Computer-Aided Process Decision-making R&D for Advanced Energy Systems

Transcription

1 Energy Systems Initiative (ESI) Computer-Aided Process Decision-making (CAPD) Carnegie Mellon University Pittsburgh, PA March 7, Computer-Aided Process Decision-making R&D for Advanced Energy Systems Stephen E. Zitney, Ph.D. Director, Collaboratory for Process & Dynamic Systems Research

2 U.S. Energy Challenges Meet increasing demand Provide secure, affordable, and clean energy Address energy-water nexus 2 U.S. data from EIA, Annual Energy Outlook 2008

3 U.S. Energy Challenges DOE 2020 Goals Clean energy Near-zero levels of NO x, SO x, PM, and Hg 90% CO 2 capture and 99%+ storage permanence Affordable energy <35% increase in COE for postand oxy-combustion capture <10% increase in COE for precombustion capture (e.g., IGCC) Energy-water nexus Reduce freshwater withdrawal and consumption by 70% or greater IGCC Power Plant 3 References: 1. Existing Plants Emissions and Capture Program Goals, U.S. DOE/National Energy Technology Laboratory, Draft Final Report, February Impact of Cost Escalation on Power Systems R&D Goals Re-baselining APS, CS & FC GPRA R&D Goals, July 2008

4 National Energy Technology Laboratory Where Energy Challenges Converge and Energy Solutions Emerge U.S. Department of Energy (DOE) national lab Advances economic and energy security by: Increasing efficiency, reliability, and economics of advanced energy systems While protecting the environment and promoting sustainability Accomplishes DOE mission by: Implementing a broad spectrum of energy and environmental R&D programs >1,800 projects with total award value over $9B Conducting cutting-edge on-site R&D Office of Research & Development Institute for Advanced Energy Solutions (IAES) NETL/University R&D partnership» CMU, Pitt, PSU, VT, and WVU» $12M/yr, 40 faculty, and 160 PhDs/post-docs» 10 R&D thrust areas, including Collaboratory for Process & Dynamic Systems Research 4

5 Collaboratory for Process & Dynamic Systems Research Goals and Objectives Accelerate R&D on advanced models, methods, and tools for process systems engineering Develop innovative solutions across energy plant lifecycle innovation, design, operations, and management Apply to existing plants and emerging advanced energy systems with carbon capture & storage (CCS) 5 APECS Co-Simulation of IGCC-CCS Plant Energy Plant Lifecycle IGCC Power Plant

6 Energy Application Areas Combustion Natural Gas Combined Cycle (NGCC) Pulverized Coal (PC) Combustion Oxygen Combustion Chemical Looping Combustion (CLC) Gasification Integrated Gasification Combined Cycle (IGCC) Polygeneration Chemicals, Liquid Fuels, H 2, SNG Chemical Looping Gasification (CLG) IGCC/Fuel Cell Hybrids (IGFC) Carbon Capture and Storage (CCS) Pre- and post-combustion Absorption (e.g., physical/chemical solvents) Adsorption (e.g., PSA/TSA/VSA, solid sorbents) Membrane Separation, Cryogenics, Hydrates Combustion Power Plant IGCC Power Plant with CCS 6

7 R&D Areas Innovation Process Synthesis Heat Exchanger/Water Networks Design Process/Equipment Co-Simulation Virtual Plant Simulation Plant-wide Optimization Risk and Uncertainty Analysis Operations Dynamic Modeling and Simulation Sensors and Control Operator/Immersive Training Systems Management Planning and Scheduling Supply Chain Management Enterprise-Wide Optimization Energy Plant Lifecycle 7

8 Process Innovation R&D Areas Process Synthesis Heat Exchanger Network Synthesis / Pinch Analysis Reactor / Separation Network Synthesis Water Network Synthesis Technology Mixed Integer NonLinear Programming (MINLP) Disjunctive Programming Projects Optimal Synthesis of IGCC Systems R. Kamath, Profs. Grossmann and Biegler (CMU) Optimization Approach to Process Synthesis with Application to Pulverized Coal Power Plants with CO 2 Capture and Water Networks Dr. Diwekar (VRI) Optimal Synthesis of Pressure Swing Adsorption Cycles for Pre- and Post-Combustion CO 2 Capture A. Agarwal, S. Vetukuri, Prof. Biegler (CMU) IGCC Superstructure 8

9 R&D Areas Innovation Process Synthesis Heat Exchanger/Water Networks Design Process/Equipment Co-Simulation Virtual Plant Simulation Plant-wide Optimization Risk and Uncertainty Analysis Operations Dynamic Modeling and Simulation Sensors and Control Operator/Immersive Training Systems Management Planning and Scheduling Supply Chain Management Enterprise-Wide Optimization Energy Plant Lifecycle 9

10 Design 10 R&D Areas Projects Process/Equipment Co-Simulation Virtual Plant Simulation Plant-wide Optimization Risk and Uncertainty Analysis Technologies Steady-State Process Simulation Computational Fluid Dynamics (CFD) Reduced Order Modeling (ROM) Nonlinear Programming (NLP) Stochastic Simulation APECS/ EKM TM VE-PSI APECS R&D, SW Dev. ANSYS, ALSTOM, AspenTech, CMU APECS Applications ALSTOM: PC, NGCC, Oxy-Fuel, CLC/G Others: REI, OSU, WVU Reduced Order Modeling Neural Networks, PCA, Kriging CMU, ANSYS Multizonal Gasification ROMs Reaction Design Virtual Power Plant Simulation APECS/VE-Suite Integration (VE-PSI) Ames Laboratory US/UK Collaboration on Virtual Simulation ALSTOM, PSE, ANSYS, Doosan, RWE Optimization of PC and IGCC Systems with CO 2 Capture and Water Networks NETL, CMU, WVU, IIT Stochastic/Multi-Objective Optimization Vishwamitra Research Institute (VRI)

11 R&D Areas Innovation Process Synthesis Heat Exchanger/Water Networks Design Process/Equipment Co-Simulation Virtual Plant Simulation Plant-wide Optimization Risk and Uncertainty Analysis Operations Dynamic Modeling and Simulation Sensors and Control Operator/Immersive Training Systems Management Planning and Scheduling Supply Chain Management Enterprise-Wide Optimization Energy Plant Lifecycle 11

12 Operations R&D Areas Process Operability Sensors and Control Operator/Immersive Training Systems Technologies Dynamic Process Simulation Virtual Plant Simulation Model Predictive Control Dynamic Optimization Projects Plant-wide IGCC Dynamic Simulation and Control + West Virginia University (WVU) Dynamic Simulator Research & Training Center + NETL, WVU, IOM, FCS, Enginomix, EPRI, Ames Lab Projects Nonlinear Model Predictive Control (NMPC) of Air Separation Units (ASUs) + R. Huang, Prof. Biegler (CMU) Plant-wide IGCC Model Predictive Control (MPC) ++ Rensselaer Polytechnic Institute MPC for GE Gasifier and Radiant Syngas Cooler +++ GE Global Research Dynamic Simulation and Advanced Controls for Hybrid Combustion- Gasification Chemical Looping +++ ALSTOM Power, Univ. of Illinois 12 + Funded by NETL IAES; ++ Funded by NETL s University Coal Research (UCR) Program +++ Funded by NETL Advanced Research Program

13 NETL Dynamic Simulator Research & Training Center R&D, education, and training for design, operation, and control of advanced energy systems Real-time dynamic simulators w/ operator training systems (OTS) Immersive training systems (ITS) IGCC plant with CO 2 capture OTS: Oct 2010; ITS: Jan 2011 NETL collaborators WVU, FCS, Enginomix, EPRI AEP, BP, Doosan, GRE, Southern, Invensys Operations Mgmt Dynsim TM, InTouch TM, EYESim TM Located at NETL & WVU/NRCCE R&D: APC, Sensors, ROMs, VE, Zitney, S.E. and D. Wilbers, NETL Advances Clean Coal Power Technology Utilizing Virtual Reality Training System, Presented at 2010 Power Plant Simulation Conference, February 21-26, San Diego, CA (2010). Zitney, S.E. et al., NETL to Establish Dynamic Simulation Research and Training Center to Promote IGCC Technology with CO 2 Capture, Proc. of the COAL-GEN 2009 Conference, August 19-21, Charlotte, NC (2009). 13

14 R&D Areas Innovation Process Synthesis Heat Exchanger/Water Networks Design Process/Equipment Co-Simulation Virtual Plant Simulation Plant-wide Optimization Risk and Uncertainty Analysis Operations Dynamic Modeling and Simulation Sensors and Control Operator/Immersive Training Systems Management Planning and Scheduling Supply Chain Management Enterprise-Wide Optimization Energy Plant Lifecycle 14

15 Management R&D Areas Planning and Scheduling Supply Chain Management Enterprise-Wide Optimization (EWO) Technology Linear Programming (LP) Multi-Period Mixed Integer Linear Programming (MILP) Stochastic Programming Potential Projects Optimal model-based planning and scheduling for polygeneration plants National supply chain model for optimal planning of the production and distribution of liquid fuels with uncertainties in demands and supplies, as well as supply disruptions Integrated energy-water-co 2 model for planning, management, and optimization purposes 15

16 Summary Accelerates R&D on advanced models, methods, and tools for process systems engineering Addresses challenges and develops innovative solutions across the energy plant lifecycle APECS with EKM and VE-PSI IGCC dynamic simulator and ITS Applies technology solutions to existing plants and emerging advanced energy systems Offers unique opportunities for collaborative R&D, technology transfer, and commercialization Energy Plant Lifecycle 16

17 Thank You Questions? For additional information, please contact: Stephen E. Zitney, NETL EML: TEL: