Artificial neural networks for analysis of process states in fluidized bed combustion

Liukkonen, Mika; Heikkinen, Mikko; Hiltunen, Teri; Hälikka, Eero; Kuivalainen, Reijo; Hiltunen, Yrjö

doi:10.1016/j.energy.2010.10.033

Cited by 63 publications

(25 citation statements)

References 27 publications

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“…For example, condition-dependent behavior is characteristic for industrial processes (Juntunen, Liukkonen, Lehtola, & Hiltunen, 2013a, 2013bLiukkonen, Havia, Leinonen, & Hiltunen, 2011;Liukkonen, Heikkinen, et al, 2011;, which usually requires flexibility and adaptivity from practical software applications. In practice, this means that the process adheres to different regularities under different conditions, which means that these conditions have to be recognized by the application, so that it can adapt to the prevailing situation.…”

Section: Introductionmentioning

confidence: 99%

“…In practice, this means that the process adheres to different regularities under different conditions, which means that these conditions have to be recognized by the application, so that it can adapt to the prevailing situation. A typical example on this is load-dependent behavior in energy and water processes (Liukkonen, Heikkinen, et al, 2011;Liukkonen & Hiltunen, 2014;. Moreover, it is shown that problems originating from fluctuation can be significant in a dynamically behaving process .…”

Section: Introductionmentioning

confidence: 99%

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Monitoring and analysis of air emissions based on condition models derived from process history

Liukkonen

Hiltunen²

2016

Cogent Engineering

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Evaluation of online information on operating conditions is necessary when reducing air emissions in energy plants. In this respect, automated monitoring and control are of primary concern, particularly in biomass combustion. As monitoring of emissions in power plants is ever more challenging because of lowgrade fuels and fuel mixtures, new monitoring applications are needed to extract essential information from the large amount of measurement data. The management of emissions in energy boilers lacks economically efficient, fast, and competent computational systems that could support decision-making regarding the improvement of emission efficiency. In this paper, a novel emission monitoring platform based on the self-organizing map method is presented. The system is capable, not only of visualizing the prevailing status of the process and detecting problem situations (i.e. increased emission release rates), but also of analyzing these situations automatically and presenting factors potentially affecting them. The system is demonstrated using measurement data from an industrial circulating fluidized bed boiler fired by forest residue as the primary fuel and coal as the supporting fuel.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring and analysis of air emissions based on condition models derived from process history

Liukkonen

Hiltunen²

2016

Cogent Engineering

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“…Boilers, with developed models aimed at predicting the performance of pulverized-coal boilers [48], predicting NO x emissions of CFB (circulating fluidised bed) boilers [49], estimating pollutant emissions in chain-grate stoker boilers [50], modelling NO x emissions in pulverized-coal boilers [51] with subsequent application of Genetic Algorithm for optimization purposes [52], predicting bottom ash depositing in pulverized-coal power plants [53] and reproducing the influence of fouling on the efficiency of biomass boilers [54].…”

Section: Introductionmentioning

confidence: 99%

A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

Rossi

Velázquez

2015

Energy

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“…Circulating fluidized bed combustion (CFB) is a good example of an energy process in which monitoring the condition and performance is increasingly important (Liukkonen et al, 2011a(Liukkonen et al, , 2011b(Liukkonen et al, , 2012a(Liukkonen et al, , 2013a(Liukkonen et al, , 2014. It is well known that in fluidized bed gasification, for example, the use of difficult fuels such as biomass can lead to severe problems, such as ash sintering or agglomeration.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Fluidized Bed Condition by Image Analysis and Modelling

Liukkonen

Hiltunen²,

Hiltunen

2015

IFAC-PapersOnLine

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Circulating fluidized bed combustion (CFB) is a good example of an energy process in which monitoring the condition and performance is important, especially when inhomogeneous solid fuels such as biofuel is used. There are two main reasons for this: 1) information on the coarseness of the bed material would provide information on problematic phenomena such as agglomeration, and 2) it is not economical to replace the bed material too often, because it leads to excessive usage of sand and premature wearing of bottom ash conveyor and related devices, so optimization of the bed change interval would be useful. Nevertheless, the possibilities for performing sampling and measurements cost-efficiently in industrial-scale CFBs are difficult because of the demanding environment. In this paper we present a costefficient measurement system and a further developed, indirect modeling methodology for evaluating fluidized bed condition, which is based on capturing and analyzing digital images from bottom ash.

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Artificial neural networks for analysis of process states in fluidized bed combustion

Cited by 63 publications

References 27 publications

Monitoring and analysis of air emissions based on condition models derived from process history

Monitoring and analysis of air emissions based on condition models derived from process history

A methodology for energy savings verification in industry with application for a CHP (combined heat and power) plant

Evaluation of Fluidized Bed Condition by Image Analysis and Modelling

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