Hybrid Process Modeling for Chemical Plant Debottlenecking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current process modeling and simulation practices face challenges in accurately capturing physical phenomena due to the complexity of chemical processes, leading to high costs and limited sustainability, especially in calibration and online execution of full-scale models.
Innovation Solution
A hybrid approach combining first principles knowledge with machine learning techniques to generate a computer-implemented method and system for modeling and simulating industrial chemical processes, which includes generating a list of features based on first principles and creating a machine learning model to enhance predictions and improve process performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-scale first-principles models are used for process modeling and simulation, then model accuracy in representing physical and chemical properties is improved, but model complexity and calibration cost increase significantly
Solution Approach 1:
The patent divides the full-scale first-principles model into modular functional blocks representing different unit operations and processes. Each module can be independently calibrated and validated, reducing the overall complexity while maintaining accurate representation of physical and chemical properties throughout the entire system.
Solution Approach 2:
The patent extracts and separates the calibration challenge from the full-scale model by identifying and isolating specific parameters and sub-systems that require calibration. This allows targeted calibration efforts on critical components rather than attempting to calibrate the entire complex model simultaneously, reducing calibration cost and complexity.
2Reliability
If full-scale first-principles models are used for online applications, then process monitoring and optimization capability is improved, but calibration cost and sustainability deteriorate
Solution Approach 1:
The patent implements self-calibration capabilities where the model automatically adjusts certain parameters using real-time process data and feedback mechanisms. This reduces the need for manual calibration efforts and ongoing expert intervention, thereby lowering calibration costs while maintaining reliable process monitoring and optimization capabilities.
Solution Approach 2:
The patent employs adaptive parameter adjustment where model parameters are dynamically modified based on changing process conditions and available data. This allows the model to maintain accuracy across different operating scenarios without requiring frequent recalibration, improving sustainability while reducing calibration costs.
3Loss of information
If full-scale first-principles models are used, then comprehensive process representation is improved, but data incorporation and model sustainment become more difficult
Solution Approach 1:
The patent develops a unified model framework that can handle multiple data types, sources, and process representations through standardized interfaces and data structures. This universal approach maintains comprehensive process representation while simplifying data incorporation and model maintenance by providing consistent methods for handling diverse information.
Solution Approach 2:
The patent implements feedback mechanisms where model predictions are continuously compared with actual process data, and discrepancies are used to identify areas needing maintenance or updates. This systematic feedback approach simplifies model sustainment by automatically highlighting specific components requiring attention rather than requiring comprehensive manual review of the entire complex model.
Data Source
AI summary
Computer-based process modeling and simulation methods and systems combine first principles models and machine learning models to benefit where either model is lacking. In one example, input values (measurements) are adjusted by first principles techniques. A machine learning model of the chemical process of interest is trained on the adjusted values. In another example, a machine learning model represents the residual (delta) between a first principles model prediction and empirical data. Residual machine learning models correct physical phenomena predictions in a first principles model of the chemical process. In another example, a first principles simulation model uses the process input data and predictions of the machine learning model to generate simulated results of the chemical process. The hybrid models enable a process engineer to troubleshoot the chemical process, enable debottlenecking the chemical process, enable optimizing performance of the chemical process at the subject industrial plant, and enable automated process control.