Around 40% to 60% of municipal solid waste originates from kitchens, offering a valuable resource for compost production. Traditional composting methods such as windrow, vermi-, and bin composting are space-intensive and time-consuming. Black soldier fly larvae (BSFL) present a promising alternative, requiring less space and offering ease of handling. This research encompasses experimental data collection, life cycle assessment, and machine learning, and employs the Levenberg–Marquardt algorithm in an Artificial Neural Network, to optimize kitchen waste treatment using BSFL. Factors such as time, larval population, aeration frequency, waste composition, and container surface area were considered. Results showed that BSFL achieved significant waste reduction, ranging from 70% to 93% by weight and 65% to 85% by volume under optimal conditions. Key findings included a 15-day treatment duration, four times per day aeration frequency, 600 larvae per kilogram of waste, layering during feeding, and kitchen waste as the preferred feed. The larvae exhibited a weight gain of 2.2% to 6.5% during composting. Comparing the quality of BSFL compost to that obtained with conventional methods revealed its superiority in terms of waste reduction (50% to 73% more) and compost quality. Life cycle assessment confirmed the sustainability advantages of BSFL. Machine learning achieved high accuracy of prediction reaching 99.5%.

The advancement of plasma technology is intricately linked with the utilization of computational fluid dynamics (CFD) models, which play a pivotal role in the design and optimization of industrial-scale plasma reactors. This comprehensive compilation encapsulates the evolving landscape of plasma reactor design, encompassing fluid dynamics, chemical kinetics, heat transfer, and radiation energy. By employing diverse tools such as FLUENT, Python, MATLAB, and Abaqus, CFD techniques unravel the complexities of turbulence, multiphase flow, and species transport. The spectrum of plasma behavior equations, including ion and electron densities, electric fields, and recombination reactions, is presented in a holistic manner. The modeling of non-thermal plasma reactors, underpinned by precise mathematical formulations and computational strategies, is further empowered by the integration of machine learning algorithms for predictive modeling and optimization. From biomass gasification to intricate chemical reactions, this work underscores the versatile potential of plasma hybrid modeling in reshaping various industrial processes. Within the sphere of plasma catalysis, modeling and simulation methodologies have paved the way for transformative progress. Encompassing reactor configurations, kinetic pathways, hydrogen production, waste valorization, and beyond, this compilation offers a panoramic view of the multifaceted dimensions of plasma catalysis. Microkinetic modeling and catalyst design emerge as focal points for optimizing CO₂ conversion, while the intricate interplay between plasma and catalysts illuminates insights into ammonia synthesis, methane reforming, and hydrocarbon conversion. Leveraging neural networks and advanced modeling techniques enables predictive prowess in the optimization of plasma-catalytic processes. The integration of plasma and catalysts for diverse applications, from waste valorization to syngas production and direct CO₂/CH₄ conversion, exemplifies the wide-reaching potential of plasma catalysis in sustainable practices. Ultimately, this anthology underscores the transformative influence of modeling and simulation in shaping the forefront of plasma-catalytic processes, fostering innovation and sustainable applications.

Biomass is a renewable energy source with great potential worldwide and in the European Union. However, valorization is necessary to turn many types of waste biomass into a tradable commodity that has the potential to replace coal in power plants without significant modifications to firing systems. Mild pyrolysis, also known as torrefaction, is a thermal valorization process of low-quality biomass that could be suitable for such a purpose. In this work, typical Spruce-Pine-Fir residues from a sawmill were tested in terms of the explosion and flame propagation properties. The ISO 1 m³ dust explosion vessel was used, with a modified and calibrated dust dispersion system that could cope with very coarse particles. The deflagration index, K_st, was higher for the torrefied sample, with a peak at 36 bar m/s compared with 27 for the raw biomass. The peak flame speeds were similar for both samples, reaching 1 m/s. The peak P_max/P_i was between 7.3 and 7.4 bar for both untreated and torrefied biomass. The mechanism for coarse particle combustion is considered to be influenced by the explosion-induced wind blowing the finer fractions ahead of the flame, which burns first, subsequently devolatilizing the coarser fractions.

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.