AI Model Integration in Process Simulation for Unified Training and Deployment
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Solution Overview
Problem
Existing process simulation systems face limitations due to the isolation of intelligent models, which restricts their capability and functionality, necessitating a solution to integrate intelligent operations within the simulation environment for improved performance and efficiency.
Innovation Solution
A computer-implemented method and apparatus that tightly integrate intelligent models with process simulation systems by using specially configured algorithms to identify and flag qualifying datasets, train models, and deploy them within the same environment, allowing for pre-processing, post-processing, and performance evaluations, all while utilizing a shared data repository for inputs and outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If intelligent models are isolated from the process simulation system, then the system structure remains simple and manageable, but the capability and functionality of the simulation system are restricted
Solution Approach 1:
The patent integrates intelligent models directly into the process simulation system by establishing a unified architecture where both traditional simulation models and intelligent models coexist and interact. The integration framework allows intelligent models to be embedded within the simulation environment, enabling seamless data exchange and joint operation, thereby enhancing the overall capability and functionality of the system.
Solution Approach 2:
The integrated system is designed to support multiple types of models (traditional simulation models and intelligent models) within a single unified framework. This multi-functional architecture enables the system to perform both conventional process simulation and advanced intelligent operations, making the simulation system more versatile and adaptable to different modeling needs.
2Productivity
If intelligent models are integrated within the process simulation system, then computational efficiency and storage efficiency are improved, but the system complexity increases
Solution Approach 1:
The patent merges the data management and computational resources of intelligent models with those of the process simulation system. By sharing common data repositories, processing units, and operational frameworks, the system achieves improved computational efficiency and resource utilization while managing complexity through unified architecture design.
Solution Approach 2:
The integrated system enables intelligent models to leverage the existing infrastructure and resources of the process simulation environment. The system automatically manages data flow, model coordination, and computational tasks, reducing the need for separate external processes and thereby improving efficiency while keeping complexity management automated.
3Ease of operation
If external processes are used for model training and data processing, then the process simulation system remains simple, but network efficiency and access flexibility are reduced
Solution Approach 1:
The patent consolidates model training, data processing, and simulation operations within a single integrated system architecture. By eliminating the separation between external processes and the core simulation system, the patent enables direct access to all functionalities, improving network efficiency and operational flexibility while managing complexity through unified design.
Solution Approach 2:
The integrated architecture acts as an intermediary that unifies access to both traditional simulation models and intelligent models. This unified interface allows users to access all model types and operations through a single system, eliminating the need for separate external processes and thereby improving ease of operation and network efficiency.
Data Source
AI summary
Embodiments of the disclosure provide for intelligent model integration within a process simulation system. Some embodiments receive data associated with the operation of a plant, determine, using at least one specially configured algorithm and based on the received data, at least one qualifying dataset determined qualified to train an intelligent model, train the intelligent model using the at least one qualifying dataset, and deploy the trained intelligent model for use.