Integration of Carbon and Hydrogen Supply Chains: First Steps

Dealing with the challenges of growing population, wealth, and energy demand leading to increased GHG emissions, the PSE Lab in Bremen focuses on preventing excessive CO2 release into the atmosphere. With a systems perspective, they address the interconnectivity of these factors and work towards innovative solutions like carbon capture via absorption technology using ILs. The lab aims to reduce CO2 emissions effectively and sustainably.

• Carbon integration
• Hydrogen supply chains
• GHG emissions
• PSE Lab
• Carbon capture

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1. LABORATORY OF PROCESS SYSTEMS ENGINEERING First steps to the integration of carbon- and hydrogen supply chains Edwin Zondervan* *ezon@uni-bremen.de Narayen K.R. Tuan Nguyen Anton Ochoa Bique Sven Charmeteau Kevin Schmidtke Sebastian Schulze Grazia Leonzio

2. Forecast Problem attacked: Population, wealth and energy-demand are growing, leading to (amongst others) increased GHG emissions. How can we prevent that more-and-more CO2 is released into our atmosphere? Insight found: In dealing with this complex problem a systems perspective is very valuable! (everything is connected to everything), that is what PSE lab in Bremen tries to do! 2

3. Outline Some numbers and interpretations on carbon dioxide/Greenhouse Gases; The (engineering) challenges that we face; How PSE lab in Bremen is working on carbon dioxide? (our first insights) 3

4. Climate change 4

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8. ...towards implementation From a concept, an innovation ... 8

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13. PSE Lab on: Carbon capture with absorption via IL s? Tuan Nguyen Capture technology: absorption with [bmim][Ac] can remove up to 90% CO2 from flue gas streams (at different compositions and flow rates) MEA_0.1 kmol/s IL_0.1 kmol/s MEA_1 kmol/s IL_1 kmol/s MEA_5 kmol/s IL_5 kmol/s MEA_10 kmol/s IL_10 kmol/s 240 220 Total cost/ton CO2 captured ($/ton) (cost includes feed dehydration, CO2 capture & compression) 200 180 160 140 120 100 80 60 40 20 0 13 0.0 0.1 0.2 0.3 0.4 0.5 0.6 Molar fraction of CO2 in feed

14. PSE Lab on : Carbon storage as CO2 mitigation option Narayen K.R. Data analysis: aggregation of sources/sinks: From 242 sources to 35 sources Model development: Mixed Integer Linear Program with approx. 7000 Decision variables Storage capacity: 85% of storage capacity is still available even after 25 years of CO2 storage Preferred technology: In this problem, PSA using MVY is preferred (13 alternatives) Cost effectiveness: Further optimization of capture technologies is necessary to reduce the cost ( 47,83 billion ( 35,43/tCO2/yr) 14

15. PSE Lab on : Carbon utilization via methanol production Sebastian Schulze Sven Charmeteau Kevin Schmidtke Data analysis: location of primary emission sources, hydrogen production- and methanol reforming sites Model development: Mixed Integer Linear Program with approx. 7000 Decision variables Anton Ochoa Bique Main driver: Flue gas composition Preferred transport: Best is to transport raw materials and product via pipeline Cost effectiveness: A Methanol supply chain based on CO2 emissions and renewable hydrogen is at this stage not feasibile: energy should cost nothing and capture costs should be dramatically reduced 15

16. PSE Lab on : Carbon capture, storage and utilization integrative design Grazia Leonzio Model development: Mixed Integer Quadratic Program for Germany including storage and utilization options ~1700 variables and constraints Data analysis: Potential for CO2 utilization in Europe Main driver: Utilization is preferred over storage Preferred technology: PSA as preferred techn. (highest efficiency, highest costs) Cost effectiveness: Capture costs take up to 90% of the total supply chain costs; production costs are 20x higher than current MeOH sales prices economic and environmental incentives required 16

17. Observations Scale: Looking only at one aspect will lead to conflicting conclusions; Time: A carbon dioxide economy might not be sustainable; hopefully CO2 emissions will not occur in the (far) future: that means that there is no need for capture, storage and utilization; 18

18. Next ... Management of (combinatorial) complexity Decision-making under uncertainty Multi-criterion decision making 17