@article{scholars17934,
            year = {2022},
         journal = {International Journal of Coal Preparation and Utilization},
       publisher = {Routledge},
           pages = {1143--1169},
          volume = {42},
            note = {cited By 2},
          number = {4},
             doi = {10.1080/19392699.2019.1694009},
           title = {Grindability and abrasive behavior of coal blends: analysis and prediction},
            issn = {19392699},
          author = {Idris, A. and Man, Z. and Bustam, A. and Rabat, N. E. and Uddin, F. and Abdul Mannan, H.},
        abstract = {Low-grade coals are blended with high-quality coals to meet economic, environmental, and quality specifications. Hence, the grindability and abrasiveness of coal blends are crucial economic and operational parameters. This work evaluates, analyzes, and predicts the grindability and abrasive behavior of coal blends. Three binary coal blends with common low-grade coal were first prepared at various ratios. Blends 1 and 2 were composed of identical and similar ranks, whereas Blend 3 was composed of different ranks. The blends were analyzed using proximate, ultimate analyzers, and a Bomb calorimeter. The grindability and abrasive behavior of the blends were measured using Hardgrove grindability index (HGI) and Yancey, Geer, and Price methods, respectively. Further, the coarser (+75{\^I}1/4m) and finer ({\^a}??75{\^I}1/4m) fractions of HGI experiment were characterized using proximate, ultimate and heating value analyses. The additivity of HGI values was observed for Blend 1 and Blend 2, whereas, the non-additive behavior was observed in Blend 3. Further, the blends{\^a}?? mineral matter contents and abrasiveness index were found to be additive. Several existing models were found to be inaccurate for HGI predictions. Therefore, a new cross-validated model using multi-linear regression was proposed. The model exhibited better HGI predictions of coal blends with a coefficient of determination R2{\^A} ={\^A} 0.9416. {\^A}{\copyright} 2019 Taylor \& Francis Group, LLC.},
        keywords = {Additives; Calorimeters; Forecasting; Predictive analytics, Abrasiveness index; Coal blends; Hardgrove grindability indices; Low-grade coal; Predictive modeling, Coal},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075333637&doi=10.1080\%2f19392699.2019.1694009&partnerID=40&md5=b9c9bda9ad45cbe2b24a80e6c02badda}
}