Design of Temperature Controller for Heating Furnace in Oil Field

Transcription

1 Available online at Physics Procedia 24 (202) International Conference on Applied Physics and Industrial Engineering Design of Temperature Controller for Heating Furnace in Oil Field Dou Zhenhai, Sun Lianyun 2 School of Electrical and Electronic Engineering Shandong University of Technology Zibo, China 2 Department of Information Engineering Shandong Youth University of Political Science Jinan, China Abstract The temperature controller is an important part of the control system design of the crude oil heating furnace. the traditional PID control is usually used in temperature control, but it can not effectively overcome the impact by the interference, load change and the parameters change of system and other factors. Based on the relationship between rules factor α and the error E, meanwhile, taking into account the change of error rate E C, the temperature controller is designed adaptive fuzzy control with variable slope. It greatly enhances the robustness of the system. By field application, works well Published Published by Elsevier by Elsevier B.V. Selection Ltd. Selection and/or peer-review and/or peer-review under responsibility under responsibility of ICAPIE Organization of [name Committee. organizer] Open access under CC BY-NC-ND license. Keyword:heating furnace, varied slope, fuzzy control.introduction Currently, PID or classical fuzzy control algorithm have a most using in the temperature control of the crude oil heating furnace, but often can not effectively overcome the interference, change of load or the system parameters and other factors, At the same time, there are many characteristics of heating system like nonlinear and strongly coupled, large time delay and others []. The expectation can not achieved by traditional control method. Based on a large number of theoretical studies, the varied slope rule self adaptive fuzzy controller has a application in crude oil heating furnace system by the algorithm turning the slope of α E curve.[2] 2. The overall structure of the system hardware Published by Elsevier B.V. Selection and/or peer-review under responsibility of ICAPIE Organization Committee. Open access under CC BY-NC-ND license. doi:0.06/j.phpro

2 2084 Dou Zhenhai and Sun Lianyun / Physics Procedia 24 (202) The system (oil furnace system) is a subsystem which has an important position of the automatic control system of Yongyi crude oil gathering station in Dongying City, Shandong Province, Shengli Petroleum Administration. PLC remote automatic control unit (EDA90Ck3) as the core in this system, and it divided two level 485 bus as a borderline, the high-grade bus contact the host computer with the PLC and all same level control module; follow the PLC is the measure/control module of this subsystem that contact with low-grade 485 bus. The system hardware consists of industrial control computer, RS converters, PLC remote automatic control unit, EDA907, EDA9022, wind valve control device, EDA9033 electrical parameters measure unit of the fan, EDA908 gas flow measurement unit, flame detectors, gas electric control valve. The hardware components shown in Figure : PLC remote automatic control unit run the fuzzy control algorithm as the core of the system. It receive data from the lower unit. It runs the fuzzy control algorithms, then send control signals for control switches; and sends data to host computer for real-time display, alarm, storage and printing. 3. Design the VARIED slope Rule Fuzzy Controller The Varied Slope Rules Adaptive Fuzzy Controller is two-dimensional structure, Input of the controller is crude oil outlet temperature error E and error change EC, output is the gas flow (corresponding to the gas valve opening)u. E=R y Where R is the setting outlet temperature, y is the measurements, EC=E( n) E( n ) Where E(n) and E ( n ) is respectively current and previous error. The fuzzy language subset of E, EC andu is defined as: {NB, NM, NS, ZO, PS, PM, PB}, and quantified in 5 levels as {-7, -6, -5, -4, -3, -2, -,0,,2,3,4,5,6,7}. Interval of the actual temperature error change: e max~ + e, the max corresponding quantitative value is -7 ~ +7.[3] For taking into account the characters of fuzzy controller like completeness, compatibility and interference, the membership function, in this case, select the graphics as triangular. All the value of intersection of the fuzzy set membership functions are about 0.5. Triangle shape is simple, little calculation, the membership function of each variable as shown in Figure 4.6, where E and EC have same function. In order to improve the sensitivity in the vicinity of the middle volume, E andec function is non-uniform distribution of symmetrical triangle, correspondingly, a uniform distribution is used inu function. Shown in Figure 2 The rule of varied slope self-adaptive fuzzy controller and the algorithm of adjust factor is as tfollow: U = αe + ec α = E N ( α ) EC ( n ) ec ( n ) max max k0 + α mid + Emid In this case, taking the rotation point coordinates of variable slope correction curve are: (3.5,0.5), the initial slope k 0 get 0.333, then the adjustment factor is calculated as: ecmax ( n ) α = E ecmax ( n ) 7 N k 0

3 Dou Zhenhai and Sun Lianyun / Physics Procedia 24 (202) DC0V B A High level 485 bus to host computer ZK DC/DC DC0/6 VCC D0+ D0- GND PLC remote automatic control unit Address:08 D+ D- D2+ D2- VCC+ GND- D+ D- EDA I 0 I I 2 I 3 I 4 I 5 I 6 I 7 VCC D+ D- EDA GND I 00 I 0 GND Measure unit of gas pressure Measure unit of crude oil entrance pressure Measure unit of crude oil entrance temperature Measure unit of crude oil exit temperature Measure unit of water sheath pressure Gas electric control valve ~380V ZK2 Wind valve control device 05 EDA V 5A 06 Flowmeter interface EDA flame detector 08 electrical parameters of the fan Flow of gas Figure Frame chart of hardware of heating furnace fuzzy control system

4 2086 Dou Zhenhai and Sun Lianyun / Physics Procedia 24 (202) After obtainu, we could get a precise control value by defuzzification method like the commonly used weighted average as follow. 4. Software Design of System u 5 i i= = 5 u μ( u ) i= μ( u ) i i NB NM NS ZO PS PM PB a) Diagram of membership function of E EC NB NM NS ZO PS PM PB b) Diagram of membership function of U Figure 2 Diagram of membership function of I/O values Software design is the key of the control system. Based on this hardware system, according to the control algorithm, author edited the application software program of this system. In order to improve the process speed of operation, the program uses the assembly language for programming language, block diagram of the fuzzy control algorithm as a core part shown in Figure 3. After a trial run, the system has a good work. First of all, the automation level of operations is enhanced, and greatly reduces the labor intensity; temperature control effect is showed in figure 4. The contrast curve in this figure is based on the date get in a conjoint double 24 hours, one is manual work, and other is varied slope fuzzy control. This curve shows that the accuracy of the controller and system stability are significantly improved. The design achieve the desired results.

5 Dou Zhenhai and Sun Lianyun / Physics Procedia 24 (202) Begin Sampling E, EC ( α ) U = αe + EC α = E k n + α mid Emid k n Fuzzify input value Defuzzify output value EC=0? Y Reconculate the slope k n Output control value U N EC>EC max? Y Modify current EC max The end N Figure 3 Frame chart of software of heating furnace fuzzy control system Figure 4 The contrast of control effect

6 2088 Dou Zhenhai and Sun Lianyun / Physics Procedia 24 (202) Conclusion In past time, the alteration of the automatic control system on oil furnace, most used PID or classical fuzzy control algorithm. For the crude oil heating furnace which has characteristic like large inertia, nonlinear, delay, those algorithm have a bad represent in adaptability and robustness. The general self-tuning fuzzy controller only considered the impact of the error E on the system, while ignoring the impact by the error change rate EC which is important equally on system performance. The varied slope fuzzy control algorithm introduced an intermediate amount - slope k ofα E curve, according to the EC to adjust the size of k, thus changing the curve, and further changed the control rule table, the system is based on the actual situation has been a self-learning function, the system's stability, robustness greatly increased. References []LIU Yu-cheng, LIU Yu-bin, LI Tai-fu, SU Ying-ying, An Exploring to an Adaptive Fuzzy Control Algorithm for Sintering system,microcomputer Information, 2009, 25(2-), P [2]Dou Zhenhai, Varied Slope Rules Self Adaptive Fuzzy Control Algorithm of Crude Oil Heating Furnace, 200 international congress on educational information technology, pp: 22-24, September 200. [3]Chen Zhiwang, Li Liang,Fuzzy MRAC for Position Control of Rolling Mill, Metallurgical Equipment 2008(0), P6-9+5.