Equation Solvability Feedback in Industrial Process Models
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Solution Overview
Problem
Complex industrial processes generate overwhelming volumes of data, making it challenging to ensure efficient operation and detect inefficiencies, particularly in simulating and optimizing processes due to the time-consuming nature of solving large systems of equations.
Innovation Solution
A system that models industrial processes using software to generate and configure multiple models, determining solvability of equations and suggesting changes to render them solvable, integrated with a three-mode simulator for steady state mass and energy balances, fluid flow network analysis, and dynamic simulation, allowing for accurate representation and optimization of processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a simulation model of a process is created to simulate and optimize the process, then the ability to detect inefficiencies and determine alterations is improved, but the time required to solve large systems of equations increases
Solution Approach 1:
The system performs preliminary analysis of the simulation model to identify and flag potential equation solvability issues before the simulation is executed. This includes checking for missing variables, inconsistent units, and other conditions that would prevent successful equation solving, thereby preventing time waste during actual simulation runtime.
Solution Approach 2:
The system provides continuous feedback to users about the solvability status of equations in the simulation model. When equations are found to be unsolvable, the system identifies specific issues and suggests corrective actions, allowing users to iteratively refine their models until all equations are solvable, thus ensuring both accuracy and efficiency.
2Productivity
If sophisticated data management techniques are used to handle large volumes of sensor data, then the ability to monitor and control industrial processes is improved, but the complexity of the system increases
Solution Approach 1:
The data management system is segmented into modular components that handle different aspects of data processing independently. Each module manages specific tasks such as data acquisition, validation, storage, and analysis, allowing the system to handle large volumes of sensor data efficiently while maintaining manageable complexity through clear separation of concerns.
3Reliability
If a simulation model accurately represents process functionality, then the optimization capability is improved, but the difficulty of ensuring model accuracy and responsive performance increases
Solution Approach 1:
The simulation model includes built-in self-validation mechanisms that automatically check for accuracy and consistency. The system performs self-diagnosis to detect modeling errors, validates equations against known physical principles, and ensures responsive performance through automated testing, reducing the burden on users to manually verify model accuracy.
Data Source
AI summary
Software instructions stored on a memory device and executable by a processor generate a plurality of models to simulate a process entity. The models include equations that mathematically represent the functionality of the process. Moreover, the models are configured to accurately represent the functionality of the process. Instructions determine whether the equations of the models are solvable after each change made to the models and indicate the result of the determination of solvability of the equations. Changes to the plurality of models are suggested that may render the equations solvable if the equations are determined to be unsolvable.