Statistical Analysis of Organosulfur Extraction Data of Ohio 5/6 Coal Using the Perchloroethylene Coal Cleaning Process
The perchloroethylene coal cleaning process removes both organic and pyritic forms of sulfur using perchloroethylene as the solvent medium. The effect of process variables including temperature, extraction time, solvent to coal ratio and particle size of coal has been studied by a systematic 24 full factorial experimental design with a single replicate. The process was found to be strongly dependent on the type of coal. Hence, this variable was controlled by choosing one single type of coal, i.e., Ohio 5/6 (1:1 mixture of Ohio 5 and Ohio 6 coals) throughout this entire investigation. The significant effects and interactions have been quantified by F-tests. The estimates of significant effects have been obtained by Yates algorithm. Residual probability and normal probability plots have been obtained to test model adequacy. Finally, a computational model has been developed to predict the organosulfur extraction efficiency of this coal at various values of process variables. The parity plots conclude that the model has a good interpolational predictive capability.
P. Vishnubhatt et al., "Statistical Analysis of Organosulfur Extraction Data of Ohio 5/6 Coal Using the Perchloroethylene Coal Cleaning Process," Fuel Science and Technology International, Taylor & Francis, Jan 1993.
The definitive version is available at https://doi.org/10.1080/08843759308916138
Chemical and Biochemical Engineering
Article - Journal
© 1993 Taylor & Francis, All rights reserved.