Current, Best and Future Practice of Life Cycle Inventory modeling for CCUS Arne Kätelhön, Niklas von der Assen and André Bardow

Transcription

1 Current, Best and Future Practice of Life Cycle Inventory modeling for CCUS Arne Kätelhön, Niklas von der Assen and André Bardow Institute of Technical Thermodynamics RWTH Aachen University, Germany Workshop: Life Cycle Assessment of CCUS, November 12, 2015, London

2 Life Cycle Inventory (LCI) 1. Goal & Scope Definition 2. Life Cycle Inventory (LCI) 4. Interpretation 3. Life Cycle Impact Assessment 2

3 Data Collection Early screening Stoichiometry 100% yield Design process [1] CO 2 -based methanol CO H 2 CH 3 OH + H 2 O stoichiometry 1375 kg CO kg H kg CH 3 OH kg H 2 O Feedstock Impacts from Databases Reference: von der Assen et al., Chem. Soc. Rev., 2014, 6, Reference: [1] Sugiyama et al., AIChE Journal, 2008, 54, 1037

4 Data Collection Early screening Stoichiometry 100% yield Process Chemistry Conversion selectivity Utilities Heat of reaction Available data Rough operating conditions from lab-scale experiments LCI approach Include energy inputs, co-products, etc. Process estimation tools (inputs: e.g. product concentration) [1] Estimation tools for chemicals based on molecular structure [2] First benchmarking possible, BUT large uncertainties 4 References: [1] Patel et al., Energy Environ. Sci., 2012, 5(9), 8430 [2] Wernet et al., Green Chem., 2009, 11, 1826

5 Data Collection Early screening Stoichiometry. 100% yield Process Chemistry Conversion selectivity Utilities Heat of reaction Conceptual design Process models Reaction kinetics Property data Available data Process simulations including reaction, separation, waste treatment, and equipment specifications From short-cuts to detailed models LCI approach Detailed LCI model, sensitivity analyses Benchmarking of products & early detection and optimization of environmentally relevant parameters 5

6 Data Collection Early screening Process Chemistry Conceptual design Industrial data Stoichiometry. 100% yield Conversion selectivity Utilities Heat of reaction Process models Reaction kinetics Property data Physical measurements Detailed highquality data LCI approach Full LCI based on high-quality data Data requests Multiple experts involved Company secrets Input 1 Input 2 Electricity Process Emission 1 Emission 2 Output 1 Output 2 Heat 6

7 Data Collection Early screening Process Chemistry Conceptual design Industrial data Stoichiometry. 100% yield Conversion selectivity Utilities Heat of reaction Process models Reaction kinetics Property data Physical measurements Detailed highquality data Data requirements Uncertainty Start early and hang in! 7

8 Handling multifunctionality System Expansion Environmental impact reductions clearly determined BUT no product-specific assessment Reference: von der Assen et al., Energy Environ. Sci., 2013, 6, 2721 Bernet et al., Energy Procedia, 2014, 63,

9 Handling multifunctionality Avoided burden Avoided Burden implies a comparison Often no stand-alone production process Reference: von der Assen et al., Energy Environ. Sci., 2013, 6,

10 Handling multifunctionality Allocation where to cut the integrated system? Easy, but too arbitrary? Reference: von der Assen et al., Energy Environ. Sci., 2013, 6,

11 Consequential LCI model Life Cycle grave Waste management Market Market effects exist! Don t neglect them, just because they are difficult to model! Impact on other life cycles Use Market Impact on other life cycles Product Market Product substitution direct effect Too big to model, too uncertain! direct CCU technology Raw materials Market Technology substitution Impact on other life cycles indirect Effect-Type indirect effect physical relationship nonphysical relationship Reference: Kätelhön et al., Environ. Sci. Technol., 2015, 49 (13), enco2re steering committee meeting, Cologne 1/25/2016

12 Impact Uncertainties Uncertainties in Process data Modeling choices Choices in the supply chain Impact assessment Indirect effects Methods for uncertainty assessment Sensitivity analysis Monte Carlo Simulation [1] Analytical error propagation [2] P 1 P 2 P 3 BUT: How certain are uncertainties? 12 References: [1] Patel et al., Energy Environ. Sci., 2012, 5(9), 8430 [2] Jung et al., Int J Life Cycle Assess, 2013, 19 (3),

13 LCA is useful and challenging: Data collection Good trade-off between data requirements and uncertainty? Assessment tool and/or decision support for process design? Life Cycle Inventory modeling How to handle multifuctionality? Consequential or attributional modeling? Uncertainty assessment How to quantify uncertainties? What software-tools to use? 13

14 Thank you for your attention! Dipl.-Wirt.Ing. Arne Kätelhön Supported by Energy Systems Engineering Institute of Technical Thermodynamics RWTH Aachen University Phone: