Hybrid System for fouling control in biomass boilers

Romeo, Luis M.; Gareta, Raquel

doi:10.1016/j.engappai.2006.01.019

Cited by 29 publications

(10 citation statements)

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“…A previous work of Teruel et al [28] was the first detailed publication about neural network models to predict fouling, in which this method was applied to a 350 MW e utility boiler furnace. ANN together with expert systems have been proposed for the optimized control of fouling in the superheater and convective passes of a 50 MW e biomass boiler [29]. Fouling and slagging in these areas show much less randomness than the furnace, and it is not measured locally but using indirect techniques based on thermal balance models.…”

Section: Introductionmentioning

confidence: 99%

Soft-computing models for soot-blowing optimization in coal-fired utility boilers

Peña

Teruel

Díez

2011

Applied Soft Computing

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Section: Introductionmentioning

confidence: 99%

Soft-computing models for soot-blowing optimization in coal-fired utility boilers

Peña

Teruel

Díez

2011

Applied Soft Computing

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“…Chlorine (38% by weight) in biomass, largely found in the corrosion deposits which is affected by various condition includes: temperature and concentrations of other components as shown in Figure 2 [25,[27][28][29]. From the literature, It has been found that Cl is the major cause of the issues concerned with the corrosion occur at metallic parts of combustors temperature ranging from 450˚C to 900˚C [29][30][31][32].…”

Section: Deposits and Corrosionmentioning

confidence: 99%

Fluidized Bed Combustion: Technology for Efficient Utilization of Biomass Residues

Sharma

2018

AJEAT

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Fossil fuels are the most common and reliable energy source, which presently fulfill 80% energy requirements all across the world. In the last few decades, over-consumption, fast pace modernization and population growth are some prominent factors which are exploiting the fossil fuels. The degradation of natural resources has gone up at an alarming rate which provoked to look for an alternate source of energy. From all available alternative renewable energy sources, biomass is the only carbon-based sustainable option. But, its diversity makes it a complex and difficult fuel. Among all technologies used for energy generation from the biomass, fluidized bed combustion is emerging as a suitable best option to handle fuel diversity. This article deals with biomass fluidization and its combustion in a fluidized bed. The difficulties encountered during biomass combustion and different solutions for the same have been highlighted. Problems like deposition, corrosion, agglomeration and trace metal emission have been discussed and their remedies to avoid the discontinuity in the operation of biomass-fired fluidized bed combustor. This technical approach will help to reduce environmental problems, improve the economic structure of the nation, and remove obstacles for sustainable energy development.

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“…Some of the well-known methods in this area are auto-regression, Markov chain, or robust optimization techniques [7][8][9]. Among the empirical methods, machine learning has been widely used to solve real world problems [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. Artificial neural networks (ANNs) are well-known machine learning systems that have been utilized to predict the solar radiation [2][3][4][29][30][31][32][33][34][35][36][37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%