Plant-Level Process Modeling Using Auto-Generated Equipment Models
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Solution Overview
Problem
In industrial plants, particularly chemical and biological production facilities, it is challenging to consistently predict and maintain product quality due to complex dependencies on process parameters, equipment state, and catalyst behavior, which complicates the use of mathematical models for performance prediction and optimization.
Innovation Solution
A computer-implemented method and system that automatically generates a plant-level model by selecting and interconnecting equipment models from a library, using a topology generator, and trains these models with historical data to compute performance parameters, allowing for dynamic modeling and monitoring of industrial plants without requiring expert users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mathematical models are used to predict product quality and performance parameters in industrial plants, then production stability and quality consistency can be improved, but the complexity of the production environment with multiple equipment, catalyst behavior changes, and time-dependent equipment state makes model setup, storage, integration, and deployment substantially difficult
Solution Approach 1:
The system performs self-service by automatically generating and training models using historical data from the plant's own operational data. The model generation process leverages the plant's existing data infrastructure and automatically adapts to different equipment configurations without requiring external expert intervention for each new modeling scenario.
Solution Approach 2:
The system creates simplified digital copies (virtual models) of the complex physical plant equipment and processes. These virtual models replicate the behavior of reactors, separators, and other equipment, allowing quality prediction and performance analysis without directly manipulating or complexly instrumenting the physical systems.
2Measurement precision
If expert knowledge is required to set up suitable models for performance prediction, then model accuracy can be improved, but this creates a substantial roadblock in usage of modeling for improving industrial production due to the need for specialized expertise
Solution Approach 1:
The system eliminates the need for expert users by making the modeling process self-service. It automatically generates appropriate models based on historical data and plant configuration, performing what previously required specialized expert knowledge in a fully automated manner accessible to operational personnel.
Solution Approach 2:
The system replaces the manual mechanical process of expert model setup with an automated computational process. Instead of experts manually configuring models based on their knowledge, the system uses algorithms to automatically generate and train models, substituting human expert activity with automated intelligence.
3Measurement precision
If catalyst behavior and equipment state are monitored to maintain product quality, then quality prediction accuracy can be improved, but the time-dependent nature of catalyst and equipment behavior adds another dimension of complexity to the production environment
Solution Approach 1:
The system performs preliminary action by proactively monitoring and modeling the time-dependent degradation of catalysts and equipment before they cause quality problems. It uses historical data to predict future states of equipment and catalyst behavior, allowing preventive maintenance and quality adjustments before actual degradation affects product quality.
Solution Approach 2:
The system incorporates dynamics by explicitly modeling the time-dependent behavior of catalysts and equipment. Rather than assuming static conditions, the models capture how catalyst activity changes over time and how equipment performance degrades, allowing accurate quality prediction despite these dynamic changes in the production environment.
Data Source
AI summary
The present teachings relate to a method for modeling an industrial plant comprising a plurality of equipment, the method comprising: providing a plant level model of the industrial plant; wherein the plant level model has been generated via a topology generator by automatically selecting and interconnecting equipment models from a model library; obtaining, using a model trainer, a trained plant level model; wherein the trained plant level model is obtained from the plant level model by training at least some of the equipment models in the plant level model using one or more historical datasets; wherein the trained plant level model is usable for computing at least one performance parameter via a model executor. The present teachings also relate to a framework, a software product, a use of the model and a use of the performance parameter.