A novel explainable modeling method for cleaned coal quality evaluation in jigged fluidized bed

References

• Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Chelgani, S. C., H. Nasiri, and M. Alidokht. 2021. Interpretable modeling of metallurgical responses for an industrial coal column flotation circuit by XGBoost and SHAP-A “conscious-lab” development. International Journal of Mining Science and Technology 31 (6):1135–44. doi:10.1016/j.ijmst.2021.10.006.
   Web of Science ®Google Scholar
• Chen, T., T. He, and M. Benesty. 2015. Xgboost: Extreme gradient boosting. R Package Version 1:1–4.
   Google Scholar
• Cierpisz, S. 2017. A dynamic model of coal products discharge in a jig. Minerals Engineering 105:1–6. doi:10.1016/j.mineng.2016.12.010.
   Web of Science ®Google Scholar
• Cierpisz, S., M. Kryca, and W. Sobierajski. 2016. Control of coal separation in a jig using a radiometric meter. Minerals Engineering 95:59–65. doi:10.1016/j.mineng.2016.06.014.
   Web of Science ®Google Scholar
• Gerling, A., H. Ziekow, A. Hess, U. Schreier, C. Seiffer, and D. O. Abdeslam. 2022. Comparison of algorithms for error prediction in manufacturing with automl and a cost-based metric. Journal of Intelligent Manufacturing 33 (2):555–73. doi:10.1007/s10845-021-01890-0.
   Web of Science ®Google Scholar
• Hou, W. 2019. Identification of coal and gangue by feed-forward neural network based on data analysis. International Journal of Coal Preparation and Utilization 39 (1):33–43. doi:10.1080/19392699.2017.1290609.
   Web of Science ®Google Scholar
• Kliegr, T., Š. Bahník, and J. Fürnkranz. 2021. A review of possible effects of cognitive biases on interpretation of rule-based machine learning models. Artificial Intelligence 295:103458. doi:10.1016/j.artint.2021.103458.
   Web of Science ®Google Scholar
• Kumar, S., and R. Venugopal. 2017. Performance analysis of jig for coal cleaning using 3D response surface methodology. International Journal of Mining Science and Technology 27 (2):333–37. doi:10.1016/j.ijmst.2017.01.002.
   Web of Science ®Google Scholar
• Lee, M. H. 2022. Identifying correlation between the open-circuit voltage and the frontier orbital energies of non-fullerene organic solar cells based on interpretable machine-learning approaches. Solar Energy 234:360–67. doi:10.1016/j.solener.2022.02.010.
   Web of Science ®Google Scholar
• Liu, C., S. Gong, and C. C. Loy. 2014. On-the-fly feature importance mining for person re-identification. Pattern Recognition 47 (4):1602–15. doi:10.1016/j.patcog.2013.11.001.
   Web of Science ®Google Scholar
• Lu, T. T., X. P. Ma, and L. Chen. 2013. Simulation of PID control of jig discharging system optimized by genetic algorithm. Gongkuang Zidonghua-Industry and Mine Automation 39 (1):67–70.
   Google Scholar
• Lundberg, S. M., and S. I. Lee. 2017. A unified approach to interpreting model predictions. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. Vol. 1. 30.
   Google Scholar
• Mackay, M., L. Giroux, and M. Holuszko. 2021. Preparation of clean coal samples using the Roben Jig. Fuel 306:121729. Wang, F., He, F., & Li, Y. (2020). Research on and Application of On-line Cleaning of Stainless Steel Tube Condense. IOP Conference Series: Earth and Environmental Science, 526. doi:10.1016/j.fuel.2021.121729.
   Web of Science ®Google Scholar
• Paul, S. R., and S. Bhattacharya. 2021. Performance analysis of coal cleaning operations: role of probable error in separation and organic efficiency. International Journal of Coal Preparation and Utilization 43 (1):1–13. doi:10.1080/19392699.2021.2000969.
   Web of Science ®Google Scholar
• Phengsaart, T., M. Ito, N. Hamaya, C. B. Tabelin, and N. Hiroyoshi. 2018. Improvement of jig efficiency by shape separation, and a novel method to estimate the separation efficiency of metal wires in crushed electronic wastes using bending behavior and “entanglement factor”. Minerals Engineering 129:54–62. doi:10.1016/j.mineng.2018.09.015.
   Web of Science ®Google Scholar
• Rao, B. V. 2021. An improved stochastic model to describe partition surfaces of entire segregated batch jig bed. Minerals Engineering 170:107064. doi:10.1016/j.mineng.2021.107064.
   Web of Science ®Google Scholar
• Shang, D., Z. Yang, J. Wang, Y. Wang, and Y. Liu. 2020. Innovative design and fuzzy logic control for an underground moving sieve jig. Mathematics 8 (12):2151. doi:10.3390/math8122151.
   Web of Science ®Google Scholar
• Shrikumar, A., P. Greenside, A. Shcherbina, and A. Kundaje. 2016. Not just a black box: Learning important features through propagating activation differences. Proceedings of the 33 rd International Conference on Machine Learning, New York, NY, USA, 2016. Vol. 48. 1–6.
   Google Scholar
• Siregar, I., Y. Niu, P. Mostaghimi, and R. T. Armstrong. 2017. Coal ash content estimation using fuzzy curves and ensemble neural networks for well log analysis. International Journal of Coal Geology 181:11–22. doi:10.1016/j.coal.2017.08.003.
   Web of Science ®Google Scholar
• Štrumbelj, E., and I. Kononenko. 2014. Explaining prediction models and individual predictions with feature contributions. Knowledge and Information Systems 41 (3):647–65. doi:10.1007/s10115-013-0679-x.
   Web of Science ®Google Scholar
• Wang, Y., W. Li, R. Fang, H. Zhu, and Q. Peng. 2022. Capacities prediction and correlation analysis for lithium-ion battery-based energy storage system. Control Engineering Practice 125:105224. doi:10.1016/j.conengprac.2022.105224.
   Web of Science ®Google Scholar
• Wen, Z., C. Zhou, J. Pan, T. Nie, R. Jia, and F. Yang. 2021. Froth image feature engineering-based prediction method for concentrate ash content of coal flotation. Minerals Engineering 170:107023. doi:10.1016/j.mineng.2021.107023.
   Web of Science ®Google Scholar
• Yin, X., Z. He, Z. Niu, and Z. S. Li. 2018. A hybrid intelligent optimization approach to improving quality for serial multistage and multi-response coal preparation production systems. Journal of Manufacturing Systems 47:199–216. doi:10.1016/j.jmsy.2018.05.006.
   Web of Science ®Google Scholar
• Zhang, D., L. Qian, B. Mao, C. Huang, B. Huang, and Y. Si. 2018. A data-driven design for fault detection of wind turbines using random forests and XGboost. Institute of Electrical and Electronics EngineersAccess 6:21020–31. doi:10.1109/ACCESS.2018.2818678.
   Google Scholar