Chemical percolation model for devolatilization. 3. Direct use of carbon-13 NMR data to predict effects of coal type

~~ ~~ ~ ~ ~~ The chemical percolation devolatilization (CPD) model describes the devolatilization behavior of rapidly heated coal based on the chemical structure of the parent coal. Percolation lattice statistics are employed to describe the generation of tar precursors of finite size based on the number of cleaved labile bonds in the infinite coal lattice. The chemical percolation devolatization model described here includes treatment of vapopliquid equilibrium and a cross-linking mechanism. The cross-linking mechanism permits reattachment of metaplast to the infinite char matrix. A generalized vapor preasure correlation for high molecular weight hydrocarbons, such as coal tar, is proposed based on data from coal liquids. Coal-independent kinetic parameters are employed. Coal-dependent chemical structure coefficients for the CPD model are taken directly from 13C NMR measurements, with the exception of one empirical parameter representing the population of char bridges in the parent coal. This is in contrast to the previous and common practice of adjusting input coefficients to precisely match measured tar and total volatiles yields. The CPD model successfully predicts the effects of pressure on tar and total volatiles yields observed in heated grid experiments for both bituminous coal and for lignite. Predictions of the amount and characteristics of gas and tar from many different coals compare well with available data, which is unique because the majority of model input coefficients are taken directly from NMR data and are not used as empirical fitting coefficients. Predicted tar molecular weights are consistent with size-exclusion chromatography (SEC) data and field ionization mass spectrometry (FIMS) data. Predictions of average molecular weights of aromatic clusters as a function of coal type agree with corresponding data from NMR analyses of parent coals. The direct use of chemical structure data as a function of coal type helps justify the model on a mechanistic rather than an empirical basis.