Steam Cracker Degradation Forecasting for Coking and Fouling
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Solution Overview
Problem
Chemical production plants face challenges in predicting the progression of degradation in equipment, such as coking or fouling, which can lead to unplanned production losses and reduced efficiency.
Innovation Solution
A computer-implemented method using a prediction model to estimate the future value of key performance indicators based on historical data and future operating parameters, allowing for the prediction of equipment degradation over a specified horizon.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If equipment operates continuously without maintenance, then productivity increases, but degradation progresses leading to unplanned downtime
Solution Approach 1:
The prediction model estimates future degradation states before they occur, enabling maintenance to be scheduled in advance. This allows operators to plan maintenance activities during scheduled downtime rather than reacting to unexpected failures, thus maintaining continuous production while preventing unplanned downtime.
2Reliability
If maintenance is performed frequently, then equipment reliability is maintained, but productivity is reduced due to downtime
Solution Approach 1:
The system uses operational data and prediction models to self-assess equipment health status and forecast degradation. This enables condition-based maintenance where interventions are triggered only when predicted degradation reaches critical thresholds, avoiding unnecessary maintenance activities and optimizing the balance between reliability and productivity.
3Reliability
If degradation is monitored continuously, then reliability is improved, but measurement complexity increases
Solution Approach 1:
The prediction model acts as an intermediary that processes complex operational data and transforms it into simplified degradation estimates. Instead of directly measuring multiple complex parameters, the model uses readily available operational data to infer degradation states, reducing measurement complexity while maintaining monitoring effectiveness.
Data Source
AI summary
In order to predict the future evolution of a health-state of an equipment and/or a processing unit of a chemical production plant, e.g., a steam cracker, a computer-implemented method is provided, which builds a data-driven model for the future key performance indicator based on the key performance indicator of today, the processing condition of today, and the processing condition over a prediction horizon.