DeepSeek R1 vs. OpenAI ChatGPT o1: A Process Engineering Showdown

DeepSeek caught my attention this past week as it became the centerpiece of global discussions in the AI world. Its recent release of the DeepSeek-R1 model, hailed as a rival to OpenAI's ChatGPT o1, is reshaping the narrative around generative AI (GenAI). With global markets reacting, Nvidia’s stock plummeting amid concerns about DeepSeek’s disruptive potential, Intrigued by its promise and controversy, I wanted to see how DeepSeek performs against ChatGPT in solving a domain-specific problem from process engineering.

As I began comparing ChatGPT O1 and DeepSeek R1, I wanted to test their ability to address a practical, real-world challenge. That’s when the Over Flash Pump Around (OPA) in a Crude Distillation Unit came to mind. OPA systems play a critical role in refinery operations, ensuring proper heat removal and fractionation in the distillation process. It’s a topic that requires both technical understanding and operational insight—perfect for testing these AI models.

But the real reason I picked this case? It’s the memories tied to it. Back in the day, when I was building digital twin projects, I recall operators at a Gulf Coast refinery jokingly referring to OPA as their "PAPA." They’d say, “OPA, my PAPA! If you’re not steady, we’ll have to run!” It was their lighthearted way of emphasizing how crucial this system was to stable operations. That phrase stuck with me over the years, perfectly capturing both the criticality of the Over Flash Pump Around system and the camaraderie of refinery life.

Now the Real Question

"In Crude Distillation unit if Over Flash Pump Around flowrate is dropping what is your operational advise. Provide possible causes and recommended actions".

Probable Cause Suggestion

When tackling the issue of dropping Over Flash Pump Around (OPA) flowrates, both models approached the problem with strong domain knowledge, leveraging logical deductions to pinpoint potential root causes. Their suggestions reflected a comprehensive understanding of refinery operations and the interplay between mechanical, process, and instrumentation factors.

Commonalities in Suggesting Probable Cause

Both models demonstrated robust domain knowledge, offering several overlapping insights into possible causes and recommended actions:

• Mechanical Issues: Both identified pump-related problems such as cavitation, mechanical failure, and motor malfunction.
• Blockages: Fouling in pump strainers, heat exchangers, or discharge lines was recognized as a likely contributor.
• Instrumentation Faults: Miscalibrated flow meters or sensor malfunctions were flagged as potential culprits.
• Low Liquid Levels: Insufficient liquid in the overflash zone was highlighted as a cause of reduced flow.

These commonalities underscore the models’ strong grasp of industrial process troubleshooting.

Divergences in Suggestiong Probable Cause

ChatGPT:

• Heat Load Variations: Emphasized changes in crude feed composition and exchanger fouling impacting reflux rates.
• Hydraulic Constraints: Highlighted excessive pressure drops and equipment degradation in the circuit.
• Vapor Maldistribution: Identified poor vapor-liquid distribution as a possible cause.

DeepSeek:

• System Leaks: Uniquely suggested that leaks in piping or equipment could be a factor.
• Column Pressure Fluctuations: Focused on how changes in pressure could affect pump performance.
• Column Flooding: Flagged vapor load increases as a precursor to potential flooding issues.

Suggested Actions

The recommendations from both ChatGPT and DeepSeek reflect industry-standard troubleshooting practices, showcasing their ability to translate technical expertise into actionable guidance. These actions emphasize the importance of a systematic approach to resolving operational challenges in complex refinery systems.

Common Recommended Actions

Both models proposed similar immediate steps to address the issue, including:

• Inspecting the Pump: Checking for cavitation, abnormal noises, and suction/discharge pressures.
• Cleaning Fouled Equipment: Addressing blockages in strainers, filters, and heat exchangers.
• Calibrating Instrumentation: Verifying flow transmitters and recalibrating faulty sensors.
• Adjusting Process Parameters: Ensuring adequate liquid levels in the overflash zone by modifying feed rates or reflux.

Differing Recommended Actions

ChatGPT:

• Mass/Energy Balance Studies: Proposed conducting studies to align pump-around systems with process demands.
• Stabilizing via Upstream Circuits: Suggested temporarily increasing upstream pump-around flow.
• Predictive Analytics: Recommended upgrading control systems to predictive models for better stability.

DeepSeek:

• Throughput Reduction: Suggested reducing throughput temporarily to maintain column stability.
• Leak Inspections: Prioritized leak detection in piping and pump seals.
• Vapor Distribution Analysis: Emphasized inspecting and stabilizing vapor-liquid flow distribution.

Conclusion

In practical applications like addressing process engineering challenges, both models have their place. ChatGPT excels in quick operational adjustments, while DeepSeek shines in deep system analysis.

However It is important to note that the training datasets of both ChatGPT and DeepSeek may differ significantly, which could influence their responses to the above use case. Variations in domain-specific data, language modeling priorities, and contextual knowledge might have a more pronounced impact on their outputs than the inherent correctness of the models themselves. This comparison should be viewed as a snapshot of their capabilities, shaped by their unique training methodologies and data sources.

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