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A molecular-level kinetic model was developed for the gasification of common plastics, including polyethylene (PE), polyethylene terephthalate (PET), polyvinyl chloride (PVC), and polystyrene (PS). Model development was divided into three steps: molecular characterization of the feed, generation of a pathway-level reaction network, and creation of the material balance differential equations (DEs). The structure of all polymers was modeled as linear with known repeat units. For PE, PVC, and PET, Flory−Stockmayer statistics were used to describe the initial polymer size distribution. PS was described using a twoparameter γ distribution. The parameters of all polymer size distributions were tuned using data from the literature. The chemistry of plastic gasification contains depolymerization, pyrolysis, and gasification reactions. The initial depolymerization of PE and PET was modeled using random-scission and Flory−Stockmayer statistics. A statistical method was created extending random scission to a generalized polymer size distribution and applied here to the breakdown of PS. The depolymerization of PVC was modeled as two steps: polyene formation followed by benzene production. Pyrolysis reactions were included on small oligomers and were broken down into two categories: cracking and formation of tar and char molecules. For gasification, incomplete combustion and steam reforming were included to break down oligomers, tar, and char molecules. Also, light gas reactions, e.g., water-gas shift, were added to the network. The final network contained 283 reactions and 85 species. After construction of the material balance DEs, kinetic parameters were tuned using literature data on each plastic. These studies involved gasification, pyrolysis, and thermogravimetric analysis (TGA) experiments each probing different aspects of depolymerization, pyrolysis, and gasification kinetics. Model results matched experimental data well.

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Insights into the products obtained from the catalytic liquefaction of lignin were gained through the experimental hydroprocessing reactions of the model compounds 4-methylguaiacol, 4-methylcatechol, eugenol, vanillin, o ,o'biphenol, o -hydroxydiphenylmethane, and phenyl ether over a sulfided CoOMo03/y-A1,03 catalyst. Compounds with aromatic methoxyl groups (4methylguaiaco1, eugenol, vanillin) underwent prlmary demethylation as their major reaction. Hydroxyl groups were removed readily at temperatures well below those required for thermal dehydroxylation. Catalytic cleavage of the interaromatic unit linkages of o-hydroxydiphenylmethane and phenyl ether was facile, while o ,o'-biphenol was converted to single-ring products through a 2-phenylphenol intermediate: dibenzofuran was also a primary product from the reaction of o ,o'-biphenol. Product yields' temporal variations were used in the analysis of lignin liquefaction. This chemical modeling suggests that catalytic lignin liquefaction should permit the recovery of 10 wt % single-ring phenols and hydrocarbons at conditions where 2 wt % would be realized through thermal fragmentation alone.The liquefaction of different coals slurried in Koppers' creosote oil at short contact time (12 min) was studied at different reaction temperatures (300-450 "C). The influence of tetralln and hydrogen on short contact time (SCT) liquefaction ylelds and vehicle Incorporation was monitored. The data support the following concepts: (1) The conversion to tetrahydrofuran (THF) and pentane solubles of the reaction products increased with increasing temperature in all cases. (2) The presence of tetraiin or hydrogen enhanced the conversion to THF and pentane solubles; when both tetralln and hydrogen were present, the effects were additive for conversion to pentane solubles but not additive for conversion to THF solubles. (3) Both high temperature and the presence of a hydrogen source minimized vehicle incorporation into the coal and coal products.

A molecular-level kinetic model for biomass gasification was developed and tuned to experimental data from the literature. The development was divided into two categories: the composition of the feedstock and the construction of the reaction network. The composition model of biomass was divided into three submodels for cellulose, hemicellulose, and lignin. Cellulose and hemicellulose compositions were modeled as linear polymers using Flory–Stockmayer statistics to represent the polymer size distribution. The composition of lignin, a cross-linked polymer, was modeled using relative amounts of structural building blocks or attributes. When constructing the full biomass composition model, the fractions of cellulose, hemicellulose, and lignin were optimized using literature-reported ultimate analyses. The reaction network model for biomass contained pyrolysis, gasification, and light-gas reactions. For cellulose and hemicellulose, the initial depolymerization was described using Flory–Stockmayer statistics. The derived monomers from cellulose and hemicellulose were subjected to a full pyrolysis and gasification network. The pyrolysis reactions included both reactions to decrease the molecule size, such as thermal cracking, and char formation reactions, such as Diels–Alder addition. Gasification reactions included incomplete combustion and steam reforming. For lignin, reactions occurred between attributes and included both pyrolysis and gasification reactions. The light-gas reactions included water-gas shift, partial oxidation of methane, oxidation of carbon monoxide, steam reforming of methane, and dry reforming of methane. The final reaction network included 1356 reactions and 357 species. The performance of the kinetic model was examined using literature data that spanned six different biomass samples and had gas compositions as primary results. Three data sets from different biomass samples were used for parameter tuning, and parity plot results showed good agreement between the model and data (y predicted = y obs0.928 + 0.0003). The predictive ability of the model was probed using three additional data sets. Again, the parity plot showed agreement between the model and experimental results (y predicted = y obs0.989 – 0.007).