Forecasting Industrial Aging Processes with Machine Learning Methods

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This invention relates to a computer-implemented method and system for forecasting the degradation (aging) processes of industrial equipment, particularly in chemical production plants, using advanced machine learning models. Instead of relying only on traditional mechanistic or simple empirical models, the approach utilizes both stateless and stateful data-driven models—including linear regressions, neural networks, recurrent neural networks (RNNs) like LSTM and ESN, and hybrid models—to predict key degradation indicators (KPIs) based on real-time sensor data and planned operating conditions. The predictions can inform plant operators about when maintenance or regeneration (e.g., cleaning, catalyst exchange) will be needed, allowing for more efficient and reliable plant operation.

Use CasesContent extracted from patent full text and abstract with AI.

• Predicting the deactivation of catalysts in chemical plants due to coking, sintering, or poisoning.
• Forecasting fouling or plugging in heat exchangers and pipes.
• Estimating erosion rates for equipment such as pipes and nozzles in reactors.
• Anticipating when maintenance or cleaning events will be needed for critical machinery in the process industry.
• Improving planned downtime scheduling and minimizing unplanned production losses in large-scale manufacturing plants.
• Enabling predictive maintenance strategies across industrial facilities based on data-driven forecasting.
• Assessing the long-term performance and degradation trends for assets managed in cycles, such as reactors and exchangers.

BenefitsContent extracted from patent full text and abstract with AI.

• Provides much more accurate and adaptable degradation forecasts compared to purely mechanistic or empirical models, especially in real plant conditions.
• Enables earlier and better maintenance planning, which reduces costs associated with unplanned downtime and equipment failures.
• Improves plant reliability, safety, and operational efficiency.
• Flexible modeling: can use various machine learning approaches (stateless/statuful/hybrid) to suit data availability and specific application needs.
• Practical for complex real-world environments by leveraging historical sensor data and operating conditions.
• Adapts to the specific degradation behaviors and operating profiles of individual assets or plants.
• Supports 'what-if' scenario simulations for different future operating plans, helping to optimize asset life and maintenance interventions.

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Patent Abstract

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.