Determining the composition of binary coal blends using Bayes theorem

• Published in: Fuel (Guildford) Vol. 82; no. 2; pp. 117 - 125
• Main Authors: Lester, Edward, Watts, David, Cloke, Mike, Langston, Paul
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 2003; Elsevier
• Subjects: Applied sciences; Bayes theorem; Coal and derived products; Coal blends; Coal petrography; Energy; Exact sciences and technology; Fuels; p.f; Structure, chemical and physical properties; Vitrinite reflectance; United Kingdom; Europe; Coal blends; p.f; Vitrinite reflectance; Coal petrography; Bayes theorem; Composition; Iterative method; Vitrinite; Algorithm; Mixture; Coal; Bayes methods; Reflectance
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/S0016-2361(02)00223-5

Binary coal blends were prepared using a typical UK steam coal with four different coals which were then analyzed using random vitrinite reflectance ( R random). Deconvolution of the vitrinite reflectance data was attempted using Bayes Theorem in order to calculate the composition of each blend on a % vol/vol basis. Modifications were made to the initial Bayes algorithm to take into account experimental error. The effect of using increasing amounts of data on the blend predictions was also investigated. Accurate predictions were achieved when using more than 100 reflectance measurements from each component and iterating the Bayes algorithm more than 100 times.