air and infrared techniques, were employed. Three different temperatures (30, 40, 50°C) and air velocities (0.5, 1, 1.5m/s) were selected for the hot-air drying process. Three levels of infrared intensity (1500, 3000, 4500W/m2), emitter-sample distance (10, 15, 20cm) and air speed (0.5, 1, 1.5m/s) were used for the infrared drying technique. According to the results, drying had a falling rate over time. Drying kinetics of peppermint leaves was explained and compared using three mathematical models. To determine coefficients of these models, non-linear regression analysis was used. The models were evaluated in terms of reduced chi-square (χ2), root mean square error (RMSE) and coefficient of determination (R2) values of experimental and predicted moisture ratios. Statistical analyses indicated that the model with the best fitness in explaining the drying behavior of peppermint samples was the Logarithmic model for hot-air drying and Midilli model for infrared drying. Moisture transfer in peppermint leaves was also described using Fick’s diffusion model. The lowest effective moisture diffusivity (1.096×10−11m2/s) occurred during hot-air drying at 30°C using 0.5m/s, whereas its highest value (5.928×10−11m2/s) belonged to infrared drying using 4500W/m2 infrared intensity, 0.5m/s airflow velocity and 10cm emitter-sample distance. The activation energy for infrared and hot-air drying were ranged from 0.206 to 0.439W/g, and from 21.476 to 27.784kJ/mol, respectively.

Mass transfer characteristics of eggplant slices during length of continuous band dryer

This study presents a mathematical modeling of eggplant slice drying process in a continuous band dryer. The experiments for drying of eggplant slices were conducted at three air temperature levels of 45, 60, and 75 °C, inlet air velocities of 1, 1.5, and 2 m/s, and belt linear speeds of 2.5, 6.5, and 10.5 mm/s. To estimate the drying kinetics of eggplant slices, different mathematical models were utilized to fit the empirical data of thin layer drying. The models were compared based on their coefficients of determination (R2), reduced Chi squares (χ2) and root mean square errors (RMSE) between the experimental and predicted moisture ratios (MR). A feed and cascade forward with back-propagation algorithm was employed to predict the moisture ratio (MR) and drying rate (DR). The effective moisture diffusivity varied from 3.40 × 10−9 to 1.13 × 10−8 m2/s. The activation energy varied from 14.18 to 25.09 kJ/mol. The obtained results show that the feed forward back-propagation network with training algorithm of Levenberg–Marquardt, 4-5-5-2 topology, threshold functions of tansig–tansig–tansig can able to predict the moisture content and drying rate with R2 values of 0.9992 and 0.9726, respectively. Comparison of ANN results with mathematical models revealed that mathematical modeling yields better accuracy to predict the moisture content and drying rate of eggplant.

Effect of temperature on antioxidant capacity during drying process of mortiño (Vaccinium meridionale Swartz)

ABSTRACTThe mortiño fruit (Vaccinium meridionale Swartz) has been recognized as an excellent source of antioxidants, phenolic compounds, and anthocyanins. Drying conditions, particularly temperature, often lead to food quality degradation. The present study investigated the influence of drying temperature (40, 50, and 60°C) on antioxidant activity, anthocyanin, and phenol content of mortiño. Four different thin layer drying models of drying kinetics (Modified page, Newton, Henderson & Pabis and, two-terms) were fitted to the experimental data. For antioxidant capacity determination, oxygen radical absorbance capacity and ferric ion reducing antioxidant power assays were used. The results showed that antioxidant capacity, and phenolic and anthocyanin content all decreased with increasing temperature and drying time. It was observed that phenols and anthocyanins were conserved in greater amounts at 60°C with 34% (5.85 mg gallic acid/g dm) and 32% (2.36 mg Cyanidin-3-glucoside/g dm) preservation of initial content, respectively. Drying kinetics models were compared based on their R2 and root mean square error values between the experimental and predicted moisture ratios. The two-terms model was found to satisfactorily describe the drying curves for all temperatures evaluated, with a determination coefficient (R2) above 0.9987 and root mean square error lower than 0.0201.

Modeling of drying kiwi slices and its sensory evaluation.

In this study, monolayer drying of kiwi slices was simulated by a laboratory‐scale hot‐air dryer. The drying process was carried out at three different temperatures of 50, 60, and 70°C. After the end of drying process, initially, the experimental drying data were fitted to the 11 well‐known drying models. The results indicated that Two‐term model gave better performance compared with other models to monitor the moisture ratio (with average R 2 value equal .998). Also, this study used artificial neural network (ANN) in order to feasibly predict dried kiwi slices moisture ratio (y), based on the time and temperature drying inputs (x 1, x 2). In order to do this research, two main activation functions called logsig and tanh, widely used in engineering calculations, were applied. The results revealed that, logsig activation function base on 13 neurons in first and second hidden layers were selected as the best configuration to predict the moisture ratio. This network was able to predict moisture ratio with R 2 value .997. Furthermore, kiwi slice favorite is evaluated by sensory evaluation. In this test, sense qualities as color, aroma, flavor, appearance, and chew ability (tissue brittleness) are considered.

MATHEMATICAL MODELLING OF DRYING OF CHLORELLA SP., NEOCHLORIS CONJUNCTA AND BOTRYOCOCCUS BRAUNII AT DIFFERENT DRYING CONDITIONS

Drying is one of the most important process in the cultivation of microalgae. Performance of drying depends on appropriate drying methods. This study aimed (1) to determine the effect of different drying methods on the Chlorella sp., Neochloris conjuncta, and Botryococcus braunii strains and (2) to identify the most suitable model for drying methods. Chlorella sp., Neochloris conjuncta and Botryococcus braunii cultures were grown in basal medium in raceways at the greenhouse condition in Isparta province. During experiment, air temperature and relative humidity, wind speed, solar irradiance, wet and dry mass and temperature of microalgae biomass were measured. Drying applications continued for approximately 3 days. Microalgae were dried at solar tunnel, outdoor, and greenhouse conditions. Furthermore, the effect of drying methods such as open sun, solar tunnel, and greenhouse drying on drying time, and drying ratio of microalgae have been investigated. The drying data were applied to ten different mathematical models, namely, Newton, Page, Henderson and Pabis, Logarithmic, Midilli and Kucuk, Wang and Singh, Two Term, Two Term Exponential, Weibull and Logistic Equation Models. The performance of these models were compared according to the coefficient of determination, standard error of estimate and residual sum of squares, between the observed and predicted moisture ratios. Results showed that the Weibull equation gave the best prediction to the drying kinetics evidenced by coefficient of determination ranging from 0.9959 – 0.9992 for all drying methods. In addition, solar tunnel drying method has been determined to be the most suitable one among tested methods for drying of microalgae. Key words: Chlorella sp., Neochloris conjuncta, Botryococcus braunii, drying, mathematical modelling, microalgae

Mathematical modeling of microwave drying of crashed cotton stalks for man-made composite material

Drying characteristics, energy consumption and drying kinetics modeling of crashed cotton stalks dried in a microwave dryer were investigated in this research. A microwave dryer with an output power of 1000 W and 2450 MHz was employed, and the effects of material load ranging from 50 g to 250 g on drying time, drying rate, drying efficiency and specific energy consumption were evaluated. The results showed that drying rate decreased with drying duration. A rising rate period was followed by a falling rate period and the overall drying process occurred in the falling rate period. Six mathematical models were used to fit the drying rates data of crashed cotton stalks, and Midilli et al. model was found the best prediction model by comparing R2, RMSE and χ2 values between experimental and predicted moisture ratios. With decrease in material load from 250 g to 50 g, effective moisture diffusivity increased from 2.8668×10-8 m2/s to 7.9817×10-8 m2/s. Results also indicated that drying efficiency and specific energy consumption significantly increased with the increase of the material load. Average drying efficiency and specific energy consumption varied in the range of 7.52%-19.78% and 12.49- 35.90 MJ/kg water, respectively. There were a lowest energy consumption of 10.99 MJ/kg water and a highest drying efficiency of 17.13% at the material load level of 250 g. Keywords: mathematical modeling, crashed cotton stalks, man-made composite material, microwave drying, kinetics DOI: 10.3965/j.ijabe.20160902.2190 Citation: Wang H T, Li P, Guo K Q. Mathematical modeling of microwave drying of crashed cotton stalks for man-made composite material. Int J Agric & Biol Eng, 2016; 9(2): 171－178.

Isothermal hot air drying behavior of municipal sewage sludge briquettes coupled with lignite additive

Isothermal drying behaviors of the municipal sewage sludge briquettes were investigated based on a laboratory-scale hot air forced convective dryer at 1.5ms−1 air speed. The influences of drying temperatures (60–180°C) and lignite as a typical additive with 10% dosage on the drying kinetics of the sewage sludge were highlighted. A considerable shrinkage and cracking of the sewage sludge briquettes presented in the drying process, and there were many hollow cavities formed in the final drying stages. The average drying rates of the sewage sludge/lignite blend in the whole falling rate periods were higher than that of the raw sewage sludge. The Midilli models gave good agreement between experimental and predicted moisture ratios of the raw sewage sludge or sewage sludge/lignite blend with the highest R2. The apparent activation energies of the raw sewage sludge or sewage sludge/lignite blend in the first falling rate periods were higher than that in the second falling rate periods. Adding lignite could obviously improve the drying behavior of the sewage sludge briquettes over 120°C.

Determination of drying kinetics and convective heat transfer coefficients of ginger slices

In the present work, the effects of some parametric values on convective heat transfer coefficients and the thin layer drying process of ginger slices were investigated. Drying was done in the laboratory by using cyclone type convective dryer. The drying air temperature was varied as 40, 50, 60 and 70 °C and the air velocity is 0.8, 1.5 and 3 m/s. All drying experiments had only falling rate period. The drying data were fitted to the twelve mathematical models and performance of these models was investigated by comparing the determination of coefficient (R2), reduced Chi-square (χ2) and root mean square error between the observed and predicted moisture ratios. The effective moisture diffusivity and activation energy were calculated using an infinite series solution of Fick’s diffusion equation. The average effective moisture diffusivity values and activation energy values varied from 2.807 × 10−10 to 6.977 × 10−10 m2/s and 19.313–22.722 kJ/mol over the drying air temperature and velocity range, respectively. Experimental data was used to evaluate the values of constants in Nusselt number expression by using linear regression analysis and consequently, convective heat transfer coefficients were determined in forced convection mode. Convective heat transfer coefficient of ginger slices showed changes in ranges 0.33–2.11 W/m2 °C.