Flexible operation and control of methanol production from fluctuating syngas feed

Transcription

1 Flexible operation and control of methanol production from fluctuating syngas feed Matthias Gootz, Robert Pardemann, Bernd Meyer TU Bergakademie Freiberg 1

2 Concepts for excess electricity storage are needed in Germany Electrolysis in combination with Demand-Side-Management in chemical industry is an important concept for electricity storage (Dechema 2015) Lignite Bypass Gasifier Syngas Water scrubber Water- gas-shift Sour gas treatment Surplus Renewable Electricity Hydrogen Electrolyzer Storage Methanol synthesis Oxygen Methanol Methanol- Annex concept (Wolfersdorf 2015) Biomass gasification and production of methanol (Hannula 2014) Process Chain 2

3 200 MW th Entrained flow gasifier with cooling screen (Siemens type) HTS + LTS Selective AGR (Rectisol type) Lignite Bypass Gasifier Syngas Water scrubber Water- gas-shift Sour gas treatment Surplus Renewable Electricity Hydrogen Storage Electrolyzer Methanol synthesis Oxygen 50 MW Alkaline Quasi-Isothermal Methanol Can methanol plant handle fluctuations caused by hydrogen input and water-gas-shift operation? Process Chain 3

4 MW electrolysis High WGS bypass 10 MW electrolysis Low WGS bypass Mole flow in kmole/h Electrolyzer H2 Syngas H2 Syngas CO Other syngas components Steady state Part load Boundary conditions for dynamic simulation Syngas composition 4

5 Reactions CC + 2 H 2 CC 3 OO CC H 2 CC 3 OO + H 2 O CC 2 + H 2 CC + H 2 O Kinetic model for Cu/ZnO/Al 2 O 3 commercial catalyst (Van den Bussche and Froment 1996, Van-Dal 2013, adjustment to Aspen Plus TM data input form) r RRRRRRRR = KKKKKKK ffffff DDDDDDD FFFFF AAAAAAAAAA tttt kkkk kk s * r CCCCC = 2 k 1 P CO2 P H2 k 6 P H2 OP CC3 OOP H 2 (1 + k 2 P H2 OP 1 0,5 H 2 + k 3 P H2 + k4 P H2 O) 3 kkkk kk s * r RRRR = 1 k 5 P CO2 k 7 P H2 OP CO P H 2 (1 + k 2 P H2 OP 2 1 0,5 H + k 3 P H2 + k4 P H2 O) kkkk kk s Methanol Kinetics 5

6 RPLUG-Model Aspen Plus TM Plug flow Fixed bed pressure drop: Ergun equation Heat transfer: Syngas - Catalyst Syngas - Coolant i=1 i=2 T Gas T Cat T Coolant Model validation against data from open literature: Reactor configuration from kinetic data source (Van den Bussche and Froment 1996) Industrial Lurgi synthesis reactor (Chen 2011) Methanol Reactor 6

7 Parameter Simulation Literature Syngas modulus at reactor inlet 2,07 2,07 (Abrol 2012) Maximum temperature inside reactor 278 C <300 C (Bertau 2014) Pressure at the reactor inlet 69 bar bar (Bertau 2014) Recycle- ratio (Bertau 2014) Yield of methanol in mole/mole (CO, CO 2 in syngas) Yield of methanol in kg/l (Catalyst in reactor) (Bertau 2014) (max) (Wurzel 2006) Control system objectives Counterbalance load changes in the methanol plant Temperature control to avoid catalyst deactivation Steady State Results 7

8 Control Problem Formulation What variables need to be controlled? What variables need to be manipulated? Relative Gain Array Are there loop interactions? How can input and output variables be matched? Singular Value Analysis Is decoupling possible? Aspen Control Design Interface Matlab Control scheme PI controller tuning Aspen Plus Dynamics Control System Design 8

9 CO TC F Q p PC F F TC TC Methanol Synthesis 9

10 Mass flow in kg/h Pressure in bar Mass flow in kg/h Pressure in bar 5000 SYNGAS INPUT RECYCLE Time in hours Time in hours Mass flow in kg/h Pressure in bar Methanol Synthesis RAW METHANOL Time in hours

11 Step Down 11

12 Step Up 12

13 Summary The proposed control design allows flexible methanol production. Step response tests show good controllability of the process. Outlook Kinetic data for methanol synthesis under non-steady-state conditions is needed Plant wide dynamic simulation is required to investigate dynamic process performance. Implementation of deactivation data from the literature could be used to predict influence of load changes on catalyst. Outlook 13

14 Thank you for your attention. Project Polygeneration-Annex Project number: 03ET7042A Matthias Gootz M.Sc. IEC Fuchsmühlenweg 9 / Haus 1 (Reiche Zeche) Freiberg Tel.: Fax: Matthias.Gootz@iec.tu-freiberg.de Webseite: Acknowledgement 14

15 Abrol, S.; Hilton, C. M., Modeling, simulation and advanced control of methanol production from variable synthesis gas feed. Computers & Chemical Engineering 40, Bertau, M.; Offermanns, H.; Plass, L.; Schmidt, F.; Wernicke, H., Methanol: The Basic Chemical and Energy Feedstock of the Future. Springer-Verlag, Berlin Chen, L.; Jiang, Q.; Song, Z.; Posarac, D., Optimization of Methanol Yield from a Lurgi Reactor. Chemical Engineering & Technology 34 (5), DECHEMA Gesellschaft für Chemische Technik und Biotechnologie e.v., Elektrifizierung chemischer Prozesse. Diskussionspapier. Available from: (accessed 06/01/2015) (in German). Hannula, I.; Co-production of synthetic fuels and district heat from biomass residues, carbon dioxide and electricity: Performance and cost analysis. Biomass and bioenergy 74, Van-Dal, E. S.; Bouallou, C., Design and simulation of a methanol production plant from CO2 hydrogenation. Journal of Cleaner Production 57, Vanden Bussche, K.M.; Froment, G.F., A steady-state kinetic model for methanol synthesis and the water gas shift reaction on a commercial Cu/ZnO/Al2O3 catalyst. Journal of Catalysis 161 (1), Wolfersdorf, C.; Boblenz, K.; Pardemann, R.; Meyer, B., Syngas based annex concepts for chemical energy storage and improving flexibility of pulverised coal combustion power plants. 7 th International Freiberg/ Inner Mongolia Conference on IGCC & XtL Technologies. Huhhot, Inner Mongolia, China. Wurzel, Th., Delivering the building blocks for future fuel and monomer demand. DGMK Conference Synthesis Gas Chemistry References 15