Structured Model Order Reduction for Parallel Units
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Advanced process control in industrial systems with parallel working units, such as boilers and pumps, faces challenges in obtaining accurate low-order models for various on/off configurations, as existing methods require extensive identification experiments and often produce inconsistent results due to the complexity of combining individual unit models with the remaining system model.
Innovation Solution
A systematic approach using structured model order reduction, which involves identifying models of individual parallel units and the remaining system separately, then combining them with a structured model reduction algorithm to obtain a reduced-order model for any configuration needed by advanced process control, thereby minimizing experimental time and resources while ensuring consistent quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If identification experiments are performed for all on/off configurations, then model accuracy is improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The patent segments the identification process into two phases: (1) identifying models of individual parallel units and the remaining system separately, and (2) combining these models using structured model order reduction to obtain the full system model. This segmentation eliminates the need to perform exhaustive identification experiments for all possible on/off configurations, significantly reducing time consumption while maintaining model accuracy.
2Loss of time
If heuristics are used to combine individual unit models, then time consumption is reduced, but model quality consistency deteriorates
Solution Approach 1:
The patent employs structured model order reduction techniques that systematically transform the combined models of individual units and the remaining system into a reduced-order model with guaranteed quality properties. This mathematical approach ensures consistent model quality across different configurations, eliminating the inconsistency associated with heuristic methods while maintaining reduced time consumption.
3Adaptability or versatility
If the number of parallel units increases, then system functionality is improved, but the number of configurations and model identification complexity increase
Solution Approach 1:
The patent develops a universal modeling framework that can handle any number of parallel units and any on/off configuration through a standardized two-phase approach. The structured model order reduction technique provides a multi-functional solution that automatically adapts to different system configurations, eliminating the need to develop separate identification procedures for each configuration and thereby reducing overall complexity.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An approach for modeling parallel working units of a system for advanced process control. The approach may be a systematic solution based on structured model order reduction. Two phases of it may incorporate model identification and model combination. The first phase is where a model of each parallel unit (24,25) and a model of the remaining system (23) without any unit may be obtained. The second phase is where the models may be combined to obtain a model of the whole system for any configuration needed by the advanced process control. The model of the whole system may be subjected to a structured model reduction to obtain a reduced order model for the advanced process control.