Systems and Methods for Fuel Type Detection for Hydronic and Other Heating Systems
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Solution Overview
Problem
Hydronic and other gas fired heating systems face inefficiencies and safety risks when the wrong fuel type is input, requiring manual adjustment of settings like gas valve and fan speed to achieve optimal air-to-fuel ratios, and incorrect fuel input can lead to malfunctions.
Innovation Solution
A gas fired appliance with a controller that runs a machine learning model to detect the fuel type based on operational data, adjusting settings such as gas valve and fan speed to optimize the air-to-fuel ratio automatically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If manual adjustment of settings is performed to achieve optimal air-to-fuel ratio, then system efficiency is improved, but labor time and complexity increase
Solution Approach 1:
The system automatically detects fuel type and adjusts gas valve and fan settings without technician intervention. The controller executes a machine learning model that analyzes operational data, identifies the fuel type, and autonomously optimizes air-to-fuel ratio parameters, enabling the system to serve itself rather than requiring manual configuration.
Solution Approach 2:
The system performs fuel type detection and settings optimization during initial system startup or commissioning phase. By executing the machine learning model early in the operational sequence, the system proactively determines optimal settings before actual heating operations begin, preventing efficiency losses that would occur with incorrect initial configurations.
2Measurement precision
If technician manually tunes system settings, then accuracy of air-to-fuel ratio is improved, but device complexity increases
Solution Approach 1:
The patent replaces manual mechanical adjustment of gas valves and fan controls with an automated electronic control system. The controller executes a machine learning model that processes operational data and automatically adjusts settings, substituting the mechanical tuning process with an intelligent software-based system that achieves comparable or superior precision without requiring technician expertise.
Solution Approach 2:
The system continuously monitors operational parameters and uses the machine learning model to analyze performance data in real-time. Based on this feedback loop, the controller automatically refines and adjusts gas valve and fan settings to maintain optimal air-to-fuel ratio, ensuring sustained accuracy without requiring repeated manual interventions.
3Ease of operation
If automated fuel detection is implemented, then ease of operation is improved, but device complexity increases
Solution Approach 1:
The controller is designed to handle multiple fuel types (natural gas, propane, oil) through a single universal machine learning model. Rather than requiring separate control systems for each fuel type, the automated detection system universally identifies the fuel type and automatically configures appropriate settings, simplifying operation while consolidating complexity into a single multi-functional controller.
Data Source
AI summary
Systems and methods are provided for gas fired systems, such as boilers, hydronic systems, and other fuel powered heating systems which are capable detecting a fuel type being consumed by the system and adjusting operation of the system based on the fuel type detected. The gas fired or other heating systems may have a controller capable of a running a machine learning model trained to detect a fuel type based on operational data corresponding to the gas fired or other heating system. Once the type of fuel is determined, operation of the gas fired or other heating system may be adjusted according to the fuel type detected. For example, the system may be powered down, a gas valve may be adjusted to adjust fuel injected into the heat exchanger, or a fan (e.g., blower) speed (e.g., revolutions per minute) may be adjusted.