Biomass chemical looping gasification for syngas production using modified hematite as oxygen carriers

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 MDF residue in an updraft fixed bed gasifier to produce heat and power via an ORC turbine

Biomass, which is a renewable resource, is regarded as an essential energy source due to its accessibility and abundance. In this study, the gasification of wood-based biomass wastes from the medium density fiberboard (MDF) facility was carried out and investigated utilizing an updraft fixed bed gasifier. The feeding capacity of the upstream gasifier is 2100 kg/h. MDF wastes are loaded into the system with feeding capacities of 1500, 1750 and 2100 kg/h. As a reference, the system has also been tested with oak wood chips at a maximum rate of 2100 kg/h. Produced syngas production rate to biomass waste is approximately 2.5 Nm3/kg. The measured gas compositions are CO, CO2, CH4, H2, O2 and N2. Test results with 2100 kg/hMDF wastes have similar gas composition compared to the test results with oak wood chips. The quality of the syngas produced by gasification is directly related to the fuel. It has been observed that the efficiency of the gasification process can be directly or indirectly impacted by the properties of the fuel, such as the moisture content, chemical compositions, and size. The temperature of the produced gas is approximately 430 °C, and it isdirectly combusted with tars and soot it contains to ensure that no chemical energy is lost. The thermal gasification system converts approximately 88% by weight of MDF residue to syngas. The calorific value of produced syngas is obtained between 6.0 and 7.0 MJ/Nm3. The hot syngas containing tars produced from the gasifier was directly burned in the thermal oil heater retrofitted to vortex syngas burner to recover thermal energy, which was then utilized in the production of energy via an ORC turbine. The thermal oil heater has a thermal capacity of 7MWh and the power generation capacity of the ORC turbine is 955 kW of electricity.

In Situ Removal of Benzene as a Biomass Tar Model Compound Employing Hematite Oxygen Carrier

Tar is an unavoidable biomass gasification byproduct. Tar formation reduces gasification efficiency and limits the further application of biomass gasification technology. Hence, efficient tar removal is a major problem to be solved in the formation and application of biomass gasification technology. Chemical looping gasification (CLG), a novel and promising gasification technology has attracted extensive attention owing to its low tar generation. Active oxygen carriers (OCs), the reduced OC in CLG, are considered to be excellent catalysts for tar cracking. In this study, the use of benzene as a typical tar model compound for tar removal using the iron ore OC is investigated. In the blank experiment, where an inert material (SiO2) is used as the carrier, the benzene cracking is relatively low, and the benzene conversion, H2 yield, and carbon conversion are 53.65%, 6.33%, and 1.24%, respectively. The addition of hematite promotes benzene cracking. A large amount of oxygen-containing gases (CO and CO2) are generated. Additionally, the conversion degrees for benzene, H2 and carbon are about 67.75%, 21.55%, and 38.39%, respectively. These results indicate that hematite performs both oxidation and catalysis during benzene cracking. The extension of the residence time facilitates benzene removal, owing to the good interaction between the gas phase and solid phase. The addition of water vapor inhibits the benzene conversion and promotes the conversion of carbon deposition. The lattice oxygen reactivity of hematite OC shows an uptrend as the cycle number is increased during the benzene conversion cycle. The experimental results confirm that CLG has a low-tar advantage and that hematite is an effective OC for benzene removal.