Experimental optimization of microwave drying zinc oxide leach residues by response surface methodology
-
Weifeng Zhang
Weifeng Zhang is pursuing his doctorate at Kunming University of Science and Technology, China, where he is currently engaged in magnetic and thermal coupling of mechanical systems, and heat and mass transfer.
Junruo Chen is a PhD supervisor in Kunming University of Science and Technology, mainly engaged in mechanical design and theory, virtual design and manufacturing technology.
,
Guo Lin
Guo Lin has started his MSc at Kunming University of Science and Technology, China. He currently carries out research on microwave energy application, and metallurgy under the supervision of Professor Libo Zhang. His main research subject is the drying and calcination of rare earths by microwave heating.
Libo Zhang is a PhD supervisor in Kunming University of Science and Technology, and mainly engages in microwave heating in the application of metallurgy, chemical engineering, and material science.
Abstract
Response surface methodology (RSM) and Box-Behnken design (BBD) were applied to experiment optimization of microwave drying zinc oxide leach residues (ZOLR). The effects of different factors involving temperature, drying time, material mass, material thickness and their interaction on moisture content and dehydration rate of ZOLR were investigated. Two second-order polynomial models for moisture content and dehydration rate of ZOLR were established by multivariate regression analysis. Analysis of variance shows that all independent variable and quadratic terms of the four factors have significant influence on moisture content and dehydration rate of ZOLR. The optimum drying conditions are as follows: temperature is 93.41°C, drying time is 15.9 min, sample mass is 77.91 g and material thickness is 17 mm. The predicted values of moisture content and dehydration rate were 10.98% and 1 g/min, which were close to experimental values 11±0.03% and 1±0.01 g/min under the optimal conditions. Infrared spectra analysis show that no chemical reactions took place in the process of microwave dying ZOLR and only a part of the absorbed water was removed in the sample.
1 Introduction
Zinc oxide leach residues (ZOLR) are classified as solid waste produced from the process of zinc hydrometallurgy. Toxic elements such as lead, arsenic and cadmium, as well as metallic elements like zinc, indium and silver are contained in the residues [1]. After being stockpiled outside long-term and exposed to natural weathering and rain wash, the toxic elements permeate through soil and water, which will seriously pollute the surrounding environment and is harmful to the health and safety of humans [2], [3], [4], [5]. Because of a higher content of zinc, indium and silver, ZOLR is a kind of precious secondary resource. Consequently, harmless treatment of ZOLR must be conducted and increase the added value of zinc hydrometallurgy.
At present, the Waelz process, Ausmelt Technology and the Kivcet process are mainly used to deal with ZOLR and recovery of valuable metals [6], [7], [8]. Because moisture content of ZOLR is very high, the leach residues must be dried to lower moisture content in order to satisfy the needs of the recycling treatment technique. A rotary kiln, fluidized bed and tunnel kiln are usually applied to dry ZOLR. However, there are some disadvantages over these methods. Rotary kilns and tunnel kilns use gas, heavy oil or coal to provide heat, which have higher energy consumption, lower thermal efficiency and higher pollution [9], [10]. Fluidized beds also have some disadvantages such as uneven heating, high local temperature and a narrow application range [11]. ZOLR are a wet sticky material; the increase of zinc ion concentration and ferric hydroxide colloids are the main reason for the wet stickiness. The appearance of crusts on the surface of ZOLR will stop heat and mass transfer in the process of drying. The main advantages of microwave drying include a faster drying rate, a shorter drying time, lower energy consumption and heat and mass transfer in the same direction [12], [13]. Microwave drying is widely used in minerals, powders, food and wood [14], [15], [16], [17]. However, up to now, there has been no report on the research of microwave drying ZOLR.
Absorbing properties of ZOLR are lower compared to water [18]. Therefore, more energy is absorbed by moisture in the sample and reaches boiling point temperature while the ZOLR is kept at a lower temperature in the drying process. Energy saving and consumption reduction is realized because of the above reasons. So, microwave drying ZOLR has obvious advantages. The ionic mobility and ionic conduction increased with increased temperature, which led to the increase of dielectric constant [19], [20], [21]. For this reason, the absorbing properties of ZOLR increased with increased temperature. Sustainable microwave input will lead to high local temperature of the material [22], [23]. Microwave drying temperature should be limited to prevent a chemical reaction from taking place in ZOLR due to a high temperature. The present article mainly researches the relationship between microwave power, drying time, material mass and moisture content of material [24], [25]. However, there are few reports about the influence of thickness and moisture content on drying efficiency. Material thickness is a very important factor in design and optimization of a large-scale microwave drying system. Literature about the optimization of microwave drying of ZOLR using the response surface methodology (RSM) approach has not been reported.
The main objective of this paper is to estimate the influence of drying temperature, drying time, material mass and material thickness on moisture content and dehydration rate of ZOLR in the microwave drying process. A three-level-four-variable Box-Behnken Design (BBD) was built to optimize the four variables on the two responses of moisture content and dehydration rate. Infrared spectra analysis was conducted to determine whether chemical reactions appeared in the process of microwave drying ZOLR.
2 Materials and methods
2.1 Materials and main equipment
ZOLR in this experiment was supplied by a smelting enterprise in Yunnan Province of China, and the moisture content was 28.97%. The chemical compositions of ZOLR were examined by ICP-OES (Agilent Technologies Inc., CA, USA) chemical composition analysis and X-ray diffraction analysis (Shimadzu, Japan). The main chemical components of ZOLR are listed in Table 1, among which the contents of lead, sulfur and zinc are very high. Figure 1 shows the X-ray diffraction pattern of ZOLR; it can be seen that lead mainly exists in the form of PbSO4, PbS, while zinc exists mainly in the form of ZnFe2O4, ZnO.
Chemical compositions of zinc oxide leach residues (%).
| Zn | Pb | Fe | Cu | SiO2 | CaO |
| 2.81 | 40.57 | 0.7 | 0.0094 | 1.02 | 0.49 |
| As | Cd | Ge | In | S | F |
| 1.85 | 0.055 | 0.0011 | 497.6 g/t | 9.81 | 0.033 |
X-ray diffraction (XRD) pattern of zinc oxide leach residues.
The experiment equipment is box-type microwave drying equipment made by Key Laboratory of Unconventional Metallurgy, Ministry of Education, Kunming University of Science and Technology, Kunming, Yunnan, China. The microwave power is continuously adjustable between 0 W and 3000 W at a frequency of 2.45 GHz and the temperature is controllable. The schematic diagram of microwave drying equipment is shown in Figure 2. The core components of the drying equipment include five parts: two magnetrons with numerical control device, cooling them with recycled water when they work. The cavity, length, width and height are 215 mm, 350 mm and 330 mm, respectively. The electronic balance can continuously record material mass loss. A thermocouple is used to monitor the change of temperature in the drying process. The glass tray is employed to contain material.
The schematic of microwave drying system.
2.2 Experimental method
A certain quantity of ZOLR was weighed with an electronic balance at the beginning of the experiments and put on a glass tray. The loaded glass tray was put in the box-type microwave drying equipment. The thermocouple was attached to the center of the material and used to measure the temperature of it. Microwave power, temperature and drying time were set, then dehydration experiments were conducted.
The moisture content and dehydration rate of ZOLR were calculated using the following Eqs. (1) and (2), respectively:
where: wt is the moisture content of the sample at any time t, %; m0 is the initial mass of the sample, g; mt is the mass of the sample at any time t, g; and δ is the moisture content of the sample (wet base), 28.97%.
where: ν is dehydration rate, g/min; and Δt is microwave drying time, min.
2.3 Experimental design
Microwave power was fixed at 1.5 kW and drying temperature controlled at under 110°C. The effects of drying temperature, drying time, material mass and material thickness on moisture content and dehydration rate of ZOLR were determined. The disadvantages of single-factor include not being able to reveal the interaction effects and optimization process among these variables [26]. Nevertheless, RSM is a process parameter optimization method and integration of mathematical methods and statistical analysis [27], which is widely used in the fields of chemical industry, biology, medical science and metallurgy [28], [29], [30], [31].
Design-Expert 8.0 software (America SE Company) was used as optimization design software. The optimal experiment was based on the RSM of BBD, and the proper range of these variables included drying temperature (X1), drying time (X2), material mass (X3) and material thickness (X4). Moisture content (Y1) and dehydration rate (Y2) of ZOLR were regarded as the response value. The interrelationship between response values (Y1, Y2) and the experimental elements (X1, X2, X3, X4) were worked out by Eq. (3) [32], [33]:
where Y was the response function, β0 was a constant, βi , βii and βij were the linear, quadratic and interactive coefficients, respectively, Xi was the coded levels of independent variables and XiXj and Xj2 represented the interaction and quadratic terms, respectively.
3 Results and discussion
3.1 Effect of temperature on moisture content and dehydration rate
In order to study the effect of temperature on moisture content and dehydration rate of ZOLR, other experimental variables were set as follows: ZOLR was 60 g, material thickness was 10 mm and microwave drying time was 15 min.
The effects of temperature on moisture content and dehydration rate of the sample are shown in Figure 3. The moisture content had an obvious tendency to decline with increasing temperature. The decline rate was very fast between 70°C and 90°C, and it was comparatively slow between 90°C and 110°C. The moisture content of the sample was 5.35% at 90°C. The dehydration rate increased with increased temperature. The dehydration rate increased rapidly between 70°C and 90°C, while it increased slowly between 90°C and 110°C. The dehydration rates were, respectively, 1.00 g/min and 1.13 g/min at 90°C and 110°C. Therefore, 90°C was chosen as the center point of temperature for further RSM experiments.
The effect of temperature on moisture content and dehydration rate of zinc oxide leach residues.
3.2 Effect of drying time on moisture content and dehydration rate
In order to study the effect of drying time on moisture content and dehydration rate of ZOLR, other experimental variables were set as follows: ZOLR was 60 g, material thickness was 10 mm and drying temperature was 90°C.
The effects of drying time on moisture content and dehydration rate of the sample are shown in Figure 4. The moisture content and dehydration rate decreased synchronously with increased drying time. The moisture content decreased quickly between 5 min and 20 min, while the decline speed slowdown between 20 min and 25 min. The moisture content declined to 2.48% in 20 min. The dehydration rate was comparatively fast between 5 min and 15 min, and was moderate from 15 min to 25 min. Consideration of higher dehydration rate, 15 min was selected as the center point of drying time for further RSM experiment.
The effect of drying time on moisture content and dehydrating rate of zinc oxide leach residues.
3.3 Effect of mass on moisture content and dehydration rate
In order to study the effect of mass on moisture content and dehydration rate of ZOLR, other experimental variables were set as follows: temperature was 90°C, drying time was 15 min and material thickness was 10 mm.
The effects of sample mass on moisture content and dehydration rate of the sample are shown in Figure 5. The moisture content and dehydration rate increased synchronously with increased material mass. The moisture content increased slowly when the material weights were between 60 g and 90 g. The moisture content increased comparatively quickly when the material weights were between 90 g and 100 g. The moisture content was 8.37% when the material weight was 100 g. The dehydration rate increased in a straight line when the material weights were between 70 g and 90 g and increased slowly when the material weights were between 90 g and 100 g. Dehydration rate was 1.11 g/min when the material mass was 90 g. In comprehensive consideration of lower energy consumption and higher dehydration rate, material masses were selected between 70 g and 90 g.
The effect of material mass on moisture content and dehydration rate of zinc oxide leach residues.
3.4 Effect of material thickness on moisture content and dehydration rate
In order to study the effect of material thickness on moisture content and dehydration rate of ZOLR, other experimental variables were set as follows: temperature was 90°C, drying time was 15 min and material mass was 80 g.
The effects of material thickness on moisture content and dehydration rate of the samples are shown in Figure 6. The moisture content increased with increased material thickness of the sample. The moisture content increased comparatively quickly when material thickness was between 10 mm and 20 mm. The moisture content increased slowly when material thickness was between 20 mm and 30 mm, and the moisture content was 17.35% when material thickness was 20 mm. The dehydration rate decreased with increased material thickness of the sample. The dehydration rate decreased quickly when thickness was between 10 mm and 20 mm, and rather slow when thickness was between 20 mm and 30 mm. Consequently, material thickness should be selected between 10 mm and 20 mm.
The effect of material thickness on moisture content and dehydration rate of zinc oxide leach residues.
3.5 Optimization of microwave drying parameters
3.5.1 Development of a regression model
Based on the single-factor experiment, the proper ranges of the drying variables including temperature (X1), drying time (X2), material mass (X3) and material thickness (X4) were determined. Then, a three-level-four-factor BBD was applied to determine the best combination of drying variables for ZOLR. The factor level and code can be seen in Table 2. Twenty-nine designed experiments in the current BBD are shown in Table 3, and five replicate experiments at the center of the design were evaluated by a pure error sum of squares.
Variables and experimental design levels for response surface.
| Factor | Code | Levels of codes | ||
|---|---|---|---|---|
| –1 | 0 | 1 | ||
| Temperature (°C) | X1 | 80 | 90 | 100 |
| Time (min) | X2 | 10 | 15 | 20 |
| Mass (g) | X3 | 70 | 80 | 90 |
| Thickness (mm) | X4 | 10 | 15 | 20 |
Box–Behnken experimental design and results.
| Codes | X1 | X2 | X3 | X4 | Y1 (%) | Y2 (%) |
|---|---|---|---|---|---|---|
| 1 | 80.00 | 20.00 | 80.00 | 15.00 | 18.82 | 0.5 |
| 2 | 80.00 | 15.00 | 80.00 | 10.00 | 15.23 | 0.87 |
| 3 | 90.00 | 10.00 | 90.00 | 15.00 | 18.25 | 1.18 |
| 4 | 90.00 | 15.00 | 80.00 | 15.00 | 10.12 | 1.12 |
| 5 | 90.00 | 15.00 | 80.00 | 15.00 | 10.68 | 1.09 |
| 6 | 90.00 | 15.00 | 70.00 | 20.00 | 18.52 | 0.6 |
| 7 | 90.00 | 15.00 | 70.00 | 10.00 | 4.57 | 1.19 |
| 8 | 90.00 | 15.00 | 90.00 | 20.00 | 21 | 0.61 |
| 9 | 100.00 | 15.00 | 70.00 | 15.00 | 3.32 | 1.24 |
| 10 | 100.00 | 20.00 | 80.00 | 15.00 | 5.49 | 0.99 |
| 11 | 90.00 | 20.00 | 80.00 | 10.00 | 2.07 | 1.1 |
| 12 | 90.00 | 15.00 | 80.00 | 15.00 | 10.82 | 1.09 |
| 13 | 90.00 | 15.00 | 80.00 | 15.00 | 10.54 | 1.1 |
| 14 | 80.00 | 15.00 | 80.00 | 20.00 | 26.87 | 0.15 |
| 15 | 90.00 | 15.00 | 80.00 | 15.00 | 10.57 | 1.1 |
| 16 | 90.00 | 15.00 | 90.00 | 10.00 | 6.84 | 1.43 |
| 17 | 80.00 | 15.00 | 90.00 | 15.00 | 21.67 | 0.56 |
| 18 | 90.00 | 20.00 | 90.00 | 15.00 | 14.88 | 0.74 |
| 19 | 100.00 | 15.00 | 80.00 | 20.00 | 13.53 | 0.95 |
| 20 | 100.00 | 15.00 | 80.00 | 10.00 | 1.57 | 1.49 |
| 21 | 90.00 | 10.00 | 80.00 | 20.00 | 21.95 | 0.72 |
| 22 | 100.00 | 10.00 | 80.00 | 15.00 | 9.69 | 1.71 |
| 23 | 90.00 | 10.00 | 80.00 | 10.00 | 13 | 1.47 |
| 24 | 80.00 | 10.00 | 80.00 | 15.00 | 23.42 | 0.58 |
| 25 | 80.00 | 15.00 | 70.00 | 15.00 | 21.09 | 0.47 |
| 26 | 100.00 | 15.00 | 90.00 | 15.00 | 8.83 | 1.33 |
| 27 | 90.00 | 10.00 | 70.00 | 15.00 | 18.64 | 0.89 |
| 28 | 90.00 | 20.00 | 70.00 | 15.00 | 8.81 | 0.78 |
| 29 | 90.00 | 20.00 | 80.00 | 20.00 | 18.72 | 0.51 |
By exert multiple regression analysis on the experiment data, the drying variables and response variables were related by the following second-order polynomial Eqs. (4) and (5), respectively.
The analysis of variance results for moisture content [Eq. (4)] and dehydration rate [Equation (5)] are shown in Tables 4 and 5, respectively. The p value was used to estimate the significance of the model and regression coefficient. The model term and regression coefficient are significant if the p value was no more than 0.05 [34]. In Eq. (4), X1, X2, X3, X4, X1X3, X2X3, X2X4, X12, X22, X32, and X42 have significant effects to the model of moisture content. In Eq. (5), X1, X2, X3, X4, X1X2, X2X3, X3X4, X12, X22, X32, and X42 have significant effects to the model of dehydration rate. The p values for the models of moisture content and dehydration rate are smaller than 0.0001 (Tables 4 and 5) which indicate these models are highly significant. The determination coefficient (R2) was used to explain the relationship between actual values and predicted values [35]. The determination coefficients of the two models are all close to 1 (Tables 4 and 5), which means that these models can be used to accurately predict moisture content and dehydration rate of ZOLR in microwave drying.
Analysis of variance for response surface model of moisture content.
| Source | Sum of squares | Degree of freedom | Mean square | F-value | p-value (prob>F) |
|---|---|---|---|---|---|
| Model | 1315.68 | 14 | 93.98 | 85.83 | <0.0001 |
| X1 | 597.42 | 1 | 597.42 | 545.63 | <0.0001 |
| X2 | 108.96 | 1 | 108.96 | 99.52 | <0.0001 |
| X3 | 22.74 | 1 | 22.74 | 20.77 | 0.0004 |
| X4 | 498.07 | 1 | 498.07 | 454.89 | <0.0001 |
| X1X2 | 0.04 | 1 | 0.04 | 0.04 | 0.8512 |
| X1X3 | 6.08 | 1 | 6.08 | 5.55 | 0.0336 |
| X1X4 | 0.03 | 1 | 0.03 | 0.02 | 0.8807 |
| X2X3 | 10.43 | 1 | 10.43 | 9.53 | 0.0080 |
| X2X4 | 14.82 | 1 | 14.82 | 13.54 | 0.0025 |
| X3X4 | 0.01 | 1 | 0.01 | 0.01 | 0.9215 |
| X12 | 23.07 | 1 | 23.07 | 21.07 | 0.0004 |
| X22 | 37.74 | 1 | 37.74 | 34.47 | <0.0001 |
| X32 | 14.54 | 1 | 14.54 | 13.28 | 0.0027 |
| X42 | 9.01 | 1 | 9.01 | 8.22 | 0.0124 |
| Residual | 15.33 | 14 | 1.09 |
R2=0.9885; Adj.R2=0.977; Pred.R2=0.9345.
Analysis of variance for response surface model of dehydration rate.
| Source | Sum of squares | Degree of freedom | Mean square | F-value | p-value (Prob>F) |
|---|---|---|---|---|---|
| Model | 3.74 | 14 | 0.27 | 96.75 | <0.0001 |
| X1 | 1.75 | 1 | 1.75 | 633.59 | <0.0001 |
| X2 | 0.31 | 1 | 0.31 | 112.51 | <0.0001 |
| X3 | 0.04 | 1 | 0.04 | 13.97 | 0.0022 |
| X4 | 1.34 | 1 | 1.34 | 485.70 | <0.0001 |
| X1X2 | 0.10 | 1 | 0.10 | 37.12 | <0.0001 |
| X1X3 | 0.00 | 1 | 0.00 | 0.00 | 1.0000 |
| X1X4 | 0.01 | 1 | 0.01 | 2.94 | 0.1087 |
| X2X3 | 0.03 | 1 | 0.03 | 9.87 | 0.0072 |
| X2X4 | 0.01 | 1 | 0.01 | 2.32 | 0.1500 |
| X3X4 | 0.01 | 1 | 0.01 | 4.79 | 0.0460 |
| X12 | 0.08 | 1 | 0.08 | 30.64 | <0.0001 |
| X22 | 0.03 | 1 | 0.03 | 12.50 | 0.0033 |
| X32 | 0.05 | 1 | 0.05 | 19.76 | 0.0006 |
| X42 | 0.04 | 1 | 0.04 | 16.16 | 0.0013 |
| Residual | 0.04 | 14 | 0.00 |
R2=0.9898; Adj.R2=0.9795; Pred.R2=0.9417.
3.5.2 Effects of process variables on moisture content
In order to better visualize the interaction of the statistically significant factors from the statistical analysis, a three-dimensional response surface plot for the effects of the statistically significant factors on moisture content of ZOLR is shown in Figure 7. This type of plot was used to show interaction effects of two factors on the response values at one time, while another two factors were kept at zero coded level. The second-order effect of drying temperature, drying time, material mass and material thickness are clearly observed in Figure 7.
Three-dimensional response surface plot of moisture content: effects of the statistically significant interaction factors on moisture content of zinc oxide leach residues.
Figure 7A represents the interaction effects of temperature and mass on moisture content of ZOLR. The moisture content decreased with increased temperature and increased with increased mass. Under the interaction of temperature and mass, moisture content of the samples showed a trend to decline in microwave drying. The effect of temperature on moisture content seems to be more significant than mass based on the slope of the surface plot, which also can be demonstrated by higher F-value of mass in Table 4. With increased temperature and evaporation of surface moisture, larger gradients of temperature and steam pressure were formed between the center layer and surface of the material which speed up water gasification. Consequently, the moisture content of the sample decreased.
Figure 7B represents the interaction effects of drying time and mass on moisture content of ZOLR. The moisture content decreased with increased drying time and increased with increased mass. Under the interaction of drying time and mass, moisture content of the sample took on a declining trend in microwave drying. Both the slope of the surface plot and the F-value in Table 4 show that drying time has more significant influence on moisture content than mass. With increased drying time, more microwave energy was absorbed by the sample, and the more moisture was removed, less moisture was left in the sample.
Figure 7C represents the interaction effects of drying time and material thickness on moisture content of ZOLR. The moisture content decreased with increased drying time and increased with increased thickness. Under the interaction of drying time and material thickness, moisture content of the sample took on increased trend in microwave drying. Both the slope of the surface plot and the F-value in Table 4 show that material thickness has a more significant influence on moisture content than drying time. This is because with the increased material thickness, microwave energy absorbed by the sample also ceased which resulted in decreased gradients of temperature and steam pressure. Meanwhile, transmission time of water increased from inside to outside of the sample; in unit time the dehydration decreased which can account for the increased moisture content of the sample.
3.5.3 Effects of process variables on dehydration rate
Figure 8 shows a three-dimensional response surface plot for the effects of statistically significant factors on dehydration rate of ZOLR. The second-order effects of drying temperature, drying time, material mass and material thickness are also clearly observed in Figure 8.
Three-dimensional response surface plot of dehydration rate: effects of the statistically significant interaction factors on dehydration rate of zinc oxide leach residues.
Figure 8A represents the interaction effects of temperature and drying time on dehydration rate of ZOLR. The dehydration rate increased with increased temperature and slightly decreased with increased drying time. Under the interaction of temperature and drying time, dehydration rate of the sample took on a trend to increase in microwave drying. Both the slope of the surface plot and the F-value in Table 5 show that temperature has a more significant influence on dehydration rate than drying time. With increased temperature, more heat energy was absorbed by moisture in the sample and reached boiling point temperature rapidly. Meanwhile, the diffusion ratio of moisture from internal to surface of the sample was accelerated.
Figure 8B represents the interaction effects of drying time and mass on the dehydration rate of ZOLR. The dehydration rate decreased with increased drying time and slightly increased with increased mass. Under the interaction of drying time and mass, dehydration rate of the sample had a trend to decrease in microwave drying. The F-value in Table 5 shows that drying time has a more significant influence on dehydration rate than mass. The mixture of water and sand-like are usually classified into three layers: the free water layer, the hygroscopic water layer and the capillary water layer [36]. In the latter stages of drying, the mobility of water decreased with decreased free water, which resulted in decreased dehydration rate.
Figure 8C represents the interaction effects of material thickness and mass on dehydration rate of ZOLR. The dehydration rate slightly increased with increased mass and decreased with increased thickness. Under the interaction of material thickness and mass, dehydration rate of the sample showed a trend to decrease in microwave drying. Both the slope of the surface plot and the F-value in Table 5 show that material thickness has a more significant influence on dehydration rate than mass. With increased thickness, microwave energy absorbed by the sample was mainly used to raise temperature of its own. The sample entered a falling rate period of drying.
3.5.4 Parameter optimization and validation
In order to get the desired moisture content as well as keeping a higher dehydration rate of ZOLR in microwave drying, the optimum drying parameters for microwave drying ZOLR were obtained from Design-Expert 8.0 software and presented as follows: temperature was 93.41°C, drying time was 15.9 min, sample mass was 77.91 g and material thickness was 17 mm (Table 6). Under the optimum conditions, the predicted data of moisture content and dehydration rate were 10.98% and 1 g/min, while the experimental values were 11±0.03% and 1±0.01 g/min (n=5), respectively. The difference between the actual value and the predicted value was not significant (p>0.05), which indicates that these models are adequate for the drying process.
Predicted and experimental values at optimum conditions.
| Temperature X1/°C | Time X2/min | Mass X3/g | Thickness X4/mm | Moisture content (%) | Dehydration rate (g/min) | ||
|---|---|---|---|---|---|---|---|
| Predicted | Actual | Predicted | Actual | ||||
| 93.41 | 15.9 | 77.91 | 17 | 10.98 | 11±0.03 | 1 | 1.0±0.01 |
3.6 Infrared spectrum analysis
Figure 9 shows the comparison of infrared spectra (Shimadzu, Japan) of the ZOLR before and after microwave drying.
The comparison of infrared spectrum of zinc oxide leach residues before and after drying.
The infrared spectrum was used to validate the effect of microwave drying on ZOLR. The absorption peak and the molecule bending-stretching vibration frequency of water appear near 1640 cm−1 and 3400 cm−1, respectively [37], [38]. The absorption peak decreased near 3400 cm−1 which means that a part of the adsorbed water in the sample had been removed. We found that the absorption peak of crystal water (near 1640 cm−1) remains the same, which means that some crystal water was contained in the dried ZOLR. Other absorption peaks were not changed in the infrared spectra, so we can conclude that there is no chemical reaction during the process of microwave drying ZOLR under controlled temperature. The infrared spectrum analysis further proved feasibility of microwave drying ZOLR.
4 Conclusions
Based on the single-factor experiment, RSM was applied to optimize the experiments of microwave drying ZOLR. The analysis of variance results showed that temperature, drying time, material mass, material thickness and quadratic terms of process parameters all had significant effects on moisture content and dehydration rate of ZOLR. The confirmed experimental optimum conditions for the ZOLR were as follows: temperature 93.41°C, drying time 15.9 min, sample mass 77.91 g, material thickness 17 mm and predicted values of moisture content and dehydration rate were 10.98% and 1 g/min. Under the optimum conditions, the experimental values are 11±0.03% and 1±0.01 g/min, respectively. The predicted values are very close to the experimental values.
The infrared spectrum of ZOLR shows that it is feasible to use microwave drying ZOLR. Only some absorbed water was removed and no chemical reaction was caused in the process of microwave drying ZOLR under temperature control.
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: 51464024
Funding statement: The authors are grateful for the financial support from National Natural Science Foundation of China (no. 51464024), the Young and Middle-aged Academic Technology Leader Backup Talent Cultivation Program in Yunnan Province, China (2012HB008) and the Yunnan Province Natural Science Foundation (2013FB096).
About the authors
Weifeng Zhang is pursuing his doctorate at Kunming University of Science and Technology, China, where he is currently engaged in magnetic and thermal coupling of mechanical systems, and heat and mass transfer.
Junruo Chen is a PhD supervisor in Kunming University of Science and Technology, mainly engaged in mechanical design and theory, virtual design and manufacturing technology.
Guo Lin has started his MSc at Kunming University of Science and Technology, China. He currently carries out research on microwave energy application, and metallurgy under the supervision of Professor Libo Zhang. His main research subject is the drying and calcination of rare earths by microwave heating.
Libo Zhang is a PhD supervisor in Kunming University of Science and Technology, and mainly engages in microwave heating in the application of metallurgy, chemical engineering, and material science.
Acknowledgments
The authors are grateful for the financial support from National Natural Science Foundation of China (no. 51464024), the Young and Middle-aged Academic Technology Leader Backup Talent Cultivation Program in Yunnan Province, China (2012HB008) and the Yunnan Province Natural Science Foundation (2013FB096).
References
[1] Esteller MV, Dominguez-Mariani E, Garrido SE, Aviles M. Environ. Earth Sci. 2015, 74, 2893–2906.10.1007/s12665-015-4315-9Search in Google Scholar
[2] Piatak NM, Seal II RR. Appl. Geochem. 2010, 25, 302–320.10.1016/j.apgeochem.2009.12.001Search in Google Scholar
[3] Mihaljevic M, Ettler V, Vanek A, Penizek V, Svoboda M, Kribek B, Sracek O, Mapani BS, Kamona AF. Water Air Soil Pollut. 2015, 12, 226.10.1007/s11270-015-2440-4Search in Google Scholar
[4] Zhang L, Ye X, Feng H, Jing Y, Ouyang T, Yu X, Liang R, Gao C, Chen W. Mar. Pollut. Bull. 2007, 54, 974–982.10.1016/j.marpolbul.2007.02.010Search in Google Scholar PubMed
[5] Salehipour M, Ghorbani H, Kheirabadi H, Afyuni M. Hum. Ecol. Risk Assess. 2015, 21, 1920–1935.10.1080/10807039.2014.1002292Search in Google Scholar
[6] Buse R, Mombelli D, Mapelli C. Metall. Ital. 2014, 106, 19–27.Search in Google Scholar
[7] Choi CY, Lee YH. Waste Treat. Clean Technol. 1999, 2, 1613–1622.Search in Google Scholar
[8] Slobodkin LV, Kluev GF. Zinc Lead Process 1998, 51, 715–722.Search in Google Scholar
[9] Forero Núñez CA, López Ma GA, Sierra Vargas FE. Tecnura 2015, 19, 93–105.10.14483/udistrital.jour.tecnura.2015.1.a07Search in Google Scholar
[10] Oba R, Possamai TS, Nicolau VP. Appl. Therm. Eng. 2014, 63, 59–65.10.1016/j.applthermaleng.2013.10.063Search in Google Scholar
[11] Goksu EI, Sumnu G, Esin, A. J. Food Eng. 2005, 66, 463–468.10.1016/j.jfoodeng.2004.04.017Search in Google Scholar
[12] Raghavan GSV, Rennie TJ, Sunjka PS, Orsat V, Phaphuangwittayakul W, Terdtoon P. Braz. J. Chem. Eng. 2005, 22, 195–201.10.1590/S0104-66322005000200005Search in Google Scholar
[13] Tuan Quoc L, Jittanit W. J. Stored Prod. Res. 2015, 61, 1–8.10.1016/j.jspr.2015.01.004Search in Google Scholar
[14] Dadali G, Apar DK, Ozbek B. Drying Technol. 2007, 25, 917–924.10.1080/07373930701372254Search in Google Scholar
[15] Wray D, Ramaswamy HS. Drying Technol. 2015, 33, 769–783.10.1080/07373937.2014.985793Search in Google Scholar
[16] Ouertani S, Hassini L, Azzouz S, Torres SS, Belghith A, Koubaa A. Drying Technol. 2015, 33, 1234–1242.10.1080/07373937.2015.1022828Search in Google Scholar
[17] Zhang Y, Jia K, Cai L, Shi SQ. Drying Technol. 2013, 31, 666–671.10.1080/07373937.2012.753610Search in Google Scholar
[18] Omar R, Idris A, Yunus R, Khalid K, Isma MIA. Fuel 2011, 90, 1536–1544.10.1016/j.fuel.2011.01.023Search in Google Scholar
[19] Al-Muhtaseb AH, Hararah MA, Megahey EK, McMinn WAM, Magee TRA. J. Food Eng. 2010, 98, 84–92.10.1016/j.jfoodeng.2009.12.013Search in Google Scholar
[20] Ahmed J, Ramaswamy HS, Raghavan VGS. J. Food Eng. 2007, 82, 351–358.10.1016/j.jfoodeng.2007.02.049Search in Google Scholar
[21] Guo W, Zhu X. Food Bioprocess Tech. 2014, 7, 3591–3601.10.1007/s11947-014-1375-xSearch in Google Scholar
[22] Uyar R, Bedane TF, Erdogdu F, Palazoglu TK, Farag KW, Marra F. J. Food Eng. 2015, 146, 163–171.10.1016/j.jfoodeng.2014.08.018Search in Google Scholar
[23] Nikolic B, Milojevic N, Stanisavljev D, Knezevic-Vukcevic J. Arch. Biol. Sci. 2014, 66, 721–728.10.2298/ABS1402721NSearch in Google Scholar
[24] Hirun S, Utama-ang N, Roach PD. J. Food Sci. Tech. Mys. 2014, 51, 2127–2133.10.1007/s13197-012-0709-9Search in Google Scholar PubMed PubMed Central
[25] Niu H, Li Y, Lei Y, Zhang L, Peng J, Guo S. Arab. J. Sci. Eng. 2012, 37, 65–73.10.1007/s13369-011-0165-2Search in Google Scholar
[26] Bas D, Boyaci IH. J. Food Eng. 2007, 78, 836–845.10.1016/j.jfoodeng.2005.11.024Search in Google Scholar
[27] Box GEP. J. Qual. Technol. 1999, 31, 67–72.10.1080/00224065.1999.11979897Search in Google Scholar
[28] Duan Xh, Srinivasakannan C, Liang Js. J Taiwan Inst. Chem. E 2014, 45, 1618–1627.10.1016/j.jtice.2014.08.031Search in Google Scholar
[29] Ilaiyaraja N, Likhith K, Babu GRS, Khanum F. Food Chem. 2015, 173, 348–354.10.1016/j.foodchem.2014.10.035Search in Google Scholar PubMed
[30] Arslan A, Veli S, Bingol D. Sep. Purif. Technol. 2014, 132, 561–567.10.1016/j.seppur.2014.05.036Search in Google Scholar
[31] Balasubramanian V, Senthilkumar J, Balasubramanian M. J. Reinf. Plast. Comp. 2008, 27, 1603–1613.10.1177/0731684407082629Search in Google Scholar
[32] Wu J, Yu D, Sun H, Zhang Y, Zhang W, Meng F, Du X. Ind. Crop Prod. 2015, 69, 68–75.10.1016/j.indcrop.2015.01.053Search in Google Scholar
[33] Afshari K, Samavati V, Shahidi SA. Int. J. Biol. Macromol. 2015, 74, 558–567.10.1016/j.ijbiomac.2014.07.023Search in Google Scholar PubMed
[34] Senthilkumar N, Tamizharasan T, Gobikannan S. Arab. J. Sci. Eng. 2014, 39, 8015–8030.10.1007/s13369-014-1320-3Search in Google Scholar
[35] Fu J, Zhao Y, Wu Q. J. Hazard Mater. 2007, 144, 499–505.10.1016/j.jhazmat.2006.10.071Search in Google Scholar PubMed
[36] Liu YH, Tang JM, Mao ZH. J. Food Eng. 2009, 93, 453–459.10.1016/j.jfoodeng.2009.02.012Search in Google Scholar
[37] Karelin AI, Leonova LS, Arsatov AV, Dobrovol’skii YA. Russ. J. Inorg. Chem. 2013, 58, 563–569.10.1134/S0036023613050094Search in Google Scholar
[38] Ganguly J, Banerjee BK. T Indian Ceram. Soc. 1985, 44, 17–18.10.1080/0371750X.1985.10822722Search in Google Scholar
©2017 Walter de Gruyter GmbH, Berlin/Boston
This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Articles in the same Issue
- Frontmatter
- In this issue
- Original articles
- A safe, efficient and environment friendly biosynthesis of silver nanoparticles using Leucaena leucocephala seed extract and its antioxidant, antimicrobial, antifungal activities and potential in sensing
- Preparation, characterization, and antioxidant activity of water-soluble oligochitosan
- Green synthesis and characterization of zero-valent iron nanoparticles using stinging nettle (Urtica dioica) leaf extract
- Green synthesis of silver nanoparticles using aqueous extract of dried Juglans regia green husk and examination of its biological properties
- Characterization and adsorption properties of La and Fe modified activated carbon for dye wastewater treatment
- Study on regeneration of spent activated carbon by using a clean technology
- Desulfurization of liquid fuel via extraction with imidazole-containing deep eutectic solvent
- Experimental optimization of microwave drying zinc oxide leach residues by response surface methodology
Articles in the same Issue
- Frontmatter
- In this issue
- Original articles
- A safe, efficient and environment friendly biosynthesis of silver nanoparticles using Leucaena leucocephala seed extract and its antioxidant, antimicrobial, antifungal activities and potential in sensing
- Preparation, characterization, and antioxidant activity of water-soluble oligochitosan
- Green synthesis and characterization of zero-valent iron nanoparticles using stinging nettle (Urtica dioica) leaf extract
- Green synthesis of silver nanoparticles using aqueous extract of dried Juglans regia green husk and examination of its biological properties
- Characterization and adsorption properties of La and Fe modified activated carbon for dye wastewater treatment
- Study on regeneration of spent activated carbon by using a clean technology
- Desulfurization of liquid fuel via extraction with imidazole-containing deep eutectic solvent
- Experimental optimization of microwave drying zinc oxide leach residues by response surface methodology
