Catalytic Reactor Operating Conditions Using Catalyst Aging Models
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Solution Overview
Problem
Current models for determining operating conditions in catalytic reactors are complex, require tedious and expensive experimental determination of kinetic and transport parameters, and do not accurately account for catalyst aging, limiting their practical utility and applicability.
Innovation Solution
A data-driven machine learning model is used to determine target operating parameters based on historical data, including catalyst age indicators, allowing for robust and reliable reactor operation and enhanced process control by predicting catalyst deactivation and optimizing operating conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If kinetic models based on kinetics, heat and mass transfer phenomena are used to describe catalyst performance, then the ability to control reactor operation is improved, but the model complexity and experimental determination cost increase
Solution Approach 1:
The patent replaces complex kinetic models with machine learning models that learn patterns directly from historical operational data. Instead of using mechanical kinetic equations requiring experimental parameter determination, the system uses data-driven algorithms (neural networks, random forests, gradient boosting) to predict catalyst performance and optimize reactor operations, thereby reducing model complexity while maintaining control reliability
Solution Approach 2:
The patent creates a virtual copy of the reactor system through machine learning models that replicate catalyst behavior patterns from historical data. These models copy the essential performance characteristics without requiring the complex physical-chemical mechanisms, enabling accurate predictions and control decisions with simplified computational structures
2Measurement precision
If kinetic models with experimentally determined parameters are used, then catalyst performance prediction accuracy is improved, but the time and cost for parameter determination increase
Solution Approach 1:
The patent performs preliminary action by training machine learning models on extensive historical operational data before actual reactor optimization is needed. The models learn catalyst performance patterns in advance from past operations, eliminating the need for time-consuming experimental parameter determination when optimization is required. The system is pre-prepared with learned knowledge that can be immediately applied
Solution Approach 2:
The system uses its own historical operational data to train and improve its predictive models without requiring external experimental campaigns. The machine learning models self-learn from the plant's accumulated operational experience, eliminating the need for separate experimental parameter determination processes and reducing both time and cost
3Ease of manufacture
If literature-based kinetic coefficients are used in hybrid models, then model development is simplified, but the applicability to specific catalysts is reduced
Solution Approach 1:
The patent changes the fundamental parameters from fixed literature-based kinetic coefficients to adaptive machine learning models that automatically adjust to specific catalyst characteristics. The system uses catalyst identifiers and operational data to learn and adapt parameters specific to each catalyst type, maintaining ease of model development while achieving catalyst-specific accuracy through data-driven parameter adaptation
4Device complexity
If catalyst aging is not considered in performance models, then model simplicity is maintained, but the long-term performance prediction accuracy deteriorates
Solution Approach 1:
The patent introduces dynamics by incorporating catalyst age as a time-varying parameter in the machine learning models. Instead of static models that assume constant catalyst performance, the system uses dynamic models that automatically adjust predictions based on catalyst age and deactivation patterns learned from historical data, maintaining relative simplicity while achieving accurate long-term performance forecasting
Data Source
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AI summary
Systems and methods for determining an operating condition of a chemical production plant including at least one catalytic reactor are provided. Via a communication interface operating data and a catalyst age indicator are received (10). At least one target operating parameter for the operating condition of a scheduled production run or a current production run are determined (14). The at least one target operating parameter for the operating condition may be used for monitoring and/or controlling the chemical production plant.