Introduction
Carbon capture systems are vital for reducing greenhouse gas emissions, but they come with high operational costs. A key challenge for engineers is optimizing energy-intensive processes, such as the reboiler duty in MEA-based CO₂ absorption units. This involves analyzing large datasets, conducting sensitivity analyses, and drafting reports—a time-consuming and repetitive effort.
AI tools like ChatGPT offer a solution by automating these tasks, making them faster and more efficient. In this blog, we’ll demonstrate how AI can streamline carbon capture optimization, using an example to illustrate the workflow.
The process begins with calculating a baseline for energy consumption and costs. Let’s consider a hypothetical scenario where the plant captures 1,000 tons of CO₂ daily, with a reboiler duty of 3.5 GJ/ton CO₂ and an energy cost of $10 per GJ. AI can instantly provide the results using this prompt:
Prompt:"The plant captures 1,000 tons/day of CO₂. Reboiler duty is 3.5 GJ/ton CO₂, and energy cost is $10 per GJ. Calculate the total daily energy consumption and cost."
Output:
Total energy consumption = 3,500 GJ/day.
Total cost = $35,000/day.
These values are only examples to demonstrate the AI workflow. You can adjust the inputs to reflect real plant data.
Next, AI can assist with a sensitivity analysis to evaluate how varying the lean amine flow rate affects reboiler duty and costs. Using the following prompt, AI automates the creation of a detailed table:
Prompt: "Assume the lean amine flow rate varies from 100 to 200 m³/h, increasing in increments of 10 m³/h. For each increment, reboiler duty decreases by 2% from the baseline. Create a table showing flow rate, reboiler duty, energy use, and cost."
Output: The generated table reveals trends such as:
At a flow rate of 150 m³/h, daily energy costs drop to $31,500, saving $3,500/day compared to the baseline.
With the sensitivity analysis complete, AI identifies the optimal operating range for maximum cost savings while maintaining CO₂ capture efficiency. The following prompt guides AI in making recommendations:
Prompt:"Based on the table, identify the flow rate range where total daily cost savings are maximized without significant loss in CO₂ capture efficiency (assume 1% drop per 10 m³/h increase)."
Output:
Optimal flow rate range = 130–150 m³/h.
Annual cost savings = $1.2M.
Finally, AI can create visualizations and summarize findings in a professional report. This can be achieved with a prompt like:
Prompt: "Generate a graph of reboiler duty (y-axis) vs. lean amine flow rate (x-axis), highlighting the optimal range, and summarize findings in a technical report."
Output: A polished chart and a concise report, ready for stakeholders.
Conclusion
By incorporating AI into their workflows, engineers can drastically reduce the time spent on repetitive tasks, such as sensitivity analyses and reporting. This not only accelerates decision-making but also ensures accuracy and consistency. The example provided highlights how AI transforms complex processes into streamlined, efficient workflows.
With AI, optimizing carbon capture systems becomes simpler and more effective, enabling engineers to focus on innovation and problem-solving. The possibilities are vast—are you ready to take the next step in integrating AI into your operations?
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