NOx Emission Modelling and Control for Fossil-fuel Power Generation Plants
In the past decade, atmospheric pollution has become a major global concern due to ever increasing demand on energy and raw material consumptions in both developed and developing countries. The major contributors to atmospheric pollution are mainly from fossil-fuel power stations (see figure 1) and transportation vehicles. Ever increasing demand on energy has made the problem even more challenging. The principal pollutants from these energy systems may include sulphur dioxide (SO2), nitrogen oxides (NOx), carbon dioxide (CO2) and particulates (soot, flyash). Among these emissions, no practical methods exist for reducing CO2 or NOx significantly, leading to increased research into these two important areas. This project focuses on the modelling, prediction and control of NOx emissions from fossil-fuel power generation plants.
Fig 1. Coal-fired power plants
Methods and results
The earliest research into NOx formation mechanism can be traced back to 1943 when Zeldovich first postulated the thermal NO formation mechanism. However, extensive research started following the discussions on acidification in the eighties. These would include papers on NOx formation, modelling and control. Technologies for NOx emission reduction may be classified into two groups:
- primary or combustion modification-based technologies, and
- secondary or flue-gas treatment-based technologies
The early techniques used in this project include some ‘black-box’ linear and nonlinear regression (ARX and NARX) models, and artificial neural networks for modelling and control purpose. Each of these models has both advantages and disadvantages, mainly on their model complexity and generalisation.
However, power plant NOx emission modelling for real-time operation and control presents some unique problems.
- The fundamental equations for NOx formation cannot be directly applied as many of the variables are either not measurable online or cannot be reliably measured. Therefore, the model structure is not readily available, thus excluding non-linear system parameter estimation techniques.
- Information such as the steady-state relationships between input and output is not readily available. This causes problems in the construction of a lumped system model, or a Wiener, Hammerstein, or Wiener-Hammerstein model of the process.
- During normal plant operation, safety and production considerations mean that test signals are highly restricted in terms of magnitudes and duration.
A further advance is the proposal of ‘engineering-genes’, which extract two types of nonlinear functions, e.g. Arrhenius and non - Arrhenius activations, to build a transparent neural networks to model the NOx emission in the combustion chamber (see figure 2).
To tackle these problems, this project then further developed a novel 'grey-box' approach, which uses equations relating the NOx formation mechanism in order to extract some fundamental functions which are referred to FEs (fundamental elements). An algorithm is then used to construct the 'best' time series NOx emission model by selecting the most significant of the functions. The developed model has been used to predict NOx emissions from two different power plants for over a month.
Fig 2. Neural mesh estimation of pollutant emissions in TPPs using Arrhenius function
The industry has mature experience on modelling and control of power plants using conventional neural networks, and CFD method has been well established as the best tool for plant design by simulating the combustion process in the furnace to estimate pollutant emissions from the TPPs. The first model type lacks transparency, the second method is however very expensive to implement and is also time consuming. The proposal of this above transparent neural network paradigm offers a new prospect in real-time power plant emission modelling and control with improved performance and model transparency.
Moreover, a software package has been developed for modelling NOx emissions (see Figure 3) using genetic algorithm.
Fig 3. NOx emission modelling software
This work has been partially supported by the Engineering and Physical Sciences Research Council (EPSRC) for funding this project (GR/S85191/01), The British Coal Utilization Research Association.
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