Optimal Synthesis of Integrated Gasification Combined Cycle (IGCC) Systems

Transcription

1 Optimal Synthesis of Integrated Gasification Combined Cycle (IGCC) Systems R. S. Kamath, I. E. Grossmann, L. T. Biegler Department of Chemical Engineering Carnegie Mellon University Pittsburgh, PA 53 March, 0

2 Problem Statement Given Type of coal (quality, composition) Power demand Mode of operation (with or without chemical/h production) Extent of CO capture Location of site Ambient conditions for utilities (air, water) Determine Optimal structural configuration and operating conditions Minimize Investment and operating costs Additional constraints to be met Environmental emissions (current or more stringent future ones)

3 Superstructure for IGCC Air E xhaust gas C oal Treated Ash Treated Wastewater Treated Tailgas S ulfur S C OAL G AS IF IC ATION S E C TION C oal C oal Handling G as ifier As h Ash/S lag Treatment R aw S yngas P roc es s C ondensate S yng as C ondens ate C ooling Treatment C ooled S yngas Ac id g as C leaning & S ulfur P rocess R ec overy S team C lean S yngas Air S eparation Unit Oxygen WHB S team S P OWE R G E NE R ATION G asifier S team S E C TION WHB S team Boiler F uel S team S uperheated Mechanical G eneration S team S team P ower Heat Turbine R ec overy C ondensate E lectrical Injection Hot E nergy S team E xhaust gas G as Turbine E lectrical E nergy P ower P roduc tion Turbine F uel S M C hemical C hemic al P roduc tion H /C O R atio Adjus tment (S hift) S C O C apture S M H P roduc tion H To C O storage 3

4 Methodology (Daichendt and Grossmann, 997) Step : Input-Outlet Level Develop basic aggregate models for all sections Connect the models to form a simplified superstructure Optimize at I/O level by minimizing only operating costs (material & energy) Further Steps: Optimizing superstructure at higher complexity levels Use results (initial point, bounds) from previous optimization runs Increase complexity of models for the sections, one at a time Repeat the procedure till superstructure of desired complexity is optimized 4

5 Aggregate Model for Coal Gasification Restricted Gibbs Energy Minimization Min s. t. Atomic balance for reacting species Component mole balance for inerts Evaluation of component Gibbs energy Add equilibrium reactions (temperature approach) Model solved as an inner minimization problem (inside an outer optimization model) 5 Non-negativity of molar flows

6 Capability of restricted Gibbs model Composition (mole %) Shell entrained gasifier (Ni and Williams, 995) GE entrained gasifier (Klara, 007) CoP entrained gasifier (Klara, 007) Actual Model Actual Model Actual Model N H CH CO CO H O H S COS NH Trace ΔT Approach Shift Reforming COS hydrolysis NH 3 synthesis

7 Superstructure for the Utility section Ref: Bruno et al. (998) 7

8 Numerical Example for Utility model Demands Electricity: 500 MW Mechanical Power No : 5 MW Mechanical Power No : 5 MW HP Heating: 5 MW MP Heating: 0 MW LP Heating: 50 MW Superstructure 6 process streams 3 HP turbines (7 modes) MP turbines (3 modes) 5 Headers (HP, MP, LP, Cond, Vac) Gas turbine (compressor, expander) 3 Boilers (HP, MP, HRSG) 4 Combustors (GT, Boilers) 5 Liquid pumps Air blowers Deaerator Logic constraints turbine Max demand (mechanical or power) Mechanical demand Either electricity or turbine MP turbine can t contribute power 8 Pressure of Steam Headers HP: 45 bar MP: 0 bar LP: 7 bar Non-convex MINLP problem Binary variables: 44 Continuous variables: 75 Constraints: 309 Modeled and solved using GAMS (Intel Core Duo.4 GHz with GB RAM)

9 Optimal Solution for Numerical Example Operating cost = $307.7 Million/yr 9

10 Superstructure for Air Separation Unit L P G OX L ow P res s ure C olumn Was te P urg e L P G AN HP G AN Integ rated condens er reboiler Multi s tream heat exc hang er Multi s tream heat exc hang er Air integration (Gas Turbine) HO C O Hig h P res s ure C olumn Ambient Air P P U 0

11 Aggregate model for complex columns Cascade model for counter-current stages (Kremser method with Edmister approximation) E xiting vapor (V ) E ntering liquid (L 0 ) Performance Poor Approximations removed Recovery fractions E ntering vapor (V N+ ) E xiting liquid (L N ) Effective Absorption/Stripping factors Better (realistic) constraints added Vapor outlet at dew point Liquid outlet at bubble point Simulation-based (like rigorous models) Columns interconnected cascades and feed/side-draw stages Optional feeds/side-draws (may or may not exist) Cascades bypassed (disappear) if corresponding feed/side-draw stages do not exist

12 Possible modes for LP column S 3 S 30 S 3 S 30 S 3 S 30 S 3 S S 9 S 6 S 7 S 9 S 6 S 7 Vapor S ide draw (optional) S 8 S 9 S 6 S 7 Vapor S ide draw (optional) S 8 S 9 S 6 S 7 S 5 S 4 S 5 S 4 S 3 S S 3 S 3 3 S S 0 S S 0 S 9 S 8 S 9 S 8 S 9 S 8 S 9 S 8 S 7 S 6 S 5 S 7 S 6 S 5 S 3 S 4 S 3 S 4 S 0 S S 0 S S 9 S S 9 S S 8 S 5 S 7 S S 6 Main Feed (always exists ) S 8 S 5 S 7 S S 6 Main Feed (always exists ) S 8 S 5 S 7 S S 6 Main Feed (always exists ) S 8 S 5 S 7 S S 6 Main Feed (always exists ) S 3 S S 3 S S 3 S S 3 S S 4 Hig h P urity O product S 4 Hig h P urity O product S 4 Hig h P urity O product S 4 Hig h P urity O product Superstructure (LP column of ASU) Only main feed exists feeds exist Main feed & Side-draw exist Both feeds & Side-draw exist

13 Simulation of LP column Vapor S ide draw (optional) S 8 S 3 S 9 S 6 S 5 S 3 S 30 4 Column hardware configuration S 7 S 4 S 0 Stages (including reboiler) : 45 Number of feeds : 3 (Top + Main + nd ) Stage location of main feed : 3 Stage location of nd feed : Stage location of vapor side-draw : 9 Operating conditions Column Pressure : 5 bar Reboil Ratio : 3.5 S 9 S 7 S 3 S 4 S 9 S S 8 S 5 S 8 S 6 S 7 S S 5 S 6 Main Feed (always exists ) Feed characteristics a) Main and nd feed Molar flowrate : 3 kmol/s Mol % N : Mol % O : Mol % Ar : Temperature : 96.7K b) Top feed Molar flowrate : kmol/s Mol % N : Mol % O : Mol % Ar : Temperature : 93.8K S 3 S S 4 Hig h P urity O produc t 3

14 Simulation Results for LP Column Vapor S ide draw (optional) S 8 S 3 S 9 S 6 S 30 4 RADFRAC (Aspen Plus) S 7 Top Product Aggregate Model (GAMS) Percentage Deviation Flowrate kmol/s S 5 S 4 N fraction O fraction S S 9 S 0 S 8 Ar fraction Temperature K S 7 S 3 S 4 S 9 S S 8 S 5 S 6 S 7 S S 5 S 6 Main Feed (always exists ) Bottom Product Flowrate kmol/s N fraction 6.8E E O fraction Ar fraction E Temperature K S 3 S S 4 Hig h P urity O produc t Reboiler Duty MW

15 Model for Multi-stream Heat Exchangers H H C C Almost no models in literature (Proprietary MHeatX model in Aspen Plus) MHeatX assumes all outlet streams have same Temperature if DOF > Need an equation-oriented model suitable for optimization Aggregate Model for MHEx Use Pinch technology to formulate an inverse problem Given a MHEx (adiabatic black box), determine feasible temperatures and heat capacity flowrates for all inlet and outlet streams. Use Duran & Grossmann (986) model with no objective function and zero utility loads Generates feasible points for outer problem in which model is embedded. No temperature intervals (useful for simultaneous optimization and heat integration) Can t estimate capital cost (handy when emphasis is on energy/utility costs) 5

16 Dealing with Phase Change in MHEx Some streams can change phase during heat transfer (difficulty in enthalpy calculation, FCp is not constant) No models exist for heat integration with phase change (phase is not known a priori) Proposed Model H H H C C S up T C,OUT P T C,OUT S ub T C,OUT C S up C P C S ub S up T C,IN P T C,IN S ub T C,IN C S up T H,IN H S up S up T H,OUT It is known that streams H and C do not change phase Streams C and H may change phase 6 H S up T H,IN S up T H,IN H P H S ub P T H,OUT S ub T H,OUT C

17 Mathematical model for phase detection For both hot and cold streams a) Region detection for inlet stream For hot streams b) Region detection for outlet stream For cold streams c) Equations for Flash calculation for -phase region 7

18 Sample flowsheet for ASU Aspen Plus (Rigorous) ~ 960 variables T = {.9,.96} Aggregate Model (GAMS) ~ 80 variables MXR T: Temperature (K) F: Flowrate (kmol/s) 5 4 T = {09.0, 09.8} MHEx 0 FLSH 4 9 HPC SPLT LPC F = {4.353, 4.355} T = {4.67, 4.45} FLSH 7 3 F = {.49,.40} T = {4.935, 4.94} F = {7.576, 7.577} 8

19 Conclusions and Future Work Conclusions Formulated the proposed superstructure for IGCC as MINLP model Methodology: solve large combinatorial problem with models of various complexity levels Developed aggregate models for coal gasification, utility section, complex distillation columns and Multi-stream Heat Exchangers in ASU Preliminary results show systematic topology optimization (Utility model) accurate predictions (Gibbs model), and good match between aggregate and rigorous models (ASU) Future Work Develop aggregate models for other sections of the IGCC plant (e.g. acid gas cleaning, Sulfur recovery, CO capture) Optimize the superstructure at input-output level (considering only operating costs) Based on previous optimization results, develop more rigorous models and optimize the superstructure at next level of complexity 9