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In order to realize low-carbon transformation in the energy industry and achieve the "double carbon" goal as scheduled, renewable energy represented by biomass has become increasingly attractive. Biomass gasification is a thermochemical treatment technology, which has seen rapid development in recent years. It is currently the most effective method for high-grade and efficient utilization of biomass energy. In this paper, the key units involved in the entrained flow gasification process, such as pulverization, transportation, and gasification were analyzed, and the feasibility of applying airflow bed technology to biomass gasification was demonstrated. Six typical agricultural and forestry biomasses were used as raw materials. Biomass entrained flow gasification simulation was carried out with the help of the Aspen Plus chemical process simulation software, and the coal and biomass co-gasification and biomass separate gasification processes were characterized. The carbon reduction effect of biomass gasification was analyzed. The results show that when a certain amount of biomass is added into coal, a good gasification performance is retained. With the increase of biomass blending ratio, the specific coal consumption gradually decreases, and the carbon reduction effect is apparent. Compared with traditional fixed bed and fluidized bed gasification, biomass entrained flow gasification has an advantageous calorific value of synthetic gas about 10 MJ/m³. It can be used as a surrogate for natural gas heating, which has economic value in achieving carbon reduction. This study is in line with the dual-carbon policy and also provides a reference for the efficient conversion and utilization of biomass. [ABSTRACT FROM AUTHOR]

Because of the insufficient stability and high time cost of single data-driven models, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN), Genetic Algorithm-Support Vector Machine (GA-SVM) and Extreme Learning Machine (ELM), a fusion modeling method of information entropy Stacking is proposed to establish linear fusion models of information entropy. Actual production data, the gasifier load, feed pressure and flow rate, and cooling water flow rate were used as inputs, while the gasifier outlet temperature, syngas flow temperature and rate, and syngas composition at the outlet of the washing tower were used as outputs. An information entropy Stacking fusion model of the gasifier was established. The Mean Relative Errors (MRE) of the predictions of the information entropy Stacking fusion model on the gasifier outlet temperature, syngas outlet temperature and flow rate, syngas CO content, and H² content were determined to be 1.89.%, 0.17%, 0.78%, 0.95%, and 0.71%, respectively. These results are more stable than those obtained by the single data driven model. The fitting speed was about 19% higher than that of the linear information entropy fusion model. Combined with the optimization algorithm, our model can be applied to the online optimization of operating conditions such as oxygen coal ratio in the gasification process, the gasification temperature of the gasifier, and the effective gas yield during the process. [ABSTRACT FROM AUTHOR]

Catalytic pyrolysis of low-rank coal is currently an effective method for producing high-quality tar and gas. In this study, catalytic upgrading of volatiles from Naomaohu (NMH) coal pyrolysis has been conducted in a two-stage fixed-bed reactor using combined CaO and Ni/olivine (Ni-loaded olivine) catalysts. The effect of catalyst distribution modes and catalytic temperature on the tar and gas quality has been investigated. Simulated distillation and GC-MS analysis have been used to investigate the distribution of tar components. The results indicated that the light oil fraction in tar dramatically increased due to the combination of CaO and Ni/olivine. The CaO-Ni/olivine mode is especially better compared to the layouts of the Ni/olivine-CaO mode and the mixed mode. The CaO-Ni/olivine mode ensures a higher light fraction in tar at 69.3% and a light oil fraction at 29.8% at a catalytic temperature of 450 °C, while the heavy tar fraction decreased to 30.7%. Meanwhile, the contents of benzene (heteroatomic substituents) in tar significantly increased from 2.55% to 6.45% compared with the blank test. In this scenario, CaO breaks down macromolecular compounds in tar and cleaves long-chain esters to produce aliphatic hydrocarbons. These hydrocarbons are then dehydrogenated to produce lighter aromatic hydrocarbons over the CaO surface. Subsequently, the volatiles pass through the Ni/olivine catalysis, where ether compounds are produced by means of dehydration reactions. In addition, the CaO absorbs the CO2 in the pyrolysis gas, leading to an elevation of CH4 and H2 concentration. Particularly, the concentration of H2 significantly increased from 16.2% to 30.37%, while the concentration of CO2 significantly decreased from 37.9% to 10.57%. These findings suggest that the usage of combined CaO and Ni/olivine catalysts is beneficial for improving both the tar and gas quality. [ABSTRACT FROM AUTHOR]