Fundamental study of smouldering combustion of peat in wildfires

Smouldering combustion is the slow, low-temperature, flameless burning of porous fuels and the most persistent type of combustion, different from flaming combustion. Smouldering is the dominant phenomena in fires of coal and natural deposits of peat which are the largest and longest burning fires on Earth. These megafires fires contribute considerably to annual greenhouse gas emissions roughly equivalent to 15% of the man-made emissions, and result in the widespread destruction of global ecosystems and regional haze events. Moreover, the atmospheric release of ancient carbon in soil and the sensitivity of peat ignition to higher temperatures and drier conditions create a positive feedback mechanism to climate change. Compared to flaming combustion, smouldering combustion can be initiated with a much weaker ignition source, and provide a hazard shortcut to flaming. Once ignited, the persistent smouldering fires can consume a huge amount of earth biomass, and burn for very long periods of time (days, years and centuries) despite extensive firefighting efforts or climate changes. For the past few decades, there have been some experimental studies on smouldering peat fires of different scales. However, very few computational work has been done to systematically study such emerging fire phenomena before the research undertaken in this thesis. This thesis is presented in a manuscript style: each chapter takes the form of an independent paper, which has been published or submitted to a journal publication. A final chapter summarizes the conclusions, and suggests potential areas of future research. Chapter 1 first proposes a comprehensive 5-step kinetic model based on thermogravimetric analysis (TGA) to describe the heterogeneous reactions in smouldering combustion of peat. The corresponding kinetic parameters are inversely modelled using genetic algorithm (GA). This 5-step (including drying) kinetic model successfully explains the TG data of four different peat soils from different geographical locations. The chemical validity of the scheme is also investigated by incorporating it into a one-dimensional (1-D) plug-flow model. The reaction and species distributions of two most common fire spread modes, lateral and in-depth spread, are successfully simulated. Chapter 2 presents a new comprehensive 1-D model of a reactive porous media to solve the conservation equations and the proposed 5-step heterogeneous chemical kinetics. This model is used to simulate several ignition experiments on bench-scale peat samples in the literature. The model first predicts the smouldering thresholds, relating to the critical moisture content (MC) and inert content (IC). The modelling results show a good agreement with experiments for a wide range of peat types and organic soils. The influences of the kinetic parameters, physical properties, and ignition protocol on initiating the peat fire are also investigated. Chapter 3 continues to optimize this 1-D model to investigate the vertical in-depth spread of smouldering fires into peat columns 20-30 cm deep with heterogeneous profiles of MC, IC and density. Modelling results reveal that smouldering combustion can spread over peat layers with a very high MC (~250%) if the layer is thin and located below a thick and drier layer. It is also found that the critical MC for extinction can be much higher than the previously reported critical MC for ignition. Furthermore, depths of burn (DOB) in peat fire is successfully predicted, and shows a good agreement with experiments on 18 field peat samples in the literature. Chapter 4 further looks into the kinetic schemes of different complexities to explain the TGA of two peat soils under various atmospheric oxygen concentration. Their best kinetic parameters are fast searched via Kissinger-genetic algorithm (K-GA) method, and the oxidation model is determined for the first time. Then, the kinetic model is applied into the 1-D model to simulate the peat experiment with fire propagation apparatus (FPA) in the literature. Try peat samples are used to minimize the influence of moisture, and ignited under both sub- and super-atmospheric oxygen concentration. Modelling results show a good agreement with experiment, and the stochastic sensitivity analysis is used to test the model sensitivity to multiple physicochemical properties. Chapter 5 investigates the interactions of atmospheric oxygen and fuel moisture in smouldering wildfires with the proposed 1-D model. Modelling results reveal a nonlinear correlation existing between the critical fuel moisture and atmospheric oxygen as MC increases, a greater increase in oxygen concentration is required for both ignition and fire spread. Smouldering fires on dry fuel can survive at a substantially lower oxygen concentration (~11%) than flaming fires, and fuel type and chemistry may play important roles especially in high MC. The predicted spread rate of smouldering peat fire is on the order of 1 mm/min, much slower than flaming fires. In addition, the rate of fire spread increases in an oxygen-richer atmosphere, while decreases over a wetter fuel. Chapter 6 presents an experimental study on smouldering fires spreading over bench-scale peat samples under various moisture and wind conditions. The periodic “overhang” phenomenon is observed where the smouldering fire spreads beneath the top surface, and the overhang thickness is found to increase with peat MC and the wind speed. Experimental results show that the lateral spread rate decreases with MC, while increases with the wind speed. As peat MC increases, the fire spread behaviour becomes less sensitive to the wind condition and its depth. A simple heat transfer analysis is proposed to explain the influence of moisture and wind on the spread rate profile, and suggests that the overhang phenomena is caused by the spread rate difference between the top and the lower peat layers. Chapter 7 summarizes the research of this thesis, and discuss the possible areas for future research.Open Acces

Thermodynamic Simulation of Carbonate Cements-Water-Carbon Dioxide Equilibrium in Sandstone for Prediction of Precipitation/Dissolution of Carbonate Cements

Carbonate cements, such as calcite, dolomite, ferrocalcite and ankerite, play important roles in the formation of pores in sandstones: precipitation of carbonate cements modifies pores and inhibits compaction, while dissolution creates secondary pores. This work proposed a precipitation-dissolution model for carbonate cements-CO2-H2O system by means of ion equilibrium concentration ([M2+], M = Ca, Mg, Fe or Mn) with different factors, such as temperature, depth, pH, [Formula: see text], variable rock composition and overpressure. Precipitation-dissolution reaction routes were also analyzed by minimization of the total Gibbs free energy (ΔG). Δ[M2+], the variation of [Ca2+], [Fe2+], [Mg2+] or [Mn2+] for every 100 m of burial depths, is used to predict precipitation or dissolution. The calculation results indicate that the increasing temperature results in decrease of equilibrium constant of reactions, while the increasing pressure results in a relatively smaller increase of equilibrium constant; As a result, with increasing burial depth, which brings about increase of both temperature and pressure, carbonate cements dissolve firstly and produces the maximal dissolved amounts, and then precipitation happens with further increasing depth; For example, calcite is dissolving from 0.0 km to 3.0 km with a maximal value of [Ca2+] at depth of 0.8 km, and then precipitates with depth deeper than 3.0 km. Meanwhile, with an increasing CO2 mole fraction in the gaseous phase from 0.1% to 10.0% in carbonate systems, the aqueous concentration of metal ions increases, e.g., dissolved amount of CaFe0.7Mg0.3(CO3)2 increases and reaches maximum of 1.78 mmol·L-1 and 8.26 mmol·L-1 at burial depth of 0.7 km with CO2 mole fraction of 0.1% and 10.0%, respectively. For the influence of overpressure in the calcite system, with overpressure ranging from 36 MPa to 83 MPa, pH reaches a minimum of 6.8 at overpressure of 51 MPa; meanwhile, Δ[Ca2+] increases slightly from -2.24 mmol·L-1 to -2.17 mmol·L-1 and remains negative, indicating it is also a precipitation process at burial depth of 3.9 km where overpressure generated. The method used in this study can be applied in assessing burial precipitation-dissolution processes and predicting possible pores in reservoirs with carbonate cement-water-carbon dioxide

Ionic Liquid Design and Process Simulation for Decarbonization of Shale Gas

Ionic liquids (ILs) have been receiving increasing attention as a potential decarbonization solvent. However, the enormous number of potential ILs that can be synthesized makes it a challenging task to search for the best IL for CO₂ removal from methane. In this work, a method was proposed to screen suitable ILs based on the COSMO-RS (conductor-like screening model for real solvents) model, an absorption mechanism, and experimental data. Besides the Henry’s constant, the viscosity and toxicity of ILs should also be taken into consideration for an industrial decarbonization process. Furthermore, process simulation was performed to evaluate the new IL-based decarbonization technology. Considering CO₂ solubility, CO₂/CH₄ selectivity and toxicity and viscosity of ILs, [bmim]­[NTf₂] has been screened to be the potential solvent among 90 classes of ILs. Based on reliable experimental data, a rigorous thermodynamic model was established. The simulation results have been found to agree well with the available experimental results. Two process flow sheet options, use of two single-stage flash operations or a multistage flash operation following the absorber, have been simulated and assessed. Compared with the well-known MDEA (methyldiethanolamine) process for CO₂ capture, the single-stage and multistage process alternatives would reduce the total energy consumption by 42.8% and 66.04%, respectively

Mining Periodic Traces of an Entity on Web

A trace of an entity is a behavior trajectory of the entity. Periodicity is a frequent phenomenon for the traces of an entity. Finding periodic traces for an entity is essential to understanding the entity behaviors. However, mining periodic traces is of complexity procedure, involving the unfixed period of a trace, the existence of multiple periodic traces, the large-scale events of an entity and the complexity of the model to represent all the events. However, the existing methods can’t offer the desirable efficiency for periodic traces mining. In this paper, Firstly, a graph model(an event relationship graph) is adopted to represent all the events about an entity, then a novel and efficient algorithm, TracesMining, is proposed to mine all the periodic traces. In our algorithm, firstly, the cluster analysis method is adopted according to the similarity of the activity attribute of an event and each cluster gets a different label, and secondly a novel method is proposed to mine all the Star patterns from the event relationship graph. Finally, an efficient method is proposed to merge all the Stars to get all the periodic traces. High efficiency is achieved by our algorithm through deviating from the existing edge-by-edge pattern-growth framework and reducing the heavy cost of the calculation of the support of a pattern and avoiding the production of lots of redundant patterns. In addition, our algorithm could mine all the large periodic traces and most small periodic traces. Extensive experimental studies on synthetic data sets demonstrate the effectiveness of our method

Fatigue Performance of SFPSC under Hot-Wet Environments and Cyclic Bending Loads

A new structural material named “steel fiber polymer structural concrete (SFPSC)” with features of both high strength and high toughness was developed by this research group and applied to the bridge superstructures in the hot-wet environments. In order to investigate the fatigue performance and durability of SFPSC under hot-wet environments, the environment and fatigue load uncoupling method and the coupling action of environment and fatigue load were used or developed. Three-point bending fatigue experiments with uncoupling action of environments and cyclic loads were carried out for SFPSC specimens which were pretreated under hot-wet environments, and the experiments with the coupling action of environments and cyclic loads for SFPSC specimens were carried out under hot-wet environments. Then, the effects of hot-wet environments and the experimental methods on the fatigue mechanism of SFPSC material were discussed, and the environmental fatigue equations of SFPSC material under coupling and uncoupling action of hot-wet environments and cyclic bending loads were established. The research results show that the fatigue limits of SFPSC under the coupling action of the environments and cyclic loads were lower about 15%. The proposed fatigue equations could be used to estimate the fatigue lives and fatigue limits of SFPSC material

Genetic variants in ADAM33 are associated with airway inflammation and lung function in COPD

BACKGROUND: Genetic factors play a role in the development and severity of chronic obstructive pulmonary disease (COPD). The pathogenesis of COPD is a multifactorial process including an inflammatory cell profile. Recent studies revealed that single nucleotide polymorphisms (SNPs) within ADAM33 increased the susceptibility to COPD through changing the airway inflammatory process and lung function. METHODS: In this paper, we investigated associations of four polymorphisms (T1, T2, S2 and Q-1) of ADAM33 as well as their haplotypes with pulmonary function and airway inflammatory process in an East Asian population of patients with COPD. RESULTS: We found that T1, T2 and Q-1 were significantly associated with the changes of pulmonary function and components of cells in sputum of COPD, and T1 and Q-1 were significantly associated with cytokines and mediators of inflammation in airway of COPD in recessive models. 10 haplotypes were significantly associated with transfer factor of the lung for carbon monoxide in the disease state, 4 haplotypes were significantly associated with forced expiratory volume in one second, and other haplotypes were associated with airway inflammation. CONCLUSIONS: We confirmed for the first time that ADAM33 was involved in the pathogenesis of COPD by affecting airway inflammation and immune response in an East Asian population. Our results made the genetic background of COPD, a common and disabling disease, more apparent, which would supply genetic support for the study of the mechanism, classification and treatment for this disease. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2466-14-173) contains supplementary material, which is available to authorized users

Hydrogen jet diffusion modeling by using physics-informed graph neural network and sparsely-distributed sensor data

Efficient modeling of jet diffusion during accidental release is critical for operation and maintenance management of hydrogen facilities. Deep learning has proven effective for concentration prediction in gas jet diffusion scenarios. Nonetheless, its reliance on extensive simulations as training data and its potential disregard for physical laws limit its applicability to unseen accidental scenarios. Recently, physics-informed neural networks (PINNs) have emerged to reconstruct spatial information by using data from sparsely-distributed sensors which are easily collected in real-world applications. However, prevailing approaches use the fully-connected neural network as the backbone without considering the spatial dependency of sensor data, which reduces the accuracy of concentration prediction. This study introduces the physics-informed graph deep learning approach (Physic_GNN) for efficient and accurate hydrogen jet diffusion prediction by using sparsely-distributed sensor data. Graph neural network (GNN) is used to model the spatial dependency of such sensor data by using graph nodes at which governing equations describing the physical law of hydrogen jet diffusion are immediately solved. The computed residuals are then applied to constrain the training process. Public experimental data of hydrogen jet is used to compare the accuracy and efficiency between our proposed approach Physic_GNN and state-of-the-art PINN. The results demonstrate our Physic_GNN exhibits higher accuracy and physical consistency of centerline concentration prediction given sparse concentration compared to PINN and more efficient compared to OpenFOAM. The proposed approach enables accurate and robust real-time spatial consequence reconstruction and underlying physical mechanisms analysis by using sparse sensor data