Process Design; Art or Engineering?

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Denis Blankenship
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1 ...Pragmatism or fundamentals? E D W I N Z O N D E R V A N T U / E J U N E 1 4 TH U V A.

2 Outline Introduction Who am I? What about TU/e and Chem. Eng? Process design What is process design? Process design in a nutshell (by example) Process creation/synthesis Reactor design Separations design Recycles Process simulation and optimization Heat and/or mass integration Economic evaluation Current research Reactive distillation Biorefinery 2

3 Born February 26th 1976 in Leeuwarden Performed bachelor in Process automation in Leeuwarden (1999) Who am I? 3 Performed master in Chemical engineering at Groningen University (Master thesis topic: System Identification for control) (2003) Works currently as assistant professor at Eindhoven University Performed doctorate at Twente University/Groningen University (2007)

4 What about TU/e and Chem. Eng? Three educational and research tracks: Chemistry Material science Process Engineering Averagely 50 students enrol each year in our Bachelor and M.Sc. program (Majority moves to PE) Averagely 15 students enrol in our post master Process and Product Design Around 150 Ph.D. Students Around 50 staff 4

5 What about TU/e and Chem. Eng? Current classes in process and product design: 6BA75 Process design, Bachelor course, 3 ECTS (40 students) Case: Hydroalkylation of toluene 6BO06 Product design and process management, Bachelor course, 4 ECTS (40 Students) Case: Micellar catalysis of propylene oxide 6PE42 Integrated process design, M.Sc. Course, 5 ECTS, (40 students) Case: Production of paraphenylene diamine 5

6 What is process design? 6 In chemical engineering, process design is the design of processes for desired physical and/or chemical transformation of materials. The design starts at a conceptual level and ultimately ends in the form of fabrication and construction plants. Nano Micro Meso Macro

7 What is process design? Several process design procedures: J.M. Douglas, Conceptual design of chemical processes, (1988) Seider et al., Product and process design principles, (2010) Grossmann et al, Systematic methods in chemical process design, (2004) Bongers, Product Driven Proces Synthesis (PDPS) All of them incorporate some kind of hierarchy with feedback. Passing through the hierarchy more level of detail is required. Process design has many (multidisciplinary) facets; engineering, environment, business. 7

8 Douglas Process design hierarchy 8 Batch versus continuous Input-output structure of the flowsheet Recycle structure of the flowsheet General structure of the separation system Vapor recovery system Liquid recovery system Heat exchanger network

9 Steps in Design and Retrofit 9 Assess Primitive Problem Detailed Process Synthesis - Algorithmic Methods Development of Base-case Plant-wide Controllability Assessment Detailed Design, Equipment sizing, Cap. Cost Estimation, Profitability Analysis, Optimization

10 Assess Primitive design problem 10 Process design begins with a primitive design problem that expresses the current situation and provides an opportunity to satisfy a societal need. Normally, the primitive problem is examined by a small design team, who begins to assess its possibilities, to refine the problem statement, and to generate more specific problems: Raw materials - available in-house, can be purchased or need to be manufactured? Scale of the process (based upon a preliminary assessment of the current production, projected market demand, and current and projected selling prices) Location for the plant Refined through meetings with engineering technical management, business and marketing. Brainstorming to generate alternatives

11 Important note... We have many systematic tools to start designing a process, which seems to make it an Engineering discipline But during the design process, it is creativity and knowledge beyound the Engineering community that leads to novel designs, which seems to make it an Art!... But in this talk the emphasis will be on pragmatism, so on Engineering!!

12 The beginning Most of the time it starts with something that seems to work in the lab! The Alchemist, Adriaan van Ostade

13 Example: The toluene hydroalkylation process Principle path: C 7 H 8 + H 2 C 6 H 6 + CH 4 Benzene is one of the intermediates that can be converted to cyclohexane, and cyclohexane can be used to produce nylon Side reaction: 2C 6 H 6 C 12 H 10 + H 2 From lab. Data: irreversible reactions, no catalyst, temp ~ o F, conversion: 75% toluene/benzene, 25% benzene/biphenyl

14 Design objective Design a plant with a capacity of 200 MMlb/year (based on a toluene conversion of 274 lbmol/hr and 330 days of operation annually)

15 Reaction operation of the hydroalkylation Excess of H 2 to prevent carbon deposition and to absorb heat Purge methane to avoid expensive separation of H 2 /CH 4

16 Adding the recycles Note: not all amounts are known yet

17 Adding the separations Note 1: Pressures not yet known Note 2: this is just one possible selection

18 Adding cooling and heating Note: Heating and cooling added to alter temperature, pressure and liq./vap. Phase

19 Task Integration: adding the unit operations

20 Process simulation After the generation of process flow-sheets you would like to analyze several things: To solve mass/energy balances, phase equilibria, mass transfer, kinetics, All with the aim of finding suitable operating conditions (Temperature, pressure, etc.) P.S. Simulators are also developed and used with the aim of training operators.

21 Process simulation For this we may use process simulators Process simulators are mostly used for steady-state and scheduling calculations. Process simulators can also be used for process dynamics and control; economic evaluation and profitability analysis; process optimization.

22 Why should we use simulators? Solution of mass/energy balances in a simultaneous manner Non-ideal thermodynamic models Detailed (rigorous) unit operation models Solve large sets of equations (typical ~ )

23 Software packages There are many process simulator packages available, basically we divide them into two types: Modular: Aspen Plus, HYSIS, CHEMCAD, PRO II, Unisim,... Equation oriented (EO): Matlab, gproms, GAMS,...

24 Modular mode Unit and thermodynamic models are self-contained subprograms (modules) These flow-sheets are called at a higher level to converge the stream connectivity of the flow-sheet (e.g. Recycle streams) Has a long history and is more popular for design work Easy to construct and debug however; it can be inflexible for various user specifications

25 Module based simulators

26 Equation oriented mode Process equations (unit, connectivity, thermodynamic) are assembled and solved simultaneously. Requires sophisticated numerical methods and software engineering concepts Is primarily applied to online modelling and optimization.

27 EO simulators

28 Historical evolution 1950s: Unit stand-alone models execution in sequence to form a flow-sheet origin of sequential modular mode. 1960s: Sequential modular in-house flow-sheeting packages (petrochemical companies). Academic research for fundamentals of EO simulators. 1970s: Advanced methods for modular flow-sheets simultaneous modular flow-sheets. More general models and advanced numerical methods. Aspen (MIT). 1980s 1990s: Considerable industrial development for equation oriented mode. User friendly interfaces and powerful algorithms. Vendor-supported software today: Consideration of special issues and addition of the respective features/modules (e.g. Supply chain problems, advanced economic analysis, etc.)

29 Heat integration Some streams in the process require heating and cooling. Heat integration is concerned with finding the best connection between hot-, cold- and utility streams Several procedures: Pinch analysis Graphical methods Algorithmic methods (e.g. With math. optimization) 29

30 Heat and/or mass integration 30 Example of ethylene production process with heat integration

31 Safety considerations Example Disaster 1 Flixborough: 1st June tons of cyclohexane were released from Nypro s KA plant (oxidation of cyclohexane) leading to release of vapor cloud and its detonation. Total loss of plant and death of 28 plant personnel. Highly reactive system - conversions low, with large inventory in plant. Process involved six, 20 ton stirred-tank reactors. 31 Discharge caused by failure of temporary pipe installed to replace cracked reactor. The so-called dog-leg was not able to contain the operating conditions of the process (10 bar, 150 o C)

32 Safety considerations Flixborough - What can we learn? Develop processes with low inventory, especially of flashing fluids ( what you don t have, can t leak ) Before modifying process, carry out a systematic search for possible cause of problem. Carry out HAZOP analysis Construct modifications to same standard as original plant. Use blast-resistant control rooms and buildings 32

33 Safety considerations Example Disaster 2 Bhopal: 3rd December Water leakage into MIC (Methyl isocyanate) storage tank leading to boiling and release of 25 tons of toxic MIC vapor, killing more than 3,800 civilians, and injuring tens of thousands more. MIC vapor released because the refrigeration system intended to cool the storage tank holding 100 tons of MIC had been shut down, the scrubber was not immediately available, and the flare was not in operation. Bhopal - What can we learn? Avoid use of hazardous materials. Minimize stocks of hazardous materials ( what you don t have, can t leak ). Carry out HAZOP analysis. Train operators not to ignore unusual readings. Keep protective equipment in working order. Control building near major hazards. 33

34 Process Control Process design steady state Dynamic behaviour unfavourable process characteristics. Control systems should keep process at desired operating level for: Safety Product specifications (quality and safety) Environmental regulations Operational constraints Economics Account for design errors 34

35 Plant wide control What to control and where to control without conflicts! 35 Example of a plant wide control scheme for production of vinyl chloride

Economic evaluation 36 You use economic evaluation to determine from a set of alternative process designs that you have made whether or not they are

36 Economic evaluation 36 You use economic evaluation to determine from a set of alternative process designs that you have made whether or not they are (economically) feasible. Cost accounting and profitability becomes more accurate as the process design becomes more detailed. But mind you that numbers can be easily 20-80% off!

basic, detailed) Procurement Construction & field Supervision Contract fees

37 Economic evaluation: Cost Accounting 37 Direct costs Indirect costs Equipment Piping Civil & steel Process control Electrical Insulation & paint Engineering (conceptual, basic, detailed) Procurement Construction & field Supervision Contract fees Estimation often on basis of cost factors, e.g Lang factors Sizing often according to power laws

38 Economic evaluation: Manufacturing costs 38 Cost of Manufacture (COM) Feedstock Utilities Labor related operations Maintanance

39 Economic evaluation: Profitability analysis Approximate profitability measures: Return of investment (ROI) Payback period (PBP) Venture profit (VP) Annualized Cost Rigorous profitability measures Net Present value (NPV) Time value of money Cash Flow and Depreciation 39

40 Optimization for process design Mathematical syntax: 40 min f ( x, d) st.. c( x, d) 0 g( x, d) 0 L x x U x L d d U d x X, d D Objective: Economic or environmental criterion Equality constraints: Mass & Energy balance, Equilibrium relations, etc. Inequality constraints: Operating limits Bound constraints: Equipment limits Set over which the variables are defined: Continuous or discrete

41 Types of optimization problems 41 Optimization Discrete Continuous CLP MIP See: Biegler & Grossmann (2004) Grossmann & Biegler (2004) MINLP G-O LP,QP,L CP NLP DFO surrogate SA,GA

42 Applications in Chem. Eng. 42 See: Biegler & Grossmann (2004) Grossmann & Biegler (2004)

43 Optimization approaches Mathematical programming Simplex method, Lagrange multiplier method, SLP, SQP, Branch & Bound, Disjunctive programming, constraint programming, etc. Global optimization / Meta-heuristic Neural networks, fuzzy logic, ant colony, simulated annealing, taboo search, genetic algorithms, etc. Other Stochastic programming, multi-objective optimization,... 43

44 Sustainable design of a reactive distillation column Background Fatty acid esters of use to the food- and cosmetics industry. Traditional production: Batch wise Reactive distillation as PI option 44 Reactive distillation column Objective Develop and test a framework to identify and optimize relevant operational and design parameters of an RD system

45 Process variables Metrics Sustainable design Selected variables for multi criterion decision analysis: Number of stages Operating pressure Ratio Conversion Process variables 45 Optimizer Decision variables Simulator Ecoinvent Proposed framework by Bojarski

46 Sustainable design 46 B Decision space A Objective space E C D min[ Z ( x) Z ( x)] st.. cx ( ) 0 gx ( ) L U x x x z 2 C D E B A x X Schilling et al. (1983) z 1

47 Sustainable design 47 Objective space z 2 D C E B A Ideal- or utopia point Non-inferior set (Pareto Optimal, or non-dominant) z 1

48 Sustainable design 48 Environmental metrics Decision space Economic metrics Catalyst loading is major player! Multi criterion decision making Optimized settings for P, N, RR and X

49 Background Optimal design of a Biorefinery 49 Crude oil will deplete, alternative feedstock required, biomass can be a renewable resource. Objective Develop a process design optimization model that can assist in selecting promising biomass conversion routes.

50 Proposed model Superstructure and transhipment model 50 Sources Products (Sinks) Mixing Side reaction Papoulias & Grossmann (1983) Dunn & Halwagi (1996) Processing steps Splitting Main reaction Feed Reactants Waste By-product Main product

51 Method Mathematical program ),, ( ),, ( ),,,, (.. ),, ( min,, k i k k i k k i i k i r k m r k i k k n k i k n f y g f y g SW MW h y s t f y f w Z Objective function Logical constraints Component balances Structural constraints The details of this MINLP model can be found in the proceeding, it contains single equations and variables (containing only 68 decision variables) nonlinear terms can be linearized out! 51

52 Optimal design 52 Linearizations: xy x {0,1} y Non-convex/nonlinear Glover Replace xy with z Lx z Ux y U (1 x) z y L(1 x) L min{ y, y } U max{ y, y } Convex/linear... yf 0 F F F L U Standard... F 0 yf F yf L U

53 Optimal design 53 Superstructure: all possible topologies. Outcome will be different for each objective

54 Process design Concluding remarks...cannot be explained in a single lecture!...it has many facets...it requires multiple disciplines...there is a lot of uncertainty...it is a hardcore engineering discipline...(but it requires a lot of creativity too) 54

SYNTHESIS AND OPTIMIZATION OF DEMETHANIZER FLOWSHEETS FOR LOW TEMPERATURE SEPARATION PROCESSES

Distillation Absorption 2010 A.B. de Haan, H. Kooijman and A. Górak (Editors) All rights reserved by authors as per DA2010 copyright notice SYNTHESIS AND OPTIMIZATION OF DEMETHANIZER FLOWSHEETS FOR LOW