Heat Exchanger s Shell and Tube Modeling for Intelligent Control Design

Transcription

1 2011 International Conferene on Computer Communiation Devies (ICCCD 2011) Heat Exanger s Sell Tube Modeling for Intelligent Control Design Dirman Hanafi 1 Mod Nor Mod Tan 2 Abdulraman A.A. Ememed 3 Tatang Mulyana 4 Amran Mod Zaid 5 6 Ayob Hj. Joari Department of Meatroni Roboti Engineeering Faulty of Eletrial Eletroni Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Batu Paat Joor Malaysia dirman@utm.edu.my Abstrat- Te sell tube of eat exanger is a medium were eat transfer proess ourred. Te auray of te eat exanger depends on te performane of bot elements. Terefore bot omponents need to be ontrolled in order to aieve a substantial result in te proess. For tis purpose te atual dynamis of bot sell tube of te eat exanger is ruial. Tis paper disusses two metods used in deriving te matematial modeling of te system. First pysial dynami modeling is obtained using pysis dynamis laws were atual parameters of te sell tube are onsidered. Seondly te model is determined by applying non-parametri system identifiation based on experimental response on te eat exanger. Two models are used to design te sell tube intelligent ontrol. Te intelligent ontrol type is a Fuzzy Proportional Derivative (FPD) ontrol. Te experiment results sows tat te sell tube eat exanger model develop using its pysial parameters ontrolled wit FPD ontroller give better response it means it an used as a model ontroller of te sell tube eat exanger. Keyword- Sell tube dynami modeling nonparametri modeling intelligent ontrol I. INTRODUCTION Nowadays eat exanger widely used in industry like emial proess oil gas nulear plant palm oil proess food meanial system et. Heat exangers are used to transfer eat from one fluid to oter [1]. Te eat transferring reasons one of te following: 1. To eat ooler fluid by otter fluid. 2. To redue te temperature of ot fluid by ool fluid. 3. To boil a liquid by te otter fluid. 4. To ondense a gaseous fluid by a ooler fluid. 5. To boil a liquid wile ondensing a otter gaseous fluid. Te eat transferring proess is appen in sell tube eat exanger. Te proess of te eat transfer two fluids wit differene temperature in sell tube eat exanger are done witout aving surfae ontat. Te output performane of a eat exanger is greatly influened by sell tube. On te oter te dems of te proess are not onstant; te eat ontent of te two fluids is not onstant eiter. Terefore te sell tube eat exanger must be ontrolled to make it operate at te partiular rate required by te proess every moment in time. In ontrol system engineering system investigation improvement are done based on te system model. Terefore it is neessary to ave a matematial model of te given system. It must be a ig fidelity matematial model apturing realisti dynami beaviors of te system [4]. Te matematial models desribe te relationsips among te system variables in terms of matematial expressions like differential equations. Tis paper is subjet to improve te performane of te eat exanger tat it is installed in UTHM proess ontrol laboratory. In tis ase two modeling teniques one type of intelligent ontrol are applied. Te modeling teniques for sell tube eat exanger derives onsisting of dynami modeling tat it as been applied pysial termo dynami laws [5]. In tis resear te model parameters are alulated based on te real values of sell tube eat exanger omponents. Next modeling tenique is system identifiation. Te non-parametri system identifiation is applied. Te eat exanger model its parameters are estimated using input output data of te eat exanger tat are olleted in experimental testing [36]. Two models develop are used to design te sell tube eat exanger intelligent ontrol. Te intelligent ontrol type is fuzzy ontrol system [78]. To improve te fuzzy ontrol performane in tis ase it ombines wit proportional derivative (PD) ontrol as fuzzy proportional derivative (FPD) ontroller. II. SHELL AND TUBE COMPONENT Tere are several varieties of a sell tube eat exanger. Te number of a basi omponent is relative small. Fig. 1 sows a sell tube eat exanger [2]. Figure 1. Sell tube eat exanger Te omponents onsist of: /11/$ IEEE V2-37

2 2011 International Conferene on Computer Communiation Devies (ICCCD 2011) 1. Tube: providing te eat transfer surfae between one fluid flowing inside te tube te oter fluid flowing aross te outside of te tubes. 2. Tube seet: were te tube is eld in plae. 3. Sell sell-side nozzles: ontainer of te sell-side fluid te inlet exit ports. 4. Tube-side annels nozzles: ontrol te flow of tube-side fluid into out of te tubes. 5. Cannel overs: round te plates. 6. Pass divider: for two tubes side pass or more tan two passes. 7. Baffles: support te tubes in te proper position prevent vibration. III. SHELL AND TUBE HEAT EXCHANGER MODELING A. Dynami modeling of sell tube eat exanger based on pysial parameters Matematial model is a system model to represent te system dynamis in matemati formulations [3]. Tis model is determined applying several pysi emial laws variables wi are appening in proess. Fig.2 sows several variables involve in sell tube eat exanger proess. fluid ( kg 3 3 / m ). V V A A U U are volume 2 m ) eat transfer ( m ) eat transfer surfae area ( 2 o oeffiient ( W / m C ) old ot fluid respetively. B. Non-parametri system identifiation of sell tube eat exanger Non-parametri system identifiation is a tenique to estimate te system model troug its step response. Te tenique is based on two points of te fration response of te system at 20% 60% as sown by te following figure: Figure 3. Real experimental te eat exanger step response T i VALVE HOT SIDE IN COOL SIDE OUT T o To HOT SIDE OUT COOL SIDE IN Figure 2. Sell tube eat exanger input output Related to te energy balane law te energy supply to eat exanger must equal to te energy removed. Tis relationsip is given by equation below: T o T o = w = w Were T i / ρ V U A / ρ V C / ρ V i p U A / ρ V C i o o p T o Ti To ot fluid temperature ( o C o ) + o o ) + o ) ) Ti (1) (2) are inlet outlet old ) respetively. w w are mass flow rate of old ot fluid ( kg / se ). C p are te eat apaity of old ot fluid C p o ( J / kg. C ). ρ ρ are te density of old ot Figure 4. Smit s art Te value of dampingξ is equal to ratio of t 20 / t 60 value of time delay τ determines from te grap of t60 /τ versus t 20 / t 60. Te transfer funtion of te system is given as: t 0 G( S) = ke /( τ 1S + 1)( τ 2S + 1) (3) Were τ 2 1 = τξ + ξ 1 τ 1 = τξ ξ 2 1 k is gain t o is time. After substitution te values need te transfer funtion of sell tube eat exanger as below: V2-38

3 2011 International Conferene on Computer Communiation Devies (ICCCD 2011) G ( S) = /(23.3S)(2.46S + 1)( ) (4) IV. FUZZY CONTROL DESIGN FOR SHELL AND TUBE HEAT EXCHANGER Fuzzy logi is based on te priniple of uman expert deision making in problem solving meanism. Were te solution is desribed in a linguisti term or every spoken language i.e. fast slow ig low et [78]. In more omplex ases tey inlude some edge terms i.e. very ig not so low et. To represent su terms a nonmatematial fuzzy set teory is needed. Fig. 5 sows te blok diagram of fuzzy logi. Tey are onsisted four omponents: Fuzzifiation Fuzzy rule based Fuzzy inferene Defuzzifiation. e e GE GCE E CE f u GU Rule base Figure 6. FPD ontroller blok diagram Te sell tube eat exanger proess dynamis take some time before a ange in te ontrol signal is notieable in te proess output te proportional ontroller will be more or less late in orreting for an error. Derivative ation elps to predit te error te proportional-derivative ontroller uses te derivative ation to improve losed-loop stability. Te basi struture of a PD ontroller is: U u( = K ( e( + T e ( ) (5) p d Figure 5. Fuzzy logi blok diagram Te fuzzy logi ontroller for te quarter ar passive suspension system is designed follow te step below: 1. Design te membersip funtion for fuzzify input output variables. 2. Implement te fuzzy inferene by a series of IF THEN rules. 3. Inferene engine derives a onlusion from te fats rules ontained in te knowledge base using various uman expert teniques. 4. Proess to maps a fuzzy set into a risp set. Fuzzy logi ontrol proposed in tis resear is design applying Matlab Fuzzy Logi Toolbox. Te fuzzy inferene system (FIS) is used to edit visualize used rules membersip funtions. Te resulting FIS model is ten tested using te Simulink Toolbox wi also gives te onveniene of building analyzing dynamial systems grapially. Fuzzy proportional derivative (FPD) ontroller Fuzzy proportional derivative (FPD) ontrol developed is a multi input single output ontroller model. Te inputs are error ( E ) ange in error ( CE ). Output is a signal ontrol (U ). Fuzzy logi ontroller an provide desirable bot small signal large signal dynami performane at same time [8]. Blok diagram of FPD ontrol tat as been designed for sell tube eat exanger is represented by Fig. 6. Notie tat definition deviates from te straigt differene ( e ( e( n 1) ) used in te early fuzzy ontrollers. Te FPD ontroller output as funtion of error ange in error given as: U ( = f ( GE * e( * GCE * e ( ) * GU (6) Were funtion f denotes te rule base mapping. It is usually nonlinear but wit a favorable oie of design a linear approximate is: f ( GE * e( * GCE * e ( ) * GU GE * e( + GCE * e ( Ten te ontrol signal beomes as below: (7) U ( = GE * GU ( e( + ( GCE / GE) e ( ) (8) Te gain fator for te linear ontroller orresponds to te proportional derivative gains are: K p = GE * GU (9) T d = GCE / GE (10) Te FPD ontroller is used to ontrol te sell tube eat exanger valve. Te linguisti terms for errors are: NE (negative error) ZE (zero error) PE (positive error). Canges in errors are: NLDE (Negative Large Derivative Error) NSDE (Negative Small Derivative Error) ZDE (Zero Derivative Error) PSDE (Positive Small Derivative Error) PLDE (Positive Large Derivative Error). Outputs are: for output are: u0 (valve 0-10) u25 (valve 0-25) u50 (valve 25-75) u75 (valve ) u100 (valve 75- V2-39

4 2011 International Conferene on Computer Communiation Devies (ICCCD 2011) 100). Te membersip funtions for input output are assumed triangular type. V. SIMULATION RESULTS Simulation for FPD ontroller of te sell tube eat exanger is done using Matlab Simulink. Te input signal is te old fluid temperature sell tube eat exanger. Two models of sell tube eat exanger are ontrolled wit FPD ontroller te results are ompared in order to deide wi one produe good result. Ea Matlab Simulink blok ontrol system related model are below: Figure 11. Comparison of two models response witout ontroller Fig are te response of pysial model system identifiation model omparison between two models response under FPD ontroller respetively. Figure 7. Matlab Simulink blok ontrol system for model from dynami modeling Figure 12. Response of pysial model wit FPD ontroller Figure 8. Matlab Simulink blok ontrol system for model from nonparametri system identifiation Fig represent te response of ea model to te input temperature. In tis ase te input value is assumed 40 degree Celsius. Te two models responses ave similar trend. Wile Fig. 11 sows te omparison between response of pysial model system identifiation model. Figure 13. Response of system identifiation model wit FPD ontroller Figure 14. Comparison of two models response wit FPD ontroller Figure 9. Response of pysial model witout ontroller Figure 10. Response of system identifiation model witout ontroller Based on te omparison of models under FPD ontroller te response of system identifiation model is not able to rea te input referene. It as steady sate error around 12.5%. Wile te response of pysial model an rea model exatly at 150 seond no oversot no steady state error as settling time 80 seond. VI. CONCLUSION In tis paper te sell tube eat exanger as been modeled ontrol using FPD Controller. Te model tat developed using sell tube eat exanger pysial parameters as good response tan te model tat identified using non-parametri system identifiation. It means te sell tube eat exanger model determined based on it pysial parameter is feasible use to analysis design it ontroller. And FPD ontroller also an apply to improve te sell tube eat exanger performane related wit it suessfully on te simulation. V2-40

5 2011 International Conferene on Computer Communiation Devies (ICCCD 2011) ACKNOWLEDGMENTS Tis work as been supported by Registrar Offier Faulty of Eletrial Eletroni Engineering Universiti Tun Hussein Onn Malaysia REFERENCES [1]. Engineer Edge Heat Exanger LLC All rigt reserved Spirax-saro Steam Consumption of Heat Exangers Spirax_Saro Limited [2]. Y. Zao S. Zou Li Li Dynami Carateristis Modeling of a Heat Exanger Using Neural Network Pro. First International Conferene on Intelligent Networks Intelligent Systems pp November [3]. Han-Sue Tan T. Bradsaw Model Identifiation of an Automotive Hydrauli Ative Suspension System Pro. Te Amerian Control Conferene Vol. 5 pp [4]. C. Harris K. Kelly T. Wang A. MCless S. Motakef Fabriation Modeling Testing of Miro-Cross Flow Heat Exangers Journal of Miroelektromeanial Systems Vol. 11(6) De pp [5]. I. Skrjan D. Matko Preditive Funtional Control Based on Fuzzy Model for Heat-Exanger Pilot Plant IEEE Transation on Fuzzy Systems Vol 8(6) De pp [6]. D. Zenjun F. Lide S. Zambiao Z. Yameng Design Simulation of Fuzzy Control System for Water Temperature of Heat Exanger Pro. 2 nd International Conferene on Computer Automation Engineering (ICCEA) Vol. 4 pp Feb [7]. J. Jantzen Foundations of Fuzzy Control Jon Wiley [8]. C. A A. B. Corripio Priniples Pratie of Automation Proess Control 3 rd Ed Wiley New York V2-41