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Toppling-sliding failure is a typical mode of deep-seated toppling failure. In this mode, massive collapsed rock masses form the main sliding body, which is sensitive to rainfall events and prone to instability under rainfall due to its unique slope structure. In the present study, based on the detailed investigation on the geology and deformation characteristics, we studied the deformation and failure mechanism of a large-scale deep-seated toppling in Nandongzi Village, Pingquan City, Hebei Province. We constructed an engineering geology model to describe the toppling-sliding failure under rainfall. In addition, based on the saturated–unsaturated seepage theory and using the SLOPE/W and SEEP/W modules in the GeoStudio software, we explored the seepage law and factors controlling the seepage failure of toppling-sliding under rainfall. From surface to interior, the slope can be divided into topplingalling zone, strong toppling zone, slight toppling zone, and non-deformation zone. The geological structure consisting of an upper strong slab and an underlying weak rock layer, controls the early deformation, and the deformation and failure mode is compressing-bending-toppling. Due to the influence of excavation and rainfall, the sliding movements occur along planar rupture planes in the toppling-falling zone in the later stage, during which the failure mode switches to creeping-cracking. At present, the stability of the slope is highly sensitive to rainfall. When the rainfall intensity exceeds 220 mm/day (50 years return period storm), the factor of safety will fall below 1.05 and subsequently the sliding failure may be triggered. Because of the difference in permeability characteristics between the toppling-falling zone and the strong toppling zone, high pore-water pressure is developed at their boundary, leading to a drastic decrease in the factor of safety. Specifically, the more considerable difference in permeability, the lower the safety factor. Overall, this study is significant in scientific guiding for evaluating and preventing such slope failures.

Landslides pose a constant threat to the lives and property of mountain people and may also cause geomorphological destruction such as soil and water loss, vegetation destruction, and land cover change. Landslide susceptibility assessment (LSA) is a key component of landslide risk evaluation. There are many related studies, but few analyses and comparisons of models for optimization. This paper aims to introduce the Tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization of three typical neural network models for LSA in Shuicheng County, China, as an example, and to compare the differences of predictive ability among the models in order to achieve higher application performance. First, 17 influencing factors of landslide multiple data sources were selected for spatial prediction, hybrid ensemble oversampling and undersampling techniques were used to address the imbalanced sample and small sample size problem, and the samples were randomly divided into a training set and validation set. Second, deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) models were adopted to predict the regional landslides susceptibility, and the TPE algorithm was used to optimize the hyperparameters respectively to improve the assessment capacity. Finally, to compare the differences and optimization effects of these models, several objective measures were applied for validation. The results show that the high-susceptibility regions mostly distributed in bands along fault zones, where the lithology is mostly claystone, sandstone, and basalt. The DNN, RNN, and CNN models all perform well in LSA, especially the RNN model. The TPE optimization significantly improves the accuracy of the DNN and CNN (3.92% and 1.52%, respectively), but does not improve the performance of the RNN. In summary, our proposed RNN model and TPE-optimized DNN and CNN model have robust predictive capability for landslide susceptibility in the study area and can also be applied to other areas containing similar geological conditions.

Among the most frequent and dangerous natural hazards, landslides often result in huge casualties and economic losses. Landslide susceptibility mapping (LSM) is an excellent approach for protecting and reducing the risks by landslides. This study aims to explore the performance of Bayesian optimization (BO) in the random forest (RF) and gradient boosting decision tree (GBDT) model for LSM and applied in Shuicheng County, China. Multiple data sources are used to obtain 17 conditioning factors of landslides, Borderline-SMOTE and Randomundersample methods are combined to solve the imbalanced sample problem. RF and GBDT models before and after BO are adopted to calculate the susceptibility value of landslides and produce LSMs and these models were compared and evaluated using multiple validation approach. The results demonstrated that the models we proposed all have high enough model accuracy to be applied to produce LSM, the performance of the RF is better than the GBDT model without BO, while after adopting the Bayesian optimized hyperparameters, the prediction accuracy of the RF and GBDT models is improved by 1% and 7%, respectively and the Bayesian optimized GBDT model is the best for LSM in this four models. In summary, the Bayesian optimized RF and GBDT models, especially the GBDT model we proposed for landslide susceptibility assessment and LSM construction has a very good application performance and development prospects.

High ash-containing paper sludge ash (PSA) has been investigated in terms of its use as a low-cost catalyst for primary pyrolysis tar cracking in the two-stage pyrolysis of biomass. Experimental results showed that tar yields decreased significantly using PSA as catalyst in comparison with the thermal cracking experiments. A consequent increase in total gas yield was obtained. The yields of H2, CH4, and CO2 increased significantly under the catalytic effect of PSA, particularly the H2 yield almost doubled, while CO yields showed a slight decrease due to the consumption reactions. Thermogravimetric analysis of the fresh and spent PSA showed that carbon particles formed along with the tar decomposition, while the catalytic reactivity of the PSA was not reduced because of the participation of carbon particles in tar reforming. XRD spectrograms revealed that calcium-ferrite oxide (Ca2Fe2O5) was formed in PSA, which has excellent stability and catalytic effect on tar cracking and H2 production. The PSA still s...

High ash-containing paper sludge which is rich in various metal oxides is employed in herb residue pyrolysis to enhance the yield of fuel gas and reduce tar yield in a drop tube fixed bed reactor. Effects of heat treatment temperature and blending ratio of paper sludge on the yields and composition of pyrolysis products (gas, tar and char) were investigated. Results indicate that paper sludge shows a significantly catalytic effect during the pyrolysis processes of herb residue, accelerating the pyrolysis reactions. The catalytic effect resulted in an increase in gas yield but a decrease in tar yield. The catalytic effect degree is affected by the paper sludge proportions, and the strongest catalytic effect of paper sludge is noted at its blending ratio of 50%. At temperature lower than 900 °C, the catalytic effect of paper sludge in the pyrolysis of herb residue promotes the formation of H2 and CO2, inhibits the formation of CH4, but shows slight influence on the formations of CO, while the formation of the four gas components was all promoted at 900 °C. SEM results of residue char show that ash particles from paper sludge adhere to the surface of the herb residue char after pyrolysis, which may promote the pyrolysis process of herb residue for more gas releasing. FT-IR results indicate that most functional groups disappear after pyrolysis. The addition of paper sludge promotes deoxidisation and aromatization reactions of hetero atoms tars, forming heavier polycyclic aromatic hydrocarbons and leading to tar yield decrease.

The co-pyrolysis characteristics of Zhundong lignite and pine sawdust were investigated in a thermogravimetric analyzer and a fixed bed reactor. This study found that the obtained activation energies were generally lower than the calculated values. Particularly in the conversion range of 0.2–0.6, most of the relative deviation values was lower than −10% for the blends, indicating positive synergistic effect between Zhundong lignite and pine sawdust in volatiles release during non-isothermal pyrolysis. From the isothermal pyroylysis in the fixed bed reactor, the experimental values of gas yield were greater than the calculated, while both experimental tar and char yields became lower. Pronounced synergy effect occurred at ZD and PS mass ratio of 1:1 and 2:1 for tar and gas product, indicating that enough hydrogen-donors could be provided to promote degradation reactions. The experimental yields of four main gas components, CO, H2, CO2 and CH4, were all higher than that of the calculated values. SEM results indicated both Zhundong lignite and pine sawdust residue chars became more porous, and metals salt in Zhundong lignite volatilized and condensed on the surface of Zhundong lignite and pine sawdust, which may perform as catalyst during co-pyrolysis.

Isothermal pyrolysis of rice husk catalyzed by iron and nickel was conducted in a microfluidized bed reactor to investigate the forming characteristics and kinetics of the main gas components. Results indicated that both Fe and Ni showed a catalytic effect on the releasing behavior of gaseous products during rice husk pyrolysis. The forming reaction rate of H2 and CO2 was significantly promoted with the addition of Fe or Ni, while that of CH4 was inhibited. For CO, Ni showed a positive effect while Fe showed a negative effect on its forming rate. The char evolution of rice husk was also influenced by the presence of Fe and Ni from the SEM-EDX and BET results. The model fitting method was used to calculate the kinetics of gas releasing to further investigate the effect of Fe and Ni, and the most probable reaction mechanism was obtained based on the relative error between experimental and calculated conversion fraction. Corresponding to the changing trend of reaction rate, the obtained activation energies f...

Co-pyrolysis behaviors of Zhundong lignite and pine sawdust under isothermal conditions were investigated with a focus on the gas releasing characteristics using a micro fluidized bed reactor. The kinetic parameters were deduced by measuring time-dependent changes in the composition of evolved gases from the pyrolysis reactions, employing the universal integral method. The gas release intensity and conversion rate of pine sawdust were quite different from those of Zhundong lignite because of the difference in chemical structure and element content. The effect of blending ratio on the degree of conversion varied significantly for different gas components; H2, CO, and CH4 conversion became quicker with an increasing biomass ratio, while the variation of CO2 was not obvious. The generally used mechanistic models for gas–solid reactions were examined as ways to interpret the experimental data, and reaction kinetics was deduced with respect to the formation of an individual gas component. The obtained activati...

Influence of potassium on the gas releasing behaviors during rice husk high-temperature pyrolysis was investigated under isothermal conditions in a two stage micro-fluidized bed reactor. Reaction kinetics for generating H2, CO, CO2 and CH4 was investigated based on the Friedman and model-fitting approaches. Results indicated that different gas species had different times to start and end the gas release process, particularly at 600 °C, representing different chemical routes and mechanics for generating these gas components. The resulting apparent activation energies for H2, CO, and CO2 decreased from 23.10 to 12.00 kJ/mol, 15.48 to 14.03 kJ/mol and 10.14 to 7.61 kJ/mol respectively with an increase in potassium concentration from 0 to 0.5 mol/kg, while that for CH4 increased from 16.85 to 19.40 kJ/mol. The results indicated that the addition of potassium could promote the generation reactions of H2, CO and CO2 while hinder the generation reactions of CH4. The pyrolysis reaction was further found to be subject to the three-dimensional diffusion model for all the samples.

Potassium is a well-known alkali catalyst in the thermal reactions of biomass. The effect of potassium on the pyrolysis behavior and kinetics of biomass was investigated through TGA and a fixed bed in this study. The addition of potassium reduced initial and peak temperature in TGA curves, promoting the decomposition process of biomass. The effect of potassium on the apparent activation energy varies at different conversion degree ( a ) and lower apparent activation energy was obtained at the initial stage ( a ⩽ 0.3) of the pyrolysis process. The influence of potassium on the volatiles releasing behavior was dependent on the amount of loading potassium. The gas yield was significantly increased with the increasing of potassium concentration when the impregnated potassium was below 0.3 mol/kg. Particularly, the yield of H 2 and CO 2 was promoted by potassium. Higher potassium content may contribute to the increase in reactivity towards tar molecules, leading to quickly decrease in tar yield. The surface chemical characteristics of char were determined by FTIR spectroscopic method. Above a certain threshold surface concentration of potassium, SEM analysis shows that agglomeration is a potential threat at high temperature, which can block the active sites and decrease the activity during biomass thermal decomposition.

This paper characterized the observed low frequency acoustic signals generated by rock falls, thunderstorm, and wind turbulence in large rocky landslide. A digital infrasonic recording system was deployed on site to capture real-time low frequency acoustic signals associated with rock falls. An advanced non-stationary signal analysis method, i.e. Empirical Mode Decomposition (EMD), was applied to get insight to the characteristics of the low frequency acoustic signals induced by the hazards. Joint time–frequency distribution spectra technique was used to detect distinctive features of the events. The study shows that the low frequency acoustic signals can be excited by rock falls, thunderstorm and wind turbulence in the field environment, but the signal varies in both time domain and frequency domain with different patterns depending on the physical processes. The results demonstrated that the EMD-based signal processing technique is capable of extracting distinctive features to differentiate acoustic signals in real environment.