A globally enhanced general regression neural network for on-line multiple emissions prediction of utility boiler

Song, Jingge; Romero, Carlos E.; Yao, Zheng; He, Boshu

doi:10.1016/j.knosys.2016.11.003

Cited by 49 publications

(9 citation statements)

References 29 publications

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“…A deep understanding of a power plant is needed to control the amount of mercury emissions [61][62][63]. Therefore, an accurate estimation of emissions is of the utmost importance to control and reduce mercury emissions [64]. Numerous investigations have been published in the literature regarding the application of artificial intelligence approaches [65][66][67][68][69].…”

Section: Literature Reviewmentioning

confidence: 99%

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

et al. 2019

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Accurate prediction of mercury content emitted from fossil-fueled power stations is of the utmost importance for environmental pollution assessment and hazard mitigation. In this paper, mercury content in the output gas of power stations’ boilers was predicted using an adaptive neuro-fuzzy inference system (ANFIS) method integrated with particle swarm optimization (PSO). The input parameters of the model included coal characteristics and the operational parameters of the boilers. The dataset was collected from 82 sample points in power plants and employed to educate and examine the proposed model. To evaluate the performance of the proposed hybrid model of the ANFIS-PSO, the statistical meter of MARE% was implemented, which resulted in 0.003266 and 0.013272 for training and testing, respectively. Furthermore, relative errors between the acquired data and predicted values were between −0.25% and 0.1%, which confirm the accuracy of the model to deal non-linearity and represent the dependency of flue gas mercury content into the specifications of coal and the boiler type.

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Section: Literature Reviewmentioning

confidence: 99%

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

et al. 2019

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“…A deep understanding of the power plant needed in order to control the amounts of mercury emissions. Therefore, an accurate estimation of emission is vital for engineers who want to control and reduce mercury emission (Song et al, 2017). The method of ANFIS proposed by Jang (Jang, 1991;Roger, 1993) and is a versatile and intelligent hybrid system.…”

Section: Theory Of Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Developing an ANFIS-PSO Based Model to Estimate Mercury Emission in Combustion Flue Gases

Shamshirband¹,

Hadipoor²,

Baghban³

et al. 2019

Preprint

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Mercury content in the output gas from boilers was predicted using an Adaptive Neuro-Fuzzy Inference System (ANFIS). Input parameters were selected from coal characteristics and the operational configuration of boilers. The ANFIS approach is capable of developing a nonlinear model to represent the dependency of flue gas mercury content into the specifications of coal and also the boiler type. In this study, operational information from 82 power plants has been gathered and employed to educate and examine the proposed ANFIS model. Resulted values from the model were compared to the collected data and it indicates that the model possesses an extraordinary level of precision with a correlation coefficient of unity. The MARE% for training and testing parts were 0.003266 and 0.013272, respectively. Furthermore, relative errors between acquired data and predicted values were between -0.25% and 0.1% which confirm the accuracy of PSO-ANFIS model.

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“…The literature reports a fair amount of works dealing with the estimation of pollutants emissions based on data-based models [ 17 , 18 , 19 ] or first principles models [ 20 ]. Unfortunately, any model-based approach depends on the intrinsic error of the model, availability of field sensors to record a referential data set, and the presence of hidden data outliers given by sensor failures or decalibration [ 21 ].…”

Section: Related Workmentioning

confidence: 99%

Analysis of Soot Propensity in Combustion Processes Using Optical Sensors and Video Magnification

Garcés

Fuentes

Reszka

et al. 2018

Sensors

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Industrial combustion processes are an important source of particulate matter, causing significant pollution problems that affect human health, and are a major contributor to global warming. The most common method for analyzing the soot emission propensity in flames is the Smoke Point Height (SPH) analysis, which relates the fuel flow rate to a critical flame height at which soot particles begin to leave the reactive zone through the tip of the flame. The SPH and is marked by morphological changes on the flame tip. SPH analysis is normally done through flame observations with the naked eye, leading to high bias. Other techniques are more accurate, but are not practical to implement in industrial settings, such as the Line Of Sight Attenuation (LOSA), which obtains soot volume fractions within the flame from the attenuation of a laser beam. We propose the use of Video Magnification techniques to detect the flame morphological changes and thus determine the SPH minimizing observation bias. We have applied for the first time Eulerian Video Magnification (EVM) and Phase-based Video Magnification (PVM) on an ethylene laminar diffusion flame. The results were compared with LOSA measurements, and indicate that EVM is the most accurate method for SPH determination.

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A globally enhanced general regression neural network for on-line multiple emissions prediction of utility boiler

Cited by 49 publications

References 29 publications

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases

Developing an ANFIS-PSO Based Model to Estimate Mercury Emission in Combustion Flue Gases

Analysis of Soot Propensity in Combustion Processes Using Optical Sensors and Video Magnification

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