AI Calorific Value Prediction for Boiler Fuel Monitoring
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Solution Overview
Problem
Thermal power plants face inefficiencies due to lack of real-time information on the calorific value of solid fuels, leading to suboptimal boiler operation and control decisions, with existing monitoring systems requiring significant hardware investments and interfering with existing infrastructure.
Innovation Solution
A method and system for on-line monitoring and determining the calorific value of solid fuels using sensors to collect operational data from boilers and mills, combined with historical data and AI algorithms to predict the current fuel value, allowing for precise optimization of boiler operation without additional hardware investments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fuel quality analysis methods are used, then measurement precision of calorific value is achieved, but loss of time occurs due to significant delay in results
Solution Approach 1:
The patent replaces traditional mechanical laboratory analysis systems with an AI-based predictive system that uses operational data from boilers and mills to estimate calorific value in real-time, eliminating the time delay inherent in physical fuel sampling and laboratory testing
Solution Approach 2:
The system creates a virtual copy of the fuel's calorific value by predicting it from operational parameters (mill power, air pressure, temperature, fuel flow rate) rather than directly measuring the physical fuel, allowing real-time estimation without physical interference
2Measurement precision
If additional hardware systems are installed to monitor fuel quality, then measurement precision improves, but device complexity and infrastructure interference increase
Solution Approach 1:
The system uses existing operational data from the boiler and mill systems to predict fuel calorific value, allowing the existing infrastructure to serve dual purposes - both operation control and fuel quality monitoring - without requiring separate dedicated hardware
Solution Approach 2:
The AI prediction system serves multiple functions simultaneously: it monitors fuel quality, optimizes boiler operation, and provides real-time calorific value data, replacing the need for multiple separate specialized systems
3Productivity
If real-time calorific value information is obtained, then productivity and operational optimization improve, but loss of information occurs in traditional systems due to delayed data
Solution Approach 1:
The system continuously predicts calorific value based on real-time operational data from mills and boilers, creating a feedback loop where current fuel quality information is constantly updated and fed back to control systems for immediate optimization
Solution Approach 2:
The AI model performs preliminary prediction of calorific value based on operational parameters before the actual fuel combustion occurs, allowing proactive adjustment of boiler operation to optimize efficiency
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time optimization of boiler operation, increased operational awareness, and accurate assessment of energy potential, allowing for improved response to adverse conditions and maximization of existing technical potential without disrupting existing infrastructure.
Implementation Method 1
a laser beam is directed onto the fuel sample in order to vaporize and atomize the sample
Implementation Method 2
the laser beam is focused in a measurement zone... forming a plasma
Implementation Method 3
the atoms are excited by the plasma and emit light at characteristic wavelengths
Implementation Method 4
a spectrometer is used to detect the light emitted by the atoms in the plasma
Data Source
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AI summary
A method for online monitoring and determining the calorific value of a solid fuel that is currently combusted in a boiler that comprises a combustion chamber (10) with a steam production system, to which the solid fuel is fed from at least one mill (13) connected to a hopper (14), characterised in that the method comprises the following steps: on-line measuring the operational data of the boiler and of at least one mill during the operation of the boiler; collecting the historical data of measurements of the calorific value of the solid fuel and the operational data of the block; calculating the energy balances of the steam production system based on the collected historical data; iteratively determining (207-212) the efficiency of the boiler based on historical data by: determining (208) sets of mill characteristics depending on a calorific value of the solid fuel and operational data of the mill while combusting that solid fuel; determining (210) the fuel mass flux based on the set of mill characteristics (208); determining (211) the actual calorific value of the fuel (LHV) for the historical data; training a model (407) based on artificial intelligence algorithms to predict the calorific value using the historical data of fuel calorific value (211) and measured operational data of the boiler; determining (408) in real time, using the trained model, the calorific value of the solid fuel that is currently combusted.