Prediction of the NO  emissions from thermal power plant using long-short term memory neural network

Yang, Guohui; Wang, Yingnan; Li, Xinli

doi:10.1016/j.energy.2019.116597

Cited by 149 publications

(84 citation statements)

References 17 publications

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“…The deep neural network used in the experiment is mainly composed of an LSTM layer and a fully connected layer, and the test data are obtained from the continuous data surface generated [11]. The location data with a latitude and longitude of (36.604 N, 97.486 W) from September 2014 to August 2015 are selected as the experimental data by the model, which is the location of Lamont, the TCCON observation site.…”

Section: Data Processing Algorithmmentioning

confidence: 99%

Research on a Prediction Method for Carbon Dioxide Concentration Based on an Optimized LSTM Network of Spatio-Temporal Data Fusion

Meng

Ding

Liu

2021

IEICE Trans. Inf. & Syst.

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In view of the different spatial and temporal resolutions of observed multi-source heterogeneous carbon dioxide data and the uncertain quality of observations, a data fusion prediction model for observed multiscale carbon dioxide concentration data is studied. First, a wireless carbon sensor network is created, the gross error data in the original dataset are eliminated, and remaining valid data are combined with kriging method to generate a series of continuous surfaces for expressing specific features and providing unified spatio-temporally normalized data for subsequent prediction models. Then, the long short-term memory network is used to process these continuous time-and space-normalized data to obtain the carbon dioxide concentration prediction model at any scales. Finally, the experimental results illustrate that the proposed method with spatio-temporal features is more accurate than the single sensor monitoring method without spatio-temporal features.

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Section: Data Processing Algorithmmentioning

confidence: 99%

Research on a Prediction Method for Carbon Dioxide Concentration Based on an Optimized LSTM Network of Spatio-Temporal Data Fusion

Meng

Ding

Liu

2021

IEICE Trans. Inf. & Syst.

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“…The advantages of using LSTM in comparison with traditional model solutions are addressed in the work of Yang et al [34]. Zhang et al [35] dealt with the efficiency of LSTM and FFNN (feedforward neural networks), where LSTM was less accurate, but with a significantly longer prediction horizon.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Zhang et al [35] dealt with the efficiency of LSTM and FFNN (feedforward neural networks), where LSTM was less accurate, but with a significantly longer prediction horizon. In the same field, Yang et al [34] proved that compared to vector modelling, LSTM showed a higher predictive accuracy, faster response time, and stronger generalization capability. In the case of renewable resources, Correa-Jullian et al [36] compared the predictive methods based on standard neural networks and LSTM.…”

Section: Literature Reviewmentioning

confidence: 99%

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

2020

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There is no doubt that the issue of making a good prediction about a company’s possible failure is very important, as well as complicated. A number of models have been created for this very purpose, of which one, the long short-term memory (LSTM) model, holds a unique position in that it generates very good results. The objective of this contribution is to create a methodology for the identification of a company failure (bankruptcy) using artificial neural networks (hereinafter referred to as “NN”) with at least one long short-term memory (LSTM) layer. A bankruptcy model was created using deep learning, for which at least one layer of LSTM was used for the construction of the NN. For the purposes of this contribution, Wolfram’s Mathematica 13 (Wolfram Research, Champaign, Illinois) software was used. The research results show that LSTM NN can be used as a tool for predicting company failure. The objective of the contribution was achieved, since the model of a NN was developed, which is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be applied to small, medium-sized and manufacturing companies alike, as well as used by financial institutions, investors, or auditors as an alternative for evaluating the financial health of companies in a given field. The model is flexible and can therefore be trained according to a different dataset or environment.

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“…Compared to theoretical studies, there are more industrial ones using ML algorithms. This section will introduce application of ML in industrial studies related to combustion, including power plant, [41][42][43][44][45] engine, [46][47][48][49][50][51][52][53][54] fuel [55][56][57][58] and other fields. [59][60][61][62][63][64] To reach the goal of efficient and clean combustion in electric generation, ML was used to classify coals, [41] monitor combustion process [42] and treat emissions [43][44][45] in power plants.…”

Section: Industrial Researchmentioning

confidence: 99%

Progress in the Application of Machine Learning in Combustion Studies

Zheng¹,

Lin²,

Yang³

et al. 2020

ES Energy Environ.

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Combustion is the main source of energy and environmental pollution. The objective of the combustion study is to improve combustion efficiency and to reduce pollution emissions. In the past decades, machine learning (ML), as a branch of artificial intelligence, has attracted increasing interests, especially in the combustion field. In the present work, the definition, current status and recent progress in the applications of ML on researches related to combustion are briefly reviewed. Combustion studies combined with ML can be divided into theoretical and industrial aspects. Studies of combustion theory include computational fluid dynamics (CFD) simulation, combustion phenomenon and fuel. ML is used to reduce the cost of CFD, including reducing the scale of combustion mechanism, saving the memory storage of the probability density function table and optimizing Large Eddy Simulation. ML helps in the research of combustion phenomena, such as detecting thermoacoustic combustion oscillation, portioning regimes of ignition and detonation, and reconstructing cellular surface of gaseous detonation. ML has been also applied to study physicochemical properties of fuels and to design the next generation fuels.In the industrial research with respect to combustion, ML is mainly applied to produce electricity and power by power plants or engines, and less to other fields. ML could figure out problems of combustion in various kinds of furnaces and postcombustion emissions in power plants. In addition, ML plays important roles in biodiesel engine, Homogenous Charge Compression Ignition (HCCI), and operation control or monitoring in the engines. Moreover, ML can also be applied to other industrial studies related to combustion, mainly to particulate matters. The methods of the mentioned studies are summarized in details and the potential applications of ML in combustion community are proposed.

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Prediction of the NO emissions from thermal power plant using long-short term memory neural network

Cited by 149 publications

References 17 publications

Research on a Prediction Method for Carbon Dioxide Concentration Based on an Optimized LSTM Network of Spatio-Temporal Data Fusion

Research on a Prediction Method for Carbon Dioxide Concentration Based on an Optimized LSTM Network of Spatio-Temporal Data Fusion

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

Progress in the Application of Machine Learning in Combustion Studies

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