Dynamic Simulation of Calcium Looping Accelerates CO2 Capture Technology Development

Why It Matters

This approach allows designers and engineers to rapidly assess new carbon capture technologies at various scales without the immediate need for expensive physical prototypes. It enables informed decision-making early in the design process, reducing development time and costs.

Key Finding

A dynamic simulation model proved effective in analyzing and scaling up a calcium looping CO2 capture process, providing insights into system behavior and design considerations from laboratory to industrial scales.

Key Findings

• The developed model framework was successfully applied to laboratory (30 kWth) and pilot scale (1.7 MWth) units.
• The model was used to design a conceptual industrial scale unit (250 MWth).
• Valuable insights were gained regarding the behavior of small-scale devices and the interconnected behavior of pilot plant reactors.
• The effect of solid fluidization on thermal and CO2 balances was analyzed.

Research Evidence

Aim: To analyze the technological feasibility of the calcium looping process for CO2 capture at different scales using a computational model.

Method: Model-based simulation and analysis

Procedure: A one-dimensional, semi-empirical dynamic model was developed and applied to simulate the behavior of interconnected circulating fluidized bed reactors in a calcium looping system. The model incorporated mass and energy balance solvers, semi-empirical models for solid behavior and chemical reactions, and accounted for fluidized bed combustion, heat transfer, and core-wall layer effects.

Context: Carbon capture technology development, specifically post-combustion CO2 capture using calcium looping.

Design Principle

Leverage computational modelling for rapid techno-economic assessment and feasibility studies of novel processes.

How to Apply

Use dynamic simulation software to model and analyze the performance of a proposed system under various operating conditions and scales, identifying potential bottlenecks or areas for optimization.

Limitations

The model is semi-empirical and one-dimensional, which may simplify certain complex phenomena. The accuracy of the model is dependent on the quality of the underlying empirical data and assumptions.

Student Guide (IB Design Technology)

Simple Explanation: Using computer simulations (models) of a carbon capture system can help designers figure out if it will work and how well it will work at different sizes, saving time and money compared to building real machines right away.

Why This Matters: Modelling allows you to explore many design possibilities and understand how your design will behave in real-world conditions without having to build expensive prototypes, which is crucial for efficient design projects.

Critical Thinking: How might the assumptions made in a simplified model lead to inaccurate predictions about the performance of a real-world system, and what steps can be taken to mitigate these risks?

IA-Ready Paragraph: Computational modelling was employed to simulate and analyze the proposed design, enabling a thorough techno-economic evaluation and assessment of technological feasibility across various scales. This approach facilitated rapid iteration and optimization of design parameters, significantly reducing the need for costly physical prototypes during the early stages of development.

Project Tips

• When developing a new product or system, consider using simulation software to test different design variations and predict performance.
• Ensure that the models used are validated against experimental data where possible to increase confidence in the results.

How to Use in IA

• Reference the use of simulation software to test design concepts and predict outcomes, demonstrating a rigorous approach to design investigation.

Examiner Tips

• Demonstrate an understanding of the limitations of the models used and discuss how these might affect the conclusions drawn.

Independent Variable: Model parameters (e.g., reactor size, sorbent properties, operating temperature).

Dependent Variable: System performance metrics (e.g., CO2 capture efficiency, energy consumption, process stability).

Controlled Variables: Fundamental physical and chemical laws governing the process (e.g., conservation of mass and energy).

Strengths

• Allows for rapid exploration of design space.
• Enables analysis of complex dynamic behaviors.
• Cost-effective compared to physical prototyping for initial studies.

Critical Questions

• To what extent does the model accurately represent the real-world system it aims to simulate?
• What are the key assumptions made in the model, and how sensitive are the results to these assumptions?

Extended Essay Application

• A detailed simulation of a complex system (e.g., renewable energy generation, advanced manufacturing process) can form the basis of an Extended Essay, exploring optimization strategies or comparative analyses of different design approaches.

Source

Model based analysis of the post-combustion calcium looping process for carbon dioxide capture · LUTPub (LUT University) · 2013