Synthesis Gas Composition Prediction for Underground Coal Gasification Using a Thermochemical Equilibrium Modeling Approach

Synergistic blending of biomass, sewage sludge, and coal for enhanced bioenergy production: Exploring residue combinations and optimizing thermal conversion parameters

Creating renewable energy from lignocellulosic biomass is essential for a sustainable future. Due to their abundance and the possibility of producing cheap and clean energy, non-lignocellulosic wastes like sewage sludge from industrial and municipal wastes have drawn attention as a feasible alternative to fossil fuels. These abundant, cost-effective resources may help minimize the effects of climate change since they produce less pollution. Several drawbacks are associated with using sewage sludge in thermal conversion procedures. These issues encompass suboptimal energy yield, elevated ash levels in the final product, and subpar biomass quality. Using these scraps in conjunction with coal might enhance energy conversion processes. This study has revealed the necessity for further investigation into how various combinations of residues interact with each other, influencing synergistic effects and degradation processes. The study's underlying objective was to provide a centralized database on the synergistic effects of mixing biomass and sewage sludge for bioenergy production, coal and biomass, and coal and sewage sludge through thermochemical processes like combustion, pyrolysis, gasification, and hydrolysis with Aspen Plus. This study will assist in enhancing biofuels' output from sewage sludge, coal, and coal/biomass blends in thermal conversion by defining the operating parameters (temperature, heat, and residence duration) of pyrolysis and combustion, features, and chemical properties that may influence these processes.

The Prediction of Possibilities of CO Poisoning and Explosion during Syngas Leakage in the UCG Process

Underground coal gasification (UCG) is an industrial process that converts coal into product gas (syngas). This technology makes it possible to obtain energy without mining coal and post-processing. The UCG process is considered a safe technology with various advantages over conventional mining techniques. However, a small amount of potentially dangerous syngas may escape from the UCG cavity, and it is necessary to pay attention to the safety of the process in this respect. This article analyses the impact of a syngas leak from UCG on a possible CO poisoning and explosion hazard in the vulnerable zones. Measured data from experiments and proposed mathematical models were used for the analysis of the UCG process and simulation studies. It is mainly a mathematical model mixing of gases, which evaluated the danger of explosion and CO poisoning in sensitive zones. This model predicts the composition of the syngas mixture with air because this mixture can be dangerous at a certain concentration. Simulation studies focused mainly on CO poisoning in vulnerable areas based on the measured data from laboratory experiments. Simulation studies have shown that the critical value of CO poisoning can achieve with a certain composition of syngas and its escape into vulnerable areas. The results of the studies here indicate a possible hazard. However, by monitoring and control of UCG process, this risk can be minimized. Based on the obtained results, the control of the supply of fresh air to the endangered area during the monitoring of the syngas composition was proposed to prevent the occurrence of possible poisoning.

Regression Models Utilization to the Underground Temperature Determination at Coal Energy Conversion

The underground coal gasification represents a technology capable of obtaining synthetic coal gas from hard-reached coal deposits and coal beds with tectonic faults. This technology is also less expensive than conventional coal mining. The cavity is formed in the coal seam by converting coal to synthetic gas during the underground coal gasification process. The cavity growth rate and the gasification queue’s moving velocity are affected by controllable variables, i.e., the operation pressure, the gasification agent, and the laboratory coal seam geometry. These variables can be continuously measured by standard measuring devices and techniques as opposed to the underground temperature. This paper researches the possibility of the regression models utilization for temperature data prediction for this reason. Several regression models were proposed that were differed in their structures, i.e., the number and type of selected controllable variables as independent variables. The goal was to find such a regression model structure, where the underground temperature is predicted with the greatest possible accuracy. The regression model structures’ proposal was realized on data obtained from two laboratory measurements realized in the ex situ reactor. The obtained temperature data can be used for visualization of the cavity growth in the gasified coal seam.