On-line prediction of clean coal ash content based on image analysis

Qiu, Zhaoyu; Dou, Dongyang; Zhou, Deyang; Yang, Jianguo

doi:10.1016/j.measurement.2020.108663

Cited by 34 publications

(11 citation statements)

References 23 publications

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“…Most of the acquired samples having a high energy capacitance (CVs) were connected to comprising higher carbonaceous maceral constituents holding high moisture content and lower ash yield. Because of contributions towards coal volatility resulting from hydrocarbons, moisture and higher grindability index (HGI) the coal wash plant products reported progressively high (CVs) [55], [56], [57], [54].…”

Section: Resultsmentioning

confidence: 99%

Coal Sample 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 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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Section: Resultsmentioning

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

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Section: Discussionmentioning

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

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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“…Considering the prediction accuracy and the generalization ability of the model, the penalty coefficient C was set to 30 to optimize the SVR model. The optimized SVR model and LR model (Dou et al, 2019a;Qiu et al, 2020) were used to predict the quality index of the carbonized material, and the effectiveness of the models was tested through the evaluation indexes MAE, MSE, and R-Square. The results are shown in Table 5 and the prediction effect of the optimized models on the quality-index data of the carbonized material is shown in Figs.…”

Section: Optimized Model and Prediction Effectmentioning

confidence: 99%

Modeling and optimization of activated carbon carbonization process based on support vector machine

Liu¹,

Zhang²,

Dou³

et al. 2021

Physicochem. Probl. Miner. Process.

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Product prediction and process parameter optimization in the production process of activated carbon are very important for production. It can stabilize product quality and improve the economic efficiency of enterprises. In this paper, three process parameters of a carbonization furnace, namely feeding rate, rotation speed, and carbonization temperature, were adopted to build a quality optimization model for carbonized materials. First, an orthogonal test was designed to obtain the preliminary relationship between the process parameters and the quality indicators of a carbonized material and prepare data for modeling. Then, an improved SVR model was developed to establish the relationship between product quality indicators and process parameters. Finally, through the singlefactor experiments and the Monte Carlo method, the process parameters affecting the quality of a carbonized material were determined and optimized. This showed that a high-quality carbonized material could be obtained with a smaller feeding rate, larger rotation speed, and higher carbonization furnace temperature. The quality of activated carbon reached its maximum when the feeding rate was 1.0 t/h, the rotation speed was 90 r/h, and the temperature was 836°C. It can effectively improve the quality of carbonized materials.

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On-line prediction of clean coal ash content based on image analysis

Cited by 34 publications

References 23 publications

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

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

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

Modeling and optimization of activated carbon carbonization process based on support vector machine

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