We improve chemical process operation by filling in missing information using next-generation, tailor-made AI models that combine data with process knowledge for enhanced accuracy and efficiency.
How Mechanistic AI works?
How Mechanistic AI fills in the missing information
Distillation columns often operate with limited or incomplete information – such as unknown product compositions, feed properties, or catalyst condition. Mechanistic AI bridges these gaps by combining real-world process knowledge with data-driven insights:
01. Hybrid Modeling
We start with a model skeleton describing key process phenomena which is the refined using the process data with machine learning behavior This hybrid approach helps the AI “learn” the column’s specific behaviors, even when direct measurements are lacking.
02. Intelligent Estimation
By learning on your data – like temperature, pressure, and flow rates – the AI estimates unmeasured variables, effectively creating virtual sensors. These estimates offer real-time insight into composition or catalyst health, without installing any additional hardware, e.g., sensors.
03. Actionable Insights
With more complete data, your APC system can take precise control actions. Operators gain visibility into key parameters previously hidden, leading to optimized throughput, energy usage, and product quality – ultimately saving significant costs and resources.
By filling in missing variables through transparent, mechanistic modeling, you gain a more holistic picture of your distillation process – without the guesswork or heavy reliance on purely data-driven black-box models.
How Mechanistic AI is implemented into the distillation process?
Pre-Study
We begin by assessing feasibility and gathering initial process data. During this phase, we analyze your current setup, goals, and any available historical data to determine if Mechanistic AI is a strong fit for your operations. This quick, low-commitment step gives us a clear roadmap for the next phases.
Model configuration
Next, we tailor the AI model to your specific process requirements. Our team refines the mechanistic parameters, incorporates your unique plant constraints, and aligns everything with your existing APC framework. By combining your plant knowledge with our AI expertise, we develop a robust model that addresses your precise distillation challenges.
Implementation
Once the model is configured, we deploy it into your environment. This includes integrating with your DCS and ensuring compatibility with existing hardware and software. We guide you through each step to minimize downtime, verify performance, and confirm that all security protocols are met.
Support
After deployment, our work doesn’t end. We provide ongoing monitoring, fine-tuning, and periodic maintenance to keep your AI model accurate and up to date. Regular check-ins help identify new optimization opportunities, so you consistently benefit from improved distillation control and cost savings. In addition, we constantly improve our modeling approach improving its predictive ability meaning even better process economics.
Interested of finding out more?
High operational costs, unpredictable plant behavior, or frequent APC adjustments – Mechanistic AI can bridge those gaps and unlock your plant’s full potential.