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Bridging the gap between the well-known technological description of gasification and the underlying theoretical understanding, this book covers the latest numerical and semi-empirical models describing interphase phenomena in high-temperature conversion processes. Consequently, it focuses on the description of gas-particle reaction systems by state-of-the-art computational models in an integrated, unified form. Special attention is paid to understanding and modeling the interaction between individual coal particles and a surrounding hot gas, including heterogeneous and homogeneous chemical reactions inside the particle on the particle interface and near the interface between the solid and gas phases. While serving the needs of engineers involved in industrial research, development and design in the field of gasification technologies, this book's in-depth coverage makes it equally ideal for young and established researchers in the fields of thermal sciences and chemical engineering with a focus on heterogeneous and homogeneous reactions.

Context Coal water slurry (CWS) is a new type of liquid coal product with low pollution, which is mainly used in the chemical industry to produce syngas (CO + H 2 ). It is of great significance to study the microscopic mechanism of CWS gasification reaction for improving the efficiency of coal gasification. In this paper, the method of molecular dynamics based on reaction force fields (ReaxFF-MD) is used to study the gasification process of CWS/O 2 system at different temperatures. The results show that, in the range of 1600–2400 K, the macromolecular network structure of lignite is decomposed into a large number of small molecular structures and a small number of light tar free radical fragments, and the types and quantities of reaction products increased rapidly. At 2400–4000 K, the free radical fragments of light tar are further decomposed and reacted with gasification agents, but the types and quantities of reaction products have little change. At 3600 K, a full gasification reaction occurred in the system, and the content of syngas is the highest. Methods The model was established and optimized by Materials Studio (MS) software. Based on ReaxFF-MD method, Lammps software was used to simulate the gasification process of CWS/O 2 system, and the reaction force field files containing C, H, O, N, and S element were used. By calculating the activation energy of gasification reaction, the rationality of the model and calculation method was illustrated. The post-processing of the results was implemented using OVITO, ChemDraw software, and self-programmed Python scripts.

The biomass-to-methanol process may play an important role in introducing renewables in the industry chain for chemical and fuel production. Gasification is a thermochemical process to produce syngas from biomass, but additional steps are requested to obtain a syngas composition suitable for methanol synthesis. The aim of this work is to perform a computer-aided process simulation to produce methanol starting from a syngas produced by oxygen–steam biomass gasification, whose details are reported in the literature. Syngas from biomass gasification was compressed to 80 bar, which may be considered an optimal pressure for methanol synthesis. The simulation was mainly focused on the water–gas shift/carbon capture sections requested to obtain a syngas with a (H2 – CO2)/(CO + CO2) molar ratio of about 2, which is optimal for methanol synthesis. Both capital and operating costs were calculated as a function of the CO conversion in the water–gas shift (WGS) step and CO2 absorption level in the carbon capture (CC) unit (by Selexol® process). The obtained results show the optimal CO conversion is 40% with CO2 capture from the syngas equal to 95%. The effect of the WGS conversion level on methanol production cost was also assessed. For the optimal case, a methanol production cost equal to 0.540 €/kg was calculated.

In order to strengthen the safety management of coal slurry preparation systems, a dynamic risk assessment method was established by using the bow-tie (BT) model and the Structure-variable Dynamic Bayesian Network (SVDBN). First, the BT model was transformed into a static Bayesian network (BN) model of the failure of a coal slurry preparation system by using the bow-tie model and the structural similarity of the Bayesian cognitive science, based on the SVDBN recursive reasoning algorithm. The risk factors of the coal slurry preparation system were deduced using the Python language in two ways, and at the same time, preventive measures were put forward according to the weak links. In order to verify the accuracy and feasibility of this method, the simulation results were compared with those obtained using GeNIe software. The reasoning results of the two methods were very similar. Without considering maintenance factors, the failure rate of the coal slurry preparation system gradually increases with increasing time. When considering maintenance factors, the reliability of the coal slurry preparation system will gradually be maintained at a certain threshold, and the maintenance factors will increase the reliability of the system. The proposed method can provide a theoretical basis for the risk assessment and safety management of coal slurry preparation systems.

To address the gap in quantitatively modeling dynamic failure mechanisms for Gasifier lock bucket valve system reliability, this study proposes an innovative method: using backpropagation (BP) neural network to optimize the prior data of dynamic Bayesian network (DBN). Firstly, based on the empirical formula for the number of hidden layer neurons, the original DBN model of the system is adapted to a structurally adaptive BP neural network to calibrate its prior parameters，and the correspondence between the prior distribution of DBN and the input-output functions of the BP network is established. Subsequently, utilizing the core characteristics of BP network, iterative optimization of DBN prior data is achieved through continuous learning of the operating performance of the lock bucket valve system. Next, the optimized DBN model is subjected to dynamic system reliability evaluation using bidirectional inference analysis. The results show that in the positive prediction, the reliability of the system after 300 hours of operation without considering maintenance is only 0.047, which can be improved to 0.302 after incorporating maintenance factors. The reliability of the optimized system is lower than before optimization, and the gap gradually widens over time. Reverse reasoning clearly identifies the weak links in the system as high-pressure coal powder flushing, adhesion between ball seats, internal deformation and wear. Targeted preventive measures can improve the reliability of the system and extend its service life.

The heterogeneity of a rock mass under high temperature and its thermo-mechanical coupling characteristics are difficult problems to investigate. This situation brings considerable difficulties to the study of underground coal gasification under thermo-mechanical coupling. The development of a numerical simulation method for the thermo-mechanical coupling of heterogeneity rock mass under high-temperature burnt conditions can provide an important foundation for related research. On the basis of the variation of mechanical properties of rock mass with temperature, a thermo-mechanical coupling simulation method, which considers the heterogeneity of a rock mass under high temperature, is proposed in this study. A test block experiment is implemented and then applied to the strata movement and failure of underground coal gasification. The results are as follows: (1) The proposed method can truly reflect the heterogeneity of a rock mass under high-temperature environment, providing an effective method for the numerical simulation of geotechnical engineering in high-temperature conditions. (2) The variation of mechanical properties of rock mass after an increase in temperature is the main reason for the change law of strata movement and failure of underground coal gasification. These factors should be considered in the investigation of underground gasification strata movement andfailure. The present study can provide an important means for the research on geotechnical engineering in high-temperature environments.

Context To investigate the impact of moisture on gas adsorption in gas-fat coal, Shanxi gas-fat coal was chosen as the research subject. The Adsorption module within molecular simulation software was utilized to construct gas-fat coal models with moisture contents of 0%, 1.14%, 2.26%, and 3.72%. The results revealed that the isothermal adsorption curves of gas-fat coal under different moisture contents all followed the Langmuir adsorption curve (Type Ι), wherein the Langmuir volume of gas-fat coal was most sensitive to changes in moisture content within the range of 0 to 1.14%. A linear equation was found to better characterize the influence of moisture on gas content in Shanxi gas-fat coal, represented by the formula η  = 1/(1 + 1.98078 w ), where, η represents the moisture impact coefficient; w is the moisture content; 1.98078 is the coefficient of moisture influence of Shanxi gasifier coal. Coupled with changes in the surface free energy of gas-fat coal, the pressure exerted a positive effect on the adsorption of gas on the coal surface, enhancing the adsorption space of methane within gas-fat coal. Methods Using Materials Studio software, the adsorption capacity and adsorption heat of methane were computed at various temperatures (323 K, 333 K, 343 K) and pressures (0.5–8 MPa). Furthermore, a comparative analysis was conducted to assess the applicability of classical linear equations, linear equations, power function equations, and exponential function equations in quantitatively characterizing the influence of moisture.

In order to solve the problem of zero-failure data and dynamic failure in gasification system, a dynamic Bayesian network (DBN) combined with Monte Carlo simulations is proposed to analyze the reliability of the gasifier lock bucket valve system. On the basis of studying the structure of the gasifier lock bucket valve system, the reliability model of the system is built based on DBN, and the structure learning is realized. The Monte Carlo simulation is used for the timed ending test in Bayesian estimation, which effectively solves the problem of zero-failure data and realizes the parameter learning. Through the Metropolis-Hastings (M-Hs) algorithm, the prior distribution of dynamic node is randomly sampled to obtain the target distribution, which makes the reliability predictive inference for DBN of the gasifier lock bucket valve system faster and more accurate. The obtained reliability prediction is a curve varying with time. The results show that the valve frequent switch node of DBN of the gasifier lock bucket valve system is identified as the weak link by the powerful reverse inference for DBN, which needs to be paid more attention to. This method can effectively improve the maintenance level of the gasifier lock bucket valve system and can effectively reduce the possibility of accidents.

Gasification models of a char particle based on the true porous structure are essential for the accurate simulation of gasifiers, and pore-scale study might provide important information for the development of the porous char particle gasification models. In this paper, a numerical study was conducted on the gasification of a single-pore char particle in supercritical water, and the emphasis was put on the gasification process inside the pore with the effects of surrounding fluid, pore structure and pore position considered. The results showed that the gasification in a pore was quite affected by pore diffusion. The increase in temperature and particle Reynolds number promoted the gasification in the pore, and convection mainly enhanced the heat transfer but had limited promotion on mass transfer in kinetically controlled regime. Increasing pore length and decreasing pore diameter caused the increase in diffusion resistance and the former had more obvious effects. However, the decreased pore diameter increased the specific surface area and benefited the whole char conversion. The pore position affected the species distribution inside the pore for non-diffusive gasification, and the impact was limited in kinetically controlled regime. Finally, study in this work will be further extended to the gasification of the porous char particle.

Converting all U.S. onroad vehicles to hydrogen fuel-cell vehicles (HFCVs) may improve air quality, health, and climate significantly, whether the hydrogen is produced by steam reforming of natural gas, wind electrolysis, or coal gasification. Most benefits would result from eliminating current vehicle exhaust. Wind and natural gas HFCVs offer the greatest potential health benefits and could save 3700 to 6400 U.S. lives annually. Wind HFCVs should benefit climate most. An all-HFCV fleet would hardly affect tropospheric water vapor concentrations. Conversion to coal HFCVs may improve health but would damage climate more than fossil/electric hybrids. The real cost of hydrogen from wind electrolysis may be below that of U.S. gasoline.