Transcript Controller Performance - Oklahoma State University

Equipment Limitations and
Challenges in Precision N
Management
R.K. Taylor, G. Dilawari, P. Bennur,
J.B. Solie, N. Wang, P. Weckler, and
W.R. Raun
Variable Rate Liquid Applicators
 Direct Injection
 Complex
 Time lag
 Fixed orifice nozzles
 Flow proportional to square root of pressure
 Difficult to achieve range in flow rates
 Pulse Width Modulation
 Variable orifice nozzles
 Pressure/Flow relationship is more complex
Objective
To determine the consistency among
nozzles and repeatability of individual
nozzles with respect to flow rate for
commercially available variable orifice
nozzles
Materials
 Nozzles: Eight of each type
 TurboDrop Nozzles (GreenLeaf
technologies): TDVR-02 and TDVR-03
 VeriTarget Nozzles (SprayTarget )
Test Stand
 Pump
 Wet boom
equipped with
TeeJet 3 nozzle
bodies
 Pressure relief
valve
 Throttling valve
Methodology
 Three nozzles were selected randomly and
tested on the three nozzle boom at 20, 30,
40, 50, 60 and 80 psi
 8 repetitions for each pressure
 Flow was adjusted to the three nozzles to
achieve the desired pressure.
 Outflow from nozzles was calculated by
measuring the volume of water collected
from each nozzle over a period of 30
seconds.
Data analysis
 Analysis of Variance was performed in SAS
9.1 (SAS, Cary, NC) using PROC ANOVA to
detect flow differences among nozzles.
 Data for each nozzle were analyzed by
pressure
 Means were separated using the LSD option
and at 0.01 level of significance
 Pressure-flow curves for each nozzle type
were plotted and compared with the
manufacturer’s pressure-flow data.
Results: TDVR-02
Pressure
(psi)
Mean
(gpm)
Min
(gpm)
Max
(gpm)
Range
(gpm)
20
0.17
0.15
0.19
0.04
9.56
30
0.20
0.18
0.23
0.05
8.88
40
0.23
0.20
0.27
0.07
9.44
50
0.33
0.32
0.37
0.05
4.68
60
0.44
0.43
0.46
0.03
2.46
80
0.60
0.57
0.63
0.06
2.59
CV (%)
Results: TDVR-02
1.00
Nozzle1
Nozzle2
Flow rate(gpm)
0.80
Nozzle3
Nozzle4
0.60
Nozzle5
Nozzle6
Nozzle7
Nozzle8
Manufacturer's data
0.40
0.20
0.00
10
20
30
40
50
60
Pressure(psi)
70
80
90
Results: TDVR-03
Pressure
(psi)
Mean
(gpm)
Min
(gpm)
Max
(gpm)
Range
(gpm)
20
0.24
0.22
0.35
0.13
19.35
30
0.31
0.27
0.37
0.10
10.89
40
0.33
0.31
0.38
0.07
8.74
50
0.45
0.42
0.47
0.05
3.20
60
0.58
0.56
0.60
0.04
2.04
80
0.78
0.75
0.87
0.12
5.31
CV (%)
Results: TDVR-03
Flow Rate (gpm)
1.00
Nozzle1
Nozzle2
Nozzle3
Nozzle4
Nozzle5
Nozzle6
Nozzle7
Nozzle8
Manufacturer's data
0.80
0.60
0.40
0.20
0.00
10
20
30
40
50
60
Pressure (psi)
70
80
90
Results: VeriTarget
Pressure
(psi)
Mean
(gpm)
Min
(gpm)
Max
(gpm)
Range
(gpm) CV (%)
20
0.17
0.16
0.22
0.06
12.34
30
0.42
0.37
0.48
0.11
7.18
40
0.81
0.76
0.89
0.13
6.65
50
1.00
0.96
1.04
0.08
2.96
60
1.12
1.10
1.16
0.06
2.32
80
1.32
1.29
1.35
0.06
1.51
Results: VeriTarget
1.6
Flow Rate (gpm)
1.4
1.2
Nozzle2
1
Nozzle3
Nozzle4
0.8
Nozzle5
0.6
Nozzle6
Nozzle7
0.4
Nozzle8
0.2
Manufacturer's data
0
10
20
30
40
50
60
Pressure (psi)
70
80
90
Conclusions
 Inconsistent behavior was observed
between the nozzles at different
pressures
 Repeatability of a nozzle was better
at pressures above 40 psi.
 Both the TurboDrop nozzles performed
according to manufacturer’s specification
 CV for VeriTarget nozzles, for most of the
nozzles, was around 10% which is
acceptable for spraying
Sensor v. Map Based VRA
 In a map based system, the controller
receives a rate change as the
applicator crosses into a new zone.
 However, with a sensor based system
the controller typically receives an
updated rate every second and does
not have the opportunity to stabilize.
Sensor Configurations
Accepted Resolution
14
12
Time, s
10
6 mph
9 mph
12 mph
15 mph
18 mph
8
6
4
2
0
0
20
40
60
80
Boom Width, ft
100
120
140
Materials
 Raven 440 controller
 Raven Fast Close valve
 Data acquisition with flow meter and
pressure transducers
Test Stand Schematic
Model
1st order valve response
Proportional Integral controller
Input Data
pass Obs Mean Minimum Maximum Std dev
Max rate Max rate
increase decrease
- L ha-1 1
162 168.4
121.4
205.8
19.6
42.8
-50.6
2
162 162.1
121.4
205.8
18.2
58.1
-27.5
3
149 179.6
134.5
205.8
17.4
50.3
-58.6
4
150 175.7
120.1
205.8
19.6
44.0
-47.2
5
175 168.2
118.3
205.8
24.4
34.9
-44.8
6
183 165.0
118.3
205.8
22.3
44.8
-36.8
7
88
175.8
118.3
205.8
19.5
58.6
-83.6
8
85
167.8
102.9
205.8
23.8
80.1
-53.3
Prescribed Rate
250
Prescription Rate, L/ha
200
150
100
50
0
0
20
40
60
80
100
Time, s
120
140
160
180
Model Output
300
Model Output, L/ha
250
y = 0.78x + 36.95
R2 = 0.45
200
150
100
50
0
0
50
100
150
Prescription Rate, L/ha
200
250
1 second lag
300
Model Output, L/ha
250
200
150
y = 1.00x - 0.89
R2 = 0.74
100
50
0
0
50
100
150
Prescription Rate, L/ha
200
250
Output Data
pass
0 sec lag
1 sec lag
r2
slope
Mean abs
error, L ha-1
r2
slope
Mean abs
error, L ha-1
1
0.45
0.78
11.6
0.74
1.00
6.0
2
0.45
0.76
11.5
0.87
1.04
4.8
3
0.35
0.67
12.8
0.84
1.03
6.1
4
0.35
0.67
14.7
0.84
1.04
7.0
5
0.67
0.87
11.6
0.93
1.02
5.1
6
0.62
0.85
11.5
0.93
1.04
5.1
7
0.26
0.61
14.7
0.85
1.11
6.9
8
0.24
0.58
18.6
0.83
1.08
11.2
Conclusions
 The modeled results showed a mean
absolute application error of 12.9 L ha-1.
 These results further indicate that the
predicted response lagged the prescribed
rate by approximately 1 second.
 This resulting misapplication could be
reduced by half if the controller delay was
reduced by 1 second.
Addendum
20
18
16
14
Rate
12
RX_RATE
RATE
10
8
6
4
2
0
180
200
220
240
260
Sample
280
300
320
340
Improved System ??
18
16
14
Rate
12
10
RX_RATE
RATE
8
6
4
2
0
1
14 27 40 53 66 79 92 105 118 131 144 157 170 183 196
Sample
Questions
Randy Taylor
[email protected]
405-744-5425