Soft sensor with shape descriptors for flame quality prediction based on LSTM regression

Sujatha, K.; Bhavani, N. P. G.; Srividhya, V.; Karthikeyan, V.; Jayachitra, N.

doi:10.1016/b978-0-12-818014-3.00006-1

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…A system of measurement sensors is built into the bench to record fuel expenditures, average chamber temperatures, and primary and secondary air flows in real time through a data acquisition system. combustion process, among which can be highlighted the Fourier transform [43], wavelet analysis [44], or recurrent neural networks [45][46][47][48][49][50]. Diagnostics of the combustion process can also use classification [51][52][53][54] and prediction [55,56].…”

Section: Measurement Systemmentioning

confidence: 99%

“…Measurement data processing is a significant research problem [41,42]. Therefore, many methods are used in the diagnosis, monitoring, and control of the combustion process, among which can be highlighted the Fourier transform [43], wavelet analysis [44], or recurrent neural networks [45][46][47][48][49][50]. Diagnostics of the combustion process can also use classification [51][52][53][54] and prediction [55,56].…”

Section: Introductionmentioning

confidence: 99%

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Application of Fuzzy Neural Networks in Combustion Process Diagnostics

Grądz,

Wójcik,

Gromaszek

et al. 2023

Energies

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Coal remains one of the key raw materials used in the energy industry to generate electricity and heat. As a result, diagnostics of the combustion process is still an important topic of scientific research. Correct implementation of the process allows the emission of pollutants into the atmosphere to be kept at a compliant level. Therefore, it is important to conduct the process in a manner that will not exceed these standards. A preliminary analysis of the measurement signals was carried out, and signal predictions of flame intensity changes were determined using the autoregressive moving average (ARMA) model. Different fuzzy neural network architectures have been investigated. Binary and multi-class classifications of flame states were conducted. The best results were obtained from the ANFIS_grid partition model, producing an accuracy of 95.46% for binary classification and 79.08% for multi-class classification. The accuracy of the recognition of flame states and the high convergence of the determined predictions with measurement signals validate the application of the proposed approach in diagnosing or controlling the combustion process of pulverized coal and its mixtures with biomass. Expert decisions determine the range of acceptable states.

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Section: Measurement Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy Neural Networks in Combustion Process Diagnostics

Grądz,

Wójcik,

Gromaszek

et al. 2023

Energies

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“…There are more than a million species which are manually classified and described under the animal kingdom. For a newly described species, the manual classification technique proves to be a tedious process in assigning a methodical place in the animal kingdom [5,6]. Despite the dissimilarities in formation and shape for diversified animals, there are some essential features universal to a variety of each and every species relative to the arrangement of cells, body regularity, and type of coelom, model of digestive, excretory, circulatory and reproductive systems.…”

Section: Current Strategy For Phylum Identification Of Various Smentioning

confidence: 99%

Identification and Classification of Animal Kingdom using Image Processing and Artificial Neural Networks

Sujatha*¹,

Srividhya²,

Aruna³

2019

IJRTE

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The biological kingdom ‘Animalia’ is composed of multi cellular eukaryotic organisms. Most of the animal species exhibit bilateral symmetry. The hierarchy of biological classification has eight taxonomy ranks. The top position in the hierarchy is occupied by the ‘domain’ and ending with the lowest position occupied by ‘species’. The classification of animal kingdom includes, Porifera, Coelenterata, Platyhelminthes, Aschelminthes, Annelida, Arthropoda, Mollusca, Echinodermata and Chordata. Manual identification of Phylum or class for each and every species, is very tedious, because there exists nearly a millions of species categorized under various classes. Hence an automated system is proposed to be developed using image segmentation and Artificial Neural Networks (ANN) trained with Back Propagation Algorithm (BPA) which is capable of assisting the scientists and researchers for class identification. This system will be useful in Museums and Archeological departments, where a huge variety of species are maintained. The classification efficiency of the proposed system is 89.1%.

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Furnace Flame Tracking for Combustion Quality Estimation in Coal Fired Boilers

Sujatha

Sinthia

Babu

et al. 2021

IOP Conf. Ser.: Mater. Sci. Eng.

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This research work focus is on optimization of the combustion quality as the combustion quality plays a vital role in minimizing NOx and CO emissions in the flue gas. Power generated is also another important factor under consideration so as to meet the demand. Flame monitoring in boilers using digital imaging provides replacement for the conventional technique of temperature measurement. The indigenous method can be replaced by incorporating an online PC based system by analyzing the flame images.

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Soft sensor with shape descriptors for flame quality prediction based on LSTM regression

Cited by 3 publications

References 8 publications

Application of Fuzzy Neural Networks in Combustion Process Diagnostics

Application of Fuzzy Neural Networks in Combustion Process Diagnostics

Identification and Classification of Animal Kingdom using Image Processing and Artificial Neural Networks

Furnace Flame Tracking for Combustion Quality Estimation in Coal Fired Boilers

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