Muhammad Aamir Bashir

Gasification technologies have the potential to turn carbonaceous feedstock, such as coal, biomass, or waste plastic, into syngas and other chemical building blocks. However, real-world implementation of gasification faces a variety of challenges related to the feedstock variability. One specific challenge noted by industry stakeholders is the need for research into chemical reaction kinetics for pyrolysis and gasification to better understand these varied feedstocks. As part of the National Laboratory Gasification Consortium (NLGC), this work uses both experimental and machine learning approaches to develop a database of chemical kinetics for pure and blended feedstocks by deriving kinetics for previously uncharacterized samples as well as incorporating existing reaction schemes from literature. Experimental investigations were carried out on Texas lignite coal, yellow pine, and plastic waste using thermogravimetric analysis (TGA) and a drop tube reactor equipped with an online mass spectrometer (MS) at different conditions (heating rates, temperatures and residence times). Gas and tar yields and product distributions were quantified for each feedstock identified with aliphatic, aromatic and oxygenated hydrocarbon compounds up to C44 using gas chromatography (GC)-MS. Solid-state intrinsic kinetics were derived using iso-conversional and modeling fitting methods for pure and blended feedstocks. Kinetic rate constants were determined using multi-step lumped kinetic model based on product yield and distributions. Using experimental data, a chemical reaction neural network was trained on the particle mass loss curves and the overall product yields. The neural network was tailored to predict a chemical reaction scheme complete with stoichiometry and kinetic parameters based on assumed first order reaction kinetics. These ML-based schemes provide simplified reaction mechanisms that can easily be incorporated into process models or computational fluid dynamics (CFD) reactor models. Some pure feedstocks, such as coal, lignocellulosic biomass, and high-density polyethylene (HDPE), have detailed reaction kinetics that provide important levels of product speciation but are too cumbersome to incorporate into CFD. To address this, a neural network was trained on an existing reaction scheme to serve as a drop-in replacement for conventional chemistry ODE solvers in CFD simulations. Incorporating neural networks in place of ODE solvers significantly reduced computational time when using detailed reaction schemes. Together, the experimental models and ML-tools provide a valuable resource for modeling gasification across a broad range of feedstocks.

The scalable production of high-quality nanoparticles is a significant challenge for advancing nanotechnology applications. This research introduces a continuous-flow liquid-plasma discharge reactor for the synthesis of silver nanoparticles at room temperature and atmospheric pressure, utilizing D-xylose as a dual-function reducing and stabilizing agent. The reactor effectively generated uniform xylose-capped silver nanoparticles (X-Ag NPs). Optimal conditions were established utilizing argon gas at a 1:100 molar ratio of Ag precursor to D-xylose, resulting in spherical X-Ag NPs with an average size of 16.89 nm, a zeta potential of −38.87 mV, and a polydispersity index of 0.22. The formation and properties of X-Ag NPs were confirmed through characterization techniques including UV-Vis spectroscopy, dynamic light scattering (DLS), Fourier-transform infrared spectroscopy (FT-IR), and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM-EDS). The findings demonstrate that uniform particle nucleation and growth occurred due to the homogeneous distribution of high-energy electrons and reactive gas species produced in the plasma phase. This environmentally sustainable, continuous-flow method shows considerable promise for the industrial-scale production of biomass-derived silver nanoparticles.

A comprehensive review of low-carbon hydrogen production via plastic waste gasification.