Determining the composition of binary coal blends using Bayes theorem

Binary coal blends were prepared using a typical UK steam coal with four different coals which were then analyzed using random vitrinite reflectance (Rrandom). 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.