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Presentation on theme: "REFINERY OPERATIONS PLANNING Dunham, Luhila, Odunuga."— Presentation transcript:


2 Overview Refinery Overview Production Planning Linear model with complex and simplified utilities Results Conclusions

3 Model Refinery: Bangchak Refinery (Thailand)

4 What is Refinery Operations Planning? How much crude should be purchased? What kind/quality of crude should be purchased? How much does the refinery want to produce? Consists of crude oil unloading, production planning and distribution

5 Problems in Refinery Operations Uncertainty in prices and demand Effects of uncertainty are minimized by using models Historically, models have been linear Use of average operating conditions Linear models are non-ideal Currently Non-linear models are ideal Linear Model with Complex Utilities

6 Features of Current Models Shah N1996Moro et al.2004Gathe-Lundgen et al.2002Glismann et al.2001Pongsakdi et al 2006Lee et al.1996Zhang et al 2001 Crude oil distillation and blendingXXX NLPXX P.planning, inventory mgt, shipmentXX Coordination of feedstock & pdt with markets XXX UncertaintyXXXX Financial riskX Decomposition StrategyXX

7 Production Planning Processing of crude oil through different units Decisions Crude oil Purchase Processing Inventory management Bangchak Refinery

8 Production Planning Routine process which guides purchasing Departments Crude oil acquisition, product sales & refinery operations Based on Market demands and prices Uncertainty

9 Production Planning Winter High fuel oil demand more fuel oil produced Summer High demand for light crudes more gasoline produced

10 Linear Modeling Method Inside Unit is a black box (inside box doesnt matter) Input and output can be related F1F1 F2F2 F3F3 Reactor Feed Light Ends Desulfurized Product CRU

11 Linear Modeling Method Linear Equation Relating Inputs and Outputs: F 3 = α 31 *F 1 What is α 31 ? It depends on the system! F1F1 F2F2 F3F3 Reactor Feed Light Ends Desulfurized Product CRU

12 Finding Alpha ( α ) Really, α ij is the result of solving an ODE Example: Concentration of Aromatics leaving a CRU Thus α ij will depend on Inlet concentrations and flows Process parameters: Temperature, Pressure

13 Hydrotreating Process to remove impurities in the stream Aromatics Hydrotreating for sulfur hydrosulfurization Reduces the aromatic content in crudes Hydrogenation Nitrogen Sulfur

14 Hydrotreating PFD

15 Hydrotreating Model Operating Conditions Reactor Temperatures range from 250°F – 550°F Main Variables Pressure Temperature Velocity H 2 /HC ratio

16 KTU Aromatics Flow Rate ƒo = Initial Flow Rate

17 Hydrotreating Empirical Model Aromatics:

18 KTU Equation Comparison Linear Equation: F ar =α 1 *F total Actual Equation:

19 Non linearity

20 Catalytic Reforming Process of increasing octane number of NPU by converting napthenes & paraffins Outputs Aromatics, light hydrocarbons (C 1 -C 5 ) & hydrogen Operating conditions VariablesRange Temperature °F900 – 950 Pressure (atm)30 – 40

21 Catalytic Reformer PFD

22 CRU -Empirical Kinetic Model Conversion of napthenes to aromatics Amount of product X1 Produced Reaction Rate Kinetic Reaction Constant Equilibrium Constant

23 Reformer Temperature Modeling

24 Process Industry Modeling Systems Allows user to analyze results graphically and to adjust variables Adds capabilities for global optimization, solution ranging, and goal programming Requires refinery-specific inputs to determine an acceptable starting point PIMS

25 PIMS and Non-linearities Benefits Identifies the solution that maximizes global profitability Validates solution and enhances planners confidence and gives an estimate on how close results are to optimal solution Eliminates need to manually search for improved solutions

26 PIMS and Non-linearities SLP is the primary non-linear modeling feature Flexibility in the types of equations that can be used in models Builds derivatives which eliminates potential errors Non-linear terms can reference existing Aspen PIMs variables or define new variables

27 PIMS Modeling PIMS finds maximum by calculating objective function gradient Maximum found by PIMS depends on initial condition X X Start 1 Max 1 Max 2 Start 2 x x

28 Linear Model with Utilities Alternative Method

29 Model Super tables Product flow of Paraffins X=f(C,T,P,F) Conc = 0.31 Temp = 800 F Pressure = 400 psi Flowrate = 16000 m 3 /day X= 2630 bbl/hr

30 Model Super tables

31 Linear Model with Utilities Our model finds the maximum by testing many discrete points Optimum value will be close to the global optimum

32 GAMS Software Algebraic modeling interface capable of solving linear and mixed integer models Non-linear equations cause problems

33 Comparison Linear or Mixed Integer Programming Discretizes continuous values Always finds best test point (near global maximum) Successive Linear Programming Uses gradient of objective function May find local maximum (depends on starting point) Linear Model (in GAMS) PIMS

34 Utility Calculations Steam and Power are produced within the refinery Steam is produced by fired steam boilers, which burn refinery fuel gas and fuel oils to create steam Electricity is produced by running high pressure steam through a turbine

35 Utility Table Equations Water Cost (Heat exchangers) Unit Fuel Gas Consumption (Heaters)

36 Utility Equations in GAMS

37 Results

38 LP Model (Individual Units) CRU Reactor Temperature Isothermal Non- Isothermal UtilitiesNo Utilities Simplified Utilities Complex Utilities Linear Model Development

39 Utility Models Linear Model with Complex Utilities: Uses tables to coordinate output values with functions of Temperature, Pressure, and Flow rate Linear Model with Simplified Utilities: Assumes Temperature and Pressure to be the average of operating conditions Linear Model without Utilities: Does not calculate utility cost

40 Results - Gross Refinery Margin ModelGRM Linear Model with Complex Utilities$34,103,151 Linear Model with Simplified Utilities$31,168,455

41 Results - Total Utility Cost ModelUtility Cost Linear Model with Complex Utilities$2,980,761 Linear Model with Simplified Utilities$3,411,319

42 Purchasing Recommendations Linear Model with Complex Utilities Crude Purchasing Recommendations (m3/day) Month 123 OM 250361265445267700 TP 353134224543073 LB 0012997 SLEB 95392 PHET 57235 MB 95392 Linear Model with Simplified Utilities Crude Purchasing Recommendations (m3/day) Month 123 OM244486262303267899 TP328534112647392 LB009041 SLEB95392 PHET57235 MB95392

43 Unit Operating Conditions

44 Discussion Catalytic Reforming Unit Isothermal Model Reactors are isothermal All reactor temperatures are the same Produces more Fuel Gas Varying Temperature Model Each reactor operates at a different temperature Temperature changes in each reactor Produces more Hydrogen Produces almost 50% more Reformate

45 Hydrogen and Refinery Fuel Gas Both utility models calculated a net production of Hydrogen and Refinery Fuel Gas Assume these are usable and can be transported around the refinery where make-up is needed Since excess is produced, no Hydrogen or RFG is purchased

46 Conclusions The Linear Model with Complex Utilities processes more crude and gives a larger GRM than a model with Simplified Utilities and a model with no utility cost calculation The Non-isothermal CRU model produces more reformate and increases overall GRM Linear models in GAMS always find a value close to the global optimum, where PIMS may find only a local optimum depending on starting point

47 Questions?

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