Simulation of a base case for future IGCC concepts with CO 2 capture

Transcription

1 Simulation of a base case for future IGCC concepts with CO 2 capture Christian Kunze, Hartmut Spliethoff Institute for Energy Systems TU München for 4 th Clean Coal Technology Conference May, 2009 Dresden, Germany

2 Agenda Background HotVeGas project Plant assumptions and configuration Model verification Concept optimization Summary Outlook

3 Background research project HotVeGas HotVeGas: high temperature gasification and gas cleaning financed by BMWi (COORETEC) and 5 industry partner (RWE, E.ON, Vattenfall, EnBW, Siemens Fuel Gasification) cooperation of 4 academic partners: research centre Jülich, TU Freiberg, GTT and TU München working package 6: provides framework for further phases research demand and potential of future technologies formulates requirements for future technology regarding their application in IGCC plants

4 Plant assumptions and configuration simplified flow sheet raw coal & CO 2 coal preparation CH 4 dry feed entrained flow gasifier ASU O 2 /N 2 compr. O 2 N 2 steam purge gas scrubber shift S tail gas Claus plant acid gas wash CO 2 compr. CO 2 to pipeline saturator flue gas to stack combined cycle power quench

5 Plant assumptions and configuration general data Plant size: based on H class turbine with 340 MW el (π 19.2; TIT 1320 C (ISO) * ) Feed: hardcoal and German lignite Gasifier: entrained flow gasifier (dry feed, full water quench) ASU: air side independent / N 2 side integrated, 98% purity CO 2 : 90 % capture, pure and at 110 bar Shift: 2 stage adiabatic raw shift (steam/dry gas >1.4, T max ~ 510 C) HRSG: 3 pressure level HRSG (170 bar, 40 bar, 6 bar) Program: Aspen Plus and Ebsilon Professional * estimated from publication

6 Model verification major subsystems verified independently (equal >90% power consumption) Gasifier: hardcoal lignite CGE* [%] (CO+H 2 ) Nm³/kg** Gas wash: P el deviation <5%, composition: <5% *** pressure test between 25 and 75 bar CO 2 comp.: 8 stage/intercooling, P el deviation < 0.5% * cold gas efficiency ** coal (daf) *** main gas species Phenomena: heat and pressure losses, gas solubility, reactor cooling slag melting energy, none equilibrium, limited conversion, electrolytic dissociation, ph, traces (HCN, NH 3, HCl, COS)

7 Concept optimization - overview focus on gasification island and ASU no investigation about the integration of ASU research area: oxygen purity CO 2 capture CO 2 purity efficiency study

8 Concept optimization - oxygen purity Linde, March 2009: 25% power reduction for 95% pure oxygen +9.3 % % - 24 % reduction of compressor, column pressure and Δp in ASU higher coal consumption to maintain gas turbine output no significant impact on raw gas composition only slight efficiency increase N 2 /O 2 pressure decreases by ~ 30-40% 98% purity for base case further integrated investigation of ASU concept

9 Concept optimization CO 2 capture find optimal flash pressure flash pressure between 1 to 3 bar flash pressure optimum at fixed H 2 S fraction in Claus gas higher fraction increases auxiliary demand CO 2 compres. refrigeration demand Claus plant

10 Concept optimization CO 2 purity S tailgas H 2 S pipeline CO 2 shift acid gas wash can reach high CO 2 purity no Sulfreen plant when recycling drying necessary for compression significant efficiency potential at lower CO 2 purity

11 Concept optimization efficiency study 135 C 165 C hardcoal 200 C quench requires hot bfw significant impact of shift requirements (H 2 O/dry gas ratio) impact of CO 2 purity lignite low temperatures sufficient for shift 165 C higher efficiency 135 C better for preheating of coal and N 2

12 Summary IGCC base case reliable model of IGCC plant η: 37% for hardcoal and 40% for lignite significant impact of CO 2 purity-potential 0.5%pts. main power consumer: ASU, acid gas removal and CO 2 compression * 80 % availability

13 Outlook exergetic and economic evaluation of base case modeling of emerging technology including: oxygen membranes hot gas clean up membrane reactor

14 Thank you for your attention! This work is part of a project supported by Bundesministerium für Wirtschaft und Technologie and industrial partner under contract number A. The author would like to thank the partners for their valuable input and discussions. Especially, Mr. Hannemann, Dr. Schingnitz, Dr. Riedel, Mr. Karkowski, Mr. Zorn and Dr. Müller.