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Magnetic Resonance Imaging of CO2 capture reactors
Carbon capture and storage is anticipated to play a central role in curbing carbon dioxide emission to the atmosphere and counteracting climate change. Here you will design and build several model reactors for the next generation carbon capture technology.
Keywords: Carbon capture, Sustainability, Magnetic resonance imaging, Fluidized beds
Carbon capture and storage is anticipated to play a central role in curbing carbon dioxide emission to the atmosphere and counteracting climate change. Fluidized bed reactors filled with granular CO2 sorbents are commonly used to capture carbon from exhaust gases. In such reactors, the exhaust gas is injected into the bottom of the fluidized bed, where it fluidized the granular sorbent, which behave similar to a boiling liquid. The size and spatial distribution of gas bubbles as well as the dynamics of the particle phase inside the reactor has a critical influence on the absorption efficiency. Controlling the size and paths of gas bubbles inside fluidized bed reactors, however, is a challenging task, since it is difficult to measure gas bubbles and particle dynamics within 3D reactors.
In our labs at ETH Zürich we have recently developed a real-time magnetic resonance imaging methodology, that can probe granular dynamics 10000 times fast compared to the state of the art measurements [1,2]. Using this technique, we could already study the infludence of a horizontal tube insert on bubble dynamics and particle mobility ([3] and Fig. 2).
During this project you will build a range of obstacle inserts (an array of vertical rods, meshes or other industrially applied inserts) for our MRI compatible fluidized bed model (Fig.1). Subsequently, you will use a full-body human MRI system (Fig. 3) to image the bubble distribution and particle dynamics in fluidized beds with your obstacles. As a final step you shoud analyze the measurements and deduce rules that might aid the construction of efficient fluidized bed reactors for carbon capture.
Type of work:
- 60% Experiments
- 30% Data analysis and interpretation
- 10% Theory and literature review
References: [1] A. Penn, T. Tsuji, D. O. Brunner, C. M. Boyce, K. P. Pruessmann, and C. R. Müller, Science Advances 3, e1701879 (2017).
[2] ETH News (2017), https://tinyurl.com/y7f5ten4
[3] A. Penn, C. M. Boyce, N. Conzelmann, G. Bezinge, K. P. Pruessmann, and C. R. Müller, Chemical Engineering Science 198, 117 (2019).
Carbon capture and storage is anticipated to play a central role in curbing carbon dioxide emission to the atmosphere and counteracting climate change. Fluidized bed reactors filled with granular CO2 sorbents are commonly used to capture carbon from exhaust gases. In such reactors, the exhaust gas is injected into the bottom of the fluidized bed, where it fluidized the granular sorbent, which behave similar to a boiling liquid. The size and spatial distribution of gas bubbles as well as the dynamics of the particle phase inside the reactor has a critical influence on the absorption efficiency. Controlling the size and paths of gas bubbles inside fluidized bed reactors, however, is a challenging task, since it is difficult to measure gas bubbles and particle dynamics within 3D reactors. In our labs at ETH Zürich we have recently developed a real-time magnetic resonance imaging methodology, that can probe granular dynamics 10000 times fast compared to the state of the art measurements [1,2]. Using this technique, we could already study the infludence of a horizontal tube insert on bubble dynamics and particle mobility ([3] and Fig. 2). During this project you will build a range of obstacle inserts (an array of vertical rods, meshes or other industrially applied inserts) for our MRI compatible fluidized bed model (Fig.1). Subsequently, you will use a full-body human MRI system (Fig. 3) to image the bubble distribution and particle dynamics in fluidized beds with your obstacles. As a final step you shoud analyze the measurements and deduce rules that might aid the construction of efficient fluidized bed reactors for carbon capture.
Type of work: - 60% Experiments - 30% Data analysis and interpretation - 10% Theory and literature review
References: [1] A. Penn, T. Tsuji, D. O. Brunner, C. M. Boyce, K. P. Pruessmann, and C. R. Müller, Science Advances 3, e1701879 (2017). [2] ETH News (2017), https://tinyurl.com/y7f5ten4 [3] A. Penn, C. M. Boyce, N. Conzelmann, G. Bezinge, K. P. Pruessmann, and C. R. Müller, Chemical Engineering Science 198, 117 (2019).
• Familiarization with fluidized bed reactors and fundamental principles of magnetic resonance imaging.
• Design and construction of a set of inserts for our MRI compatible fluidized bed model reactor.
• Guided MRI measurements using a full-body human MRI system.
• Documentation of results in a project report.
• Familiarization with fluidized bed reactors and fundamental principles of magnetic resonance imaging. • Design and construction of a set of inserts for our MRI compatible fluidized bed model reactor. • Guided MRI measurements using a full-body human MRI system. • Documentation of results in a project report.
Supervisor: Alexander Penn, apenn [at ]ethz.ch, ETZ F97, Tel. +41 44 632 7443 , please send an email for further details and application. See https://www.youtube.com/watch?v=rf5y33vj_sg for a short general introduction to the project.
Professor: Christoph R. Müller, D-MAVT or Klaas P. Prüssmann, D-ITET
Supervisor: Alexander Penn, apenn [at ]ethz.ch, ETZ F97, Tel. +41 44 632 7443 , please send an email for further details and application. See https://www.youtube.com/watch?v=rf5y33vj_sg for a short general introduction to the project. Professor: Christoph R. Müller, D-MAVT or Klaas P. Prüssmann, D-ITET