Method, apparatus, and storage medium for controlling heating system
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Solution Overview
Problem
Uncertainty in parameters such as resistance coefficient and heat dissipation coefficient in heating systems poses challenges for modeling and control, as these values are affected by various factors like pipeline conditions and environmental changes.
Innovation Solution
A method is developed to estimate system parameters like resistance coefficient and heat dissipation coefficient using an objective function and constraints, considering dynamic characteristics and moving horizon estimation, which allows for more accurate modeling and control of the heating system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If empirical formulas are used to calculate resistance coefficient and heat dissipation coefficient, then the modeling and control process is simplified, but the accuracy of system parameters deteriorates due to uncertainties from pipeline conditions and environmental changes
Solution Approach 1:
The patent implements a feedback mechanism by continuously monitoring actual system parameters (temperatures, pressures, flows) and using these measurements to update and refine the resistance coefficient and heat dissipation coefficient estimates. The objective function compares predicted versus actual measurements, creating a closed-loop system that adapts to changing pipeline conditions and environmental factors, thereby maintaining parameter accuracy without complicating the control process
Solution Approach 2:
The patent dynamically adjusts system parameters (resistance coefficient, heat dissipation coefficient) based on real-time operating conditions rather than using fixed empirical values. By formulating these parameters as variables to be optimized through the objective function rather than constants from empirical formulas, the system adapts to changing conditions while maintaining a relatively simple control structure
2Measurement precision
If actual resistance coefficient variations are accounted for (due to pipeline operating time, diameter, material, corrosion), then parameter accuracy improves, but the complexity of modeling and control increases
Solution Approach 1:
The patent creates a universal objective function that can handle multiple sources of parameter variation (pipeline operating time, diameter, material, corrosion) through a single unified mathematical framework. Rather than creating separate models for each factor, the objective function universally accommodates all these variations by optimizing resistance coefficients based on actual system performance, simplifying the overall modeling approach while maintaining comprehensive accuracy
Solution Approach 2:
The system performs self-characterization by automatically determining its own resistance coefficients and heat dissipation parameters through the optimization process, without requiring external manual adjustments or complex separate characterization procedures. The objective function enables the system to self-adapt to pipeline conditions, material properties, and corrosion effects through continuous parameter estimation based on operational data
3Reliability
If environmental factors affecting heat dissipation coefficient are considered, then modeling accuracy improves, but the control process becomes more complex
Solution Approach 1:
The patent merges the consideration of environmental factors into the unified objective function that also handles hydraulic parameters. Rather than treating heat dissipation coefficient optimization as a separate complex process, it combines thermal and hydraulic parameter estimation into a single integrated optimization framework, maintaining control process simplicity while improving overall modeling accuracy
Data Source
AI summary
The disclosure provides a method, an apparatus, and a storage medium for controlling heating system. The method includes: establishing an objective function and constraints for estimating system parameters of the heating system, in which the heating system includes nodes, pipelines and equivalent branches, the equivalent branch is configured to represent a heating resource or a heating load in the heating system, the system parameters include a resistance coefficient of each of the pipelines and equivalent branches, and a heat dissipation coefficient of each of the pipelines; solving the objective function based on the constraints to obtain the system parameters; modeling the heating system based on the obtained system parameters to obtain control parameters of the heating system; and controlling the heating system based on the control parameters.