FCC PILOT PLANT Research Tool

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2 C PILOT PLANT Research Tool BPR-601 GC WTM PC-601 PT-601 PT-501 PT-301 REGENERATOR STABILIZER STRIPPER F-501 PCV-601 RISER H.E.-601 LC- LI LCV- S.V.-301 V-60 LI-1 V-603 LC-1 F(T 1,T,T 3 ) PC-301 LCV-1 FEE VESSEL P-51 V-604 PROUCT VESSEL V-501 T R H.E.-501 S.V.-101 PC PCV-501 GC BPR-501 WTM-1 Accurate simulation of industrial processes Operation at constant feedstock or catalyst Operation within large operating spans Study kinetics Study dynamics Catalyst Benchmarking Feedstock Benchmarking Large experimental database 7 Steady State: ynamic Operation: Catalyst Feedstock Wide ranges Process recording FLUI CATALYTIC CRACKING INUSTRY & LABORATORY is the workhorse of refinery High economic importance High interest in optimization pilot plants serve as research tools Need to improve pilot plant efficiency Final correlation for the prediction of feed conversion ( nd order kinetics): yx k x E x n = C catalyst type F feed quality exp x tc:rs 100 y WHSV RT x RX Final correlation for the prediction of the catalytic coke yield: k c E c nc yc = C ( ) ( ) exp x c catalyst Fc feed tc:rs WHSV RTRX Expressions for the calculation of the Weight Hourly Space Velocity, the catalyst residence time and the riser temperature: WHSV W& F:RS 3600 W& F =, tc:rs = 3600 ρp( V:RS ( 1 ε:rs ) + VC:RS ( 1 εc:rs ) + VF:RS ( 1 εf:rs )) WHSV W& C :RS :RS Assumption of pseudo-plug flow conditions Assumption of pseudo-isothermal riser operation

3 Regenerator Model F F F F MOELING PILOT Homogeneous PLANT Riser Reactor Fluid ynamics MOEL ASSUMPTIONS ilute Phase Plug Flow Homogeneous-HeterogeneousHeterogeneous Reactions The dense bed includes a bubble and an emulsion phase Bubbles Phase Plug Flow Homogeneous Reactions Emulsion Phase Fully Mixed Homogeneous-HeterogeneousHeterogeneous Reactions Two phase flow: 1) dilute phase ) dense phase Two phases in dense bed: a) emulsion b) bubbles Emulsion: CSTR Heterogeneous Bubbles: PFR Homogeneous ilute phase: PFR Heterogeneous H t In high-density CFB a "ense Suspension Upflow" regime is observed: Averaged top voidage: u ρ A p F gf : :RS :RS ε = y :RS W& C + u ρpaf F:RS F g : :RS :RS :RS Main contributors to the overall heat balance in an riser: The enthalpy of cracking ΔH crack The enthalpy of vaporization of the feedstock ΔH vap The enthalpy content of various process streams H c H b ΔP regenerated catalyst nitrogen feedstock The slip factor is a function of the riser diameter, gas volumetric flow and solids terminal velocity: Slip factor correlation: Averaged bottom voidage: y = Fr F:RS tf : :RS FrgF : :RS The voidage of the riser-bottom (mixing) section estimated by the correlation of Richardson & Zaki: 1/ z ug : :RS ε = :RS ut:rs H + H + H + H + H = -oil 0 crack vap gas cat Loss The heat of cracking is a function of: Conversion (y x ) Feed Molecular Weight (MW F ) Riser Temperature (T RX ) H = y atrx yx + atrx + amwf + btrx + btrx + bmwf crack 13 bubble phase TWO PHASE MOEL excess gas gas to freeboard freeboard gas interchange combustion air entrained solids emulsion phase cyclones coked catalyst regenerated catalyst 1 dfif VF:RG dlf 1 dfif VF:RG dlf 1 dq f VF:RG dlf W& ( ) ( ) = = 0 0 dc c c l l 1 homo ( 1 ) ge ie ie ie = εe εe εe f KMidl fe ak i f ak i e Re e Re dt ρge V :RG 0 W& W& ( 1) ( 1) ( ) ( 1) l = = = = 1 ( 1 ) ( ) dc C cie c C cif c l l l ie ε ie :RG ie :CY 1 = + + ε f f ak ρp ρp dt V V :RG :RG ( solids ) gas dv ( ) Te :RG 1 εe + f ccp ie ie f εe ccp ie ie = e e dt i i e e e e i Re ( ( ( ( ) ( ) ( l l l l l l = = = = 1) :RG 0) :RG = 1) :CY 0 :CY 0 = 1) QC QC QC QC Q Q Qloss ge ge 1 homo ( 1 ) K = + ε α ε α f i Rf f ikrf = ( 1 ε ) α f ik Rf homo ( ) ( ) ( ) ε 1 ε = H + H K K f R Rf f R Rf Emulsion phase: homo ( ) ( 1 ) ( ) ε ε + H + H V K dl f V K f V K :RG :RG :RG H e e R Re e e R Re 0 emulsion (t) df V dl dq V dl freeboard (l) bubble (l) Bubbles phase: = + K f ak = + H KH fb K R Rb 15 MOELING PILOT PLANT Stripper Model Mass balance: dv W& C = W& C ( 1) ( ) l l = = 0 :ST :ST :ST dt ρ 1 ε p mf ( ) ( ) l = 1 = 1 ( 1 εmf ) V p :ST dc W& C = ci c ( l ) ( ) :ST i :ST :ST i:st dt ρ Perfectly mixed reactor Minimum fluidization conditions Stripping efficiency 100% Stripper temperature is controlled by heaters T sp ST disengager stripper 17 3

4 ynamic Simulation Open Loop 130% increase in feed preheat temperature: Open-loop operation: Higher sensible heat in the feed stream experiment simulation Higher riser temperature Small changes in the regenerator input variables Constant operation of regenerator MOELING PILOT PLANT 1 Models Integration Satisfactory agreement between simulated and experimental results Error in the prediction of regenerator temperature Assumption of pseudo-steady state operation of riser Assumption of pseudo-steady state operation of liftline and standpipe ynamic behavior driven by the operation of the regenerator ynamic behavior includes dynamic performance of the stripper Closed loop operation Regenerator slide valve controls riser temperature Open loop operation Regenerator slide valve set to constant opening 19 MOELING PILOT PLANT ynamic Simulation Closed Loop 15% reduction in feed rate: Closed-loop operation: Control for constant riser temperature Reduction in catalyst circulation rate Lower coke rate and catalyst rate entering the regenerator Less combustion, higher catalyst residence times, lower regenerator temperature experiment simulation In the closed loop operation changes in riser input variables lead to: Oscillation of the unit Faster responses predicted by the simulator MOELING PILOT PLANT ynamic Simulation Closed Loop 130% increase in feed preheat temperature: Closed-loop operation: Control for constant riser temperature Reduction in catalyst circulation rate Higher catalyst residence time in the regenerator Increase in the regenerator temperature 3 Conventional control robustness shows sluggish behavior More robust control can enhance the steadiness in the operation 4

5 MOEL - BASE PREICTIVE CONTROL THEORY PROS & CONS yˆx ˆ i ˆθ ynamic model improves control actions within specified horizon The estimator manages to improve future predictions of the model MOEL PREICTIVE CONTROL Principles Theory theory pros & cons error, e k+1 esired traectory Model prediction rolling control horizon uk+ 1 subect to: x=f & x,u NP N C N C sp ss = yk+ yk+ + Δu + y k+ 1 u u k+ u u wk+ w k k+ 1 wk+ 1 = 1 = 1 = 1 min J ˆ y=g x,u meas pred ek = + y y 1 k+ 1 t k- Past control actions t k-1 t k t k+1 t k+ t k+3 Future control actions yˆ = y + e pred k+ k+ k+ 1 l u u uk + u 1 NC = TC Tk / tc, NP = TP Tk / tp 7 u k- u k-1 u k u k+1 u k+ u k+3 Past and present control actions affect the future response of the process Minimize the difference between desired traectory and predictions Long prediction horizon compensates for slower dynamics but Short control horizon leads to aggressive control actions controlled variables move suppression factor steady state optimality Piecewise additive disturbance model compensates for model error Integral action guarantees for zero controller offset MOEL - BASE PREICTIVE CONTROL THEORY PROS & CONS improves operability and efficiency Follow desired traectory within specified horizon Move suppression factor for operability-stability Steady state optimality factor for process optimization Need an accurate dynamic model SIMULATION STUY 5

6 Obective Function 1 ( ) ( ( )) ( ) 33 The Problem in Industry The Pilot Plant Control Problem Obectives: Obectives: Maximum Profit Maximum Capacity Maximum Conversion Constant Temperature esired Selectivity Product specifications Environmental restrictions Unit Stability Reduce Redundant Experiments Catalyst Benchmarking Feedstock Benchmarking Constant Conversion Constant Riser Temperature Examine Catalyst Selectivity Environmental restrictions Unit Stability isturbances: Feedstock Catalyst isturbances: Feedstock Catalyst 31 3 The Structure t y n i= 1 k+ yˆi t sp y tk i u n i= 1 ˆ u n i= 1 y u u 1 / k i i i k k i J = w uˆi t k 1 + w u t + w 1 ss ui Controlled Variables: Conversion Riser Temperature Manipulated Variables: Catalyst Circulation Feed Preheat isturbances: Move suppression factor: Steady state optimality: Control horizon: 10 min Prediction horizon: 0 min Kinetic constants Control robustness Problem escription esired steady state Two instances of the model a VP and a SIM Conventional PI Control VP was depicted by a flawless version of the model Significant amount of mismatch in the reaction kinetics used in the SIM Equivalent to the control problem in the real pilot process level Manipulate catalyst circulation rateto to Control riser 34 temperature yˆi Model Predictive Control Manipulate catalyst circulation rate and feed preheat temperature to Control riser temperature and conversion isturbance: unknown catalyst quality different kinetic constants

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8 Infrastructure and Architecture App Real-Time Control Framework : Key concept : Automated real-time and flexible control scheme Multi level framework that handles/transfers data from distributed applications Include technical aspect as well as communication issues Procedure requirements : Main focus : shift from a static user required environment to a dynamic runtime behavior Improve the procedure to minimize the time spent for repeated user actions Independent software interconnection and ata sharing through network Time specific actions synchronization and handling of unpredicted model execution time Proper Procedure steps interpretation in action that could be coded 45 ISTRIBUTE / INTER- APPLICATION COMMUNICATION rives for development Software Specific limitations: User requirements were beyond the initial design/scope of the each software Unable to parameterize the gproms software Human Computer Interface App 47 istributed & Interapplication App Software / System Used in the Framework : gproms : develop dynamic model and system Matlab : lineralised state space model used in the state and parameter estimation 46 Microsoft Office Excel : processing and management of the process data Plant SCAA system (GE ifix/fix3) Process Information Management System (OSISoft PI) used for data archiving specifics : Plant / Framework : NetE (Net ynamic ata Exchange) Interapplication data transfer using Windows OLE standard (Excel Matlab, gproms - Excel) Structure and interoperability requirements: Embed to framework security considerations that were necessary for process control network, due to integration with business network Reduce the amount of time that was required to gather and setup the environment Framework development Control framework development stages : Requirement gathering & Architecture definition Identify existing assets & component development Software system integration & Testing Standalone - Simulation Networked Plant interaction App 48 8

9 ata Flow Framework Area gproms SCAA System ata Files MATLAB Control Scheme Area PI App 51 User ata Flow Procedure steps App 50 Initialization Procedure Matlab : Prepare/Build a COM Obect to communicate with Excel (code restructure) gproms : Initial Process execution (initial simulation and plant model) Excel : Open files and reset values Control scheme Software : Parameter and settings Optimization, Simulation, Linear Time Update elay Number of Iterations Main Procedure gproms : Optimization Stage gproms : Simulation Stage Mathematic model Process Model (with noise/disturbance) Linear Model MATLAB : Kalman Filter Future improvements / potentials A C B Procedure steps : Simulation model execution (A) Results to Excel (A B) Plant simulation model or Plant system execution (A) Results to Excel (A B) Linearisation model (A) ata Files generation (A C) Update flag status Trigger condition (A B) Matlab model run (Kalman Filter) (C, ) Results to Excel ( B) App 5 Further improvements : efine rules for the interface in order to interoperate with CAPE able applications (Computer-Aided Process Engineering standard) Parametric model execution via software interface Provide the ability to change the order of the sequence actions irect interaction with LIMS & PIMS system to retrieve data used in the procedure evelop an OPC server tο communicate with the Plant evelop a software component that utilizes COM/COM to distribute data to Windows Applications CONCLUSIONS future work CONCLUSIONS Mathematical modeling for process optimization Fluid Catalytic Cracking process: Workhorse of refinery - High economic incentives Complex, non-linear, uncertain, constrained process High interest in simulation and optimization Fluid Catalytic Cracking pilot plant: Research tool Catalyst benchmarking Need to improve efficiency ynamic simulator of the pilot plant: Steady state operation of the pilot riser, liftline and standpipe ynamic behavior of the regenerator and stripper The dynamic simulator was verified with pilot experiments 54 9

10 AKNOWLEGMENTS r. S.S. Voutetakis, THANK YOU 10