Machine Learning Forecasting of Catalyst Aging in Chemical Plants
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Solution Overview
Problem
Mechanistic degradation models for chemical production plants are rarely used in real-world environments due to their inability to accurately predict degradation dynamics of critical assets, as they are based on 'clean' laboratory conditions and fail to account for additional effects present in production settings.
Innovation Solution
A data-driven method using machine learning models, such as recurrent neural networks (RNNs) and hybrid models, that incorporate historical data and current process conditions to predict degradation key performance indicators (KPIs) in chemical production plants, allowing for short-term and long-term forecasting of degradation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If mechanistic degradation models are used, then insights into degradation dynamics are obtained, but prediction accuracy in real-world production environments deteriorates
Solution Approach 1:
The patent introduces data-driven models as an intermediary between mechanistic models and real-world production data. These models are trained on historical process data and degradation measurements to learn the complex relationships that mechanistic models cannot capture, thereby bridging the gap between theoretical understanding and practical prediction accuracy in production environments
Solution Approach 2:
The patent transforms the approach by changing from fixed mechanistic parameters to adaptive data-driven parameters. The system continuously learns from operational data, adjusting model parameters to reflect actual production conditions rather than idealized laboratory conditions, thereby improving prediction reliability while maintaining mechanistic insights
2Reliability
If data-driven models are used, then prediction accuracy in production environments improves, but model interpretability and mechanistic understanding deteriorate
Solution Approach 1:
The patent merges data-driven models with mechanistic models into a hybrid approach. The data-driven component captures complex real-world patterns from historical data, while the mechanistic component provides physical and chemical understanding of degradation processes. This combination maintains both prediction accuracy and mechanistic interpretability by integrating empirical observations with theoretical knowledge
Solution Approach 2:
The system implements feedback loops where prediction results and actual degradation measurements are continuously fed back to refine both data-driven and mechanistic models. This feedback mechanism ensures that the data-driven model remains accurate while the mechanistic understanding is continuously validated and updated against real-world observations
Data Source
AI summary
By accurately predicting industrial aging processes (IAP), such as the slow deactivation of a catalyst in a chemical plant, it is possible to schedule maintenance events further in advance, thereby ensuring a cost-efficient and reliable operation of the plant. So far, these degradation processes were usually described by mechanistic models or simple empirical prediction models. In order to accurately predict IAP, data-driven models are proposed, comparing some traditional stateless models (linear and kernel ridge regression, as well as feed-forward neural networks) to more complex stateful recurrent neural networks (echo state networks and long short-term memory networks). Additionally, variations of the stateful models are discussed. In particular, stateful models using mechanistical pre-knowledge about the degradation dynamics (hybrid models). Stateful models and their variations may be more suitable for generating near perfect predictions when they are trained on a large enough dataset, while hybrid models may be more suitable for generalizing better given smaller datasets with changing conditions.