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TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 1 Process Integration for Environmental Control in Engineering.

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Presentation on theme: "TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 1 Process Integration for Environmental Control in Engineering."— Presentation transcript:

1 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 1 Process Integration for Environmental Control in Engineering Curricula (PIECE) Program for North American Mobility in Higher Education (NAMP) in Higher Education (NAMP)

2 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 2 Module Steady State Process Simulation 2

3 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 3 Understand and simulate processes in steady state. Solve technical and economic problems more quickly, efficiently and successfully. Propose This module has been developed to help the students:

4 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 4 Statement of intent The student will. Review basic concepts used in steady – state simulation. Understand the purpose of steady – state simulation. Develop models of a processes in steady state. Simulate processes with help of computer simulators.

5 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 5 Contents This module is divided in 3 tiers Tier 1. Introduction to simulation tool. Tier 2. How to use computer tool. Tier 3. How to apply in real world.

6 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 6 Tier 1 Introduction to Steady State, Process Simulation tool

7 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 7 1.Basic concepts. 2.Steady – state simulation in a process integration context. 3.Steady – state simulation in a broader context. Tier 1 is divided in 3 sections Contents

8 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 8 1 Basic Concepts

9 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 9 Show the basic concepts of steady – state simulation. Improve process simulation skills. Create your own simulation flowsheets. Recognize why simulation is useful in the process industries. Basic concepts Statement of intent

10 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 10 Basic Concepts  Steady – state.  Models and simulation.  Creating models.  Unit efficiencies.  Stream components.  Units.  Performing a steady – state simulation study. Contents

11 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 11 By steady state we mean, in most systems, the conditions when nothing is changing with time. Mathematically this corresponds to having all time derivatives equal to zero, or to allowing time to become very large (go to infinity). Steady – State Steady – state

12 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 12 Steady – State The design of process systems requires both: Steady – state model. Dynamic models. One use for the steady – state models is in determining the possible region of steady – state operation for a process that can be limited by constraints such as safety, product quality, and equipment performance. Steady – state

13 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 13 Model A model is an abstraction of a process operation used to build, change, improve or control a process. Uses of a model: Equipment design, sizing and selection. Comparison of possible configurations. Evaluation of process performance against limits (e.g. Concentrations, effluent discharge rates). De-bottlenecking and optimization. Control strategy development and evaluation. Models & simulation

14 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 14 Model The model is an abstraction of the real word Models vary by: Phenomena represented (energy, classifications phase change). Level of detail and granularity Assumptions (perfect mixing, zero heat loss). Kind of input required Functions performed (constraint satisfaction, optimization). Nature of output generated Models & simulation

15 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 15 Models vary by purpose and category Purpose Operator training simulator. Control strategy evaluation. Investment justification (e.g. new equipment purchase). Other… Category Physical (e.g. mimic panel) vs. Mathematical. Qualitative vs. Quantitative. Empirical vs. First principle based. Steady state vs. Dynamic state. Models & simulation

16 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 16 Physical Model From a balance: Mathematical Model Models & simulation

17 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 17 Quantitative Using non – numeric descriptors. Fuzzy, logic. Expert system. Turn an alarm on. Qualitative Using numbers, and quantifying the magnitude of the response. Models & simulation

18 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 18 Empirical Derived from observation. Often simple. May or may not have theoretical foundation. Valid only within range of observation. First – principle based Derived from fundamental physical laws. Most reliable, but we often don’t have them. Models & simulation

19 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 19 Steady – State Snapshot of a unit operation or plant Movie of plant operation Balance at equilibrium conditionTime dependent results Equilibrium results for all unit operations Equilibrium conditions not assumed for all units Equipment sizes, in general not needed Equipment sizes needed Amount of information required: small to medium Amount of information required: medium to large Dynamic Models & simulation

20 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 20 Requirements of a good model Accuracy: close enough to the target. It is required in quantitative and qualitative models. Validity: we must consider the range of the model. The model must have a solid foundation or justification. Right level of complexity: models can be simple, usually macroscopic, or detailed, usually microscopic. The detail level of phenomena should be considered. Easy to understand. Computational efficiency: the models should be calculable using reasonable amounts of time and computing resources. Models & simulation

21 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 21 Simulation Predicts the behavior of a plant by solving the mathematical relationships that describe the behavior of the plant’s constituent components. Involves performing a series of experiments with a process model. Models & simulation

22 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 22 Importance of steady – state simulation Better understanding of the process. Consistent set of typical mill data. Objective comparative evaluation of options for return on investment etc. Identification of bottlenecks, instabilities, etc. Ability to perform many experiments cheaply once model built. Avoidance of ineffective solutions. Models & simulation

23 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 23 Constructing a model When we try to represent a phenomena, to predict future conditions, or to know how the process will behave in certain situation, it is common to use mathematical expressions.   V V SV dVBdSnFdVb dt d Models & simulation

24 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 24 Constitutive relations Relate the diffusive flux of a certain quantity with the local properties of the material and with the transport driving force. Express the movement of a certain quantity in the decreasing gradient direction of the quantity. Creating models

25 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 25 Constitutive relations Fourier’s law: Fick’s first law: Thermal diffusion Mass diffusion Newton’s law: Kinematic viscosity Creating models

26 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 26 Variation Equations Conservation Equations or Equations of change Those relate the accumulation of a quantity with the rate of entrance or formation of the same quantity in a specific volume. Those are based in fundamental principles and have universal description. Creating models

27 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 27 Conservation of mass In a differential element: It is common practice to express the balance in a differential element, and convert the equation to a differential form. Creating models

28 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 28 Conservation of mass For a pure component: Conservation of chemical species: Note: steady – state no change in the time. Creating models

29 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 29 Conservation of energy Note: steady – state no change in the time. Where H V is rate of heat generated by external source (electricity, compression, chemical reactions, etc.). Creating models

30 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 30 Unit efficiencies An engineer may define energy efficiency in a very restrictive equipment sense. Energy efficiency has been used to describe what actually may be conservation. Energy efficiency in a more subjective sense may refer to the relative economy with which energy inputs are used to provide services. Unit efficiencies

31 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 31 Typical Efficiencies Values Compressors  = 0.8 Motor  = 0.9 Turbine  = 0.8 Pump  = 0.5 Unit efficiencies

32 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 32 Stream Components Ideal gas law and equations of state. Solubility relations (solid in liquid and gas in liquid). Reaction stoichiometry and equilibrium. Simple vapor/liquid relationships such as Raout’s law. Overall stream flows and components are calculated based on physical and chemical properties such as: Stream components

33 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 33 Conversion of stream components Mechanical work. A B C A B AB Via chemical reaction. Heat. Stream components

34 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 34 Engineering Units The official international system of units is the SI. But older systems, particularly the centimeter – gram – second (cgs) and foot – pound – second (fps), are still in use. It was originated in France, in 1790 by the French Academy of Science. The units should be based on unvarying quantities in nature. Multiples of units should be decimal. The base units should be used to derive other units. Units

35 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 35 Engineering Units Units

36 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 36 Steady state model derivation. Calculation order. Recycle streams. Convergence and iteration. Recycle convergence methods. Granularity model. Performing a Steady – State simulation Study Performing a SS simulation study

37 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 37 Steady state model derivation 1.- Define Goals. a) Specific design decisions. b) Numerical values. c) Functional relationships. d) Required accuracy. 2.- Prepare information. a) Sketch process and identify system. b) Identify variables of interest. c) State assumptions and data. Performing a SS simulation study

38 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 38 Steady state model derivation 3.- Formulate model. a) Conservation balances. b) Constitutive equations. c) Rationalize (combine equations and collect terms). d) Check degrees of freedom. e) Dimensionless groups (Pr, Nu, Re, etc.). 4.- Determine solution. a) Analytical. b) Numerical. Performing a SS simulation study

39 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 39 Steady state model derivation 5.- Analyze results a) Check results for correctness Limiting and approximate answers Accuracy of numerical method b) Interpret results Plot solution Relate results to data and assumptions Evaluate sensitivity Answer “what if questions” Performing a SS simulation study

40 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 40 Steady state model derivation 6.- Validate model. a) Select key values for validation. b) Compare with experimental results. Performing a SS simulation study

41 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 41 Calculation Order In most process simulators, the units are computed (simulated) one at a time. The calculation order is automatically computed to be consistent with the flow of information in the simulation flowsheet, where the information flow depends on the specifications for the chemical process Performing a SS simulation study

42 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 42 Recycle Flows A simulation flowsheet usually contains information recycle loops. That is, cycles for which too few streams variables are known to permit the equation for each unit to be solved independently For these processes, a solution technique is needed to solve the equations for all the units in the recycle loop. Performing a SS simulation study

43 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 43 Solution technique Consist in guessing a value for the recycle stream. This value is generally not going to equal the calculated value, this represent another problem which is solved by “iteration”. Calculation Initial guessing values New values from The calculation Performing a SS simulation study

44 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 44 Iteration Convergence units use convergence subroutines to compare the newly computed variables (in the feed stream to the convergence unit) with guessed values (in the product stream from the convergence unit) and to compute new guess values when the two streams are not identical to within convergence tolerances. This procedure is call iteration. It involves re – calculating the flowsheet. Performing a SS simulation study

45 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 45 Convergence Is the process to compare the guessed value with the computed value, until find a value within the tolerance range. Guess value – calculated value < Tolerance Guess value Yes No Convergence When the criteria is achieve, the solution is found, and is time to stop the iteration. Performing a SS simulation study

46 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 46 Convergence Initialize each uni t Convergence? Start t = 0, k = 0 Guess torn streams no Stop k = k + 1 X ij y ij no Performing a SS simulation study

47 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 47 Recycle convergence methods Where is the vector of guesses for n recycle (tear) variables and is the vector of the recycle variable computed from the guesses after one pass through the simulation units in the recycle loop. Clearly, the objective of the convergence unit is to adjust so as to drive toward zero. Performing a SS simulation study

48 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 48 Successive substitution as the basic and obvious method Also call direct iteration. In this method the new guess for x is simply made equal to f(x * ). Performing a SS simulation study x0*x0* x1*x1* f(x * ) Locus of Iterates When the slope of the locus of iterates (f(x),x) is close to unity, a large number of iterations may be required before convergence occurs

49 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 49 Other convergence methods When the method of successive substitutions requires a large number of iterations, another methods are used to accelerate convergence: Wegstein’s method. Newton – Raphson method. Broyden’s quasi – Newton method. The dominant – eigenvalue method. Performing a SS simulation study

50 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 50 Wegstein’s method In this method, the two previous iterates of f(x * ) and x * are extrapolated linearly to obtain the next value of x as the point of intersection. x0*x0* x1*x1* f(x * ) Locus of Iterates Performing a SS simulation study

51 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 51 Granularity of modeling With the advance in technology, it is possible to combine on a single computer the full capability of a high fidelity simulation models. High fidelity process simulation is commonly used by many industries in the design of a process. Granularity of modeling Is the level of detail taken into account in a simulation.

52 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 52 Comparing Coarse vs. Fine models Granularity of modeling Coarse: Bleaching tower A coarse model represent the equipment with few detail.

53 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 53 Fine model Bleaching tower Liquors Fibers Chromophores Chemicals PFR CSTR The same equipment is divided in 3, and the substances into account are more than just an approximation. Granularity of modeling More than 1 direction

54 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 54 Benefits The detail level is low The time involve is less The solution effort is few The solution is approximated The detail level is big Time require is big The solution effort is big The solution is exact Granularity of modeling Coarse Models Fine Models

55 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 55 2 Steady state simulation in a process integration context

56 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 56 Steady state simulation in a process integration context Recognize the components in a simulation flowsheet. Check the procedure to create a process. What is the importance of the computer. What can we obtain as a result of a simulation. Statement of intent

57 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 57 Steady – state simulation in a process integration context  Process flowsheets.  Simulation flowsheets.  Process synthesis methodologies.  Minimal time and expense.  Computer – based process.  Data reconciliation.  Process insights resulting from simulation. Table of content

58 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 58 Process flowsheets Process flowsheets are the language of chemical processes. Like a work of art, they describe an existing process or a hypothetical process in sufficient detail to convey the essential features. Process flowsheets

59 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 59 Process flowsheet A process flowsheet is a collection of icons to represent process units and arcs to represent the flow of materials to and from the units. Fresh Feed Steam Heater Reactor Flash Distillation Product Process flowsheets

60 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 60 Simulation The analysis of a simulation, is the tool chemical engineers use to interpret process flowsheets, to locate malfunctions, and to predict the performance of the process. Simulation flowsheets

61 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 61 Simulation Flowsheet A simulation flowsheet, on the other hand, is a collection of simulation units, each representing a computer program (subroutine or model) that simulates a process unit, and arcs to represent the flow of information among the simulation units. Mixer Heater Reactor Flash Column Mathematical Convergence Unit Simulation flowsheets

62 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 62 Process synthesis methodologies Total enumeration of an explicit space: is the most obvious. Here we generate and evaluate every alternative design. We locate the better alternative by directly comparing the evaluations. Evolutionary methods: follow from the generation of a good base case design. Designers can then make many small changes, a few at a time, to improve the design incrementally. Structured Decision Making: following a plan that contains all the alternatives. Process synthesis methodologies

63 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 63 Process synthesis methodologies Design to target: these have been especially useful in designing heat recovery and reactor networks. The utility requirements become the targets for the design. Problem abstraction: Here the search for better design alternatives begins by formulating a less detailed problem statement and attempting to solve this more abstract problem first. Process synthesis methodologies

64 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 64 Minimal time Fresh Feed Steam Heater Reactor Flash Distillation Product Change in Heat Duty Change in Reactor Properties Change in Column Properties Change composition In feed With a simulation, you can simulate one day of process operation in just seconds, and make as many changes as you want. Minimal time and expense

65 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 65 Minimal expense Simulated “learning experiences” are much less costly than making real mistakes in the real plant. Is easy to model the process with different kind of equipment without having to invest in it. Minimal time and expense

66 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 66 Computer – Based process representation which can be re - used Most of the times, there are already models which can be adapted to the process under study, with minimal changes. This minimizes the time needed to set up complicated equations. Re-using models is much easier than building new ones, specially if the process is being modeled for the first time. Computer based process

67 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 67 Data reconciliation Data reconciliation is a technique for improving the quality of measured plant data. These measurements are inherently inaccurate due to instrument failures, limitations of measurement techniques, etc. As a result, data are obtained that violate mass and energy balance constraints of describe a physically infeasible process. Data reconciliation

68 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 68 How Data reconciliation works t F Reconciling errors Find a set of data that: Constitutes some kind of “best fit” (least squares) to the observed data. Satisfies mass – energy balance and other criteria. Data reconciliation

69 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 69 Opportunity to do data reconciliation This amounts to validation of the process data using knowledge of the plant structure and the plant measurement system Data reconciliation

70 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 70 Process insights resulting from modeling 1.Identification: We can find the structure and parameters in the model. 2.Estimation: If the internal structure of model is known, we can find the internal states in model. 3.Design: If the structure and internal states of model are known, we can study the parameters in the model. MODEL Process insights resulting from modeling

71 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 71 Process insights resulting from modeling If the model is known, we have two uses for our model: 1.Direct: input is specified, output is studied (simulation). 2.Inverse: output is specified, input is studied. Used when an objective must be met (production, composition). Process insights resulting from modeling

72 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 72 3 Steady – State Simulation in a Broader Context

73 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 73 Steady – State Simulation in a Broader Context Show how to take a decision to create a process. Know if the process is viable, in terms of stability and economic. Taking in count security aspects. Statement of intent

74 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 74 Aspects of Process Design  Process design.  Stability and sensitivity.  Process optimization.  Economic evaluation of alternatives.  Operator training. Table of content

75 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 75 Process design The design of chemical products begins with the identification and creation of potential opportunities to satisfy societal needs and to generate profit. The scope of chemical product is extremely broad. They can be roughly classified as: 1.basic chemical products. 2.Industrial products. 3.Consumer products. Process design

76 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 76 Process design Manufacturing Process Natural Resources Basic chemical Products Manufacturing Process Basic Chemical Process Industrial Products Manufacturing Process Basic Chemical Industrial Products Consumer Products Process design

77 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 77 Motivation for design projects 1.Desires of customers for chemicals with improved properties for many applications. 2.New inexpensive source of a raw material with new reaction paths and methods of separation. 3.New markets are discovered. Process design

78 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 78 Steps in a Process Design 1.Process Design – Questions to Answer Is the chemical structure known? Is a process required to produce the chemicals? Is the gross profit favorable? Is the process still promising after further elaboration? Is the process and/or product feasible? Process design

79 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 79 Steps in a Process Design Create and assess primitive problem. Find chemicals or chemical mixtures that have the desired properties and performance. Process creation. Development of base case. Detailed design, equipment sizing, and optimization. Startup assessment. Reliability and safety analysis. Written design report and oral presentation. Plant design, construction, startup and operation. 2.Process Design – Steps Process design

80 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 80 3.Process Design – Procedure

81 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 81 Detailed Process Synthesis Using Algorithmic Methods Create and evaluate chemical reactor networks for conversion of feed to product chemicals. Separation trains for recovering species in multi- component mixture. Reactor separator recycle networks. Locate and reduce energy usage. Create and evaluate efficient networks of heat exchangers with turbines for power recovery. Networks of mass exchangers to reduce waste. Process design

82 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 82 Feasible Region The region within which the process can be operated is called the operating window or feasible operating region. Feasible region g 3 =0 g 2 =0 g 1 =0 Stability and Sensitivity

83 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 83 Feasible Region One can not in general say a priori how a thermodynamic model will behave when extrapolated beyond the region in which data were available for determining its empirical parameters. Stability and Sensitivity

84 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 84 Stability of the process When a process is disturbed from an initial steady state, it will, in general, respond in one of 3 ways. a)Proceed to a steady state and remain there. Stability and sensitivity

85 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 85 Stability of the process b)Fail to attain to steady – state conditions because its output grows indefinitely. Stability and sensitivity

86 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 86 Stability of the process c)Fail to attain steady – state conditions because the process oscillates indefinitely with a constant amplitude. Stability and sensitivity

87 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 87 Stability of the process A steady state system x s is said to be stable if for each possible region of radios  >0 around the steady state, there is an initial state x 0 at t=t 0 falling within a radius  >0 around the steady state that causes the dynamic trajectory to stay within the region (x-x s ) t 0. Steady state x s Region  >0 Radius  State x Stability and sensitivity

88 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 88 Sensitivity analysis In many cases, it is useful to know how a chemical process respond when a equipment parameter or stream variable is varied, rather than running simulation only in few parameters. The sensitivity analysis permits the tabulation of output variables at equal increments over a specified range of parameter or variable values. Stability and sensitivity

89 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 89 Sensitivity analysis Example: Carbon monoxide and hydrogen are reacted to form methanol. The reaction is exothermic; consider an adiabatic reactor. 100% of the carbon monoxide is converted. For a fixed flow rate of carbon monoxide, it is desired to know how the outlet temperature varies with respect to the flow rate of hydrogen in the feed stream. Stability and sensitivity

90 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 90 Sensitivity analysis The temperature decreases as the mole flow in feed increases. Stability and sensitivity

91 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 91 Sensitivity analysis One of the most important contributions of sensitivity analysis is that it allows one to identify those variables which, when changed, have the greatest impact on the process output. Stability and sensitivity

92 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 92 Optimization Completely specified case. Over-specified case. Under-specified. From a Mathematical point of view, chemical engineers encounter 3 situations when solving equations. Process optimization

93 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 93 Completely specified case N equations = N variables When the number of equations is equal to the number of variables, then we can proceed to solve the problem. 3x – 2y + 9z = 3 6x – 11y + z = 7 x – 15y + 4z = 25 In this case, we have 3 equations with 3 unknowns. Process optimization

94 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 94 Over – specified case N variables < N equations which is commonly referred to as the reconciliation (data reconciliation and rectification) problem. Many variables are determined in >1 way – values must be reconciled Process optimization

95 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 95 Under – specified case N variables > N equations Also called optimization problems. The optimization is used to maximize or minimize a specified objective function by manipulating decision variables (feed stream, block input, or other input variables). Some variables are undetermined – can be manipulated to optimize the process. Process optimization

96 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 96 Optimization N variables – N equations = N D The decision variable, d, is iteratively adjusted to achieve the optimal solution to a specified objective. Some methods commonly used are: Successive linear programming (SLP). Successive quadratic programming (SQP). (used by Aspen plus, Hysys.plant) Generalized reduced gradient (GRG). Process optimization

97 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 97 Optimization Any optimization problem can be represented as: Is the objective function. Is the set of m equations in n variables x. The equality constraints Is the set of r inequality constraints. Those bound the feasible region of operation. Process optimization

98 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 98 Optimize on multiple criteria Some common objectives in optimization of an industrial process are: Achieve lower capital cost design. Increase production. Reduce unit operation cost. Reduce environmental impact. Reduce energy consumption. Process optimization

99 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 99 Degrees of freedom A degree of freedom analysis is incorporated in the development of each subroutine that simulate a process unit. These subroutines solve sets of N equations involving N variables. N D = N equations – N variables. Degrees of freedom are the number of input variables you need to specify. Process optimization

100 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 100 Quantitative comparison of alternatives In almost every case encountered by chemical engineer, there are several alternative methods which can be used for any given process or operation. Formaldehyde production: 1.By catalytic dehydrogenation of methanol. (By controlled oxidation of natural gas) 2.By direct reaction between CO and H 2 (under special conditions of catalyst, temperature, and pressure) Economic evaluation of alternatives

101 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 101 Optimum Economic Design If there are two or more methods for obtaining exactly equivalent final results, the preferred method would be the one involving the least total cost. Alternative designs do not give final products or results that are exactly equivalent. It then becomes necessary to consider the quality of the product or the operation as well as the total cost. $ Economic evaluation of alternatives

102 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 102 Economic evaluation of alternatives Throughout the design process, estimates of the cost of equipment and other costs related to the capital investment play a crucial role in selecting from among the design alternatives. The total capital investment (TCI). The annual cost of manufacture (COM). Economic evaluation of alternatives

103 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 103 Is a one – time expense for the design, construction, and start – up of a new plant or a revamp of an existing plant. Total capital investment Estimation of the total capital investment 1.Order – of magnitude estimate based on bench – scale laboratory experiments. 2.Study estimate based on a preliminary process design. 3.Preliminary estimate based on detailed process design studies lading to an optimized process design. 4.Definitive estimate based on a detailed plant design. Economic evaluation of alternatives

104 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 104 Investment justification Objective: to evaluate the costs and benefits of investment in process modifications. Inputs and outputs with costs attached must be accurately represented. Differences between candidate solutions must be accurately modelled. Level of detail just enough to enable cost- benefit calculations. Other parts of the process can be a “black box”. Economic evaluation of alternatives

105 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 105 Direct cost Indirect cost Economic evaluation of alternatives

106 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 106 Quantify cost – benefit of various possibilities When designing a greenfield plant, there are many possibilities which can be evaluated to get the best cost – benefit ratio. Economic evaluation of alternatives

107 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 107 Cost sensitivity analysis Sensitivity analysis is important in order to avoid information overload: It usually is best to do an initial analysis using only the data you have, being careful about indicating where the data is weak or you are using best guesses. Economic evaluation of alternatives

108 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 108 Cost sensitivity analysis “Planning should stimulate thinking, not overwhelm it” Economic evaluation of alternatives

109 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 109 Operator training Today, cheap computer power allows virtually any operator to have enough capability to simulate large flowsheets with considerable detail on the desktop. Process flowsheet simulators now have a sophisticated user interface, large physical properties databanks, and many thermodynamic models. Operator training

110 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 110 Running sophisticated process simulations does not guarantee correct results. You need to understand the thermodynamic assumptions underlying the program and how to ensure proper application. Operator training The personnel using the simulators, should be trained beforehand, and be aware of problems that may appear. Operator training

111 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 111 Operator Training simulation Objective: to mimic response of displays to simulate process excursion and operator inputs. integration with physical operator console. simplified process representation, just enough to generate appropriate responses. progressive series of exercises as part of system. trainee evaluation as part of system. Operator training

112 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 112 Opportunity to increase process and systems awareness in operating personnel A simulated process can be easily executed in a computer, without the expense of real equipment and without the risk of disrupting the real plant’s production. In this virtual world, computer simulations allow all manner of extreme conditions and “what – if” scenarios to be tested safely. Operator training

113 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 113 Develop competence in unusual, undesirable, or dangerous process operation conditions The only certain way to test how a proposed control system will handle every conceivable situation is to design it, install it, and try it out. Operator training

114 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 114 Simulators avoid dangerous process operation A simulated control system can be installed in a simulated plant without the expense of real equipment and without the risk of disrupting the real plant’s production. Computer simulation allow all manner of extreme conditions and “what if” scenarios to be tested safely. Operator training

115 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 115 Increase comfort level with advanced technology A simulator trainer substitutes for the real plant and the real control system. If the simulation is realistic, the trainees don’t know the difference. Operator training

116 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 116 Simulation in Process Design and Operation Before constructing a plant, or making any changes to it, it is always desirable to know how it is going to behave. Steady – state simulation, is the tool one use. In this tier, the basics tools to understand a process design, construct it and simulate it in a computer, were shown. Summary

117 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 117 Quiz A first pass simulation using mill data indicates that the boiler is generating more steam than the heating value of the fuel will provide (i.e. efficiency greater than 100 %). An appropriate response would be: a)Ignore the problem as insignificant. b)Replace the boiler simulation model with one that will give you realistic results. c)Recommend a certificate of appreciation for outstanding performance be presented to the boiler operating crew. d)Double check the accuracy of the measurements and arrange for test to be performed on the boiler fuel.

118 TIER 1 STEADY STATE SIMULATION PIECE PAPRICAN ECOLE POLYTECHNIQUE UNIVERSIDAD DE GUANAJUATO 118 a)Inaccuracies in the process data. b)Incompatible process specifications in different parts of the sequential flowsheet that are “fighting” each other. c)The actual process in fact never balances. d)Unrealistic assumptions about unit efficiencies. e)To many recycle loops. Your simulation flowsheet is failing to converge. What would be the most likely cause of this problem?


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