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Introduction

Process Simulation. Introduction. Classification of the models. Black box – white box Black box – know nothing about process in apparatus, only dependences between inputs and outputs are established. Practical realisation of Black box is the neural network

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Introduction

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  1. Process Simulation Introduction

  2. Classification of the models • Black box – white box • Black box – know nothing about process in apparatus, only dependences between inputs and outputs are established. Practical realisation of Black box is the neural network • White box – process mechanism is well <??> known and described by system of equations

  3. Classification of the models • Deterministic – Stochastic • Deterministic – for one given set of inputs only one set of outputs iscalculated with probability equal 1. • Stochastic – random phenomenon affects on process course (e.g. weather), output set is given as distribution of random variables

  4. Classification of the models • Microscopic- macroscopic • Microscopic – includes part of process or apparatus • Macroscopic – includes whole process or apparatus

  5. Elements of the model • Balance dependences • Based upon basic nature laws • of conservation of mass • of conservation of energy • of conservation of atoms number • of conservation of electric charge, etc. • Balance equation (for mass): (overall and for specific component without reaction)Input – Output = Accumulation or (for specific componentif chemical reactions presents)Input – Output +Source = Accumulation

  6. Elements of the model • Constitutive equations • Newton eq. – for viscous friction • Fourier eq. – for heat conduction • Fick eq. – for mass diffusion

  7. Elements of the model • Phase equilibrium equations – important for mass transfer • Physical properties equations – for calculation parameters as functions of temperature, pressure and concentrations. • Geometrical dependences – involve influence of apparatus geometry on transfer coefficients – convectional streams.

  8. Structure of the simulation model • Structure corresponds to type of model equations • Structure depends on: • Type of object work: • Continuous, steady running • Periodic, unsteady running • Distribution of parameters in space • Equal in every point of apparatus – aggregated parameters (butch reactor with ideal mixing) • Parameters are space dependent– displaced parameters

  9. Structure of the model

  10. Process simulation • the act of representing some aspects of the industry process (in the real world) by numbers or symbols (in the virtual world) which may be manipulated to facilitate their study.

  11. Process simulation (steady state) • Flowsheeting problem • Specification (design) problem • Optimization problem • Synthesis problem by Rafiqul Gani

  12. FLOWSHEET SCHEME INPUT PRODUCTS OPERATING CONDITIONS EQUIPMENT PARAMETERS Flowsheeting problem • Given: • All of the input information • All of the operating condition • All of the equipment parameters • To calculate: • All of the outputs

  13. R.Gani

  14. FLOWSHEET SCHEME INPUT PRODUCTS OPERATING CONDITIONS EQUIPMENT PARAMETERS Specyfication problem • Given: • Some input& some output information • Some operating condition • Some equipment parameters • To calculate: • Undefinedinputs&outputs • Undefinedoperating condition • Undefinedequipment parameters

  15. Specyfication problem • NOTE: degree of freedom is the same as in flowsheeting problem.

  16. Given: feed composition and flowrates, target product composition Assume value to be guessed: D, Qr Find: product flowrates, heating duties Solve the flowsheeting problem Adjust D, Qr Is target product composition satisfied ? STOP

  17. Process optimisation • the act of finding the best solution (minimize capital costs, energy... maximize yield) to manage the process (by changing some parameters, not apparatus)

  18. Given: feed composition and flowrates, target product composition Assume value to be guessed: D, Qr Find: product flowrate, heating duty Solve the flowsheeting problem Adjust D, Qr Is target product composition satisfied AND =min. STOP

  19. Process synthesis/design problem • the act of creation of a new process. • Given: • inputs (some feeding streams can be added/changed latter) • Outputs (some byproducts may be unknown) • To find: • Flowsheet (topology) • equipment parameters • operations conditions

  20. Process synthesis/design problem flowsheet undefined INPUT OUTPUT

  21. Given: feed composition and flowrates, target product composition Assume value to be guessed: D, Qr, N, NF, R/D etc. Find: product flowrate, heating duty, column param. etc. Solve the flowsheeting problem Adjust D, Qr As well as N, NF, R/D etc. Is target product composition satisfied AND =min. STOP

  22. Process simulation - why? • COSTS • Material – easy to measure • Time – could be estimated • Risc – hard to measure and estimate

  23. Software for process simulation • Universal software: • Worksheets – Excel, Calc (Open Office) • Mathematical software – MathCAD, Matlab • Specialized software – process simulators. Equipped with: • Data base of apparatus models • Data base of components and mixtures properties • Solver engine • User friendly interface

  24. Software process simulators (flawsheeting programs) Started in early 70’ At the beginning dedicated to special processes Progress toward universality Some actual process simulators: ASPEN Tech /HYSYS ChemCAD PRO/II ProSim Design II for Windows

  25. Chemical plant system • The apparatus set connected with material and energy streams. • Most contemporarysystems are complex, i.e. consists of many apparatus and streams. • Simulations can be use during: • Investigation works – new technology • Project step – new plants (technology exists), • Runtime problem identification/solving – existing systems (technology and plant exists)

  26. Chemical plant system • characteristic parameters can be specified for every system separately according to: • Material streams • Apparatus

  27. Apparatus-streams separation Assumption: All processes (chemical reaction, heat exchange etc.) taking places in the apparatus and streams are in the chemical and thermodynamical equilibrium state. Why separate? It’s make calculations easier

  28. Streams parameters Flow rate (mass, volume, mol per time unit) Composition (mass, volume, molar fraction) Temperature Pressure Vapor fraction Enthalpy

  29. Streams degrees of freedom DFs=NC+2 • e.g.: NC=2 -> DFs=4 • Assumed: F1, F2, T, P • Calculated: • enthalpy • vapor fraction

  30. Apparatus parameters & DF Characteristics for each apparatus type. E.g. heat exchanger : Heat exchange area, A [m2] Overall heat-transfer coefficient, U (k) [Wm-2K-1] Log Mean Temperature Difference, LMTD [K] degrees of freedom are unique to equipment type

  31. Calculation subject Number of equations of mass and energy balance for entire system Can be solved in two ways:

  32. Types of balance calculation Overall balance (without use of apparatus mathematical model) Detailed balance on the base of apparatus model

  33. Overall balance Apparatus is considered as a black box Needs more stream data User could not be informed about if the process is physically possible to realize.

  34. 3 2 1 4 Overall balance – Example Countercurrent, tube-shell heat exchanger Given three streams data: 1, 2, 3 hence parameters of stream 4 can be easily calculated from thebalance equation. DF=5 There is possibility thatcalculated temp. of stream 4 can be higher then inlet temp. of heating medium (stream 1).

  35. 3, mA 2 1, mB 4 Overall balance – Example Given: mA=10kg/s mB=20kg/s t1= 70°C t2=40°C t3=20°C cpA=cpB=idem

  36. Apparatus model involved • Process is being described with use of modeling equations (differential, dimensionless etc.) • Only physically acceptable processes taking place • Less stream data required (smaller DF number) • Heat exchange example: given data for two streams, the others can be calculated from a balance and heat exchange model equations

  37. Loops and cut streams • Loops occur when: • some products are returned and mixed with input streams • when output stream heating (cooling) inputs • some input (also internal) data are undefined • To solve: • one stream inside the loop has to be cut (tear stream) • initial parameters of cut stream have to be defined • Calculations have to be repeated until cut streams parameters are converted.

  38. Loops and cut streams

  39. Simulation of system with heat exchanger using MathCAD

  40. I.Problem definition Simulate system consists of: Shell-tube heat exchanger, four pipes and two valves on output pipes. Parameters of input streams are given as well as pipes, heat exchanger geometry and valves resistance coefficients. Component 1 and 2 are water. Pipe flow is adiabatic. Find such a valves resistance to satisfy condition: both streams output pressures equal 1bar.

  41. II.Flawsheet 5 s6 s7 2 4 1 3 s1 s2 s3 s4 s5 s8 7 6 s10 s9

  42. Numerical data: Stream s1 Ps1 =200kPa, ts1 = 85°C, f1s1 = 10000kg/h Stream s6 Ps6 =200kPa, ts6 = 20°C, f2s6 = 10000kg/h

  43. Equipment parameters: • L1=7m d1=0,025m • L2=5m d2=0,16m, s=0,0016m, n=31... • L3=6m, d3=0,05m • z4=50 • L5=7m d5=0,05m • L6=10m, d6=0,05m • z7=40

  44. III. Stream summary table • Uknown:Ts2, Ts3, Ts4, Ts5, Ts7, Ts8, Ts9, Ts10, Ps2, Ps3, Ps4, Ps5, Ps7, Ps8, Ps9, Ps10, f1s2, f1s3, f1s4, f1s5, f2s7, f2s8, f2s9, f2s10 number of unknown variables: 26 WE NEED 26 INDEPENDENT EQUATIONS.

  45. Equations from equipment information • f1s2= f1s1 f1s7= f1s6 f1s3= f1s2 f1s8= f1s7 f1s4= f1s3 f1s9= f1s8 f1s5= f1s4 f1s10= f1s9 14 equations. Still do define 26-14=12 equations

  46. Heat balance equations New variable: Q Still to define: 12+1-2=11 equations

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