research article parameters online detection and model

Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2013 Article ID 924698 7 pageshttpdxdoiorg1011552013924698

Research ArticleParameters Online Detection and Model Predictive Controlduring the Grain Drying Process

Lihui Zhang12 Helei Cui1 Hongli Li1 Feng Han1 Yaqiu Zhang1 and Wenfu Wu1

1 School of Biological and Agricultural Engineering Jilin University Changchun 130022 China2 School of Electrical and Electronic Information Engineering Jilin Jianzhu University Changchun 130118 China

Correspondence should be addressed to Wenfu Wu dzzlhhsinacom

Received 4 March 2013 Revised 12 May 2013 Accepted 12 May 2013

Academic Editor Shane Xie

Copyright copy 2013 Lihui Zhang et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

In order to improve the grain drying quality and automation level combined with the structural characteristics of the cross-flow circulation grain dryer designed and developed by us the temperature moisture and other parameters measuring sensorswere placed on the dryer to achieve online automatic detection of process parameters during the grain drying process A dryingmodel predictive control system was set up A grain dry predictive control model at constant velocity and variable temperature wasestablished in which the entire process was dried at constant velocity (ie precipitation rate per hour is a constant) and variabletemperature Combining PC with PLC and based on LabVIEW a system control platform was designed

1 Introduction

Drying is an important part of the grain processing afterharvest while drying monitoring and control are directlyrelated to themanagement of the drying operation the dryingefficiency and the quality of the dried grain At presentin the actual production the degree of automation andinformatization of dryingmonitoring is not high it is difficultto accurately monitor the whole drying process which affectsthe accuracy of the automatic control Grain drying processis a typical multivariable time-varying nonlinear and largedelay industrial production process the drying process isdifficult to achieve accurate automatic control [1]

In this paper specific to the drying process of thecross-flow circulation grain dryer a drying model predictivecontrol system was set up Using the virtual instrumenttechnology an automatic detection and control system forthe circulation grain dryer were developed to detect andcontrol the influencing factors in the drying process in orderto achieve the automation of the control process

2 Process Design of the Dryer

Shown in Figure 1 is the flow diagram of our developed cross-flow circulation grain dryer

Generally the cross-flow circulation grain dryer adoptsdrying and retarding processes In the cross-flow phasethe hot air flows vertically through the top-down grainlayer removes the excess moisture while in the retardingphase mainly the grain moisture gradient decreases and theparticles internal moisture migrates outward thus ensuringthe grain quality [2]

When operation the grain in the hopper is transferred tothe top of the dryer by an grain elevator then will be evenlythrown into the up storage by the up auger next moves downto the drying section by gravity The speed is controlled bythe revolution of the discharge motor In the drying sectionthe hot air passes the grain layers through hot air pipingand air plate After drying the moist air is discharged fromthe rear through duct by a centrifugal blower and thus thegrain is dried Then the dried grain enters into the downstorage for retarding and the retarded grain enters into thedown drying section to continue drying and thus a drying-retarding-drying-retarding process forms The dried grain isdischarged to the down storage by the discharge auger Atthe down storage by a chain driven blade the grain will betransferred into the feed hopper and will be conveyed againto the top of the dryer by the elevator again and again untilthe grains reach the target moisture content after discharge

2 Mathematical Problems in Engineering

1 2 3 4 5 6 7 8 9

10

11

12

Figure 1 Dryer process flow diagram (1) belt conveyor (2)precleaner (3) bucket elevator (4)warehouse for unprocessed grain(5)manual or pneumatic gate (6) flat belt conveyor (7) cooling fan(8) chain conveyor (9) heat source device (10) hot air blower (11)drying tower (12) bucket elevator

3 Parameters Online Detection

The system adopts distributed control as shown in Figure 2The system consists of a host (Industrial PC) PLC ambi-ent temperature and moisture sensors temperature sensorsresistive online moisture meter actuators and the relevantinstruments The main parameters include hot air tem-perature grain temperature offgas temperature ambienttemperature and humidity the raw grain moisture and dryerexit grain moisture the operative part includes PLC inverterand other peripheral lines

Data exchange between host and temperatureinstruments moisture meter and actuators through theRS232RS485 convertermodule I-7520 serial communicationdata bus On the one hand the host receives data of lowermachine from the temperature instruments and moisturesensors on the other hand the host sends control words tolower machine such as PLC temperature instruments andmoisture sensors to make corresponding controls [3 4]

Figure 3 shows sensors and controllers layout includingduct hot air temperatures (1198791 1198792) hot air temperaturesensors (1198793 1198794) grain temperature sensors (1198795 1198796) grainmoisture meters (M1 M2) grain level sensors (L1 L2)actuators (including PLC (C1) hot air speed inverter (C2)and grain discharge speed inverter (C3)) and ambient tem-perature and moisture sensors (1198797 H1) PLC inverter andambient temperature and moisture sensors are not shown inthe layout diagram

Moisture online detection is the key in the grain dryingcontrol system Our self-developed online moisture metersadopted by the system were installed in the inlet and outletelevators respectively

The resistive online moisture meter consists of a hostand a moisture sensor The moisture sensor is shown inFigure 4 For the moisture meter according to the measuredresistance in the relationship with the moisture content whenwheels crush the grain by measuring the resistance to onlinemeasure the grain moisture level The output resistance isalso associated with parameters such as grain temperaturevarieties and maturity With automatic and manual controlmodes the moisture meter has functions of grains switch-ing moisture setting moisture correction and temperature

compensation The target moisture value can be preset Themeter will respond upon arrival at this value to avoid overand insufficient drying The meter can be intermittent cycleautomatic start timing start online monitoring moisturewith high sensitivity and good stability

4 Cross-Flow Dryer Model Predictive Control

The characteristics of complexity time-varying hysteresisand nonlinearity in grain drying bring a lot of difficulties tothe traditional control applications MPC (model predictivecontrol) is a composite optimal control algorithm based onthe model by rolling implementation combined with feed-forward forecast and feedback correction Its control outputcan track set-points particularly effective for nonlinear andlarge delay control [5] Some foreign researchers developed anew type of model predictive controller for cross-flow dryerwhich was successfully applied to actual production [6]

The MPC system structure of the cross-flow circulationgrain dryer is shown in Figure 5 [6 7] including a feed-forward loop (consists of process model reverse processmodel and grain discharge speed optimizer) and a feedbackloop (parameter estimationcorrection) The system has thefollowing input parameters grain initial moisture grain tar-get drying moisture and dry air temperature The parameterthat needs to be optimized is the grain discharge speed

41 Drying Process Model Assume that the medium temper-ature can be precisely controlled and the ambient temperatureand humidity are relatively stable The grain is divided into anumber of thin layer to analyze in order to set up grain dryingcontrol model According to the structure of the dryingsection of the cross-flow circulation grain dryer and hot airdrying characteristics the grain columns of the dryer sectioncan be seen as a collection of a series of rectangles differentialunit in the vertical direction [7] as shown in Figure 6 Foreach rectangle differential unit a rectangular width is thethickness of the drying section that is the thickness of thegrain layer and the height is the differential Δ119884 or 119889119910 oflength of the drying section it may be sufficiently small Alsoassume that the hot air temperature and humidity change orthe difference in the 119884 direction is negligible

Constant velocity (equal precipitation rate) variable tem-perature control and target control are combined to controlthe grain precipitation rate within less than 15h Comparetarget moisture with the measured moisture and stop dryingafter the safe moisture or the final set value arrived

First we assumed the following parameters initial mois-ture119872

0 target moisture119872

119879 single cycle target precipitation

rate 120578 and grain discharge speed 119866 120575 is a moisturedifference in order to achieve a safe moisture The model isderived as follows

The grain target drying moisture10038161003816100381610038161198720 minus119872119879

1003816100381610038161003816 le 120575 (1)Cycles to achieve the target moisture

119899 =119872119879minus1198720

120578 (2)

Mathematical Problems in Engineering 3

Level sensors

Ambient temperatureand humidity sensor

Dischargefrequency control

Industrial PC

Moisture meter

PLC

I-7520RS232485

Feed control device

Grain temperaturesensor

Hot airtemperature sensor

Hot airregulation device

Heat sourcecontrol device

Figure 2 The composition block diagram of the control system

Heat source

Hot air channel

Air distribution chamber

L1Cereals

M1

M2L2

Dry container

T1 T2

T5

T4

T3

T6

Figure 3 Sensors and controllers layout

Figure 4 Moisture sensor

During the entire grain drying process themoisture changes of the grain over time is as follows119872011987211198722 119872

119894 119872

119899 Then there is the following

relationship

1198721minus1198720= 1198722minus1198721

= sdot sdot sdot = 119872119894+1minus119872119894= sdot sdot sdot = 119872

119899+1minus119872119899

(3)

Grain drying model PAGE

MR119894+1=119872119894+1minus119872119890119894

119872119894minus119872119890119894

=119872119894minus 120578 minus119872

119890119894

119872119894minus119872119890119894

MR119894+1= exp [minus119896

119894Δ119899119894

119894+1]

119896119894+1= 119896119894= 001313 sdot exp (00175119879

119894)

119899119894+1= 0748 + 0163 sdot 119881

119894+1

(4)


Inverseprocessmodel

Dynamicoptimization

Dryingprocess

Processmodel

Parameterestimationcorrection

Targetmoisture

+

minus

Unit dischargespeed of each layer

Initial moistureDry air temperature

Disturbance

Predictmoisture

Measuredmoisture

Grain dischargerate of optimized

Error

Figure 5 The MPC system structure of the cross-flow circulationgrain dryer

Wet grain

Hot air inlet

X (thickness)

Y

(height)

Dry grains

Hot air outlet

Figure 6 Schematic of cross-flow drying

During the drying process it is assumed that the inletgrain moisture of a unit layer is119872

119895 the outlet grain moisture

is 119872119895+1

Δ119884 is the height of the unit layer and the dryerdischarge speed at the time is 119866

119892 then

Δ119905=Δ119884

119866119892

(5)

119872119895+1= (119872

119895minus119872119890119894)MR119894+1+119872119890119894 (6)

MR119894+1= exp[minus119896

119894(Δ119884

119866119892

)

119899119894

] (7)

119872119890119894= radic

ln (1 minus RH119894)

382 times 10minus5 (18119879119894+ 82)

(8)

In which 119879119894is drying hot air temperature (∘C) 119872

119894is

the grain moisture after drying a certain period time (drybasis) MR is the grain moisture ratio119872

119890is the equilibrium

moisture content of the grain (dry basis) 119896 is a constant 119899 isthe number of the grain thin layer RH is the hot air relativehumidity 119881

119894is the speed of hot air (ms) and 119894 is the sample

number 119894 = 1 2 119899Thus using (6) (7) and (8) all unit layers of the entire

drying section can be solved and the final dischargemoisturefrom the cross-flow dryer and the grainmoisture distributionwithin the whole drying section were further obtained

42 Reverse Drying Process Model For drying productiongenerally the grain initial and targetmoisture are known to setthe appropriate parameters in order to achieve the purpose ofdrying

When the grain discharge moisture119872119891is set the reverse

drying process model can be used to calculate the dryertheoretical grain discharge speed 119866

0 Grain moisture at 119884

away from the discharge outlet is 119872119910 The grain discharge

speed 119866119910will be obtained if119872

119910reaches the target moisture

as follows

119866119910=

119884

119890119873radicln[minus(1119896) ln((119872119891minus119872119890)(119872119910minus119872119890))]

119872119890= radic

ln (1 minus RH)382 times 10minus5 (18119879 + 82)

(9)

43 The Grain Discharge Speed Optimization Due to thepresence of environment interference as well as the modelmismatch as a result of the simplified model the theoreticalgrain discharge speed of each unit layer calculated by (9)may be unequal But in the actual drying process all unitlayers in the drying section can only move down at thesame discharge rate Therefore in the MPC control systema dynamic optimizer is needed to optimize the calculation ofthe grain discharge speed in order to obtain an optimal valueIn this system zero average error method was applied [8]

Assume that the grainmoisture at the 119895th unit layer is119872119895

in the next 119895 sampling periods the real grain discharge speedbefore the 119895th unit layer reaches the outlet is 119866

119892 then the

moisture of these grains when they arrive at the outlet canbe estimated as follows

119872119891119895= 119872119895minus 120572119895Δ119884

119866119892

(10)

where 119872119891119895

is the grain final moisture the value of lay unit119895 is 1 2 3 119899 starting from inlet to the outlet 119895Δ119884119866

119892is

the time required of the grain from the current unit layer tothe discharge outlet 120572 is a constant dependent on the dryingmodel and conditions

Assuming that the target moisture after drying is119872119905and

the ideal dryer discharge speed is 119866119892119895 then

119872119905= 119872119895minus 120572119895Δ119884

119866119892119895

(11)

In the next sampling period when the actual graindischarge speed 119866

119892and the ideal grain discharge speed 119866

119892119895


Control platform Row of food control Model predictive control Data reportData acquisition

Grain temperature

510

minus81

113585

195

178

61

60

63

60

Moisture sensorNumber M2 correction

Documents

Storage path

1

1

1


ASRL2

ASRL1

Water port selection

148Export grain moisture

145

Date Time

52

50

53

50

80

minus20

5080

minus20

5080

minus20

5080

minus20

TimeTime50005000

70

minus20

0

20

40

70

minus20

0

20

40

Hot air temperature

Ambient humidity ()

Raw grain moisture

Target moisture settings

Row of grain motor speed

2012-12-17 080252

Temperature port selection

T1 T2T5

T4T3

T6

T1

T2

T5

T4

T3

T6

Ambient temperature (∘C)

Temperature sensor number

Set T1 Set T2

Set T3 Set T4

Figure 7 Front panel of data display

are not equal then there will be deviation between the finalgrain moisture and the target moisture the result is

119890119895= 119872119891119895minus119872119905= 120572Δ119884(

119895

119866119892119895

minus119895

119866119892

) (12)

This error is only for one unit layer so for the whole graincolumn the average error is

119890 =1

119899

119899

sum119895=1

119890119895=120572Δ119884

119899

119899

sum119895=1

(119895

119866119892119895

minus119895

119866119892

) (13)

Assumed 119890 = 0 the real optimized grain discharge speedcan be

119866119892=

119899 (119899 + 1)

2sum119899

119895=1(119895119866119892119895) (14)

44 Feedback Correction of the Control Model The processmodel error is defined as the difference between themeasuredoutlet grain moisture and the target grain moisture If theerror is not zero drying constant 119896 of the process and reverseprocess models is corrected using the empirical coefficient 120574In the feedback loop parameter estimationcorrection is asfollows [9]

119896119898= 120574119896 (15)

120574 = 120574119897+ 120573 ln

119872119891119901

119872119891119898

(16)

wherein 119896119898 is the drying constant after correction 120574 is anexperience correction coefficient obtained from the formula

(16) which is the current correction coefficient 120574119897is a

correction coefficient of a previous cycle (default is 08) 120573 is afilter coefficient determined by experiment by adjusting thecontroller119872

119891119901is the predicted grain moisture and119872

119891119898is

the actual measured moisture

5 System Software Design

Graphical programming language LabVIEW is used fordesign of the system operation platform The modularstructure design is adopted and system software consists ofmodules of drying model predictive control system detec-tion and control of hot air temperature grain temperaturedetection grainmoisture detection and correction and graindischarge speed control Specifically it is divided into thesystem initializationmodule the data acquisition and displaymodule the data storage and processing module and thesystem control module By the system initialization modulethe control system parameters can be set and modified Dataacquisition and display module will realize real-time datacollection from instrument transmission and visually displayin the man-machine interface Data storage and processingmodule will store the collected data while the collected datais graphed on the man-machine interface According to datavalues and graphs for each time the system control moduleis in accordance with the model predictive control algorithmfor real-time automatic controlThe data acquisition interfaceis shown in Figure 7 [10]

Themodel predictive control systemwas test simulated inLabVIEW The simulation results show that when the grainfeed moisture and the drying air temperature are within acertain range of variation by the control of the MPC thegrain discharge moisture closes to the set target moisture


Figure 8 Application site

Table 1 Initial Conditions of the Drying Test (average value)

Item 15h 10h 05hAmbient temperature∘C minus80 minus72 minus91Ambient humidity 59 65 55Raw grain moisture 193 195 194

6 System Application

In November 2012 production test experiment of drying ricewas carried out on the system The application indicates thatthe system has the advantages of simple hardware structurehigh precision high on-site anti-interference ability highdegree of automation and work friendly interface Figure 8shows the application site of the cross-flow circulation graindryer We tested working conditions of the circulation dryerat precipitation rates of 15h 10h and 05hThe initialdrying conditions are shown in Table 1 Figure 9 shows theoutlet grain moisture curve at the three drying precipitationrates From the curve we observed that the precipitation rateis approximately constant velocity and outlet rice moisturedetection is more accurate Figure 10 is the hot air temper-ature variation curve at the three drying precipitation ratesUnder the premise of maintaining the equal precipitationrate during the whole drying process using the constantvelocity variable temperature predictionmodel to control thetemperature of hot air hot air temperature fluctuations arelittle and can be better controlled When the constant speedprecipitation rate is 15h the average hot air temperatureof 68∘C is needed the higher the temperature of the dryingmedium the poorer the quality of grain after drying Whenthe constant speed precipitation rate is 05h the dried ricequality is good but the drying time and energy consumptionare high Comprehensive comparison shows that constantvelocity precipitation rate of 10h has a good dried ricequality the highest drying efficiency and the lowest energyconsumption

15h10h

05h

13

14

15

16

17

18

19

20

0 1 2 3 4 5 6 7 8

Time (h)

Wat

er co

nten

t of

grai

n(W

b

)

Figure 9 Outlet grain moisture at different drying precipitationrates

minus20

minus10

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500

Time (min)

Tem

pera

ture

(∘C)

15h10h

05h

Figure 10 Hot air temperature changes at different drying precipi-tation rates

7 Conclusion

According to the structural characteristics of the cross-flowcirculation grain dryer process and system control objectivesand requirements a grain drying parameters detection sys-temwas establishedThe system can automatically detect anddisplay the parameters of the grain temperature hot air tem-perature and grain moisture A grain drying process modelpredictive control system was established and the modelpredictive control combined moisture model prediction withthe advantages of the control function of grain dischargespeed optimization can compensate for changes of graindrying conditions and is particularly effective for nonlinearand large delay drying control A constant velocity variabletemperature of drying process predictive control model wasestablished System software was developed using LabVIEWto achieve automatic control of the grain drying process andto better guarantee the quality of the grain drying

Conflict of Interests

In this paper ldquoLabVIEW softwarerdquo is only used in the designof the system software for academic research and the authors


have no interest relationship and conflict with LabVIEWcompany (National Instruments)

Acknowledgment

Thiswork is supported by theHigh-TechResearch andDevel-opment Program of China (863 Program) (2006AA10Z256)

References

[1] X Liu Complexity Analysis and Process Control for GrainDrying Jilin University Changchun China 2003

[2] Y Zhang Moisture Detection and Automatic Control of GrainDrying Process Jilin University Changchun China 2012

[3] F Han W-F Wu Y-Q Zhang and H Zhu ldquoAutomatic controlsystem of grain dryer based on virtual instrumentrdquo Journal ofJilin University vol 39 no 3 pp 643ndash647 2009

[4] J Zhang Y Lu H Liu and X Tang ldquoOn-line measurementand intelligent prediction control of grain parameters in dryingprocessrdquo Transactions of the Chinese Society of AgriculturalMachinery vol 34 no 2 pp 50ndash53 2003

[5] H-O Wang Z-C Hu K Tu F-L Ji Y-Q Chen and L-LHu ldquoApplication research of model-predictive control in graindryingrdquo Drying Technology amp Equipment vol 6 no 6 pp 267ndash271 2008

[6] Q Liu andFWBakker-Arkema ldquoAmodel-predictive controllerfor grain dryingrdquo Journal of Food Engineering vol 49 no 4 pp321ndash326 2001

[7] Q Liu and F W Bakker-Arkema ldquoAutomatic control of cross-flow grain dryers part 1 development of a process modelrdquoJournal of Agricultural Engineering Research vol 80 no 1 pp81ndash86 2001

[8] Q Liu and F W Bakker-Arkema ldquoAutomatic control ofcrossflow grain dryers part 2 design of a model-predictivecontrollerrdquo Journal of Agricultural Engineering Research vol 80no 2 pp 173ndash181 2001

[9] Q Liu and F W Bakker-Arkema ldquoAutomatic control of cross-flow grain dryers part 3 field testing of a model-predictivecontrollerrdquo Journal of Agricultural Engineering Research vol 80no 3 pp 245ndash250 2001

[10] J Travis and J Kring LabVIEW for Everyone Graphical Pro-grammingMade Easy and Fun Publishing House of ElectronicsIndustry 3rd edition 2008

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1 2 3 4 5 6 7 8 9

10

11

12

Figure 1 Dryer process flow diagram (1) belt conveyor (2)precleaner (3) bucket elevator (4)warehouse for unprocessed grain(5)manual or pneumatic gate (6) flat belt conveyor (7) cooling fan(8) chain conveyor (9) heat source device (10) hot air blower (11)drying tower (12) bucket elevator

3 Parameters Online Detection

The system adopts distributed control as shown in Figure 2The system consists of a host (Industrial PC) PLC ambi-ent temperature and moisture sensors temperature sensorsresistive online moisture meter actuators and the relevantinstruments The main parameters include hot air tem-perature grain temperature offgas temperature ambienttemperature and humidity the raw grain moisture and dryerexit grain moisture the operative part includes PLC inverterand other peripheral lines

Data exchange between host and temperatureinstruments moisture meter and actuators through theRS232RS485 convertermodule I-7520 serial communicationdata bus On the one hand the host receives data of lowermachine from the temperature instruments and moisturesensors on the other hand the host sends control words tolower machine such as PLC temperature instruments andmoisture sensors to make corresponding controls [3 4]

Figure 3 shows sensors and controllers layout includingduct hot air temperatures (1198791 1198792) hot air temperaturesensors (1198793 1198794) grain temperature sensors (1198795 1198796) grainmoisture meters (M1 M2) grain level sensors (L1 L2)actuators (including PLC (C1) hot air speed inverter (C2)and grain discharge speed inverter (C3)) and ambient tem-perature and moisture sensors (1198797 H1) PLC inverter andambient temperature and moisture sensors are not shown inthe layout diagram

Moisture online detection is the key in the grain dryingcontrol system Our self-developed online moisture metersadopted by the system were installed in the inlet and outletelevators respectively

The resistive online moisture meter consists of a hostand a moisture sensor The moisture sensor is shown inFigure 4 For the moisture meter according to the measuredresistance in the relationship with the moisture content whenwheels crush the grain by measuring the resistance to onlinemeasure the grain moisture level The output resistance isalso associated with parameters such as grain temperaturevarieties and maturity With automatic and manual controlmodes the moisture meter has functions of grains switch-ing moisture setting moisture correction and temperature

compensation The target moisture value can be preset Themeter will respond upon arrival at this value to avoid overand insufficient drying The meter can be intermittent cycleautomatic start timing start online monitoring moisturewith high sensitivity and good stability

4 Cross-Flow Dryer Model Predictive Control

The characteristics of complexity time-varying hysteresisand nonlinearity in grain drying bring a lot of difficulties tothe traditional control applications MPC (model predictivecontrol) is a composite optimal control algorithm based onthe model by rolling implementation combined with feed-forward forecast and feedback correction Its control outputcan track set-points particularly effective for nonlinear andlarge delay control [5] Some foreign researchers developed anew type of model predictive controller for cross-flow dryerwhich was successfully applied to actual production [6]

The MPC system structure of the cross-flow circulationgrain dryer is shown in Figure 5 [6 7] including a feed-forward loop (consists of process model reverse processmodel and grain discharge speed optimizer) and a feedbackloop (parameter estimationcorrection) The system has thefollowing input parameters grain initial moisture grain tar-get drying moisture and dry air temperature The parameterthat needs to be optimized is the grain discharge speed

41 Drying Process Model Assume that the medium temper-ature can be precisely controlled and the ambient temperatureand humidity are relatively stable The grain is divided into anumber of thin layer to analyze in order to set up grain dryingcontrol model According to the structure of the dryingsection of the cross-flow circulation grain dryer and hot airdrying characteristics the grain columns of the dryer sectioncan be seen as a collection of a series of rectangles differentialunit in the vertical direction [7] as shown in Figure 6 Foreach rectangle differential unit a rectangular width is thethickness of the drying section that is the thickness of thegrain layer and the height is the differential Δ119884 or 119889119910 oflength of the drying section it may be sufficiently small Alsoassume that the hot air temperature and humidity change orthe difference in the 119884 direction is negligible

Constant velocity (equal precipitation rate) variable tem-perature control and target control are combined to controlthe grain precipitation rate within less than 15h Comparetarget moisture with the measured moisture and stop dryingafter the safe moisture or the final set value arrived

First we assumed the following parameters initial mois-ture119872

0 target moisture119872

119879 single cycle target precipitation

rate 120578 and grain discharge speed 119866 120575 is a moisturedifference in order to achieve a safe moisture The model isderived as follows

The grain target drying moisture10038161003816100381610038161198720 minus119872119879

1003816100381610038161003816 le 120575 (1)Cycles to achieve the target moisture

119899 =119872119879minus1198720

120578 (2)


Level sensors



Industrial PC

Moisture meter

PLC

I-7520RS232485

Feed control device






Heat source

Hot air channel


L1Cereals

M1

M2L2

Dry container

T1 T2

T5

T4

T3

T6




119894 119872


relationship

1198721minus1198720= 1198722minus1198721


119899+1minus119872119899

(3)


MR119894+1=119872119894+1minus119872119890119894

119872119894minus119872119890119894

=119872119894minus 120578 minus119872

119890119894

119872119894minus119872119890119894

MR119894+1= exp [minus119896

119894Δ119899119894

119894+1]

119896119894+1= 119896119894= 001313 sdot exp (00175119879

119894)

119899119894+1= 0748 + 0163 sdot 119881

119894+1

(4)


Inverseprocessmodel

Dynamicoptimization

Dryingprocess

Processmodel


Targetmoisture

+

minus



Disturbance

Predictmoisture

Measuredmoisture


Error


Wet grain

Hot air inlet

X (thickness)

Y

(height)

Dry grains

Hot air outlet




is 119872119895+1


119892 then

Δ119905=Δ119884

119866119892

(5)

119872119895+1= (119872

119895minus119872119890119894)MR119894+1+119872119890119894 (6)

MR119894+1= exp[minus119896

119894(Δ119884

119866119892

)

119899119894

] (7)

119872119890119894= radic


382 times 10minus5 (18119879119894+ 82)

(8)


119894is














as follows

119866119910=

119884


119872119890= radic


(9)




119892 then the


119872119891119895= 119872119895minus 120572119895Δ119884

119866119892

(10)

where 119872119891119895


119892is




119872119905= 119872119895minus 120572119895Δ119884

119866119892119895

(11)



119892119895



Grain temperature

510

minus81

113585

195

178

61

60

63

60


Documents

Storage path

1

1

1


ASRL2

ASRL1



145

Date Time

52

50

53

50

80

minus20

5080

minus20

5080

minus20

5080

minus20

TimeTime50005000

70

minus20

0

20

40

70

minus20

0

20

40

Hot air temperature

Ambient humidity ()

Raw grain moisture



2012-12-17 080252


T1 T2T5

T4T3

T6

T1

T2

T5

T4

T3

T6



Set T1 Set T2

Set T3 Set T4



119890119895= 119872119891119895minus119872119905= 120572Δ119884(

119895

119866119892119895

minus119895

119866119892

) (12)


119890 =1

119899

119899

sum119895=1

119890119895=120572Δ119884

119899

119899

sum119895=1

(119895

119866119892119895

minus119895

119866119892

) (13)


119866119892=

119899 (119899 + 1)

2sum119899

119895=1(119895119866119892119895) (14)


119896119898= 120574119896 (15)

120574 = 120574119897+ 120573 ln

119872119891119901

119872119891119898

(16)





119891119898is











15h10h

05h

13

14

15

16

17

18

19

20

0 1 2 3 4 5 6 7 8

Time (h)

Wat

er co

nten

t of

grai

n(W

b

)


minus20

minus10

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500

Time (min)

Tem

pera

ture

(∘C)

15h10h

05h


7 Conclusion






Acknowledgment


References


















Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Level sensors



Industrial PC

Moisture meter

PLC

I-7520RS232485

Feed control device






Heat source

Hot air channel


L1Cereals

M1

M2L2

Dry container

T1 T2

T5

T4

T3

T6




119894 119872


relationship

1198721minus1198720= 1198722minus1198721


119899+1minus119872119899

(3)


MR119894+1=119872119894+1minus119872119890119894

119872119894minus119872119890119894

=119872119894minus 120578 minus119872

119890119894

119872119894minus119872119890119894

MR119894+1= exp [minus119896

119894Δ119899119894

119894+1]

119896119894+1= 119896119894= 001313 sdot exp (00175119879

119894)

119899119894+1= 0748 + 0163 sdot 119881

119894+1

(4)


Inverseprocessmodel

Dynamicoptimization

Dryingprocess

Processmodel


Targetmoisture

+

minus



Disturbance

Predictmoisture

Measuredmoisture


Error


Wet grain

Hot air inlet

X (thickness)

Y

(height)

Dry grains

Hot air outlet




is 119872119895+1


119892 then

Δ119905=Δ119884

119866119892

(5)

119872119895+1= (119872

119895minus119872119890119894)MR119894+1+119872119890119894 (6)

MR119894+1= exp[minus119896

119894(Δ119884

119866119892

)

119899119894

] (7)

119872119890119894= radic


382 times 10minus5 (18119879119894+ 82)

(8)


119894is














as follows

119866119910=

119884


119872119890= radic


(9)




119892 then the


119872119891119895= 119872119895minus 120572119895Δ119884

119866119892

(10)

where 119872119891119895


119892is




119872119905= 119872119895minus 120572119895Δ119884

119866119892119895

(11)



119892119895



Grain temperature

510

minus81

113585

195

178

61

60

63

60


Documents

Storage path

1

1

1


ASRL2

ASRL1



145

Date Time

52

50

53

50

80

minus20

5080

minus20

5080

minus20

5080

minus20

TimeTime50005000

70

minus20

0

20

40

70

minus20

0

20

40

Hot air temperature

Ambient humidity ()

Raw grain moisture



2012-12-17 080252


T1 T2T5

T4T3

T6

T1

T2

T5

T4

T3

T6



Set T1 Set T2

Set T3 Set T4



119890119895= 119872119891119895minus119872119905= 120572Δ119884(

119895

119866119892119895

minus119895

119866119892

) (12)


119890 =1

119899

119899

sum119895=1

119890119895=120572Δ119884

119899

119899

sum119895=1

(119895

119866119892119895

minus119895

119866119892

) (13)


119866119892=

119899 (119899 + 1)

2sum119899

119895=1(119895119866119892119895) (14)


119896119898= 120574119896 (15)

120574 = 120574119897+ 120573 ln

119872119891119901

119872119891119898

(16)





119891119898is











15h10h

05h

13

14

15

16

17

18

19

20

0 1 2 3 4 5 6 7 8

Time (h)

Wat

er co

nten

t of

grai

n(W

b

)


minus20

minus10

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500

Time (min)

Tem

pera

ture

(∘C)

15h10h

05h


7 Conclusion






Acknowledgment


References


















Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Inverseprocessmodel

Dynamicoptimization

Dryingprocess

Processmodel


Targetmoisture

+

minus



Disturbance

Predictmoisture

Measuredmoisture


Error


Wet grain

Hot air inlet

X (thickness)

Y

(height)

Dry grains

Hot air outlet




is 119872119895+1


119892 then

Δ119905=Δ119884

119866119892

(5)

119872119895+1= (119872

119895minus119872119890119894)MR119894+1+119872119890119894 (6)

MR119894+1= exp[minus119896

119894(Δ119884

119866119892

)

119899119894

] (7)

119872119890119894= radic


382 times 10minus5 (18119879119894+ 82)

(8)


119894is














as follows

119866119910=

119884


119872119890= radic


(9)




119892 then the


119872119891119895= 119872119895minus 120572119895Δ119884

119866119892

(10)

where 119872119891119895


119892is




119872119905= 119872119895minus 120572119895Δ119884

119866119892119895

(11)



119892119895



Grain temperature

510

minus81

113585

195

178

61

60

63

60


Documents

Storage path

1

1

1


ASRL2

ASRL1



145

Date Time

52

50

53

50

80

minus20

5080

minus20

5080

minus20

5080

minus20

TimeTime50005000

70

minus20

0

20

40

70

minus20

0

20

40

Hot air temperature

Ambient humidity ()

Raw grain moisture



2012-12-17 080252


T1 T2T5

T4T3

T6

T1

T2

T5

T4

T3

T6



Set T1 Set T2

Set T3 Set T4



119890119895= 119872119891119895minus119872119905= 120572Δ119884(

119895

119866119892119895

minus119895

119866119892

) (12)


119890 =1

119899

119899

sum119895=1

119890119895=120572Δ119884

119899

119899

sum119895=1

(119895

119866119892119895

minus119895

119866119892

) (13)


119866119892=

119899 (119899 + 1)

2sum119899

119895=1(119895119866119892119895) (14)


119896119898= 120574119896 (15)

120574 = 120574119897+ 120573 ln

119872119891119901

119872119891119898

(16)





119891119898is











15h10h

05h

13

14

15

16

17

18

19

20

0 1 2 3 4 5 6 7 8

Time (h)

Wat

er co

nten

t of

grai

n(W

b

)


minus20

minus10

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500

Time (min)

Tem

pera

ture

(∘C)

15h10h

05h


7 Conclusion






Acknowledgment


References


















Volume 2014




Journal of











Journal of


Function Spaces






Algebra











Grain temperature

510

minus81

113585

195

178

61

60

63

60


Documents

Storage path

1

1

1


ASRL2

ASRL1



145

Date Time

52

50

53

50

80

minus20

5080

minus20

5080

minus20

5080

minus20

TimeTime50005000

70

minus20

0

20

40

70

minus20

0

20

40

Hot air temperature

Ambient humidity ()

Raw grain moisture



2012-12-17 080252


T1 T2T5

T4T3

T6

T1

T2

T5

T4

T3

T6



Set T1 Set T2

Set T3 Set T4



119890119895= 119872119891119895minus119872119905= 120572Δ119884(

119895

119866119892119895

minus119895

119866119892

) (12)


119890 =1

119899

119899

sum119895=1

119890119895=120572Δ119884

119899

119899

sum119895=1

(119895

119866119892119895

minus119895

119866119892

) (13)


119866119892=

119899 (119899 + 1)

2sum119899

119895=1(119895119866119892119895) (14)


119896119898= 120574119896 (15)

120574 = 120574119897+ 120573 ln

119872119891119901

119872119891119898

(16)





119891119898is











15h10h

05h

13

14

15

16

17

18

19

20

0 1 2 3 4 5 6 7 8

Time (h)

Wat

er co

nten

t of

grai

n(W

b

)


minus20

minus10

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500

Time (min)

Tem

pera

ture

(∘C)

15h10h

05h


7 Conclusion






Acknowledgment


References


















Volume 2014




Journal of











Journal of


Function Spaces






Algebra















15h10h

05h

13

14

15

16

17

18

19

20

0 1 2 3 4 5 6 7 8

Time (h)

Wat

er co

nten

t of

grai

n(W

b

)


minus20

minus10

0

10

20

30

40

50

60

70

80

0 100 200 300 400 500

Time (min)

Tem

pera

ture

(∘C)

15h10h

05h


7 Conclusion






Acknowledgment


References


















Volume 2014




Journal of











Journal of


Function Spaces






Algebra











Acknowledgment


References


















Volume 2014




Journal of











Journal of


Function Spaces






Algebra
















Volume 2014




Journal of











Journal of


Function Spaces






Algebra







