Coal/biomass co-combustion investigation by thermogravimetric analysis

Bampenrat, Akarasingh; Sukkathanyawat, Hussanai; Seangwattana, Thidaporn

doi:10.1051/e3sconf/202130201002

Cited by 7 publications

(4 citation statements)

References 23 publications

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“…In our study, a Thermo-gravimetric Analyzer (TGA -Q600) computer system was utilized to assess the proximate analysis geo-components on five (5) coal material collected having a variable hardness property, as part of the chemical analysis. For sample comparison, all principal coal properties linked to coal fracture and coal comminution behavior were central to the (TGA) chemical composition results, the coal material work index and the Hardgrove Grindability Value(s) for each coal material collected (see Table 1) [38], [39], [40], [41], [42].…”

Section: Proximate Analysismentioning

confidence: 99%

Coal Sampe Preparation Strategy using the TENCAN GQM series Ball Milling device.

Gaesenngwe,

RAGHUPATRUNI,

MAMVURA

et al. 2024

Preprint

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The application of Semi Autogenous Grinding strategy in mineral beneficiation engineering is well defined and established unit operation that is qualified for liberation of valuable final products from various rock ores of various kinds. The TENCAN GQM series Ball Mill equipment was deployed throughout our investigation to learn on optimum setting factors that are crucial in achieving effective product quality outcome during coal beneficiation. Therefore, a Semi-autogenous grinding methodology was deployed for grinding five (5) different coal samples into a product stream ranging in particle size from below [−600μm, +38μm] through an optimized procedure that syndicate three(3) control parameters being the {A − powder filling (fc), B − ball loading (JB) and C − residence time or grinding duration (t)}. Moreover, to analyse the grinding kinetics during interaction to produce coal mass recovery at desirable particle size specification relevant for specific manufacturing industry. However, initially the coal sample material was acquired and characterized via three spectroscopy techniques {x – ray diffraction (XRD), x – ray fluorescence (XRF), scanning electron microscopy (SEM)}, to establish contrast of each sample material in composition, morphology, hardness, and the relative abundance of occurring species entrenched within the coal structure (inorganic matter). Hence using the Proximate testing, Ultimate testing, and petrography analysis the examination was possible for each sample type collected. The coal samples that were initially collected were assorted in particle sizes classification and were grouped as run-of-mine coals (−200mm), Cobbles (−75mm, +40mm), Nuts (−40mm, +25mm), Peas (−32mm, +14mm) and fines (−14mm) according to our laboratory sieve specifications. However, the acquired coal species were initially subjugated to a hardness testing using the Hardgrove grindability Index device that reported a variable coal grindability index value with material hardness increased from Nuts (60.37), Cobbles (64.58), ROM (66.84), Fines (66.99) to Peas (67.37) which are relatively soft compared to other coal samples. Process engineers, researchers and students in different organizations studying metallurgy and coal beneficiation etc., must be informed about factors affecting coal material preparation innovations and the health-safety risk encounter by the mineral engineer personal during handling and processing. Moreover, production efficacy is significantly improved through adopting optimized model functions that accurately increase the product recovery at lower power rating for the cumulative production path hence relevantly reducing the cost(s) of exacerbated by tedious and unnecessary methods.

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Section: Proximate Analysismentioning

confidence: 99%

Coal Sampe Preparation Strategy using the TENCAN GQM series Ball Milling device.

Gaesenngwe,

RAGHUPATRUNI,

MAMVURA

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…For sample comparison, all principal coal properties linked to coal fracture and coal comminution behavior were central to the (TGA) chemical composition results, the coal material work index and the Hardgrove Grindability Value(s) for each coal material collected (see Table 1) [38], [39], [40],…”

Section: Proximate Analysismentioning

confidence: 99%

Coal Sample Preparation Strategy using the TENCAN GQM series Ball Milling device.

Gaesenngwe,

RAGHUPATRUNI,

MAMVURA

et al. 2024

Preprint

View full text Add to dashboard Cite

The application of Semi Autogenous Grinding strategy in mineral beneficiation engineering is well defined and established unit design method that is qualified for liberation of valuable of quality products from numerous rock ores of metals, coal seam, kimberlite ores etc. The TENCAN GQM series Ball Mill equipment was deployed throughout our investigation to investigate on optimum setting factors that are crucial and influential in achieving effective product quality outcome during coal beneficiation by milling operation. Therefore, a Semi-autogenous milling strategy was taken advantage of for the grinding activity that involved five (5) different coal samples and the optimum product stream in the particle size range between [−600μm, +38μm] was researched by an optimized procedure that syndicate three(3) control parameters being the {A − powder filling (fc), B − ball loading (JB) and C − residence time or grinding duration (t)}. Moreover, to understand the grinding kinetics and particle dynamics during interaction that yield a coal mass stream at desirable particle size relevant for target manufacturing companies is of profound interest in our study. Therefore, initially the acquired sample material was gently prepared using splitter equipment, sieve plates and the coal composition was analysed via Thermogravimetric machine and the Proximate results were acquired. To establish contrast amongst the samples, in compositional and coal formational morphology, hardness, and the relative abundance of occurring species were deployed as key factors that influenced the comminution patterns obtained from the trials. Samples that were initially collected assorted in particle sizes classification in a fully heterogenous format grouped as run-of-mine coals (−200mm), Cobbles (−75mm, +40mm), Nuts (−40mm, +25mm), Peas (−32mm, +14mm) and fines (−14mm), scaling via the laboratory sieve standard specifications. However, the acquired coal species were initially subjugated to a hardness testing using the Hardgrove grindability Index device that reported a variable coal grindability index value with material hardness increased from Nuts (60.37), Cobbles (64.58), ROM (66.84), Fines (66.99) to Peas (67.37) which are soft compared to other coal samples. Process engineers, researchers and students in different organizations studying metallurgy and coal beneficiation etc., must be informed about factors affecting coal material preparation innovations and the health-safety risk encounter by the mineral engineer personal during handling and processing. Moreover, production efficacy is significantly improved through adopting optimized model functions that accurately increase the product recovery at lower power rating for the cumulative production path hence relevantly reducing the cost(s) exacerbated by tedious and unnecessary design approaches.

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“…These biomass varieties are locally available and are commonly used in households and the industrial sector to fulfill their energy requirements. The biomass samples include fast-growing trees, i.e., Alnus nepalensis (11), Pinux roxiburghii (14), Bombusa vulagris (13), Bombax ceiba (11), and Eucalyptus camaldulensis (12), as well as agricultural residues, i.e., Zea mays (cob) (13), Zea mays (shell) (11), Zea mays (stover) (11), Oryza sativa (10), and Saccharum officinarun (9). A total of 115 samples were collected for this experiment.…”

Section: Sample Preparationmentioning

confidence: 99%

“…TGA is typically employed to determine combustion characteristic parameters for evaluating different combustion performance indices [12]. Biomass combustion in TGA mainly consists of three stages: (i) water evaporation, (ii) volatile release and its combustion, and (iii) char combustion [13][14][15]. TGA logs the mass loss of biomass as a function of time and temperature.…”

Section: Introductionmentioning

confidence: 99%

Near-Infrared Spectroscopy Modeling of Combustion Characteristics in Chip and Ground Biomass from Fast-Growing Trees and Agricultural Residue

Shrestha,

Posom,

Pornchaloempong

et al. 2024

Energies

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This study focuses on the investigation and comparison of combustion characteristic parameters and combustion performance indices between fast-growing trees and agricultural residues as biomass sources. The investigation is conducted through direct combustion in an air environment using a thermogravimetric analyzer (TGA). Additionally, partial least squares regression (PLSR)-based models were developed to assess combustion performance indices via near-infrared spectroscopy (NIRS), serving as a non-destructive alternative method. The results obtained through the TGA reveal that, specifically, fast-growing trees display higher average ignition temperature (227 °C) and burnout temperature (521 °C) in comparison to agricultural residues, which exhibit the values of 218 °C and 515 °C, respectively. Therefore, fast-growing trees are comparatively difficult to ignite, but sustain combustion over extended periods, yielding higher temperatures. However, despite fast-growing trees having a high ignition index (Di) and burnout index (Df), the comprehensive combustion performance (Si) and flammability index (Ci) of agricultural residue are higher, indicating the latter possess enhanced thermal and combustion reactivity, coupled with improved combustion stability. Five distinct PLSR-based models were developed using 115 biomass samples for both chip and ground forms, spanning the wavenumber range of 3595–12,489 cm−1. The optimal model was selected by evaluating the coefficients of determination in the prediction set (R2P), root mean square error of prediction (RMSEP), and RPD values. The results suggest that the proposed model for Df, obtained through GA-PLSR using the first derivative (D1), and Si, achieved through full-PLSR with MSC, both in ground biomass, is usable for most applications, including research. The model yielded, respectively, an R2P, RMSEP, and RPD, which are 0.8426, 0.4968 wt.% min⁻4, and 2.5; and 0.8808, 0.1566 wt.%2 min⁻2 °C⁻3, and 3.1. The remaining models (Di in chip and ground, Df, and Si in chip, and Ci in chip and ground biomass) are primarily applicable only for rough screening purposes. However, including more representative samples and exploring a more suitable machine learning algorithm are essential for updating the model to achieve a better nondestructive assessment of biomass combustion behavior.

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Coal/biomass co-combustion investigation by thermogravimetric analysis

Cited by 7 publications

References 23 publications

Coal Sampe Preparation Strategy using the TENCAN GQM series Ball Milling device.

Coal Sampe Preparation Strategy using the TENCAN GQM series Ball Milling device.

Coal Sample Preparation Strategy using the TENCAN GQM series Ball Milling device.

Near-Infrared Spectroscopy Modeling of Combustion Characteristics in Chip and Ground Biomass from Fast-Growing Trees and Agricultural Residue

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