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The availability of low-grade coal with a high amount of ash has urged the improvisation of separation equipment with minimal or no water utilization. The present work addresses the study on the separation equipment performance with different moisture coal. The experimental results were obtained in terms of separation efficiency. After obtaining the experimental results, the mathematical modeling results were obtained using different techniques. The cubic regression and cascade neural network models were considered to study the mathematical correlation with experimental results. The R-squared value of each mathematical modeling technique was correlated with the model fitting to check the model's validity. The results clearly showed that the cubic model fitting for the experimental condition had provided an excellent R-squared value varying from 92% to 99%. The cascade model fitting for the experimental condition has provided a higher R-squared value, i.e., more than 99%. Results show that for all experimental conditions, the cascade model fitting of the neural network technique provides the significant mathematical modeling technique suitable for predicting the separation equipment's performance compared to the cubic model of the regression technique. [ABSTRACT FROM AUTHOR]

In this paper, coal screening in vibrating screen was carried out with the size ranges of −6 mm + 4 mm, −4 mm + 2 mm, and −2 mm + 0.5 mm. The vibrating screen was newly designed with flexibility in angle and frequency. The vibrating screen experimentation was carried out by varying screen mesh, angle, and screen frequency. During the screening, the angle was kept constant, and frequency was varied to obtain each size range's screening efficiency. The experimental results of screening efficiency were evaluated for each size fraction range of coal. The maximum efficiency for screening coal with −6 mm+4 mm, −4 mm+2 mm, and −2 mm+0.5 mm size range obtained was 87.60%, 80.93%, and 62.96%, respectively. Further, the prediction model was developed for each size range using a feed-backward artificial neural network (ANN) to consider the back-propagation error technique. For each screening condition, 10 ANN models were developed with the variation in 1–10 different neurons. ANN has provided mathematical models with a 99.9% regression coefficient for predicting each size range's screening efficiency. Furthermore, the residuals of each optimal ANN model were analyzed using a normal probability plot and histogram. The ANN model's accuracy was obtained from the residual analysis by evaluating four different model conditions, i.e., independence, homoscedasticity, normality, and mean error. [ABSTRACT FROM AUTHOR]

In India, coal is one of the prime sources of energy used in the power generation and metallurgy sector. The processing of coal below 3 mm is not successfully carried out in India. The quality of coal below 3 mm can be improved by decreasing the coal's particle size, which reduces the ash percentage of coal. Screening is one of the significant beneficiation techniques used to reduce the size fraction of coal. The difficult to process coal of size −3 + 1 mm was selected in the present work. In this work, an attempt has been made to screen the coal of size −2 + 1 mm from −3 + 1 mm using a 2 mm screen mesh in the vibrating screen generated at different moisture content, angle, and frequency of the deck. The performance of the vibrating screen was evaluated using screening efficiency. Furthermore, prediction using a feed backward artificial neural network (ANN) model was developed on the experimental results for ten different neuron conditions. From the results, it was clear that the prediction results obtained from the ANN model were in good correlation with the experimental results. [ABSTRACT FROM AUTHOR]

• Effect of different M: S ratios in magnetic roller design have studied using FEMM. • Prediction analysis of FEMM data using ANN modelling technique. • Fabrication of optimized magnetic roller and development of new lab-scale PRMS. • Design calculations of newly developed/fabricated lab-scale PRMS. • Separation tests using fabricated PRMS indicates the better Fe grade and recovery %. In the present work, an attempt has been made to develop a PRMS in a cost effective and environmental friendly way through FEMM analysis of magnetic roller (active part of PRMS). The FEMM analysis indicates that, the optimized magnetic roller having magnet-to-steel disk thickness ratio of 5 mm: 2.5 mm was proved to be gainful in beneficiating paramagnetic minerals due to the best magnetic field value from the roller surface that is, 0.89 to 2.59 T. Prediction analysis was performed on FEMM data using artificial neural network (ANN) modelling technique. Further, the design calculations of lab scale PRMS in terms of power requirements and belt tensions were addressed. The fabricated PRMS was tested on paramagnetic mineral (hematite ore) assayed 51.24% of Fe, 10.20% of SiO 2 , and 2.98% of Al 2 O 3 for different roller speeds and the belt thickness. The result showed that, at 0.5 mm belt thickness with 180 rpm roller speed the fabricated lab scale PRMS works well in terms of improvement in the Fe content up to 59.5% at the concentrate along with the Fe recovery of 71.41%. The obtained results suggest that, the FEMM analysis is more suitable to optimize the effective magnetic roller for the PRMS. [ABSTRACT FROM AUTHOR]