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Abstract Fatigue can cause human error, which is the main cause of accidents. In this study, the dynamic fatigue recognition of unmanned electric locomotive operators under high-altitude, cold and low oxygen conditions was studied by combining physiological signals and multi-index information. The characteristic data from the physiological signals (ECG, EMG and EM) of 15 driverless electric locomotive operators were tracked and tested continuously in the field for 2 h, and a dynamic fatigue state evaluation model based on a first-order hidden Markov (HMM) dynamic Bayesian network was established. The model combines contextual information (sleep quality, working environment and circadian rhythm) and physiological signals (ECG, EMG and EM) to estimate the fatigue state of plateau mine operators. The simulation results of the dynamic fatigue recognition model and subjective synchronous fatigue reports were compared with the field-measured signal data. The verification results show that the synchronous subjective fatigue and simulated fatigue estimation results are highly consistent (correlation coefficient r = 0.971**), which confirms that the model is reliable for long-term dynamic fatigue evaluation. The results show that the established fatigue evaluation model is effective and provides a new model and concept for dynamic fatigue state estimation for remote mine operators in plateau deep mining. Moreover, this study provides a reference for clinical medical research and human fatigue identification under high-altitude, cold and low-oxygen conditions.

IntroductionActivating Signal Cointegrator 1 Complex, Subunit 3 (ASCC3) has been implicated in the pathogenesis of neurodevelopmental disorders and neuromuscular diseases (MIM: 620700). This paper analyzes the clinical manifestations of three patients with developmental delay caused by ASCC3 genetic variation. Additionally, we discuss the previously reported clinical features of these patients along with our own findings, thereby enhancing our understanding of these genetic disorders and providing valuable insights into diagnosis, treatment, and potential interventions for affected individuals.MethodsIn this study, we utilized trio-whole-exome sequencing (Trio-WES) and trio-copy number variations sequencing (Trio-CNV-seq) to analyze three unique families diagnosed with developmental delay caused by variation in ASCC3. Additionally, we retrospectively examined eleven previously reported ASCC3 genetic variations exhibiting similar clinical features.ResultsProband I (family 1) and Proband III (family 3) exhibited global developmental delays, characterized by intellectual disability, motor impairment, language retardation, lower muscle strength, and reduced muscle tone in their extremities. Proband II (family 2) presented poor response and dysphagia during feeding within 7 days after birth, clinical examination displayed short limbs, long trunk proportions, and clenched fists frequently observed alongside high muscle tone in his limbs -all indicative signs of developmental delay. Trio-WES revealed compound heterozygous variants in ASCC3 inherited from their parents. Proband I carried c. [489 dup]; [1897C>T], proband II carried c. [2314C>T]; [5002T>A], and proband III carried c. [5113G>T]; [718delG] variations, respectively.ConclusionThis study present the first report of Chinese children carrying compound heterozygous genetic variants in ASCC3 with LOF variants, elucidating the relationship between these variants and various aspects of intellectual disability. This novel finding expands the existing spectrum of ASCC3 variations.

Wet dust removal systems used to control dust in the polishing or grinding process of Mg alloy products are frequently associated with potential hydrogen explosion caused by magnesium-water reaction. For purpose of avoiding hydrogen explosion risks, we try to use a combination of chitosan (CS) and sodium phosphate (SP) to inhibit the hydrogen evolution reaction between magnesium alloy waste dust and water. The hydrogen evolution curves and chemical kinetics modeling for ten different mixing ratios demonstrate that 0.4wt% CS + 0.1wt% SP yields the best inhibition efficiency with hydrogen generation rate of almost zero. SEM and EDS analyses indicate that this composite inhibitor can create a uniform, smooth, tight protective film over the surface of the alloy dust particles. FTIR and XRD analysis of the chemical composition of the surface film show that this protective film contains CS and SP chemically adsorbed on the surface of ZK60 but no detectable Mg(OH)2, suggesting that magnesium-water reaction was totally blocked. Our new method offers a thorough solution to hydrogen explosion by inhibiting the hydrogen generation of magnesium alloy waste dust in a wet dust removal system.

Excessive cavity pressure may result in a sand casting explosion, and corresponding measures should be adopted to prevent these consequences. In this study, the pressure variations in the cavity were first investigated based upon on-site testing by taking the resin contents into consideration, and then the evolution characteristics of sand casting explosion accidents were analyzed in depth by system dynamics, chaos theory, and the bow-tie model. When the resin contents are 1.3 wt%, 1.4 wt%, and 1.5 wt%, the pressures of the gas vent increase by 27.0 Pa, 32.8 Pa, and 35.6 Pa, respectively. To reduce the pressure of the cavity, the resin content should be reduced. The evolutionary process of sand casting explosion accidents has a noticeable butterfly effect and randomness, whose occurrence is comprehensively affected by human, object, environment, management and emergency subsystems. The leading causes of sand casting explosion accidents mainly include the extensive gas evolution characteristics of foundry sand, cavity exhaust blockage, and inadequate safety monitoring. The leading consequences of sand casting explosion accidents mainly include casualties, secondary disasters, and social panic. The implications of these findings concerning sand casting explosion accidents can be regarded as the foundation for accident prevention in practice.

China has promised to reach its peak carbon dioxide emissions by 2030. Power generation emits the most carbon dioxide in China. Fossil fuels are currently still the largest sources of energy for electricity generation in China. As the most commonly used method for estimating emissions from thermal power generation, emission factor data has a statistical lag, which brings inconvenience to carbon emission accounting and the formulation of energy conservation and emission reduction policies. It is of great significance to predict the emission factors of fossil fuel-fired power generation in China. In this paper, the correlations between the data of coal-fired, gas-fired, and oil-fired power generation from 2012 to 2019 and the emission factors of China's power generation and the correlations between the data of fossil fuel-fired power generation from 2006 to 2019 and the emission factors of China's power generation are evaluated using SPSS. On this basis, two models for predicting emission factors of China's power generation from 2020 to 2030 are established and the relevant data are obtained. The calculation results show that when the carbon peak reaches in 2030, the emission factor of China's power generation may be 0.6290 and 0.7363 corresponding to the two prediction models.

Abstract Mental load affects the work efficiency and mental health of operators, and it has a vital effect on the efficiency and reliability of human–machine systems. In this study, the evaluation index system of operators’ mental load was used to quantitatively evaluate the mental load state of workers. The system was established by selecting indices from the operators’ physiological parameters, subjective feelings, and time perception. We propose an extended cloud evaluation model of mental load states that combines cloud model (CM) theory with analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) and provides mental load levels. An energetic material initiation experiment was conducted to evaluate the mental load state of the operators using the proposed method, and the results of a fuzzy comprehensive evaluation and subjective questionnaire were used to verify the performance of the method. The results show that the extended CM evaluation method scientifically and reliably quantified the mental load state. Applying the AHP-TOPSIS integration extended with the CM theory evaluation method in mental load state evaluation provides a new scientific method for studying the quantification of the mental load state and occupational health of workers in hazardous environments. The results of this study are a reference for assessing the mental state of personnel and analyzing occupational suitability for dangerous posts.

The pouring of sand casting is accompanied by severe heat conduction, and there is an urgent need to investigate the pyrolysis properties of foundry sand. The main purpose of this study was to investigate the pyrolysis behaviors of resin sand, including precoated sand (PCS), hot box sand (HBS), and warm box sand (WBS), at heating rates of 20 °C/min, 30 °C/min, and 40 °C/min in nitrogen and air atmospheres. The mass loss of the resin sand was monitored continuously with a simultaneous thermal analyzer, and the kinetic parameters of the resin sand were calculated based on the Coats–Redfern method and thermal data. The average mass loss of the resin sand during pyrolysis was 3.03%, which was much smaller than that of the other sands. The volatile release characteristic index of resin sand could not be calculated based on this concept. To solve this issue, the term Tstv/mloss was established, and its value was determined. With increasing heating rates from 20 °C/min to 30 °C/min and from 30 °C/min to 40 °C/min, the mass losses of the resin sand increased by 0.79% and 0.64%, respectively, and the volatile release characteristic indices of the resin sand increased by 3.8 × 10−10 and 1.06 × 10−9, respectively. In addition, the mass losses and volatile release characteristic indices of resin sand in an air atmosphere were greater than those in a nitrogen atmosphere. With increasing heating rate, the activation energy of the resin sand decreased in a nitrogen atmosphere. The findings concerning the thermal decomposition behaviors of resin sand provided a theoretical basis for the pouring step of the sand casting process.

Abstract Mining is a high-risk industry and a crucial economic driver that has a crucial role in the economies of countries worldwide. The implications of the labor market on the sustainability of the mining industry have increased the importance of sustainable human resource management at the strategic level of mining and safety management. In this article, from the perspective of management research in an energy production enterprise, we investigated the relationship between employee loyalty and employee satisfaction through a survey that targets employee loyalty, work quality, and job satisfaction and the relationship between enterprise image and switching costs. Based on service profit chain theory, we established a research model for mining employee loyalty, and 500 miners in a typical extreme mining environment in China were surveyed. The study hypotheses were tested using a structural equation model and an employee loyalty model, followed by empirical testing of the models. Employee loyalty was significantly associated with enterprise image and employee satisfaction, work quality indirectly affected loyalty through satisfaction, and the impact of switching costs on employee loyalty was not significant. We provide strong empirical evidence to help enterprises improve sustainable human resource management and regulatory policies, with important implications for safety production. Our study also provides a useful reference for further studies of sustainable human resource management in mining.

Abstract Background Biodegradation of antibiotics is a promising method for the large-scale removal of antibiotic residues in the environment. However, the enzyme that is involved in the biodegradation process is the key information to be revealed. Results In this study, the beta-lactamase from Ochrobactrum tritici that mediates the biodegradation of penicillin V was identified and characterized. When searching the proteins of Ochrobactrum tritici, the β-lactamase (OtLac) was identified. OtLac consists of 347 amino acids, and predicted isoelectric point is 7.0. It is a class C β-lactamase according to BLAST analysis. The coding gene of OtLac was amplified from the genomic DNA of Ochrobactrum tritici. The OtLac was overexpressed in E. coli BL21 (DE3) and purified with Ni2+ column affinity chromatography. The biodegradation ability of penicillin V by OtLac was identified in an in vitro study and analyzed by HPLC. The optimal temperature for OtLac is 32 ℃ and the optimal pH is 7.0. Steady-state kinetics showed that OtLac was highly active against penicillin V with a Km value of 17.86 μM and a kcat value of 25.28 s−1 respectively. Conclusions OtLac demonstrated biodegradation activity towards penicillin V potassium, indicating that OtLac is expected to degrade penicillin V in the future.

Rural energy is related to the domestic energy supply, consumption, and improvement of living standards of more than one-third of the population in China. In the “14th Five-Year Plan,” it has been clearly pointed out that it is necessary to strengthen the clean utilization of coal and implement the construction of rural clean energy projects. At present, the energy consumption structure of rural areas in China is transiting from traditional solid energy to commercial energy and clean energy. Based on this background, this paper reviews the transition trend, influencing factors, and regional differences of China’s rural household energy consumption structure from the 1990s. Taking into account China’s goal of carbon peaking by 2030 and carbon neutrality by 2060, carbon dioxide and pollutant emissions generated in the process of energy consumption and the energy-saving potential of rural households are analyzed and discussed. Moreover, the evolution of rural energy policies in China is presented and related proposals are also made. This review aims to provide reference for relevant researchers and policy makers.

The coordinated development of the Beijing-Tianjin-Hebei region has become China's national strategy with great and far-reaching significance. The construction of Energy Internet is an important measure to strengthen the coordination of green energy development in Beijing-Tianjin-Hebei region and promote the development of renewable energy. In this paper, based on the development status of renewable energy in the Beijing-Tianjin-Hebei region, the problems and challenges existing in the renewable energy development in the Beijing-Tianjin-Hebei region and related considerations for their solution are reviewed and discussed. Energy Internet has also become a booster for the rapid development of clean and renewable energy. The policies on the Energy Internet Construction issued by Beijing, Tianjin, and Hebei during the 13th Five-Year Plan Period (2016–2020) and at the beginning of the 14th Five-Year Plan Period (2021–2025) are summarized. The focuses of all these policy documents are analyzed. The development status of seven Energy Internet demonstration projects in Beijing-Tianjin-Hebei region is reviewed. On this basis, some considerations on Energy Internet construction in the Beijing-Tianjin-Hebei region are analyzed and proposed.

This study focused on the extreme heavy rainstorm that occurred in Zhengzhou in July 2021; approximately 380 people were killed or missing as a result of this storm. To investigate the evolution behaviors of this rainstorm and take corresponding prevention measures, several methods and models were adopted, including cloud modeling, preliminary hazard analysis (PHA), fault tree analysis (FTA), bow-tie modeling, and chaos theory. The main reasons for this rainstorm can be divided into the following three aspects: force majeure, such as terrain and extreme weather conditions, issues with city construction, and insufficient emergency rescue. The secondary disasters caused by this rainstorm mainly include urban water logging, river flooding, and mountain torrents and landslides. The main causes of the subway line-5 accident that occurred can be described as follows: the location of the stabling yard was low, the relevant rules and regulations of the subway were not ideal, insufficient attention was given to the early warning information, and the emergency response mechanism was not ideal. Rainstorms result from the cross-coupling of faults in humans, objects, the environment, and management subsystems, and the evolution process shows an obvious butterfly effect. To prevent disasters caused by rainstorms, the following suggestions should be adopted: vigorously improve the risk awareness and emergency response capabilities of leading cadres, improve the overall level of urban disaster prevention and mitigation, reinforce the existing reservoirs in the city, strengthen the construction of sponge cities, and improve the capacity of urban disaster emergency rescue.

Human errors are considered to be the main causation factors of high-temperature molten metal accidents in metallurgical enterprises. The complex working environment of high- temperature molten metal in metallurgical enterprises has an important influence on the reliability of human behavior. A review of current human reliability techniques confirms that there is a lack of quantitative analysis of human errors in high-temperature molten metal operating environments. In this paper, a model was proposed to support the human reliability analysis of high-temperature molten metal operation in the metallurgy industry based on cognitive reliability and error analysis method (CREAM), fuzzy logic theory, and Bayesian network (BN). The comprehensive rules of common performance conditions in conventional CREAM approach were provided to evaluate various conditions for high-temperature molten metal operation in the metallurgy industry. This study adopted fuzzy CREAM to consider the uncertainties and used the BN to determine the control mode and calculate human error probability (HEP). The HEP for workers involved in high-temperature melting in steelmaking production process was calculated in a case with 13 operators being engaged in different high-temperature molten metal operations. The human error probability of two operators with different control modes was compared with the calculation result of basic CREAM, and the result showed that the method proposed in this paper is validated. This paper quantified point values of human error probability in high-temperature molten metal operation for the first time, which can be used as input in the risk evaluation of metallurgical industry.

ABSTRACT Substantial concentrations of penicillin V potassium (PVK) have been found in livestock manure, soil, and wastewater effluents, which may pose potential threats to human health and contribute to the emergence of penicillin-resistant bacterial strains. In this study, bacterial strains capable of degrading PVK were isolated from sludge and characterized. Strain X-2 was selected for biodegradation of PVK. Based on morphological observations and 16S rRNA gene sequencing, strain X-2 was identified as an Ochrobactrum tritici strain. To enhance the PVK degradation ability of PVK, a whole-cell biodegradation process of Ochrobactrum tritici X-2 was established and optimized. In the whole-cell biodegradation process, the optimal temperature and pH were 30°C and 7.0, respectively. Under the optimized conditions, the degradation rate using 0.5 mg/ml PVK reached 100% within 3 h. During biodegradation, two major metabolites were detected: penicilloic acid and phenolic acid. The present study provides a novel method for the biodegradation of PVK using Ochrobactrum tritici strains, which represent promising candidates for the industrial biodegradation of PVK. IMPORTANCE Substantial concentrations of penicillin V potassium (PVK) have been found in the environment, which may pose potential threats to human health and contribute to the emergence of penicillin-resistant bacterial strains. In this study, antibiotic-degrading bacterial strains for PVK were isolated from sludge and characterized. Ochrobactrum tritici was selected for the biodegradation of PVK with high efficiency. To enhance its PVK degradation ability, a whole-cell biodegradation process was established and optimized using Ochrobactrum tritici. The degradation rate with 0.5 mg/ml PVK reached 100% within 3 h. The potential biodegradation pathway was also investigated. To the best of our knowledge, the present study provides new insights into the biodegradation of PVK using an Ochrobactrum tritici strain, a promising candidate strain for the industrial biodegradation of β-lactam antibiotics.

In the process of anaerobic fermentation of Propionibacterium freudenreichii to produce vitamin B12 deoxyadenosylcobalamin (ADO), the intermediate adenosylcobinamide (CBI) and 5,6-dimethylbenzimidazole (DMB) are combined to produce the product ADO. At present, there is lack of detection means for vitamin B12. To solve the problem, taking CBI as the research goal, the metabolic regulation method is used to improve the addition time of DMB, and the effects of different DMB adding time on the growth of bacteria and ADO synthesis are studied. Under the conditions of batch fermentation, the CBI synthesis amount and ADO yield of Propionibacterium are determined, and the growth curve of the bacteria is drawn. It is found that the inhibitory effect of DMB on the growth of the cells is not obvious, but the unit cell yield could be inhibited. The fermentation cycle is 120 h, and the optimal addition time is 90~100 h. Compared with 60 h, the addition of DMB can increase the fermentation unit by 59.08%. The method is simple, rapid, efficient, and has a certain guiding effect on the study of fermentation regulation of vitamin B12.

In this paper, a method for waste incineration slag is proposed. An incineration acidification alkalization modification was carried out based on the characteristics of the oxides (SiO2, CaO, Al2O3, Fe2O3, and MgO) of waste incineration slag. With modified slag as the carrier and NaHCO3 as the supporter, a slag-based composite powder explosion inhibitor was prepared with the solvent-crystallization wet coating (WCSC), ball milling dry coating (DCBM), and air impact dry coating (DCAI) methods. The advantages and disadvantages of the three methods were compared and analyzed. Explosion suppression experiments on oil shale dust were carried out, and the explosion suppression mechanism was described. The explosion suppression process of the modified slag–NaHCO3 composite powder explosion inhibitor for oil shale dust was found to involve a synergy of physical and chemical inhibition. This explosion suppression mechanism indicates three requirements for the preparation and application of industrial solid waste-based composite powder explosion inhibitors. The feasibility of preparing composite powder explosion inhibitors from waste incinerator slag was discussed from the experimental point of view and its explosion suppression performance on oil shale dust was studied with the intention of providing a new form of resource utilization for waste incinerator slag.

Background: Although hydraulic support can help enterprises in their production activities, it can also cause fatal accidents. Methods: This study established a composite risk-assessment method for hydraulic support failure in the mining industry. The key basic event of hydraulic support failure was identified based on fault tree analysis and gray relational analysis, and the evolution mechanism of hydraulic support failure was investigated based on chaos theory, a synthetic theory model, and cause-and-effect-layer-of-protection analysis (LOPA). Results: After the basic events of hydraulic support failure are identified based on fault tree analysis, structure importance (SI), probability importance (PI), critical importance (CI), and Fussell–Vesely importance (FVI) can be calculated. In this study, we proposed the Fussell–Vesely–Xu importance (FVXI) to reflect the comprehensive impact of basic event occurrence and nonoccurrence on the occurrence probability of the top event. Gray relational analysis was introduced to determine the integrated importance (II) of basic events and identify the key basic events. According to chaos theory, hydraulic support failure is the result of cross-coupling and infinite amplification of faults in the employee, object, environment, and management subsystems, and the evolutionary process has an obvious butterfly effect and inherent randomness. With the help of the synthetic theory model, we investigated the social and organizational factors that may lead to hydraulic support failure. The key basic event, jack leakage, was analyzed in depth based on cause-and-effect-LOPA, and corresponding independent protection layers (IPLs) were identified to prevent jack leakage. Implications: The implications of these findings with respect to hydraulic support failure can be regarded as the foundation for accident prevention in practice.

At present, the world is committed to the development of environmentally friendly, sustainable and industrial safety. The effective treatment of industrial solid waste can be applied in the field of industrial safety. It is one of the ways to apply industrial solid waste to industrial safety to modify industrial solid waste and combine active powder to prepare industrial solid waste-based composite powder explosion inhibitors and apply it to underground coal dust explosion. This paper introduces the modification and preparation methods of industrial solid waste, and analyzes the good explosion suppression effect and good economic benefit of industrial solid waste-based composite powder explosion inhibitors on coal dust explosion. In this paper, four kinds of industrial solid wastes (red mud, slag, fly ash and sludge) were modified, and the modified solid waste materials with good carrier characteristics were obtained. Combined with a variety of active powders (NaHCO3, KH2PO4 and Al(OH)3), the industrial solid waste-based composite powder explosion inhibitors were obtained by solvent-crystallization (WCSC) and dry coating by ball milling (DCBM). Those kinds of explosion inhibitors can suppress the explosion of pulverized coal in 40–50% of cases. Compared with the powder explosion inhibitor commonly used in industry, it has a lower production cost and better explosion suppression effect. Those kinds of explosion inhibitors have a good industrial application prospect.

The influence of heating rate, gas flow, and biomass particle size on the pyrolysis and thermal dynamics of corncobs (CC) was investigated experimentally using the quantitative method of thermogravimetric analysis (TGA) coupled with mass spectrometry (MS), and the obtained results were compared in depth. For the examined heating rates of 5, 10, and 20 °C/min, the CC pyrolysis at higher heating rates resulted in a more complete decomposition. The initial pyrolysis temperature decreased when gas flow was increased from 30 to 90 mL/min, whereas the weight loss increased. Particle sizes (d ≤ 74 μm, 74 μm < d ≤ 154 μm, 154 μm < d ≤ 280 μm, and 280 μm < d ≤ 450 μm) had pronounced effects on the thermal decomposition and bio-syngas compounds (CO, CO2, CH4, and H2) distribution. The emission intensities of most the gaseous products increased at the elevated heating rate, while they decreased with increasing gas flow. In sum, the pyrolysis of CC particles of 154 μm < d ≤ 280 μm under 20 °C/min and in a gas flow of 30 to 60 mL/min was the most appropriate for bio-syngas production in industrial applications.

As a member of the POU (Pit-Oct-Unc) transcription factor family, OCT4 (Octamer-binding transcription factor 4) is associated with the cellular proliferative. However, the roles of OCT4 in regulating the transition from preantral follicle to early antral follicle are still remains unclear. To evaluate the effect of OCT4 on cellular development in ovary, mice were injected with eCG in vivo or granulosa cells were co-cultured with FSH in vitro. The results showed that eCG up-regulated ovarian OCT4 expression. Meanwhile, OCT4 expression in granulosa cells was also up-regulated by FSH, and knockdown of OCT4 by siRNA significantly decreased FSH-induced cellular viability. Moreover, gonadotropin increased p-GSK3β (Glycogen synthase kinase 3-beta) level, β-catenin expression and its translocation to nuclear in ovarian cells. In addition, the inhibition of GSK3β activity by CT99021 significantly increased the expression of β-catenin and OCT4 in granulosa cells. And knockdown β-catenin by siRNA dramatically abolished FSH-induced OCT4 expression and cellular development. Furthermore, FSH-induced the phosphorylation of GSK3β, expression of β-catenin and OCT4, and translocation of β-catenin were mediated by the PI3K/Akt pathway. Taken together, the present study demonstrates that FSH regulated OCT4 expression via GSK3β/β-catenin pathway, which was mediated by the PI3K/Akt pathway. And these regulations are involved in ovarian cell development.

Due to a wide range of applications, sand casting occupies an important position in modern casting practice. The main purpose of this study was to optimize the performance parameters of sand casting based on grey relational analysis and predict the missing data using back propagation (BP) neural network. First, the influence of human factors was eliminated by adopting the objective entropy weight method, which also saved manpower. The larger variation degree in the evaluation indicators, indicating that the evaluated projects had good discrimination in this regard, the larger weight should be given to these evaluation indicators. Second, the performance parameters of sand casting were optimized based on grey relational analysis, providing a reference for sand milling. The larger the grey relational degree, the closer the evaluated project was to the ideal project. Third, this paper provided a new method for determining the number of hidden neurons in a network according to the mean square error of training samples, and venting quality was predicted based on BP neural network. The relevant theory was deduced before predicting missing data, such that there will be a general understanding regarding the prediction principle of BP neural network. Fourth, to demonstrate the validity of BP neural network adopted in the process of missing data prediction, grey system theory was applied to compare the result of missing data prediction.

Three typical biomass fuels—rice husk, rice straw, and corn cobs—were combusted to understand the effects of ashing temperature on ash fouling and slagging characteristics. The ashes generated from combustion at 600 °C and 815 °C were characterized thoroughly with regard to their chemical composition. The systematic slagging/fouling indices of biomass were used to study the effects of ashing temperature on ash fouling and slagging propensities. The results showed that ashing temperature had a remarkable influence on ash composition, particle size distribution, ash morphology, ash fusibility, and thermal properties. Increased ashing temperature resulted in the expansion of ash particles together with the volatilization of alkali metals in the form of inorganic salts. Morphology analysis indicated that high ashing temperatures promoted biomass ash slagging. Ash fusion points increased at elevated ashing temperatures, while the ash content decreased. As a result of the volatilization and decomposition of biomass ash, a four-step mechanism of weight loss was clearly identified by thermal analysis. All prepared biomass ashes resulted in slagging and fouling problems at different levels during the thermo-chemical conversion of biomass.

The thermogravimetric characterization and gas emission properties during the pyrolysis of rice husk (RH) and rice straw (RS) were compared, and the properties of waste rice husk ash (RHA) and rice straw ash (RSA) from the combustion process were investigated. The results showed that the pyrolysis of RH and RS followed a four-step mechanism. Comparing the emission properties of small molecule bio-syngas in the pyrolysis of these two biomass wastes implied that RS was more suitable for use as feedstock for thermochemical conversion. Chemical and phase analysis results indicated that the RSA was rich in K, Ca, and P and had good potential for use as a soil amendment or as a material for silicate ceramics. The RHA was rich in SiO2 and could be an ideal silica source for silicon compound preparation or a pozzolan source for blended cement concrete production. Morphology analysis on the ash samples revealed that the high content of alkali metals may cause the higher agglomeration tendency of RSA with respect to RHA. The contrast in pore properties of biomass char wastes isolated from the ashes indicated that the char wastes recovered from RHA would be better used as a low-cost precursor in activated carbon production.

Harnessing energy from biomass is environmentally friendly because of the essentially zero net CO2 impact. As a common agricultural byproduct, corncobs are abundant in quantity. This study was carried out to examine the thermo-physical properties of corncobs and characterize the properties of corncob ash produced from gasification, in order to provide a basis for transforming it into value-added products. The results showed that the pyrolysis of corncobs followed a three-step, stepwise mechanism. Activation energies calculated by the Coats-Redfern method at heating rates of 5, 10, and 20 °C/min were 79.08, 76.73, and 75.78 kJ·mol-1, respectively, implying that the corncobs could be decomposed easily at high heating rates. The emissions of CO, CO2, CH4, H2, H2O, and O2 during pyrolysis corresponded well with thermal curves. Corncob ash could be a good fertilizer because of its high contents of K, P, and Ca. The high SiO2 content makes the corncob ash suitable for ceramics and blended cement concrete. Sylvite (KCl) and quartz (SiO2) were the two major crystal phases in the corncob ash. Relatively large particles of unburnt carbon residues in the ash indicated that low-cost adsorbent could be developed from these carbon residues.

Hazard assessment is critical for a biomass gasification station because it includes various hazardous factors. Set pair analysis (SPA) is a convenient and effective method for hazard assessment, but it has limitations, including (1) the inability to reflect the difference when the data belong to the same hazard grade and (2) the assessment results lack precision and accuracy. This study proposes an improved method designated as general set pair analysis (GSPA). Connection measure degree (CMD), which is based on the cosine function, as well as weighting deviation degree (WDD), relative membership degree (RMD), and comprehensive index (CI) were proposed in GSPA, and the algorithms were generated. The calculated results of these different methods can be utilized to overcome the shortcomings of SPA during hazard assessment. A case study of two biomass gasification stations in Shenyang City, Liaoning Province, Northeast China, was performed. The hazard assessment results of the GSPA were compared with those of SPA. The results showed that GSPA is a more effective, precise, and accurate method for hazard assessment of a biomass gasification station.

As a by-product generated from the processing of corn, the production in China of corncob (CC) is abundant, with up to 3.87 million tons per year. The biomass gasification industries make use of the CC residue as feedstock, but large volumes of generated corncob ash (CCA) requires daily disposal. In this study, CCA was characterized by laser particle size analyzer (LPSA), X-ray fluorescence (XRF), X-ray diffraction (XRD), thermal gravimetric and differential thermal analysis (TG-DTA), scanning electron microscopy, and energy dispersive X-ray (SEM-EDX). XRF results showed that the CCA was rich in K, Ca, and P, indicating its potential as a soil amendment. High content of SiO2 in CCA revealed its potential as a pozzolan in blended cement concrete. XRD showed the presence of crystal phases such as potassium carbonate, sylvite, arcanite, quartz, calcite, and nitrite. SEM images revealed the high agglomeration of CCA. EDX gave evidence of the external surface of agglomerated particles coated with KCl. TG-DTA analysis indicates that decomposition of CCA has stepwise mechanism. The CCA powders through a 0.154 mm sieve showed a high specific surface area of 162.32 m2/g, average pore size of 12.17 Å with pore volume of 0.116 cm3/g. The carbon residue separated from CCA has the potential to be used as activated carbon.

Safe production is the foundation of the normal operations of petrochemical enterprises, and it helps maintain social stability. The main purpose of this study is to prevent petrochemical enterprise accidents by proposing a composite safety assessment approach based on the cloud model, preliminary hazard analysis–layer of protection analysis (PHA–LOPA) and the bow-tie model. First, the petrochemical enterprise and its relevant indicators were evaluated based on the cloud model. Second, the quantitative effect of the uncertainty transformation on the evaluation result of the cloud model was further analysed. This mainly includes the error analysis of the numerical characteristics under the conditions of few samples and small values. Third, the critical indicators such as shock and noise can be weakened and prevented by corresponding safety measures based on PHA–LOPA and the bow-tie model. After adopting two independent protection layers, the risk levels of shock and noise decrease from 3 to 2. Then, shock and noise were analysed in depth with the bow-tie model, and the causes and consequences were identified. Moreover, corresponding safety measures were taken to prevent accidents. The case study validated the validity and feasibility of the composite safety assessment approach proposed here.

To effectively control air pollution, it is necessary to obtain a preliminary assessment of air quality. The purpose of this study was to introduce a cloud model method in air pollution assessment. First, the standard cloud models of air pollution indicators were obtained, and the calculating process of numerical characteristics employed by the standard cloud model was explained. Second, the levels of air pollution indicators were presented based on the qualitative and quantitative analysis of cloud models, which realized the uncertainty conversion between qualitative concepts and their corresponding quantitative values, as well as taking the fuzziness and randomness into account. Air quality assessment results including SO2, NO2, CO, O3, PM10 and PM2.5 were analysed. Third, the cloud model adopted in the assessment process of air quality was validated by grey relational analysis, and the results confirmed the validity of cloud model assessment. Fourth, the air pollution level of the air quality index (AQI) was determined, and the fuzziness and randomness of the assessment results were thoroughly analysed by taking entropy and hyper entropy into consideration. Fifth, seasonal variations in different air pollution indicators were analysed to proffer a series of recommendations for government policy decision-makers and travellers. The cloud model provided a new method for air quality assessment.

Mine safety assessment is a precondition for ensuring orderly and safety in production. The main purpose of this study was to prevent mine accidents more effectively by proposing a composite risk analysis model. First, the weights of the assessment indicators were determined by the revised integrated weight method, in which the objective weights were determined by a variation coefficient method and the subjective weights determined by the Delphi method. A new formula was then adopted to calculate the integrated weights based on the subjective and objective weights. Second, after the assessment indicator weights were determined, gray relational analysis was used to evaluate the safety of mine enterprises. Mine enterprise safety was ranked according to the gray relational degree, and weak links of mine safety practices identified based on gray relational analysis. Third, to validate the revised integrated weight method adopted in the process of gray relational analysis, the fuzzy evaluation method was used to the safety assessment of mine enterprises. Fourth, for first time, bow tie model was adopted to identify the causes and consequences of weak links and allow corresponding safety measures to be taken to guarantee the mine's safe production. A case study of mine safety assessment was presented to demonstrate the effectiveness and rationality of the proposed composite risk analysis model, which can be applied to other related industries for safety evaluation.

Sand casting, currently the most popular approach to the casting production, has wide adaptability and low cost. The thermal decomposition characteristics of foundry sand for cast iron were determined for the first time in this study. Thermogravimetry was monitored by simultaneous thermal analyser to find that there was no obvious oxidation or combustion reaction in the foundry sand; the thermal decomposition degree increased as the heating rate increased. There was an obvious endothermic peak at about 846 K due to the transition of quartz from β to α phase. A novel technique was established to calculate the starting temperature of volatile emission in determining the volatile release parameter of foundry sand for cast iron. Foundry sand does not readily evaporate because its volatile content is only about 2.68 wt% and its main components have high-temperature stability. The thermal decomposition kinetics parameters of foundry sand, namely activation energy and pre-exponential factor, were obtained under kinetics theory. The activation energy of foundry sand for cast iron was small, mainly due to the wide temperature range of thermal decomposition in the foundry sand.

Sand casting operations, though commonplace, pose a significant threat of explosion accidents. This paper presents a novel sand casting safety assessment technique based on fault tree analysis, Heinrich accident triangle, hazard and operability–layer of protection analysis (HAZOP–LOPA) and bow tie model components. Minimal cut sets and minimal path sets are first determined based on fault tree analysis, then the frequency of sand casting explosion accidents is calculated based on the Heinrich accident triangle. Third, the risk level of venting quality can be reduced by adopting HAZOP–LOPA; the residual risk level of venting quality remains excessive even after adopting two independent protective layers. The bow tie model is then adopted to determine the causes and consequences of venting quality. Five preventative measures are imposed to enhance the venting quality of foundry sand accompanied by 16 mitigative safety measures. Our results indicate that the risk attributable to low foundry sand venting quality can be minimized via bow tie analysis.

Safety assessments are a crucial first step in preventing coal and gas outburst accidents. The main purpose of this study was to create a new accident prevention technique using a novel safety assessment method based on fault tree basic event importance, grey relational analysis and the bow tie model. The innovation of the proposed method lies in generating the composite importance of a basic event from the fundamental importance via grey relational analysis; bow tie analysis serves to reveal the most critical basic event. First, the minimal cut sets and minimal path sets of a coal and gas outburst accident are determined by fault tree analysis. The role of minimal cut and path sets is determined and the coal and gas outburst occurrence frequency is calculated accordingly. Second, the structure, probability, critical and Fussell–Vesely importance ranked basic events differently due to different aspects of the basic events as investigated. We establish a composite importance to represent single basic events and achieved new ranking results by grey relational analysis. Third, the critical basic event low permeability coefficient is analysed via bow tie model and safety measures are defined which prevent the dangerous consequences of a low permeability coefficient. An actual coal and gas outburst accident is used as a case study to test the feasibility and effectiveness of the proposed method.

Because a biomass gasification station includes various hazard factors, hazard assessment is needed and significant. In this article, the cloud model (CM) is employed to improve set pair analysis (SPA), and a novel hazard assessment method for a biomass gasification station is proposed based on the cloud model-set pair analysis (CM-SPA). In this method, cloud weight is proposed to be the weight of index. In contrast to the index weight of other methods, cloud weight is shown by cloud descriptors; hence, the randomness and fuzziness of cloud weight will make it effective to reflect the linguistic variables of experts. Then, the cloud connection degree (CCD) is proposed to replace the connection degree (CD); the calculation algorithm of CCD is also worked out. By utilizing the CCD, the hazard assessment results are shown by some normal clouds, and the normal clouds are reflected by cloud descriptors; meanwhile, the hazard grade is confirmed by analyzing the cloud descriptors. After that, two biomass gasification stations undergo hazard assessment via CM-SPA and AHP based SPA, respectively. The comparison of assessment results illustrates that the CM-SPA is suitable and effective for the hazard assessment of a biomass gasification station and that CM-SPA will make the assessment results more reasonable and scientific.

Biomass gasification stations are facing many hazard factors, therefore, it is necessary to make hazard assessment for them. In this study, a novel hazard assessment method called extended set pair analysis (ESPA) is proposed based on set pair analysis (SPA). However, the calculation of the connection degree (CD) requires the classification of hazard grades and their corresponding thresholds using SPA for the hazard assessment. In regard to the hazard assessment using ESPA, a novel calculation algorithm of the CD is worked out when hazard grades and their corresponding thresholds are unknown. Then the CD can be converted into Euclidean distance (ED) by a simple and concise calculation, and the hazard of each sample will be ranked based on the value of ED. In this paper, six biomass gasification stations are introduced to make hazard assessment using ESPA and general set pair analysis (GSPA), respectively. By the comparison of hazard assessment results obtained from ESPA and GSPA, the availability and validity of ESPA can be proved in the hazard assessment for biomass gasification stations. Meanwhile, the reasonability of ESPA is also justified by the sensitivity analysis of hazard assessment results obtained by ESPA and GSPA.

Biomass gasification technology has been rapidly developed recently. But fire and poisoning accidents caused by gas leakage restrict the development and promotion of biomass gasification. Therefore, probabilistic safety assessment (PSA) is necessary for biomass gasification system. Subsequently, Bayesian network-bow-tie (BN-bow-tie) analysis was proposed by mapping bow-tie analysis into Bayesian network (BN). Causes of gas leakage and the accidents triggered by gas leakage can be obtained by bow-tie analysis, and BN was used to confirm the critical nodes of accidents by introducing corresponding three importance measures. Meanwhile, certain occurrence probability of failure was needed in PSA. In view of the insufficient failure data of biomass gasification, the occurrence probability of failure which cannot be obtained from standard reliability data sources was confirmed by fuzzy methods based on expert judgment. An improved approach considered expert weighting to aggregate fuzzy numbers included triangular and trapezoidal numbers was proposed, and the occurrence probability of failure was obtained. Finally, safety measures were indicated based on the obtained critical nodes. The theoretical occurrence probabilities in one year of gas leakage and the accidents caused by it were reduced to 1/10.3 of the original values by these safety measures.