The impact of secondary air boundary conditions on CFD results in small-scale wood pellet combustion

Zadravec, Tomas; Rajh, Boštjan; Kokalj, Filip; Samec, Niko

doi:10.1016/j.fuel.2022.124451

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…Based on CFD simulations results, the relationships between the temperature distribution and operating parameters are analyzed. Accordingly, the influence of different parameters on the combustion process can be analyzed, including the boiler load ( Xu, Azevedo & Carvalho, 2001 ; Laubscher & Rousseau, 2019 ), burner arrangement and tilt angle ( Choi et al, 2020 ; Tan et al, 2017 ), separated over-fire air (SOFA) ratio and SOFA location ( Ma et al, 2015 ), yaw and tilt angles ( Jin et al, 2021 ), distribution modes [11], secondary air boundary conditions ( Zadravec, Rajh & Kokalj, 2022 ), and air staging combustion ( Zhang et al, 2015 ; Wang & Zhou, 2020 ). With the development of new sources of energy and deep peak-load regulations, it is necessary to modify conventional thermal power units to work under ultra-low loads.…”

Section: Introductionmentioning

confidence: 99%

Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine

Zhao

Cao

et al. 2023

PeerJ Computer Science

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In this article, a data-driven model based on the incremental deep extreme learning machine (IDELM) algorithm is proposed to predict the temperature distribution in the furnace. To this end, computational fluid dynamics (CFD) simulations are carried out first to get temperature distributions under typical working conditions. Based on the air distribution mode, the simulation results are divided into six subclasses. Then the K-means clustering method is applied to find out the benchmark working condition of each subclass. Moreover, the random sampling method is used to extract samples to reduce computational complexity. Modeling inputs are selected according to the CFD boundary conditions and combustion mechanisms, and data sets are reconstructed based on the increments of each actual working condition from the benchmark working condition. Finally, an IDBN-based prediction model is built in each subclass. The experimental results show that the IDBN-based model has a promising predictive ability with less than 11% symmetric mean absolute percentage error.

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Section: Introductionmentioning

confidence: 99%

Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine

Zhao

Cao

et al. 2023

PeerJ Computer Science

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“…Three types of green energy are derived from biomass, such as biofuel, biodiesels, and indurate wood pellets made from renewable lignocellulosic raw material derived from Agro-forest biomass [14]. Biomass is a renewable energy resource that, when combined with other renewable energy sources, can eventually help us achieve our goal of being a carbon-neutral society by replacing fossil fuels [15]. Resources of biomass from agricultural, Forestry, and municipal waste are comprised of various components, including woods, agricultural leftovers, sawdust, straw, manure, paper trash, household wastes, and wastewater [16].…”

Section: Introductionmentioning

confidence: 99%

Environmental and Socio-Economic Assessment of Biomass Pellets Biofuel in Hazara Division, Pakistan

et al. 2023

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A thorough life cycle assessment (LCA) was conducted to determine whether wood pellets were a viable substitute for non-renewable fuels like oil and gas, especially for heating. To evaluate the properties of wood pellets and their effects on the environment, the study was conducted in the Hazara division of Khyber Pakhtunkhwa, Pakistan. A few factors were investigated, including the carbon and water footprints and the identification of potential growth opportunities in the production of wood pellets. One kilogram of wood pellets served as the analysis reference unit. Raw materials were obtained from sawmills and furniture stores to make the wood pellets. Sawdust, a bio binder, and lubricating oil were used in the production process along with the pelletizer machine. SimaPro 9.2 software was used in the environmental footprint assessment to evaluate several environmental effects, including eutrophication, ozone depletion, abiotic depletion, rusting, human toxicity, and aquatic ecotoxicity. The highest contribution was shown by the wood pellets produced from the softwood sawdust as 149.8558 in marine aquatic ecotoxicity. The study’s findings showed that using lubricating oil during the production of wood pellets significantly affected the overall environmental results. The characterization of wood pellets showed that the Higher heating Values (HHV) resulted from burning wood pellets made from sawdust of Melia azedarach as 24.79 MJ/kg. Softwood mixed species recorded the highest water footprint and damage assessment impact and the highest carbon footprint of 0.186 CO2 e. With a 3.84 × 10−7 DALY (disability-adjusted life years) measurement, softwood mixed species showed the highest contribution to human health damage among the damage categories. In terms of cost, producing one kilogram of wood pellets from softwood mixed species was priced at 22 PKR, the lowest among the assessed species. The highest cost of 26 PKR was associated with producing wood pellets from Parthenium hysterophorus and Diospyros lotus.

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“…Kayu Gelam, or Melaleuca cajuputi is a species that grows naturally in swamp forests and is found abundantly in peat swamp forests of South Kalimantan [8]. Gelam wood has high specific gravity and has the potential to be used as raw material for charcoal and wood pellets [9]. The demand for wood pellets from year to year always increases, driven by policies to reduce greenhouse gas emissions and increase the use of renewable energy [10].…”

Section: Introductionmentioning

confidence: 99%

Effect of Variation of Mixture (Wood Gelam+Rice Husk) on Bio-Pellet on the Value of Temperature, Rate and Pressure of Combustion

Subagyo¹

2022

Civense

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The increasing demand for energy causes the depletion of fuel fossil. To overcome this, it is necessary to utilize biomass and biomass waste. The purpose of this study was to simulate the effect of bio-pellet density on temperature, rate and pressure of combustion made from a mixture of gelam wood and rice husk. The method uses ANSYS simulation with literature review. The results showed that the increasing composition of rice husks had an effect on the decreasing combustion rate, this was due to the calorific value of the pellets making up the material. Where the lower calorific value of rice husk has an effect on the rate of combustion. Pellets that have a large density affect the rate of combustion so that it extends the burning time. The density of pellets is influenced by the pressure of pellet molding, the greater the pressure makes the fuel denser and has a large density. The highest combustion air pressure occurs at 100% gelam composition and the lowest at 100% husk composition. This shows that the addition of rice husk composition reduces the combustion pressure and vice versa applies to the addition of gelam composition

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The impact of secondary air boundary conditions on CFD results in small-scale wood pellet combustion

Cited by 6 publications

References 26 publications

Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine

Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine

Environmental and Socio-Economic Assessment of Biomass Pellets Biofuel in Hazara Division, Pakistan

Effect of Variation of Mixture (Wood Gelam+Rice Husk) on Bio-Pellet on the Value of Temperature, Rate and Pressure of Combustion

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