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Development of a NOx Emission Model using advanced regression techniques

Romkema, J.F. (2020) Development of a NOx Emission Model using advanced regression techniques.

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Abstract:Polluted air is a big problem nowadays. As air pollution has many downsides, governments have laws in place to minimise the amount of these substances in the air. One of the measurements is that gas turbines need to know the amount of emission they produce. One way to achieve this is by measuring the amount of emission with the aid of measurements tools. However, the measurements tools are expensive and require much maintenance. In recent years, we have therefore seen the use of prediction instead of measuring. A predictive emission monitoring system (PEMS) is one way to track the emission in gas turbines. A PEMS consists of an emission model which is able to calculate the emission based on sensors within the gas turbine. As it is important that the emission is accuractly tracked, the legislation is quite strict. PEMSs need to comply with many rules to be put into use. This makes building a PEMS an labour-intensive task where a lot of field knowledge is required. Emission Care is an example of a company that builds PEMS for their clients. A big part of this job is the cleaning of the data and the selection of the input features. In their work they experience the amount of time it takes to retrieve those insights. Currently they select the input features based on physics and years of experience. To model their findings they license the software to build their neural network-based PEMS. However, the iterative strategy they apply now is time- and labour expensive and would benefit from a supporting tool. In this research we focused on the creation of an emission model and how data-driven techniques and machine learning can be used to support this process. The aim was to support the modeller by finding appropriate input combinations as well as validating them with the use of our model in order to reduce the time-spent on building a PEMS. In this research we first did a literature study to understand PEMS better and to determine how other studies approached the development of a PEMS. Furthermore, we looked into various regression techniques and how we could incorporate historical information to models that are not built for time series. The conducted research has resulted in a feature selection algorithm that is able to support the process of selecting feature combinations. The feature combinations proposed are tested with the current CEM software and similar scores, R2-score of 0.97, are obtained as for model currently used. Furthermore, the proposed feature combinations are also applied to our own developed emission models. These models are based on linear regression, tree-based methods, support vector regression and neural networks. We found that the models based on data with historical aspects performed similar to the ones without the historical aspect. If we compare the scores to the CEM software scores, we find that results are comparable for the tree-based method and the support vector regression model which would make those emission models good candidates to substitute the current emission model. However, the emission model is only a small part of the PEMS and therefore, the emission models found in this research serve as proof that the current CEM software peforms well. 1
Item Type:Essay (Master)
Clients:
Emission Care, Nederland
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science MSc (60300)
Link to this item:http://purl.utwente.nl/essays/82782
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