Real-Time Boiler Control Optimization with Machine Learning
In coal-fired power plants, it is critical to improve the operational efficiency of boilers for sustainability. In this work, we formulate real-time boiler control as an optimization problem that looks for the best distribution of temperature in different zones and oxygen content from the flue to improve the boiler's stability and energy efficiency. We employ an efficient algorithm by integrating appropriate machine learning and optimization techniques. We obtain a large dataset collected from a real boiler for more than two months from our industry partner, and conduct extensive experiments to demonstrate the effectiveness and efficiency of the proposed algorithm.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningFLUESimilar Papers 제목 키워드 기반
Fault Detection for Non-Condensing Boilers using Simulated Building Automation System Sensor Data
Building performance has been shown to degrade significantly after commissioning, resulting in increased energy consumption and associated greenhouse gas emissions. Continuous Commissioning using existing sensor networks…
Fault DetectionNovel Modelling and Control Strategies for a Steam Boiler under Fast Load Dynamics
This paper describes a new nonlinear dynamic model for a natural circulation boiler. The model is based on physical principles, i.e. mass, energy and momentum balances. A systematic approach is followed leading to new in…
Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates
The steam drum water level is a critical parameter that directly impacts the safety and efficiency of power plant operations. However, predicting the drum water level in boilers is challenging due to complex non-linear p…
Data Driven based Dynamic Correction Prediction Model for NOx Emission of Coal Fired Boiler
The real-time prediction of NOx emissions is of great significance for pollutant emission control and unit operation of coal-fired power plants. Aiming at dealing with the large time delay and strong nonlinear characteri…
feature selectionDeveloping an ANFIS PSO Model to Estimate Mercury Emission in Combustion Flue Gases
Accurate prediction of mercury content emitted from fossil fueled power stations is of utmost important for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of po…
FLUE