The isosteric heats of adsorption of the components of a gas mixture are critical variables for design of adsorbers for gas separation. They can be unambiguously defined by the Gibbsian Surface Excess (GSE) model of multicomponent adsorption. These variables can be experimentally measured by multicomponent differential calorimetry (MDC) and directly used to describe nonisothermal behavior of practical adsorbers. There is no need to make simplified assumptions about the nature and size of the adsorbed phase, as required by conventional adsorption thermodynamic models, to define the isosteric heats. Pure gas isosteric heats of adsorption of N and CO on a pelletized silicalite sample were measured using a MDC and a data analysis algorithm based on the GSE model. The silicalite sample behaved like a homogeneous adsorbent for weakly polar N adsorption. The presence of polar alumina binder in the silicalite sample introduced significant heterogeneity for more polar CO adsorption.

The isotope exchange technique (IET) was used to measure equilibria and kinetics for adsorption of pure N2, CH4, and Kr on a 4A zeolite sample. The intracrystalline self-diffusivities for these gases were measured under truly isothermal conditions. The self-diffusivities of Kr and CH4 were smaller than that of N2 by 2 and 1 order of magnitudes, respectively. The self-diffusivity of N2 was independent of its surface coverage but the self-diffusivities of CH4 and Kr increased with increasing coverages in the high-pressure region. This effect was much more pronounced for Kr. The activation energies for self-diffusion of the gases increased in the order Kr > CH4 > N2 while the isosteric heats of adsorption of these gases in the Henry's law region were very close. The activation energies were larger than the corresponding isosteric heats of adsorption for each gas.

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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.

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).