The depletion of fossil-based fuels, fluctuating fuel market, and environmental deterioration demand an aggressive approach towards the advancement of renewable energy technologies. By the time reliable technology for a clean and abundant energy supply is established, existing sources must be economized. Biomass gasification is the way forward in that direction. CFD modeling shows promise in the development of advanced gasification systems. A simplified 3D CFD model of a downdraft gasifier is developed to investigate the effect of gasifying agent composition on the quality of syngas. Simulation results are compared with published experimental data and found to be in reasonably good agreement. Mixing CO₂ with a gasification agent is also investigated as a possible carbon capture and utilization (CCU) strategy. An air-steam mixture is used as a base-case gasification agent. Firstly, the effect of air-to-steam ratio on syngas composition is investigated. Secondly, the effect of oxygen and mixing CO₂ with a gasification agent is investigated in two separate cases. A 50%-50% air-steam mixture is found to produce the best quality syngas. Oxygen is found to have a negligible impact on the quality of syngas. The air-steam-CO₂ = 23%-50%-15% mixture is found to be optimum regarding syngas quality.

This study examines the sustainable decomposition reactions of benzene using non-thermal plasma (NTP) in a dielectric barrier discharge (DBD) reactor. The aim is to investigate the factors influencing benzene decomposition process, including input power, concentration, and residence time, through kinetic modeling, reactor performance assessment, and machine learning techniques. To further enhance the understanding and modeling of the decomposition process, the researchers determine the apparent decomposition rate constant, which is incorporated into a kinetic model using a novel theoretical plug flow reactor analogy model. The resulting reactor model is simulated using the ODE45 solver in MATLAB, with advanced machine learning algorithms and performance metrics such as RMSE, MSE, and MAE employed to improve accuracy. The analysis reveals that higher input discharge power and longer residence time result in increased tar analogue compound (TAC) decomposition. The results indicate that higher input discharge power leads to a significant improvement in the TAC decomposition rate, reaching 82.9%. The machine learning model achieved very good agreement with the experiments, showing a decomposition rate of 83.01%. The model flagged potential hotspots at 15% and 25% of the reactor’s length, which is important in terms of engineering design of scaled-up reactors.

The textile industry, being a significant consumer of fresh water, generates large amounts of wastewater, leading to serious environmental problems. The high levels of pollutants present in textile wastewater pose a significant risk to the environment and disrupt the ecosystem. As a result, it is essential to implement effective treatment steps to mitigate these harmful effects. This research focuses on the performance of a novel catalyst, namely microcrystalline cellulose zeolitic imidazolate frameworks (MCC@ZIF-67), in a heterogeneous catalytic ozonation process (HCOP) for the treatment of real textile effluent. The textile wastewater was characterized by a chemical oxygen demand (COD) of 490 mg/L, a 5 days biochemical oxygen demand (BOD₅) of 206 mg/L, a total dissolved solids (TDS) concentration of 3500 mg/L, and intense red color. The study investigated the influence of several parameters like ozone dose, pH, catalyst dosage, and treatment time. The experimental results showed a 79.01 % decolourization, 72.40 % COD removal, and 66.51 % BOD₅ removal after 30 min of treatment at optimum conditions of pH 9, ozone concentration 0.4 mg/min, and MCC@ZIF 67 dose of 50 mg/L. The comparison with control tests revealed the beneficial synergistic effects of HCOP with a process efficiency in the order of HCOP(O₃/MCC@ZIF-67) > O₃ > MCC@ZIF-67. Scavenging tests allowed to better support the explanation of possible degradation mechanisms. This study demonstrates the potential of MCC@ZIF-67 in the HCOP process for the effective treatment of textile effluent and, depending on the characteristics of textile wastewater and standards for effluent disposal, may serve either as standalone process, a pre-treatment for subsequent biological process or tertiary treatment.