Experimental microwave-assisted air gasification of biomass in fluidized bed reactor

A particle-scale study showing microwave energy can effectively decarbonize process heat in fluidization industry

Microwave heating converts electromagnetic energy directly into thermal energy within the heated material, thereby overcoming the limitations of traditional indirect heat transfer methods. However, microwaves are well-known to have limited penetration depth, which remains a significant challenge that inhibits the use of microwaves in processes requiring uniform heating. Here, we show that fluidized beds of particles with sufficient electrical conductivity break the limitations imposed by microwave penetration depth, enabling uniform heating in large-scale reactors. Results suggest that the alternating magnetic field penetrates the entire studied reactor to induce eddy currents everywhere, causing each particle to be heated. The power absorption density for Geldart A and B particles across the bed is uniform, with no evidence of exponential attenuation, introducing unexpected penetration depth under the magnetic field component. Utilizing microwave energy, sourced by clean electricity, to heat fluidized beds offers a transformative solution to decarbonize industry, significantly reducing greenhouse gas emissions.

Investigation of biomass gasification and fluidization behaviour for pilot double tapered bubbling fluidized bed reactor

The present investigation discusses syngas production through gasification from six different bamboo biomass species available in India's northeast region (Mizoram). The thermochemical conversion of biomass to syngas is accomplished in a novel double-tapered reactor. The pre-processing stage of biomasses includes various shredding, drying, and sieving methods. In the processing stage, the biomass (20 g) is fed to the reactor regularly. The heating element in the interior of the reactor supplies the heat required (600-1000 °C) for the combustion and gasification of biomass samples. The influence of various particle sizes on bed voidage, pressure drop, bed height, suspension density, gas yield, heat transfer, and carbon conversion efficiency is studied. The tapered reactor enhanced the bed hydrodynamics. It is observed that the H2 and CO2 composition increases with the temperature (> 900 °C), whereas the CO composition is reduced as a result of the shift reaction. However, the CH4 yield is enhanced at a temperature (< 800 °C) but lessened at higher temperatures due to a reduction in moisture content. The carbon conversion efficiency (CCE) and the dry gas yield (YG) values obtained are better for the particle size of 1.18 mm than the other particle sizes. The energy from biomass resources is promising regarding sustainability, availability, and efficacy. The current approach discusses the syngas generation from bamboo biomass through gasification under varied temperature ranges (600-1000 °C), equivalence ratio (0.2), particle sizes (380 µm, 600 µm, and 1.18 mm), superficial velocity (0-2 m/s) in a pilot 5 kg/h DTBFBR of height 1.2 m and maximum inner diameter of 16.5 cm.

Essential aspects of the CFD software modelling of biomass gasification processes in downdraft reactors

Mathematical modelling and simulation of gasification processes are increasingly used in the scientific field. This review explores the application of computational fluid dynamics (CFD) in modeling biomass gasification processes in downdraft gasifiers. It discusses the different types of gasification agents used, the composition of syngas, and key operational parameters influencing the process. The review then delves into the aspects of CFD modeling, focusing on the implementation of sub-models within ANSYS fluent software. The limitations of the existing literature are addressed, and strategies for enhancing downdraft gasifier performance are proposed to facilitate successful commercialization.

Sulfur release behavior and sulfur fixation mechanism during biomass microwave co-pyrolysis of Ascophyllum and rice straw

Co-pyrolysis with low-sulfur biomass is expected to improve the yield and quality of bio-fuels, without the usage of calcium-based desulfurizer. Sulfur transformation during microwave fluidized-bed co-pyrolysis between terrestrial and marine biomass (Ascophyllum, AS; Rice straw, RS) was investigated. Sulfur release was promoted during biomass co-pyrolysis, but it was inhibited during pyrolysis between AS and low-sulfur char. Thermal cracking of biomass was promoted during co-pyrolysis between biomass, accelerating the combination of H atoms and -SH radicals. Introduction of low-sulfur bio-char (CA) inhibited the generation of bio-char and the release of sulfur. Released sulfur was captured by -OH/C = C functional groups on bio-char through dehydration reactions/addition reactions, forming mercaptan in bio-char. Furthermore, introduction of microwave and bio-char promoted the cyclization and aromatization reaction, converting mercaptan to thiophene and improving the thermal stability of solid sulfur, and thus increasing in-situ sulfur fixation rate.

Artificial intelligence methods for modeling gasification of waste biomass: a review

Gasification is a highly promising thermochemical process that shows considerable potential for the efficient conversion of waste biomass into syngas. The assessment of the feasibility and comparative advantages of different biomass and waste gasification schemes is contingent upon a multifaceted combination of interrelated criteria. Conventional analytical approaches employed to facilitate decision-making rely on a multitude of inadequately defined parameters. Consequently, substantial efforts have been directed toward enhancing the efficiency and productivity of thermochemical conversion processes. In recent times, artificial intelligence (AI)-based models and algorithms have gained prominence, serving as indispensable tools for expediting these processes and formulating strategies to address the growing demand for energy. Notably, machine learning (ML) and deep learning (DL) have emerged as cutting-edge AI models, demonstrating exceptional effectiveness and profound relevance in the realm of thermochemical conversion systems. This study provides an overview of the machine learning (ML) and deep learning (DL) approaches utilized during gasification and evaluates their benefits and drawbacks. Many industries and applications related to energy conversion systems use AI algorithms. Predicting the output of conversion systems and subjects linked to optimization are two of this science's critical applications. This review sheds light on the burgeoning utility of AI, particularly ML and DL, which have garnered significant attention due to their applications in productivity prediction, process optimization, real-time process monitoring, and control. Furthermore, the integration of hybrid models has become commonplace, primarily owing to their demonstrated success in modeling and optimization tasks. Importantly, the adoption of these algorithms significantly enhances the model's capability to tackle intricate challenges, as DL methodologies have evolved to offer heightened accuracy and reduced susceptibility to errors. Within the scope of this study, an exhaustive exploration of ML and DL techniques and their applications has been conducted, uncovering existing research knowledge gaps. Based on a comprehensive critical analysis, this review offers recommendations for future research directions, accentuating the pivotal findings and conclusions derived from the study.

Gasification of biomass for syngas production: Research update and stoichiometry diagram presentation

Gasification is a thermal process that converts organic materials into syngas, bio-oil, and solid residues. This mini-review provides an update on current research on producing high-quality syngas from biomass via gasification. Specifically, the review highlights the effective valorization of feedstocks, the development of novel catalysts for reforming reactions, the configuration of novel integrated gasification processes with an assisted field, and the proposal of advanced modeling tools, including the use of machine learning strategies for process design and optimization. The review also includes examples of using a stoichiometry diagram to describe biomass gasification. The research efforts in this area are constantly evolving, and this review provides an up-to-date overview of the most recent advances and prospects for future research. The proposed advancements in gasification technology have the potential to significantly contribute to sustainable energy production and reduce greenhouse gas emissions.