Statistical studies on high ash Indian coal crushed to (−3 mm) using 76 mm dense medium cyclone

Sahu, Dejalin; Chaurasia, Ram Chandra; Suresh, N.

doi:10.1080/19392699.2019.1605987

Cited by 11 publications

(4 citation statements)

References 46 publications

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“…The use of an ANN and RSM to model HCs was already seen in the work of Sahu et al [19] They studied the treatment of high ash Indian coals and observed that ANN presented an improved correlation compared to the RSM. Other works investigating the use of both RSM and ANN to model problems can be found in the literature.…”

Section: Introductionmentioning

confidence: 91%

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Comparison between response surface methodology and artificial neural network: Application in three‐product hydrocyclones

et al. 2022

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Modelling a process or equipment is a profitable strategy to build better control strategies, predict fault conditions, and optimize the processes. Different approaches could be explored to achieve the development of better models. This paper investigates the use of experimental data generated by a central composite rotatable design (CCRD) to develop models capable of predicting the performance of a three‐product hydrocyclone for several setups with different dimensional parameters values. Two different modelling strategies are explored: response surface methodology (RSM) and artificial neural networks (ANN). With the RSM models, it was possible to evaluate the statistical importance of the input variables to each output variable. The ANN models showed improved coefficients of determinations (R2) compared to the RSM models, presenting values higher than 97% for all cases, while the RSM models ranged from 79.07%–88.83%. The ANN was demonstrated to be the most effective method to model the physical problem of three‐product hydrocyclones, and it captured its non‐linearities. It was shown that the combination of the design of experiments and ANN to analyze this physical problem is successful and may also be applied to other problems. As far as we have knowledge, a work regarding the comparison of both RSM and ANN methods applied to three‐product hydrocyclones was not found in the literature; this absence was the motivation for this work.

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Section: Introductionmentioning

confidence: 91%

“…As p-value < 0.05 in the ANOVA, the null hypothesis is rejected, which means that at least one coefficient is significantly different from the others. The regression equation of the OUOW Rec is presented in Equation (19).…”

Section: Ouow Recmentioning

confidence: 99%

Comparison between response surface methodology and artificial neural network: Application in three‐product hydrocyclones

et al. 2022

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“…MGS was applied for the treatment of ultrafine coal as well as tailing pond deposits [ 276 , 277 ]. Statical optimization using response surface methodology based on Box-Behnken design (RSM-BBD) was used to analyze the effects of input parameters (i.e., drum inclination, drum rotation speed, and shaking frequency) on the output parameters (i.e., %yield, %ash, and %recovery) [ [277] , [278] , [279] ]. Similarly, empirical equations to estimate as a coal recovery were derived by Özgen et al (2011) using a least square method via Minitab15 [ 274 ].…”

Section: Enhanced Gravity Separation Technologiesmentioning

confidence: 99%

Conventional and recent advances in gravity separation technologies for coal cleaning: A systematic and critical review

Phengsaart¹,

Srichonphaisan²,

Kertbundit³

et al. 2023

Heliyon

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“…The relative importance of mixing parameters was further analyzed using two different methods such as Olden's and Garson's algorithms [8][9]. These methods were used to evaluate the influence of multiple significant variables based on the weighting matrixes of input-hidden and output-hidden obtained from the BP-ANN model.…”

Section: Relative Importance Of Parametersmentioning

confidence: 99%

Artificial Neural Network Model for Predicting the Energy Loss of Natural Rubber Foam

Kaewpradit

Uthaipan

Dechwayukul

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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In this work, a back-propagation artificial neural network model was optimally developed based on 25 experimental datasets for predicting the energy loss percentage of natural rubber foam. The foam specimens were prepared in a Banbury internal mixer at various conditions of mixing temperature (40-80°C), rotor speed (40-80 rpm), and mastication time (1-5 min). Stress-strain loops were analyzed by applying compressive force at aspeed test of 500 mm/min with the capacity of load cell 2.5 kN, the energy loss was further calculated. In model development, the experimental datasets were randomly divided into 70:15:15 for training, validation, and testing respectively. Levenberg-Marquardt algorithm was used as a training function was used because of its fast convergence. The prediction results revealed that the average prediction accuracy of the three models is higher than 90%. From a material design point of view, the developed model could be implemented to find the proper mixing conditions to obtain the material with the maximum energy dissipation.

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Statistical studies on high ash Indian coal crushed to (−3 mm) using 76 mm dense medium cyclone

Cited by 11 publications

References 46 publications

Comparison between response surface methodology and artificial neural network: Application in three‐product hydrocyclones

Comparison between response surface methodology and artificial neural network: Application in three‐product hydrocyclones

Conventional and recent advances in gravity separation technologies for coal cleaning: A systematic and critical review

Artificial Neural Network Model for Predicting the Energy Loss of Natural Rubber Foam

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