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Effects of Operating Parameters for Dynamic PWM Variable Spray System on Spray Distribution Uniformity
Agriculture 5th 5th IFAC IFAC Conference Conference on on Sensing, Sensing, Control Control and and Automation Automation for for 5th IFAC14-17, Conference on Sensing, Control and Automation for August 2016. Seattle, Washington, USA Agriculture Available online at www.sciencedirect.com Agriculture Agriculture August 14-17, 2016. Seattle, Washington, USA August 14-17, 2016. Seattle, Washington, USA August 14-17, 2016. Seattle, Washington, USA
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IFAC-PapersOnLine 49-16 (2016) 216–220 Effects of Operating Parameters for Dynamic PWM Variable Spray System on Effects of Operating Parameters for Dynamic PWM Variable Spray System on Effects for PWM Spray Distribution Uniformity Effects of of Operating Operating Parameters Parameters for Dynamic Dynamic PWM Variable Variable Spray Spray System System on on Spray Distribution Uniformity Huanyu Jiang, Lijun Zhang, Weinan Shi Spray Distribution Uniformity Spray Uniformity Huanyu Distribution Jiang, Lijun Zhang, Weinan Shi
Huanyu Huanyu Jiang, Jiang, Lijun Lijun Zhang, Zhang, Weinan Weinan Shi Shi College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, College of Biosystems Engineering and Food Science, University, Hangzhou 310058, China (Tel: 0571-88982140; e-mail:Zhejiang [email protected]) College Engineering and Zhejiang University, College of of Biosystems Biosystems Engineering and Food Food Science, Science, Zhejiang University, Hangzhou Hangzhou 310058, 310058, China (Tel: 0571-88982140; e-mail: [email protected]) China (Tel: 0571-88982140; e-mail: [email protected]) China (Tel: 0571-88982140; e-mail: [email protected])
Abstract: A dynamic PWM (Pulse Width Modulation) variable spray experiment platform was built up Abstract: dynamic PWM (Pulse Width Modulation) variable spray experiment platform was built up to study theA spray distribution characteristics of PWM spray progress. The homogeneity of single nozzle Abstract: A dynamic PWM (Pulse Width Modulation) variable spray experiment platform was built up Abstract: Aspray dynamic PWM (Pulse Width Modulation) variable spray The experiment platform wasCarmine built up to study the distribution characteristics of PWM spray progress. homogeneity of single nozzle dynamic spraying was evaluated based on this platform under different experiment condition. to study the spray distribution characteristics of PWM spray progress. The homogeneity of single nozzle to study the spray distribution characteristics of PWM spray progress. The homogeneity of single nozzle dynamic spraying was evaluated based on thiswas platform experiment condition. Carmine solution concentration-absorbance technology adoptedunder to getdifferent the CV (Coefficient of Variation) value dynamic spraying was evaluated based on platform under different experiment condition. Carmine dynamic spraying wascoverage. evaluated RSM based on this thiswas platform under different experiment Carmine solution concentration-absorbance technology adopted to get the CV (Coefficient of Variation) value of percent area spray (Response Surface Methodology) with three condition. variables including solution concentration-absorbance technology was adopted to get the CV (Coefficient of Variation) value solution concentration-absorbance technology adopted to get theof CVspray (Coefficient of Variation) value of percent area coverage. RSM (Response with three variables frequency, duty spray cycle of PWM control signal was and Surface forward Methodology) velocity was employed to including study the of percent area spray coverage. RSM (Response Surface Methodology) with three variables including of percent area spray coverage. RSM (Response Surface Methodology) with three variables including frequency, duty cycle of PWM control signal forward velocity of spray was employed to study the effects of these three factors on dynamic spray and distribution uniformity. Compared with previous common frequency, duty cycle of PWM control signal and forward velocity of spray was employed to study the frequency, dutyspraying, cyclefactors ofthe PWM control signal and forward velocity of Compared spray employed to study the effects these three on dynamic spray distribution uniformity. with previous common study ofof static research on this paper can evaluate the actual spraywas distribution uniformity of effects of these three factors on dynamic spray distribution uniformity. Compared with previous common effects of these three factors on dynamic spray distribution uniformity. Compared with previous common study of static spraying, the research on this paper can evaluate the actual spray distribution uniformity of dynamic spraying more accurately. Moreover, the platform and the research method can provide study spraying, the research on this can the actual spray distribution uniformity of study of of static static spraying, theaccurately. onMoreover, this paper paperspray can evaluate evaluate the actual spray uniformity of dynamic spraying more the platform and the research method can provide references for the settings ofresearch PWM variable-rate operation parameters in distribution practical plant protection dynamic spraying more accurately. Moreover, the platform and the research method can provide dynamic spraying more accurately. Moreover, the platform and the research method can provide references for the settings of PWM variable-rate spray operation parameters in practical plant protection work. references for references for the the settings settings of of PWM PWM variable-rate variable-rate spray spray operation operation parameters parameters in in practical practical plant plant protection protection work. work. © 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights work. Keywords: Dynamic variable spray, Pulse width modulation, Carmine solution, Spray reserved. distribution Keywords: Dynamic variable spray, Pulse width modulation, Carmine solution, Spray distribution uniformity, Coefficient of variation. Keywords: Dynamic spray, Keywords: Coefficient Dynamic variable variable spray, Pulse Pulse width width modulation, modulation, Carmine Carmine solution, solution, Spray Spray distribution distribution uniformity, of variation. uniformity, uniformity, Coefficient Coefficient of of variation. variation.
and measured the carmine solution content by and measured the carmine solution content by spectrophotometry. and and measured measured the the carmine carmine solution solution content content by by spectrophotometry. spectrophotometry. spectrophotometry. According to the above researches and considering the According to the above researches and considering discontinuous action of nozzles in dynamic spray progress,the a According to the above researches and considering the According to action the above researches and spray considering thea discontinuous of nozzles in dynamic progress, dynamic PWM variable spray experimental platform was discontinuous action of nozzles in dynamic spray progress, aa discontinuous action of nozzles inexperimental dynamic spray progress, dynamic PWM variable platform was built to study the effects ofspray operating parameters on dynamic dynamic PWM variable spray experimental platform was dynamic PWM variable spray experimental platform was built to study the effects of operating parameters on dynamic distribution uniformity. Carmine solution concentrationbuilt to study the effects of operating parameters on dynamic built to study the effects of operating parameters on dynamic distribution uniformity. absorbance technology wasCarmine adopted solution to get theconcentrationcoefficient of distribution uniformity. Carmine solution concentrationdistribution uniformity. Carmine solution concentrationabsorbance technology was adopted to get the coefficient of variation (CV) value of percent area spray coverage. absorbance technology was to the absorbance technology was adopted adopted to get getcoverage. the coefficient coefficient of of variation (CV) value of percent area spray variation (CV) value of percent area spray coverage. variation (CV) value of percent area spray coverage. 2. MATERIALS AND METHODS 2. MATERIALS AND METHODS 2. 2. MATERIALS MATERIALS AND AND METHODS METHODS 2.1 Dynamic PWM Variable Spray Experiment Platform 2.1 Dynamic PWM Variable Spray Experiment Platform 2.1 2.1 Dynamic Dynamic PWM PWM Variable Variable Spray Spray Experiment Experiment Platform Platform The experimental platform was designed as shown in Fig.1. The experimental platform designed in Fig.1. The platform was was designed as as shown shown in 3 1 platform was 2 designed as shown The experimental experimental in Fig.1. Fig.1. 1 1 1
RS232
3 3 3
2 2 2
RS232 RS232 RS232
9 9 9 9
13 12 12 12 13 13 13 14 15 15 15 15
14 14 14
M
12
M MM
1. INTRODUCTION 1. INTRODUCTION 1. 1. INTRODUCTION INTRODUCTION Environmentally friendly pesticide application technologies, Environmentally friendly pesticide application technologies, utilizing target spray or variable spray technologies, have Environmentally friendly pesticide application technologies, Environmentally friendly pesticide application technologies, utilizing target spray or variable spray technologies, have become increasingly important recent years (Loghavi et al., utilizing target or spray technologies, have utilizing target spray spray or variable variable spray technologies, have become increasingly important recent years (Loghavi et al., 2008). Adjusting solenoid valve by using PWM (Pulse Width become increasingly important recent years (Loghavi et become increasingly important years (Loghavi et al., al., 2008). Adjusting solenoid by PWM (Pulse Width Modulation) technology is valve a kindrecent ofusing variable spray method to 2008). Adjusting solenoid valve by using PWM (Pulse Width 2008). Adjusting solenoid valve by using PWM (Pulse Width Modulation) technology is a kind of variable spray method to control flow,technology which is easier to of be variable realized spray compared with Modulation) is a kind method to Modulation) technology is a kind of variable spray method to control flow, which is easier to be realized compared with other methods and also has less influence oncompared droplet sizes control flow, which is easier to be realized with control flow, which is easier to be realized compared with other methods also has less etinfluence on droplet sizes (Kunavut et al.,and 2000; Guzmán al., 2008). The flow of other methods and also has influence on sizes other methods and also has less less et influence on droplet droplet sizes (Kunavut et al., 2000; Guzmán al., 2008). The flow of PWM variable spray is adjusted by regulating opening and (Kunavut et 2000; et al., 2008). The flow of (Kunavut et al., al., 2000;is Guzmán Guzmán et al., 2008). which The flow of PWM variable spray adjusted by regulating opening and closing time of solenoid valve in one cycle, brings PWM variable spray is adjusted by regulating opening and PWM variable spray is adjusted by regulating opening and closing time solenoid in one cycle, which brings difficulty to of control the valve homogeneity of spray especially closing time of solenoid valve in one cycle, which brings closing time of solenoid valve inMany one scholars cycle, which bringsa difficulty to control the homogeneity of spray especially along the sprayer travel direction. have done difficulty to control the homogeneity of spray especially difficulty to control the homogeneity of spray especially along sprayer travel lot of the researches on the direction. model of Many PWMscholars variablehave spraydone flowaa along the sprayer travel direction. Many scholars have done along the sprayer travel Many scholars have done a lot of researches on the model of PWM variable spray flow but fewer researches ondirection. the spray distribution uniformity, lot of researches on the model of PWM variable spray flow lot of researches on the model of PWM variable spray flow but fewer researches on the spray distribution uniformity, especially the dynamiconuniformity. Tian et al. (2000) tested but fewer researches the distribution uniformity, but fewer the researches the spray spray Tian uniformity, especially dynamic uniformity. et al. (2000) tested 2D spatial static sprayondistribution ofdistribution two kinds of nozzles especially the dynamic uniformity. Tian et al. (2000) tested especially the dynamic uniformity. Tian et kinds al. (2000) tested 2D spatial static spray distribution of two of nozzles under condition of 206.7 kPa spray pressure, 10Hz PWM 2D spatial static spray distribution of two kinds of nozzles 2D spatial static spray distribution of two kinds of nozzles under condition 206.7 kPaand spray pressure, 10Hz PWM controlling signalof frequency 75% duty cycle, then a under condition of 206.7 spray pressure, 10Hz PWM under condition ofdynamic 206.7 kPa kPa spray pressure, 10Hz PWMa controlling signal frequency and 75% duty cycle, then model to simulate spray distribution was established controlling signal frequency and 75% cycle, then aa controlling frequency anddistribution 75% duty duty cycle, model to simulate but had notsignal beendynamic verifiedspray effectively. Weiwas et established al. then (2013) model to simulate dynamic spray distribution was established model tothe simulate dynamic spray distribution was established but had not been verified effectively. Wei et al. (2013) studied static spray deposition distribution characteristics but had not been verified effectively. Wei et al. (2013) but hadthe notstatic been verified effectively. Wei characteristics et al.droplets (2013) studied spray deposition distribution of PWM-based nozzle by using a matrix-styled studied the static spray deposition distribution characteristics studied the static spray deposition distribution characteristics of PWM-based nozzle by using aa matrix-styled droplets collection device. They established static deposition of PWM-based nozzle by using droplets of PWM-based nozzle byhollow-cone using a matrix-styled matrix-styled droplets collection device. They established static deposition distribution model of the nozzle and analysed collection device. They established static deposition collection device. They established static deposition distribution of the hollow-cone nozzle analysed the influencemodel of spray pressure, duty cycle and and frequency of distribution model of hollow-cone nozzle and analysed distribution model of the the hollow-cone nozzle and analysed the influence of spray pressure, duty cycle and frequency of PWM controlling signal on the spray deposition. While the the influence of spray pressure, duty cycle and frequency of the influence of spray pressure, duty cycle and frequency of PWM controlling signal on the spray deposition. While the matrix-styled droplets collection device can get static spray PWM controlling signal on the spray deposition. While the PWM controlling signal on the spray deposition. While the matrix-styled droplets collection device can get static spray deposition distribution conveniently, it’s not easy to measure matrix-styled droplets collection device can get spray matrix-styled droplets collection device can easy get static static spray deposition distribution conveniently, it’s not to measure the spray quality because the spray volume per unit area is deposition distribution conveniently, it’s not easy to measure deposition distribution conveniently, it’s not easy to measure the spray quality because the spray volume per unit area is small. Qiuquality et al. (2013) employed carmine solution asarea spray the spray because the spray volume per unit is the spray quality because the spraycarmine volume per unit area is small. Qiu et al. (2013) employed solution as spray liquid to measure the spray distribution indirectly. They used small. Qiu et al. (2013) employed carmine solution as spray small. Qiu et al. (2013) employed carmine solution as spray liquid to measure the spray distribution indirectly. They used glass samplers of 90 mm internal diameter to collect droplets liquid to the spray distribution indirectly. They used liquid to measure measure themm spray distribution indirectly. They used glass samplers of 90 internal diameter to collect droplets glass samplers of 90 mm internal diameter to collect droplets glass samplers of 90 mm internal diameter to collect droplets
8 10
8 8 8
7
5
7 7 7
5 5 5
5 65 5 5 6 6 6
4 4 4 4
10 10 10 11 11 11 11
Fig.1 The dynamic PWM variable spray experiment Fig.1 The dynamic PWM variable spray experiment platform Fig.1 variable Fig.1 The The dynamic dynamic PWM PWM variable spray spray experiment experiment platform platform platform
Copyright © 2016, 2016 IFAC 221Hosting by Elsevier Ltd. All rights reserved. 2405-8963 © IFAC (International Federation of Automatic Control) Copyright 2016 IFAC 221 Peer review© of International Federation of Automatic Copyright ©under 2016 responsibility IFAC 221Control. Copyright © 2016 IFAC 221 10.1016/j.ifacol.2016.10.040
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2.4 Experimental Methods
human-machine interface (1), STM32 controller (2), spray tank (3), pressure relief valve (4), ball valve (5), pressure tank (6), pressure sensor (7), one-way valve (8), air pump (9), electric motor (10), pressure gauge (11), filters (12), highspeed solenoid valve (13), nozzle(14), conveyer(15)
2.4.1 Calibration of Carmine Solution The Carmine solution absorption curve at the mass concentration of 0.04g/L, 0.06g/L and 0.08g/L are showed as Fig.3. From the picture, the maximum absorption wavelength is 508nm, which is the desirable detection wavelength to obtain stable measurement results.
The platform consisted of voltage supply and regulation part, man-machine control part and PWM variable spray part. The voltage supply and regulation part was made up of spray tank, pressure relief valve, ball valve, pressure tank, pressure sensor (Hangzhou Yeli Industrial Co. Ltd., China, type WMB2012-HS), one-way valve, air pump, electric motor and pressure gauge. Human-machine interface (Shenzhen Kinco Co. Ltd., China, type Eivew ET070) and STM32 controller formed the man-machine control part. And the PWM variable spray part consisted of solenoid valve (Chongqing Ke Si Valve Co. Ltd., China, type ZCB), fan nozzle (Spray systems Co., American, type H-VV9515) and speed regulating conveyer (Hangzhou Sandi NC Equipment Co. Ltd., China). In this platform, Air pump was driven by electric motor to supply pressure tank with air pressure. Frequency and duty cycle of PWM controlling signals can be written into humanmachine interface which communicated with STM32F407 controller through RS232 interface and Modbus communication protocol. Electric motor adjusted by frequency converter could drive the speed of conveyer up to 1m/s.
4
0.08g/l 0.06g/l 0.04g/l
Absorbance
3
2 (506, 1.1069) (506, 0.8556)
1
(506, 0.5813)
0
200
400
600
800
Wavelength (nm)
Fig.3 Carmine solution absorption curve
2.2 Droplets Collection Device
Five concentrations of carmine solution at 0.02g/L, 0.04g/L, 0.06g/L, 0.08g/L, 0.1g/L and 0.12g/L were prepared for calibration. Fig.4 shows that the mass concentration of carmine solution is linearly proportional (R2=1) to the absorbance and the deposition concentration could be calculated by the following formulas.
The droplets collection device was a matrix-styled catch tray of size 32cm*48cm as shown in Fig.2. The catch tray had 384 closely arranged round holes, the volume and diameter of each hole was 3ml and 2cm, respectively.
C=0.0733A-0.000917
(1) (2)
=3000/
Where C, A and L are the mass concentration of carmine solution (g/L), the measured absorbance, and the deposition concentration of carmine solution (µg/cm2), respectively. 0.12
Mass Concentration (g/L)
0.11
Fig.2 Droplets collection device 2.3 Experimental Solution Carmine solution was used instead of pesticide. And ultraviolet visible spectrophotometer (Molecular Devices CO., American, type SpectraMax M5) was employed to measure the absorbance of carmine solution. Due to the measurement range limitation of this devise, the mass concentration of the experimental solution was prepared into 10g/L.
222
0.1 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Absorbance Fig.4 Carmine solution standard concentration curve
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2.4.2 Evaluation Method of Spray Distribution Uniformity
BBD were coded in the range of -1, 0, and 1. According to preliminary experiment, the frequencies of PWM control signal were set to 5Hz, 7Hz and 9Hz, the duty cycles of PWM control signal were adjusted to 20%, 40% and 60%, forward velocities of spray were set to 0.5m/s, 0.6m/s and 0.7m/s. Based on the BBD design table, 15 times of experiments need to be run to get the wave data under different situations.
Carmine solution concentration-absorbance technology was applied to get the coefficient of variation (CV) value of percent area spray coverage to evaluate the uniformity of spray. CV value could be calculated by the following formulas. CV =
S=
n
∑(X i =1
i
S ×100% X 2
− X ) / (n − 1)
(3)
3. RESULTS AND DISCUSSION (4)
3.1 Model Fit The results of the experiment carried out in the laboratory are given in Table 1. Twelve runs were factorial experiments and others were zeros-point tests. The analysis of variance and the responses were achieved by Design-Expert 8.05 software.
Where S is the standard deviation of deposition concentrations of holes on one catch tray. Xi is the deposition concentration of carmine solution in each hole, µg/cm2. X is the average deposition concentration of holes on one catch tray, µg/cm2.
Table 1. Box-Behnken experimental design matrix and the responses
2.4.3 Dynamic PWM Variable Spray Experiment
Length (cm)
Deposition Concentration (µg/cm2)
Width (cm)
The spray pressure and height of nozzle of platform were set to 2MPa and 30cm, respectively. The process to perform the experiment was as follows: (1) Set the operating parameters such as frequency and duty cycle of PWM control signal, spray pressure, speed of conveyer, position and direction of nozzle. (2) Operate the experiment platform, and after the spraying and running of conveyer worked steadily, put the catch tray on the conveyer and collect the droplets. (3) After collection of droplets, dry the catch tray naturally. (4) Inject 3ml water into every hole to dissolve the carmine, then remove 200µl liquid from each hole into ELISA plate. (5) Use the ultraviolet visible spectrophotometer to measure the absorbance of each hole and apply the above formula to calculate CV value. The deposition concentrations of one catch tray could be described visually as fig.5 shows. Each of the experiments can produce such a distribution map.
Runs
Duty Cycle (X1, %)
Frequency (X2, Hz)
Velocity (X3, m/s)
CV (Y, %)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
40 (0) 20 (-1) 60 (+1) 40 (0) 40 (0) 60 (+1) 20 (-1) 60 (+1) 40 (0) 40 (0) 60 (+1) 20 (-1) 40 (0) 20 (-1) 40 (0)
9 (+1) 9 (+1) 5 (-1) 9 (+1) 5 (-1) 7 (0) 5 (-1) 9 (+1) 7 (0) 7 (0) 7 (0) 7 (0) 7 (0) 7 (0) 5 (-1)
0.7 (+1) 0.6 (0) 0.6 (0) 0.5 (-1) 0.5 (-1) 0.5 (-1) 0.6 (0) 0.6 (0) 0.6 (0) 0.6 (0) 0.7 (+1) 0.7 (+1) 0.6 (0) 0.5 (-1) 0.7 (+1)
18.36 16.747 22.555 7.717 27.313 7.796 53.644 8.418 20.267 18.007 20.187 32.064 16.216 15.892 43.852
The equation generated by Design-Expert software is listed as follow formula. Y = 107.312 - 1.269X1 - 29.157X2 + 152.721X3 + 0.142X1X2 0.473X1X3 - 7.37X2X3 + 2.315E-003X12 + 1.563X22 10.454X32 (5) Unreliable results may be produced unless the model exhibits adequate reliability (Omwamba et al., 2009), so it is always important to check whether the model is suitable for further analysis. The analysis of variance and statistical parameters of the model are listed in Table 2 and fit statistics for the models are showed in Table 3.
Fig.5 Distribution map of deposition concentrations on one catch tray RSM with three variables including frequency, duty cycle of PWM control signal and forward velocity of spray was employed to study effect of three factors on dynamic spray distribution uniformity. Three factors and three levels of Box-Behnken design (BBD) were utilized. The factors of the
The Model F-value of 20.98 implies the model is significant. There is only a 0.19% chance that a "Model F-Value" this large could occur due to noise. Values of "Pr>F" less than 223
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Predicted values of CV (%)
0.0500 indicate model terms are significant (Rožić et al., 2010). In this case X1, X2, X3, X1X2, X22 are significant model terms. The "Lack of Fit F-value" of 4.20 implies the Lack of Fit is not significant relative to the pure error. There is a 19.83% chance that a "Lack of Fit F-value" this large could occur due to noise which means suitability of models to accurately predict the variation (Quanhong et al., 2005).
219
The R2 and the adjusted R2 for response are slightly different and they are all larger than 0.9 which indicates that the relationship between response and independent variables are significant and the regression model is thought to be appropriate. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable.
CV (%)
Table 2. Analysis of variance and statistical parameters of the model for response Source
Sum of Squares
df
Model
2272.516
9
252.5018 20.98473 0.0019
X1
440.9114
1
440.9114 36.64293 0.0018
X2
1154.93
1
1154.93
X3
388.4381
1
388.4381 32.28202 0.0024
X 1X 2
129.5044
1
129.5044 10.76275 0.0219
X 1X 3
3.57399
1
3.57399
X 2X 3
8.690704
1
8.690704 0.722261 0.4342
X12
3.16578
1
3.16578
X2
144.3096
1
144.3096 11.99318 0.0180
X3
0.040353
1
0.040353 0.003354 0.9561
Residual 60.16322 Lack of 51.92126 Fit Pure Error 8.241961
5
12.03264
3
17.30709
2
4.12098
Cor Total 2332.679
14
2 2
Mean Square
F Value
Pr>F Actual values of CV (%) Fig.6 Comparison between predicted and actual values of CV (%)
95.98304 0.0002
3.2 Effects of Operating Parameters on Spray Distribution Uniformity
0.297024 0.6092
The response surfaces for the effects of forward velocity of spray, duty cycle and frequency of PWM control signal on spray distribution uniformity are prodeced by Design-Expert software and showed as Fig.7.
0.263099 0.6298
Y (CV, %) 4.19975
CV (%)
0.1983
Table 3. Fit Statistics for the models Model
R2
Adjusted R2
CV (%)
Adeq Precision
Y
0.9742
0.9278
15.81
15.864
Sometimes, R2 can not reflect whether the results obtained from the models are approximate to actual results sufficiently or not. Unless the model shows a satisfactory fit, poor or misleading results may be caused in proceeding with an investigation and optimization of the fitted response surface (Beg et al., 2003). In order to avoid this problem, actual versus predicted plots were developed and presented in Fig. 6. The actual and predicted response values lie reasonably close to the diagonal “y=x” line which suggests that the model is suitable (Karunanithy et al., 2011).
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X1 (Duty Cycle, %)
X2 (Frequency, Hz)
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Y (CV, %)
CV (%)
X1 (Duty Cycle, %)
X3 (Velocity, m/s)
CV (%)
Y (CV, %)
X3 (Velocity, m/s)
Kunavut, J., Schueller, J. K., & Mason, P. A. C. (2000). Continuous control of a sprayer pinch valve. Transactions of the ASAE, 43(4), 829. Guzmán, J. L., Rodríguez, F., Sánchez-Hermosilla, J., & Berenguel, M. (2008). Robust pressure control in a mobile robot for spraying tasks. Transactions of the ASABE, 51(2), 715-727. Tian, L., & Zheng, J. (2000). Dynamic deposition pattern simulation of modulated spraying. Transactions of the ASAE, 43(1), 5. Wei, X., Yu, D., Bai, J., & Jiang, S. (2013). Static spray deposition distribution characteristics of PWM-based intermittently spraying system. Transactions of the Chinese Society of Agricultural Engineering, 29(5), 1924. Qiu, B., Wang, L., Cai, D., Wu, J., Ding, G., & Guan, X. (2013). Effects of flight altitude and speed of unmanned helicopter on spray deposition uniform. Transactions of the Chinese Society of Agricultural Engineering, 29(24), 25-32. Omwamba, M., & Hu, Q. (2009). Antioxidant capacity and antioxidative compounds in barley (Hordeum vulgare L.) grain optimized using response surface methodology in hot air roasting. European Food Research and Technology, 229(6), 907-914. Rožić, L., Novaković, T., & Petrović, S. (2010). Modeling and optimization process parameters of acid activation of bentonite by response surface methodology. Applied Clay Science, 48(1), 154-158. Quanhong, L., & Caili, F. (2005). Application of response surface methodology for extraction optimization of germinant pumpkin seeds protein. Food chemistry, 92(4), 701-706. Li, Y., Liu, Z., Zhao, H., Xu, Y., & Cui, F. (2007). Statistical optimization of xylanase production from new isolated Penicillium oxalicum ZH-30 in submerged fermentation. Biochemical Engineering Journal, 34(1), 82-86. Beg, Q. K., Sahai, V., & Gupta, R. (2003). Statistical media optimization and alkaline protease production from Bacillus mojavensis in a bioreactor. Process Biochemistry, 39(2), 203-209. Karunanithy, C., & Muthukumarappan, K. (2011). Influence of extruder and feedstock variables on torque requirement during pretreatment of different types of biomass–A response surface analysis. Biosystems Engineering,109(1), 37-51.
X2 (Frequency, Hz)
Fig.7 Response surface for the effects of operating parameters on spray distribution uniformity Table 2 shows that forward velocity, duty cycle and frequency of PWM control signal all have significant effects on the spray distribution uniformity for dynamic PWM variable spray system. And the effects generated by these parameters are strengthened orderly. From Eq. (5) and Fig.7, duty cycle and frequency of PWM control signal have positive effectsjiao on improving the spray distribution uniformity. In contrast, raise the forward speed of spray can’t increase the uniformity. In addition, Table 2 also indicates that duty cycle and frequency have an interaction for each other. These results and also the research methods can provide references for the settings of PWM variable-rate spray operation parameters in practical plant protection work. REFERENCES Loghavi, M., & Mackvandi, B. B. (2008). Development of a target oriented weed control system. Computers and electronics in agriculture, 63(2), 112-118.
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IFAC-PapersOnLine 49-16 (2016) 216–220 Effects of Operating Parameters for Dynamic PWM Variable Spray System on Effects of Operating Parameters for Dynamic PWM Variable Spray System on Effects for PWM Spray Distribution Uniformity Effects of of Operating Operating Parameters Parameters for Dynamic Dynamic PWM Variable Variable Spray Spray System System on on Spray Distribution Uniformity Huanyu Jiang, Lijun Zhang, Weinan Shi Spray Distribution Uniformity Spray Uniformity Huanyu Distribution Jiang, Lijun Zhang, Weinan Shi
Huanyu Huanyu Jiang, Jiang, Lijun Lijun Zhang, Zhang, Weinan Weinan Shi Shi College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, College of Biosystems Engineering and Food Science, University, Hangzhou 310058, China (Tel: 0571-88982140; e-mail:Zhejiang [email protected]) College Engineering and Zhejiang University, College of of Biosystems Biosystems Engineering and Food Food Science, Science, Zhejiang University, Hangzhou Hangzhou 310058, 310058, China (Tel: 0571-88982140; e-mail: [email protected]) China (Tel: 0571-88982140; e-mail: [email protected]) China (Tel: 0571-88982140; e-mail: [email protected])
Abstract: A dynamic PWM (Pulse Width Modulation) variable spray experiment platform was built up Abstract: dynamic PWM (Pulse Width Modulation) variable spray experiment platform was built up to study theA spray distribution characteristics of PWM spray progress. The homogeneity of single nozzle Abstract: A dynamic PWM (Pulse Width Modulation) variable spray experiment platform was built up Abstract: Aspray dynamic PWM (Pulse Width Modulation) variable spray The experiment platform wasCarmine built up to study the distribution characteristics of PWM spray progress. homogeneity of single nozzle dynamic spraying was evaluated based on this platform under different experiment condition. to study the spray distribution characteristics of PWM spray progress. The homogeneity of single nozzle to study the spray distribution characteristics of PWM spray progress. The homogeneity of single nozzle dynamic spraying was evaluated based on thiswas platform experiment condition. Carmine solution concentration-absorbance technology adoptedunder to getdifferent the CV (Coefficient of Variation) value dynamic spraying was evaluated based on platform under different experiment condition. Carmine dynamic spraying wascoverage. evaluated RSM based on this thiswas platform under different experiment Carmine solution concentration-absorbance technology adopted to get the CV (Coefficient of Variation) value of percent area spray (Response Surface Methodology) with three condition. variables including solution concentration-absorbance technology was adopted to get the CV (Coefficient of Variation) value solution concentration-absorbance technology adopted to get theof CVspray (Coefficient of Variation) value of percent area coverage. RSM (Response with three variables frequency, duty spray cycle of PWM control signal was and Surface forward Methodology) velocity was employed to including study the of percent area spray coverage. RSM (Response Surface Methodology) with three variables including of percent area spray coverage. RSM (Response Surface Methodology) with three variables including frequency, duty cycle of PWM control signal forward velocity of spray was employed to study the effects of these three factors on dynamic spray and distribution uniformity. Compared with previous common frequency, duty cycle of PWM control signal and forward velocity of spray was employed to study the frequency, dutyspraying, cyclefactors ofthe PWM control signal and forward velocity of Compared spray employed to study the effects these three on dynamic spray distribution uniformity. with previous common study ofof static research on this paper can evaluate the actual spraywas distribution uniformity of effects of these three factors on dynamic spray distribution uniformity. Compared with previous common effects of these three factors on dynamic spray distribution uniformity. Compared with previous common study of static spraying, the research on this paper can evaluate the actual spray distribution uniformity of dynamic spraying more accurately. Moreover, the platform and the research method can provide study spraying, the research on this can the actual spray distribution uniformity of study of of static static spraying, theaccurately. onMoreover, this paper paperspray can evaluate evaluate the actual spray uniformity of dynamic spraying more the platform and the research method can provide references for the settings ofresearch PWM variable-rate operation parameters in distribution practical plant protection dynamic spraying more accurately. Moreover, the platform and the research method can provide dynamic spraying more accurately. Moreover, the platform and the research method can provide references for the settings of PWM variable-rate spray operation parameters in practical plant protection work. references for references for the the settings settings of of PWM PWM variable-rate variable-rate spray spray operation operation parameters parameters in in practical practical plant plant protection protection work. work. © 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights work. Keywords: Dynamic variable spray, Pulse width modulation, Carmine solution, Spray reserved. distribution Keywords: Dynamic variable spray, Pulse width modulation, Carmine solution, Spray distribution uniformity, Coefficient of variation. Keywords: Dynamic spray, Keywords: Coefficient Dynamic variable variable spray, Pulse Pulse width width modulation, modulation, Carmine Carmine solution, solution, Spray Spray distribution distribution uniformity, of variation. uniformity, uniformity, Coefficient Coefficient of of variation. variation.
and measured the carmine solution content by and measured the carmine solution content by spectrophotometry. and and measured measured the the carmine carmine solution solution content content by by spectrophotometry. spectrophotometry. spectrophotometry. According to the above researches and considering the According to the above researches and considering discontinuous action of nozzles in dynamic spray progress,the a According to the above researches and considering the According to action the above researches and spray considering thea discontinuous of nozzles in dynamic progress, dynamic PWM variable spray experimental platform was discontinuous action of nozzles in dynamic spray progress, aa discontinuous action of nozzles inexperimental dynamic spray progress, dynamic PWM variable platform was built to study the effects ofspray operating parameters on dynamic dynamic PWM variable spray experimental platform was dynamic PWM variable spray experimental platform was built to study the effects of operating parameters on dynamic distribution uniformity. Carmine solution concentrationbuilt to study the effects of operating parameters on dynamic built to study the effects of operating parameters on dynamic distribution uniformity. absorbance technology wasCarmine adopted solution to get theconcentrationcoefficient of distribution uniformity. Carmine solution concentrationdistribution uniformity. Carmine solution concentrationabsorbance technology was adopted to get the coefficient of variation (CV) value of percent area spray coverage. absorbance technology was to the absorbance technology was adopted adopted to get getcoverage. the coefficient coefficient of of variation (CV) value of percent area spray variation (CV) value of percent area spray coverage. variation (CV) value of percent area spray coverage. 2. MATERIALS AND METHODS 2. MATERIALS AND METHODS 2. 2. MATERIALS MATERIALS AND AND METHODS METHODS 2.1 Dynamic PWM Variable Spray Experiment Platform 2.1 Dynamic PWM Variable Spray Experiment Platform 2.1 2.1 Dynamic Dynamic PWM PWM Variable Variable Spray Spray Experiment Experiment Platform Platform The experimental platform was designed as shown in Fig.1. The experimental platform designed in Fig.1. The platform was was designed as as shown shown in 3 1 platform was 2 designed as shown The experimental experimental in Fig.1. Fig.1. 1 1 1
RS232
3 3 3
2 2 2
RS232 RS232 RS232
9 9 9 9
13 12 12 12 13 13 13 14 15 15 15 15
14 14 14
M
12
M MM
1. INTRODUCTION 1. INTRODUCTION 1. 1. INTRODUCTION INTRODUCTION Environmentally friendly pesticide application technologies, Environmentally friendly pesticide application technologies, utilizing target spray or variable spray technologies, have Environmentally friendly pesticide application technologies, Environmentally friendly pesticide application technologies, utilizing target spray or variable spray technologies, have become increasingly important recent years (Loghavi et al., utilizing target or spray technologies, have utilizing target spray spray or variable variable spray technologies, have become increasingly important recent years (Loghavi et al., 2008). Adjusting solenoid valve by using PWM (Pulse Width become increasingly important recent years (Loghavi et become increasingly important years (Loghavi et al., al., 2008). Adjusting solenoid by PWM (Pulse Width Modulation) technology is valve a kindrecent ofusing variable spray method to 2008). Adjusting solenoid valve by using PWM (Pulse Width 2008). Adjusting solenoid valve by using PWM (Pulse Width Modulation) technology is a kind of variable spray method to control flow,technology which is easier to of be variable realized spray compared with Modulation) is a kind method to Modulation) technology is a kind of variable spray method to control flow, which is easier to be realized compared with other methods and also has less influence oncompared droplet sizes control flow, which is easier to be realized with control flow, which is easier to be realized compared with other methods also has less etinfluence on droplet sizes (Kunavut et al.,and 2000; Guzmán al., 2008). The flow of other methods and also has influence on sizes other methods and also has less less et influence on droplet droplet sizes (Kunavut et al., 2000; Guzmán al., 2008). The flow of PWM variable spray is adjusted by regulating opening and (Kunavut et 2000; et al., 2008). The flow of (Kunavut et al., al., 2000;is Guzmán Guzmán et al., 2008). which The flow of PWM variable spray adjusted by regulating opening and closing time of solenoid valve in one cycle, brings PWM variable spray is adjusted by regulating opening and PWM variable spray is adjusted by regulating opening and closing time solenoid in one cycle, which brings difficulty to of control the valve homogeneity of spray especially closing time of solenoid valve in one cycle, which brings closing time of solenoid valve inMany one scholars cycle, which bringsa difficulty to control the homogeneity of spray especially along the sprayer travel direction. have done difficulty to control the homogeneity of spray especially difficulty to control the homogeneity of spray especially along sprayer travel lot of the researches on the direction. model of Many PWMscholars variablehave spraydone flowaa along the sprayer travel direction. Many scholars have done along the sprayer travel Many scholars have done a lot of researches on the model of PWM variable spray flow but fewer researches ondirection. the spray distribution uniformity, lot of researches on the model of PWM variable spray flow lot of researches on the model of PWM variable spray flow but fewer researches on the spray distribution uniformity, especially the dynamiconuniformity. Tian et al. (2000) tested but fewer researches the distribution uniformity, but fewer the researches the spray spray Tian uniformity, especially dynamic uniformity. et al. (2000) tested 2D spatial static sprayondistribution ofdistribution two kinds of nozzles especially the dynamic uniformity. Tian et al. (2000) tested especially the dynamic uniformity. Tian et kinds al. (2000) tested 2D spatial static spray distribution of two of nozzles under condition of 206.7 kPa spray pressure, 10Hz PWM 2D spatial static spray distribution of two kinds of nozzles 2D spatial static spray distribution of two kinds of nozzles under condition 206.7 kPaand spray pressure, 10Hz PWM controlling signalof frequency 75% duty cycle, then a under condition of 206.7 spray pressure, 10Hz PWM under condition ofdynamic 206.7 kPa kPa spray pressure, 10Hz PWMa controlling signal frequency and 75% duty cycle, then model to simulate spray distribution was established controlling signal frequency and 75% cycle, then aa controlling frequency anddistribution 75% duty duty cycle, model to simulate but had notsignal beendynamic verifiedspray effectively. Weiwas et established al. then (2013) model to simulate dynamic spray distribution was established model tothe simulate dynamic spray distribution was established but had not been verified effectively. Wei et al. (2013) studied static spray deposition distribution characteristics but had not been verified effectively. Wei et al. (2013) but hadthe notstatic been verified effectively. Wei characteristics et al.droplets (2013) studied spray deposition distribution of PWM-based nozzle by using a matrix-styled studied the static spray deposition distribution characteristics studied the static spray deposition distribution characteristics of PWM-based nozzle by using aa matrix-styled droplets collection device. They established static deposition of PWM-based nozzle by using droplets of PWM-based nozzle byhollow-cone using a matrix-styled matrix-styled droplets collection device. They established static deposition distribution model of the nozzle and analysed collection device. They established static deposition collection device. They established static deposition distribution of the hollow-cone nozzle analysed the influencemodel of spray pressure, duty cycle and and frequency of distribution model of hollow-cone nozzle and analysed distribution model of the the hollow-cone nozzle and analysed the influence of spray pressure, duty cycle and frequency of PWM controlling signal on the spray deposition. While the the influence of spray pressure, duty cycle and frequency of the influence of spray pressure, duty cycle and frequency of PWM controlling signal on the spray deposition. While the matrix-styled droplets collection device can get static spray PWM controlling signal on the spray deposition. While the PWM controlling signal on the spray deposition. While the matrix-styled droplets collection device can get static spray deposition distribution conveniently, it’s not easy to measure matrix-styled droplets collection device can get spray matrix-styled droplets collection device can easy get static static spray deposition distribution conveniently, it’s not to measure the spray quality because the spray volume per unit area is deposition distribution conveniently, it’s not easy to measure deposition distribution conveniently, it’s not easy to measure the spray quality because the spray volume per unit area is small. Qiuquality et al. (2013) employed carmine solution asarea spray the spray because the spray volume per unit is the spray quality because the spraycarmine volume per unit area is small. Qiu et al. (2013) employed solution as spray liquid to measure the spray distribution indirectly. They used small. Qiu et al. (2013) employed carmine solution as spray small. Qiu et al. (2013) employed carmine solution as spray liquid to measure the spray distribution indirectly. They used glass samplers of 90 mm internal diameter to collect droplets liquid to the spray distribution indirectly. They used liquid to measure measure themm spray distribution indirectly. They used glass samplers of 90 internal diameter to collect droplets glass samplers of 90 mm internal diameter to collect droplets glass samplers of 90 mm internal diameter to collect droplets
8 10
8 8 8
7
5
7 7 7
5 5 5
5 65 5 5 6 6 6
4 4 4 4
10 10 10 11 11 11 11
Fig.1 The dynamic PWM variable spray experiment Fig.1 The dynamic PWM variable spray experiment platform Fig.1 variable Fig.1 The The dynamic dynamic PWM PWM variable spray spray experiment experiment platform platform platform
Copyright © 2016, 2016 IFAC 221Hosting by Elsevier Ltd. All rights reserved. 2405-8963 © IFAC (International Federation of Automatic Control) Copyright 2016 IFAC 221 Peer review© of International Federation of Automatic Copyright ©under 2016 responsibility IFAC 221Control. Copyright © 2016 IFAC 221 10.1016/j.ifacol.2016.10.040
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2.4 Experimental Methods
human-machine interface (1), STM32 controller (2), spray tank (3), pressure relief valve (4), ball valve (5), pressure tank (6), pressure sensor (7), one-way valve (8), air pump (9), electric motor (10), pressure gauge (11), filters (12), highspeed solenoid valve (13), nozzle(14), conveyer(15)
2.4.1 Calibration of Carmine Solution The Carmine solution absorption curve at the mass concentration of 0.04g/L, 0.06g/L and 0.08g/L are showed as Fig.3. From the picture, the maximum absorption wavelength is 508nm, which is the desirable detection wavelength to obtain stable measurement results.
The platform consisted of voltage supply and regulation part, man-machine control part and PWM variable spray part. The voltage supply and regulation part was made up of spray tank, pressure relief valve, ball valve, pressure tank, pressure sensor (Hangzhou Yeli Industrial Co. Ltd., China, type WMB2012-HS), one-way valve, air pump, electric motor and pressure gauge. Human-machine interface (Shenzhen Kinco Co. Ltd., China, type Eivew ET070) and STM32 controller formed the man-machine control part. And the PWM variable spray part consisted of solenoid valve (Chongqing Ke Si Valve Co. Ltd., China, type ZCB), fan nozzle (Spray systems Co., American, type H-VV9515) and speed regulating conveyer (Hangzhou Sandi NC Equipment Co. Ltd., China). In this platform, Air pump was driven by electric motor to supply pressure tank with air pressure. Frequency and duty cycle of PWM controlling signals can be written into humanmachine interface which communicated with STM32F407 controller through RS232 interface and Modbus communication protocol. Electric motor adjusted by frequency converter could drive the speed of conveyer up to 1m/s.
4
0.08g/l 0.06g/l 0.04g/l
Absorbance
3
2 (506, 1.1069) (506, 0.8556)
1
(506, 0.5813)
0
200
400
600
800
Wavelength (nm)
Fig.3 Carmine solution absorption curve
2.2 Droplets Collection Device
Five concentrations of carmine solution at 0.02g/L, 0.04g/L, 0.06g/L, 0.08g/L, 0.1g/L and 0.12g/L were prepared for calibration. Fig.4 shows that the mass concentration of carmine solution is linearly proportional (R2=1) to the absorbance and the deposition concentration could be calculated by the following formulas.
The droplets collection device was a matrix-styled catch tray of size 32cm*48cm as shown in Fig.2. The catch tray had 384 closely arranged round holes, the volume and diameter of each hole was 3ml and 2cm, respectively.
C=0.0733A-0.000917
(1) (2)
=3000/
Where C, A and L are the mass concentration of carmine solution (g/L), the measured absorbance, and the deposition concentration of carmine solution (µg/cm2), respectively. 0.12
Mass Concentration (g/L)
0.11
Fig.2 Droplets collection device 2.3 Experimental Solution Carmine solution was used instead of pesticide. And ultraviolet visible spectrophotometer (Molecular Devices CO., American, type SpectraMax M5) was employed to measure the absorbance of carmine solution. Due to the measurement range limitation of this devise, the mass concentration of the experimental solution was prepared into 10g/L.
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0.1 0.09 0.08 0.07 0.06 0.05 0.04 0.03 0.02
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Absorbance Fig.4 Carmine solution standard concentration curve
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2.4.2 Evaluation Method of Spray Distribution Uniformity
BBD were coded in the range of -1, 0, and 1. According to preliminary experiment, the frequencies of PWM control signal were set to 5Hz, 7Hz and 9Hz, the duty cycles of PWM control signal were adjusted to 20%, 40% and 60%, forward velocities of spray were set to 0.5m/s, 0.6m/s and 0.7m/s. Based on the BBD design table, 15 times of experiments need to be run to get the wave data under different situations.
Carmine solution concentration-absorbance technology was applied to get the coefficient of variation (CV) value of percent area spray coverage to evaluate the uniformity of spray. CV value could be calculated by the following formulas. CV =
S=
n
∑(X i =1
i
S ×100% X 2
− X ) / (n − 1)
(3)
3. RESULTS AND DISCUSSION (4)
3.1 Model Fit The results of the experiment carried out in the laboratory are given in Table 1. Twelve runs were factorial experiments and others were zeros-point tests. The analysis of variance and the responses were achieved by Design-Expert 8.05 software.
Where S is the standard deviation of deposition concentrations of holes on one catch tray. Xi is the deposition concentration of carmine solution in each hole, µg/cm2. X is the average deposition concentration of holes on one catch tray, µg/cm2.
Table 1. Box-Behnken experimental design matrix and the responses
2.4.3 Dynamic PWM Variable Spray Experiment
Length (cm)
Deposition Concentration (µg/cm2)
Width (cm)
The spray pressure and height of nozzle of platform were set to 2MPa and 30cm, respectively. The process to perform the experiment was as follows: (1) Set the operating parameters such as frequency and duty cycle of PWM control signal, spray pressure, speed of conveyer, position and direction of nozzle. (2) Operate the experiment platform, and after the spraying and running of conveyer worked steadily, put the catch tray on the conveyer and collect the droplets. (3) After collection of droplets, dry the catch tray naturally. (4) Inject 3ml water into every hole to dissolve the carmine, then remove 200µl liquid from each hole into ELISA plate. (5) Use the ultraviolet visible spectrophotometer to measure the absorbance of each hole and apply the above formula to calculate CV value. The deposition concentrations of one catch tray could be described visually as fig.5 shows. Each of the experiments can produce such a distribution map.
Runs
Duty Cycle (X1, %)
Frequency (X2, Hz)
Velocity (X3, m/s)
CV (Y, %)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
40 (0) 20 (-1) 60 (+1) 40 (0) 40 (0) 60 (+1) 20 (-1) 60 (+1) 40 (0) 40 (0) 60 (+1) 20 (-1) 40 (0) 20 (-1) 40 (0)
9 (+1) 9 (+1) 5 (-1) 9 (+1) 5 (-1) 7 (0) 5 (-1) 9 (+1) 7 (0) 7 (0) 7 (0) 7 (0) 7 (0) 7 (0) 5 (-1)
0.7 (+1) 0.6 (0) 0.6 (0) 0.5 (-1) 0.5 (-1) 0.5 (-1) 0.6 (0) 0.6 (0) 0.6 (0) 0.6 (0) 0.7 (+1) 0.7 (+1) 0.6 (0) 0.5 (-1) 0.7 (+1)
18.36 16.747 22.555 7.717 27.313 7.796 53.644 8.418 20.267 18.007 20.187 32.064 16.216 15.892 43.852
The equation generated by Design-Expert software is listed as follow formula. Y = 107.312 - 1.269X1 - 29.157X2 + 152.721X3 + 0.142X1X2 0.473X1X3 - 7.37X2X3 + 2.315E-003X12 + 1.563X22 10.454X32 (5) Unreliable results may be produced unless the model exhibits adequate reliability (Omwamba et al., 2009), so it is always important to check whether the model is suitable for further analysis. The analysis of variance and statistical parameters of the model are listed in Table 2 and fit statistics for the models are showed in Table 3.
Fig.5 Distribution map of deposition concentrations on one catch tray RSM with three variables including frequency, duty cycle of PWM control signal and forward velocity of spray was employed to study effect of three factors on dynamic spray distribution uniformity. Three factors and three levels of Box-Behnken design (BBD) were utilized. The factors of the
The Model F-value of 20.98 implies the model is significant. There is only a 0.19% chance that a "Model F-Value" this large could occur due to noise. Values of "Pr>F" less than 223
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Predicted values of CV (%)
0.0500 indicate model terms are significant (Rožić et al., 2010). In this case X1, X2, X3, X1X2, X22 are significant model terms. The "Lack of Fit F-value" of 4.20 implies the Lack of Fit is not significant relative to the pure error. There is a 19.83% chance that a "Lack of Fit F-value" this large could occur due to noise which means suitability of models to accurately predict the variation (Quanhong et al., 2005).
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The R2 and the adjusted R2 for response are slightly different and they are all larger than 0.9 which indicates that the relationship between response and independent variables are significant and the regression model is thought to be appropriate. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable.
CV (%)
Table 2. Analysis of variance and statistical parameters of the model for response Source
Sum of Squares
df
Model
2272.516
9
252.5018 20.98473 0.0019
X1
440.9114
1
440.9114 36.64293 0.0018
X2
1154.93
1
1154.93
X3
388.4381
1
388.4381 32.28202 0.0024
X 1X 2
129.5044
1
129.5044 10.76275 0.0219
X 1X 3
3.57399
1
3.57399
X 2X 3
8.690704
1
8.690704 0.722261 0.4342
X12
3.16578
1
3.16578
X2
144.3096
1
144.3096 11.99318 0.0180
X3
0.040353
1
0.040353 0.003354 0.9561
Residual 60.16322 Lack of 51.92126 Fit Pure Error 8.241961
5
12.03264
3
17.30709
2
4.12098
Cor Total 2332.679
14
2 2
Mean Square
F Value
Pr>F Actual values of CV (%) Fig.6 Comparison between predicted and actual values of CV (%)
95.98304 0.0002
3.2 Effects of Operating Parameters on Spray Distribution Uniformity
0.297024 0.6092
The response surfaces for the effects of forward velocity of spray, duty cycle and frequency of PWM control signal on spray distribution uniformity are prodeced by Design-Expert software and showed as Fig.7.
0.263099 0.6298
Y (CV, %) 4.19975
CV (%)
0.1983
Table 3. Fit Statistics for the models Model
R2
Adjusted R2
CV (%)
Adeq Precision
Y
0.9742
0.9278
15.81
15.864
Sometimes, R2 can not reflect whether the results obtained from the models are approximate to actual results sufficiently or not. Unless the model shows a satisfactory fit, poor or misleading results may be caused in proceeding with an investigation and optimization of the fitted response surface (Beg et al., 2003). In order to avoid this problem, actual versus predicted plots were developed and presented in Fig. 6. The actual and predicted response values lie reasonably close to the diagonal “y=x” line which suggests that the model is suitable (Karunanithy et al., 2011).
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X1 (Duty Cycle, %)
X2 (Frequency, Hz)
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Y (CV, %)
CV (%)
X1 (Duty Cycle, %)
X3 (Velocity, m/s)
CV (%)
Y (CV, %)
X3 (Velocity, m/s)
Kunavut, J., Schueller, J. K., & Mason, P. A. C. (2000). Continuous control of a sprayer pinch valve. Transactions of the ASAE, 43(4), 829. Guzmán, J. L., Rodríguez, F., Sánchez-Hermosilla, J., & Berenguel, M. (2008). Robust pressure control in a mobile robot for spraying tasks. Transactions of the ASABE, 51(2), 715-727. Tian, L., & Zheng, J. (2000). Dynamic deposition pattern simulation of modulated spraying. Transactions of the ASAE, 43(1), 5. Wei, X., Yu, D., Bai, J., & Jiang, S. (2013). Static spray deposition distribution characteristics of PWM-based intermittently spraying system. Transactions of the Chinese Society of Agricultural Engineering, 29(5), 1924. Qiu, B., Wang, L., Cai, D., Wu, J., Ding, G., & Guan, X. (2013). Effects of flight altitude and speed of unmanned helicopter on spray deposition uniform. Transactions of the Chinese Society of Agricultural Engineering, 29(24), 25-32. Omwamba, M., & Hu, Q. (2009). Antioxidant capacity and antioxidative compounds in barley (Hordeum vulgare L.) grain optimized using response surface methodology in hot air roasting. European Food Research and Technology, 229(6), 907-914. Rožić, L., Novaković, T., & Petrović, S. (2010). Modeling and optimization process parameters of acid activation of bentonite by response surface methodology. Applied Clay Science, 48(1), 154-158. Quanhong, L., & Caili, F. (2005). Application of response surface methodology for extraction optimization of germinant pumpkin seeds protein. Food chemistry, 92(4), 701-706. Li, Y., Liu, Z., Zhao, H., Xu, Y., & Cui, F. (2007). Statistical optimization of xylanase production from new isolated Penicillium oxalicum ZH-30 in submerged fermentation. Biochemical Engineering Journal, 34(1), 82-86. Beg, Q. K., Sahai, V., & Gupta, R. (2003). Statistical media optimization and alkaline protease production from Bacillus mojavensis in a bioreactor. Process Biochemistry, 39(2), 203-209. Karunanithy, C., & Muthukumarappan, K. (2011). Influence of extruder and feedstock variables on torque requirement during pretreatment of different types of biomass–A response surface analysis. Biosystems Engineering,109(1), 37-51.
X2 (Frequency, Hz)
Fig.7 Response surface for the effects of operating parameters on spray distribution uniformity Table 2 shows that forward velocity, duty cycle and frequency of PWM control signal all have significant effects on the spray distribution uniformity for dynamic PWM variable spray system. And the effects generated by these parameters are strengthened orderly. From Eq. (5) and Fig.7, duty cycle and frequency of PWM control signal have positive effectsjiao on improving the spray distribution uniformity. In contrast, raise the forward speed of spray can’t increase the uniformity. In addition, Table 2 also indicates that duty cycle and frequency have an interaction for each other. These results and also the research methods can provide references for the settings of PWM variable-rate spray operation parameters in practical plant protection work. REFERENCES Loghavi, M., & Mackvandi, B. B. (2008). Development of a target oriented weed control system. Computers and electronics in agriculture, 63(2), 112-118.
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