Prediction Of Boilers Emission Using Polynomial Networks

In this paper we investigate the problem of NOx pollution using a model of furnace of an industrial boiler, and propose Functional Networks (FunNets) for high performance prediction of NOx as well as O2. The objective is to develop low cost inferential sensing techniques that would help in operating the boiler at the maximum possible efficiency while maintaining the NOx production within a specified limit. The studied boiler is 160 MW, gas fired with natural gas, water-tube boiler, having two vertically aligned burners. The boiler model is a 3D problem that involves turbulence, combustion, radiation in addition to NOx modeling. The 3D computational fluid dynamic model is developed using Fluent simulation package, where the volume of the furnace was divided into 371000 control volumes with more concentration of grids near solid walls and regions of high property gradients. The model provides calculations of the 3D temperature distribution as well as the rate of formation of the NOx pollutant, enabling a better understanding on how and where NOx are produced. The boiler was simulated under various operating conditions. The generated data is then used to train and test the developed neural network softsensors for emission prediction based on the conventional process variable measurements. The softsensors were constructed using Polynomial Networks (PolyNets), which are a special class of the recently introduced Functional Networks. PolyNets compose complex Neural Networks from simple transfer polynomials with weights that are computed efficiently by ordinary least-squares. The performance of the proposed PolyNet softsensor is evaluated in detail in the paper and compared with the traditional MLP neural networks. It is shown that PolyNets achieve better accurac

Prediction Of Boilers Emission Using Polynomial Networks

In this paper we investigate the problem of NOx pollution using a model of furnace of an industrial boiler, and propose Functional Networks (FunNets) for high performance prediction of NOx as well as O2. The objective is to develop low cost inferential sensing techniques that would help in operating the boiler at the maximum possible efficiency while maintaining the NOx production within a specified limit. The studied boiler is 160 MW, gas fired with natural gas, water-tube boiler, having two vertically aligned burners. The boiler model is a 3D problem that involves turbulence, combustion, radiation in addition to NOx modeling. The 3D computational fluid dynamic model is developed using Fluent simulation package, where the volume of the furnace was divided into 371000 control volumes with more concentration of grids near solid walls and regions of high property gradients. The model provides calculations of the 3D temperature distribution as well as the rate of formation of the NOx pollutant, enabling a better understanding on how and where NOx are produced. The boiler was simulated under various operating conditions. The generated data is then used to train and test the developed neural network softsensors for emission prediction based on the conventional process variable measurements. The softsensors were constructed using Polynomial Networks (PolyNets), which are a special class of the recently introduced Functional Networks. PolyNets compose complex Neural Networks from simple transfer polynomials with weights that are computed efficiently by ordinary least-squares. The performance of the proposed PolyNet softsensor is evaluated in detail in the paper and compared with the traditional MLP neural networks. It is shown that PolyNets achieve better accurac

Prediction Of Boilers Emission Using Polynomial Networks

In this paper we investigate the problem of NOx pollution using a model of furnace of an industrial boiler, and propose Functional Networks (FunNets) for high performance prediction of NOx as well as O2. The objective is to develop low cost inferential sensing techniques that would help in operating the boiler at the maximum possible efficiency while maintaining the NOx production within a specified limit. The studied boiler is 160 MW, gas fired with natural gas, water-tube boiler, having two vertically aligned burners. The boiler model is a 3D problem that involves turbulence, combustion, radiation in addition to NOx modeling. The 3D computational fluid dynamic model is developed using Fluent simulation package, where the volume of the furnace was divided into 371000 control volumes with more concentration of grids near solid walls and regions of high property gradients. The model provides calculations of the 3D temperature distribution as well as the rate of formation of the NOx pollutant, enabling a better understanding on how and where NOx are produced. The boiler was simulated under various operating conditions. The generated data is then used to train and test the developed neural network softsensors for emission prediction based on the conventional process variable measurements. The softsensors were constructed using Polynomial Networks (PolyNets), which are a special class of the recently introduced Functional Networks. PolyNets compose complex Neural Networks from simple transfer polynomials with weights that are computed efficiently by ordinary least-squares. The performance of the proposed PolyNet softsensor is evaluated in detail in the paper and compared with the traditional MLP neural networks. It is shown that PolyNets achieve better accuracy with simpler structures, and could be trained faster than MLP NN by a factor of 6-8 times

Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant

There is increasing need for tighter controls of coal-fired plants due to more stringent regulations and addition of more renewable sources in the electricity grid. Achieving this will require better process knowledge which can be facilitated through the use of plant models. Drum-boilers, a key component of coal-fired subcritical power plants, have complicated characteristics and require highly complex routines for the dynamic characteristics to be accurately modelled. Development of such routines is laborious and due to computational requirements they are often unfit for control purposes. On the other hand, simpler lumped and semi empirical models may not represent the process well. As a result, data-driven approach based on neural networks is chosen in this study. Models derived with this approach incorporate all the complex underlying physics and performs very well so long as it is used within the range of conditions on which it was developed. The model can be used for studying plant dynamics and design of controllers. Dynamic model of the drum-boiler was developed in this study using NARX neural networks. The model predictions showed good agreement with actual outputs of the drum-boiler (drum pressure and water level)

Technoeconomic and whole-energy system analysis of low-carbon heating technologies

Despite developments in renewable electricity production, space heating and hot-water provision still account for a high proportion of the total greenhouse gas emissions in the world. Decarbonising heating requires an in-depth understanding of the candidate technology options. Should investments in energy systems focus on large-scale/centralised options, or small-scale/distributed ones? How should end-users operate their heating systems to maximise economic and environmental benefits? Should manufacturers design high-performance yet high-cost technologies and reduce the transition cost to the wider electricity system infrastructure, or should they promote more affordable, lower-performance end-use alternatives at a cost to the wider system? In this thesis, technoeconomic models that capture the cost and performance characteristics of heating technologies are developed and used to analyse the design and operation of competing solutions from the perspectives of different stakeholders. An extensive analysis of commercially available air-source and ground-source heat pumps, combined heat and power systems, district heating infrastructure and thermal energy storage systems on the UK market is first conducted. Fitting techniques are used to determine relationships arising from the collected data and quantify the related uncertainty in technology characteristics between the data and fitted relationships. Then, thermodynamic and component-costing models are developed for technologies for which there is a substantial spread in the available data, or for which data are not available. These include electricity- and hydrogen-driven heat pumps and involve dedicated compressor efficiency maps, heat exchanger models, and equipment-costing methods. The resulting technoeconomic models are first used to assess the economic and environmental performance of different centralised and distributed low-carbon heat provision pathways, with a London district as a case study. Centralised gas-fired combined heat and power systems are found to be favourable in terms of annual total cost. However, in recent years, the carbon footprint of grid electricity has reduced significantly, meaning that heat pumps installed at household or community level achieve a higher degree of decarbonisation. Furthermore, an uncertainty propagation analysis reveals the significance of properly accounting for technology performance and cost variations when modelling energy systems. In fact, the use of technoeconomic models is shown to reduce the uncertainty in the results by more than 75% compared to the use of black-box approaches. Two different optimisation studies are then conducted to investigate smart operation strategies of heating technologies in the domestic and commercial sectors. First, thermal network models of a domestic electric heat pump coupled to a hot-water cylinder or to two phase-change material thermal stores are developed and used to optimise heat pump operation for different objective functions. As demonstrated, smart heat pump operation can lead to a decrease in operational costs of more than 20% and an increase in self-sufficiency by up to four times. For the commercial sector, a multi-objective control framework is designed and installed on an existing combined heat and power system that provides heat and electricity to a supermarket. By using a stochastic optimisation approach and considering the uncertainty related to the price of exporting electricity, energy savings higher than 35% can be achieved compared to using a typical gas boiler. The integration of technoeconomic models of technologies within whole-energy system models can be used to extend the capabilities of the latter, so that they can, apart from optimising network infrastructures, provide explicit information about future technology design. Thermodynamic and component-costing models of a domestic electric heat pump, a hydrogen boiler and a hydrogen-driven absorption heat pump, as well an existing whole-energy system model of the UK, are used to compare electrification and hydrogen pathways for the domestic sector. The technologies are compared for different weather conditions and fuel-price scenarios, first from a homeowner’s and then from a whole-energy system perspective. It is shown that, in the UK, hydrogen technologies can be economically favourable only if hydrogen is supplied to domestic end-users at a price below half of the electricity price. From a whole-energy system perspective, electric heat pumps are the least-cost decarbonisation pathway under the investigated scenarios. Lastly, this thesis includes an effort to demonstrate how different component choices when designing domestic electric heat pumps can influence the national energy generation mix and heat-decarbonisation transition cost. Using the developed electric heat pump model, a set of optimal heat pump configurations representing competing components is obtained. The size of heat exchangers and the choice of compressor type and working fluid are shown to have a remarkable influence on the technology’s performance and cost. These configurations are integrated into an existing whole-energy system capacity-expansion and unit-dispatch model, to show that, from a UK energy system perspective, although high-performance heat pumps enable a reduction in the required installed electricity generation capacity by up to 50 GW, low-to-medium performance heat pumps can lead to a reduction of more than 10% in the total system transition cost and end-user investment requirements.Open Acces

A Review of using Data Mining Techniques in Power Plants

Data mining techniques and their applications have developed rapidly during the last two decades. This paper reviews application of data mining techniques in power systems, specially in power plants, through a survey of literature between the year 2000 and 2015. Keyword indices, articles’ abstracts and conclusions were used to classify more than 86 articles about application of data mining in power plants, from many academic journals and research centers. Because this paper concerns about application of data mining in power plants; the paper started by providing a brief introduction about data mining and power systems to give the reader better vision about these two different disciplines. This paper presents a comprehensive survey of the collected articles and classifies them according to three categories: the used techniques, the problem and the application area. From this review we found that data mining techniques (classification, regression, clustering and association rules) could be used to solve many types of problems in power plants, like predicting the amount of generated power, failure prediction, failure diagnosis, failure detection and many others. Also there is no standard technique that could be used for a specific problem. Application of data mining in power plants is a rich research area and still needs more exploration

Soft Sensor for NOx Emission using Dynamical Neural Network

In this paper we propose a soft sensor for prediction of NOx emission from the combustion unit of industrial boilers. The soft sensor is based on a dynamical neural network model. A simplified structure of the dynamical neural network model is achieved by grouping the input variables using basic knowledge of the system. Neural network model is trained using real data logs of an industrial boiler. Principal Component Analysis (PCA) is used to reduce number of input variables. Lag space for the model is found by using genetic algorithm to find the best time delayed model. Lag space obtained from the linear model is then used for constriction of the dynamical neural network. The proposed model is validated using different data from the same boiler and its ability to accurately predict NOx emission from the boiler is demonstrated