Investigations of the effects of two typical jet crushing methods on the atomization and dust reduction performance of nozzles

Single-fuid nozzles and dual-fuid nozzles are the two typical jet crushing methods used in spray dust reduction. To distinguish the atomization mechanism of single-fuid and dual-fuid nozzles and improve dust control efciency at the coal mining faces, the atomization characteristics and dust reduction performance of the two nozzles were quantitatively compared. Results of experiments show that, as water supply pressure increased, the atomization angle of the swirl pressure nozzle reaches a maximum of 62° at 6 MPa and then decreases, but its droplet size shows an opposite trend with a minimum of 41.7 μm. The water supply pressure helps to improve the droplet size and the atomization angle of the internal mixing air–liquid nozzle, while the air supply pressure has a suppressive efect for them. When the water supply pressure is 0.2 MPa and the air supply pressure reaches 0.4 MPa, the nozzle obtains the smallest droplet size which is 10% smaller than the swirl pressure nozzle. Combined with the dust reduction experimental results, when the water consumption at the working surface is not limited, using the swirl pressure nozzle will achieve a better dust reduction efect. However, the internal mixing air–liquid nozzle can achieve better and more economical dust reduction performance in working environments where water consumption is limited.

Dynamic Forecasting of Traffic Event Duration in Istanbul:A Classification Approach with Real-Time Data Integration

Today,urban traffic,growing populations,and dense transportation networks are contributing to an increase in traffic incidents.These incidents include traffic accidents,vehicle breakdowns,fires,and traffic disputes,resulting in long waiting times,high carbon emissions,and other undesirable situations.It is vital to estimate incident response times quickly and accurately after traffic incidents occur for the success of incident-related planning and response activities.This study presents a model for forecasting the traffic incident duration of traffic events with high precision.The proposed model goes through a 4-stage process using various features to predict the duration of four different traffic events and presents a feature reduction approach to enable real-time data collection and prediction.In the first stage,the dataset consisting of 24,431 data points and 75 variables is prepared by data collection,merging,missing data processing and data cleaning.In the second stage,models such as Decision Trees(DT),K-Nearest Neighbour(KNN),Random Forest(RF)and Support Vector Machines(SVM)are used and hyperparameter optimisation is performed with GridSearchCV.In the third stage,feature selection and reduction are performed and real-time data are used.In the last stage,model performance with 14 variables is evaluated with metrics such as accuracy,precision,recall,F1-score,MCC,confusion matrix and SHAP.The RF model outperforms other models with an accuracy of 98.5%.The study’s prediction results demonstrate that the proposed dynamic prediction model can achieve a high level of success.

Dust Storm Ensemble Forecast Experiments in East Asia

The ensemble Kalman filter （EnKF）, as a unified approach to both data assimilation and ensemble forecasting problems, is used to investigate the performance of dust storm ensemble forecasting targeting a dust episode in the East Asia during 23-30 May 2007. The errors in the input wind field, dust emission intensity, and dry deposition velocity are among important model uncertainties and are considered in the model error perturbations. These model errors are not assumed to have zero-means. The model error means representing the model bias are estimated as part of the data assimilation process. Observations from a LIDAR network are assimilated to generate the initial ensembles and correct the model biases. The ensemble forecast skills are evaluated against the observations and a benchmark/control forecast, which is a simple model run without assimilation of any observations. Another ensemble forecast experiment is also performed without the model bias correction in order to examine the impact of the bias correction. Results show that the ensemble-mean, as deterministic forecasts have substantial improvement over the control forecasts and correctly captures the major dust arrival and cessation timing at each observation site. However, the forecast skill decreases as the forecast lead time increases. Bias correction further improved the forecasts in down wind areas. The forecasts within 24 hours are most improved and better than those without the bias correction. The examination of the ensemble forecast skills using the Brier scores and the relative operating characteristic curves and areas indicates that the ensemble forecasting system has useful forecast skills.

Forecasting Emergency Paediatric Asthma Hospital Admissions in Trinidad and Tobago: Development of a Local Model Incorporating the Interactions of Airborne Dust and Pollen Concentrations with Meteorological Parameters and a Time-Lag Factor

Respiratory diseases such as asthma and rhinitis are multifaceted disorders which are exacerbated by various factors including: gender, age, diet, genetic background, biological materials, allergens (pollen and spores), pollutants, meteorological conditions and dust particles. It is hypothesized that, the number of valid physician diagnosed cases of paediatric asthma, which has resulted in emergency room visits in Trinidad can be expressed as a function of the magnitude of pollen counts, particulate matter (PM10), and selected meteorological parameters. These parameters were used to develop a 7-day predictive model for paediatric asthma admittance. The data showed no obvious, strong correlations between paediatric asthma admissions and dust concentrations, and paediatric asthma admissions and pollen concentrations, when considered in isolation or in a linear fashion. However, using polynomial regression analysis, which looked at combinations of interactions, a strong 7-day predictive model for paediatric asthma admissions, was developed. The model was tested against actual data collated during the study period and showed a strong correlation (R2 = 0.85) between the regression model and the actual admissions data.

Statistical Guidance on Seasonal Forecast of Korean Dust Days over South Korea in the Springtime

This study aimed to develop the seasonal forecast models of Korean dust days over South Korea in the springtime. Forecast mode was a ternary forecast （below normal, normal, above normal） which was classified based on the mean and the standard deviation of Korean dust days for a period of 30 years （1981-2010）. In this study, we used three kinds of monthly data： the Korean dust days observed in South Korea, the National Center for Environmental Prediction in National Center for Atmospheric Research （NCEP/NCAR） reanalysis data for meteorological factors over source regions of Asian dust, and the large-scale climate indices offered from the Climate Diagnostic Center and Climate Prediction Center in NOAA. Forecast guidance consisted of two components; ordinal logistic regression model to generate trinomial distributions, and conversion algorithm to generate ternary forecast by two thresholds. Forecast guidance was proposed for each month separately and its predictability was evaluated based on skill scores.

Flood Forecasting of Malaysia Kelantan River using Support Vector Regression Technique

The rainstorm is believed to contribute flood disasters in upstream catchments,resulting in further consequences in downstream area due to rise of river water levels.Forecasting for flood water level has been challenging,present-ing complex task due to its nonlinearities and dependencies.This study proposes a support vector machine regression model,regarded as a powerful machine learning-based technique to forecast flood water levels in downstream area for different lead times.As a case study,Kelantan River in Malaysia has been selected to validate the proposed model.Four water level stations in river basin upstream were identified as input variables.A river water level in downstream area was selected as output of flood forecasting model.A comparison with several bench-marking models,including radial basis function(RBF)and nonlinear autoregres-sive with exogenous input(NARX)neural network was performed.The results demonstrated that in terms of RMSE error,NARX model was better for the proposed models.However,support vector regression(SVR)demonstrated a more consistent performance,indicated by the highest coefficient of determination value in twelve-hour period ahead of forecasting time.The findings of this study signified that SVR was more capable of addressing the long-term flood forecasting problems.

A VAE-Bayesian deep learning scheme for solar power generation forecasting based on dimensionality reduction

The advancements in distributed generation(DG)technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems.However,the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties.This paper proposes a novel probabilistic scheme for renewable solar power generation forecasting by addressing data and model parameter uncertainties using Bayesian bidirectional long short-term memory(BiLSTM)neural networks,while handling the high dimensionality in weight parameters using variational auto-encoders(VAE).The forecasting performance of the proposed method is evaluated using various deterministic and probabilistic evaluation metrics such as root-mean square error(RMSE),Pinball loss,etc.Furthermore,reconstruction error and computational time are also monitored to evaluate the dimensionality reduction using the VAE component.When compared with benchmark methods,the proposed method leads to significant improvements in weight reduction,i.e.,from 76,4224 to 2,022 number of weight parameters,quantifying to 97.35%improvement in weight parameters reduction and 37.93%improvement in computational time for 6 months of solar power generation data.

An air quality forecasting system in Beijing-Application to the study of dust storm events in China in May 2008

An air pollution forecast system,ARIA Regional,was implemented in 2007–2008 at the Beijing Municipality Environmental Monitoring Center,providing daily forecast of main pollutant concentrations.The chemistry-transport model CHIMERE was coupled with the dust emission model MB95 for restituting dust storm events in springtime so as to improve forecast results.Dust storm events were sporadic but could be extremely intense and then control air quality indexes close to the source areas but also far in the Beijing area.A dust episode having occurred at the end of May 2008 was analyzed in this article,and its impact of particulate matter on the Chinese air pollution index （API） was evaluated.Following our estimation,about 23 Tg of dust were emitted from source areas in Mongolia and in the Inner Mongolia of China,transporting towards southeast.This episode of dust storm influenced a large part of North China and East China,and also South Korea.The model result was then evaluated using satellite observations and in situ data.The simulated daily concentrations of total suspended particulate at 6：00 UTC had a similar spatial pattern with respect to OMI satellite aerosol index.Temporal evolution of dust plume was evaluated by comparing dust aerosol optical depth （AOD） calculated from the simulations with AOD derived from MODIS satellite products.Finally,the comparison of reported Chinese API in Beijing with API calculated from the simulation including dust emissions had showed the significant improvement of the model results taking into accountmineral dust correctly.

Impact-based forecasting for improving the capacity of typhoon-related disaster risk reduction in typhoon committee region

The two most common types of disasters caused by natural hazards in the Asia-Pacific region are floods and storms,many of them associated with typhoon(tropical cyclone)related impacts.To improve the capacity of typhoon-related disaster risk reduction so as to maximum reduce the losses of people’s life and properties,the decision makers and the public are imminently demanding the information of the targeted impact caused by typhoon.As the front line in hydro-meteorological disaster prevention and mitigation against the typhoon-related disasters,National Meteorological and Hydrological Services(NMHSs)in TC Members have recognized that forecasting impact became more important than forecasting pure causing-disaster elements.Impact-based forecasting signals an evolution from“what the weather will be”to“what the weather will do”.Many things change as impact based forecasts evolve from previous weather forecasts.To enhance impact-based typhoon forecasting,the Typhoon Committee added it into the new updated Strategic Plan 2022–2026.This paper briefed generally the concept of impact based forecasting,introduced the implementation and progresses on typhoon impact based forecasting in TC Members in recent years,and initially discussed the measures and direction for enhancement of impact-based typhoon forecasting and early warning services in future.

Solar Flux Measuring and Optical Efficiency Forecasting of the Linear Fresnel Reflector Concentrator after Dust Accumulation

The linear Fresnel reflector concentrator(LFRC)is widely used in the field of solar energy utilization due to its simple structure,low cost,and excellent wind resistance.Nevertheless,the LFRC operates outdoors all year round,and the dust accumulation on the mirror will reduce the optical efficiency of the system,so it needs to be perfected and improved.In this paper,a focal plane energy flux experimental device was designed to test the energy flux of the system under different dust accumulation times.The results indicate that,the dust density on the mirror increased and the energy flux on the focal plane decreased with increase of dust accumulation time.After undergoing dust accumulation for 35 days,the dust density on the mirror reached 4.33 g/m^(2)and the average energy flux on the focal plane decreased to 1.78 kW/m^(2).Additionally,the variation of reflectivity caused by dust accumulation on mirror was taken as the quantitative index,and a prediction model for the impact of dust on the optical efficiency of the system was proposed.The results will provide guidance for improving the optical efficiency of the LFRC.

Evaluating droplet distribution of spray-nozzles for dust reduction in livestock buildings using machine vision

Previous studies have demonstrated the negative effects of sub-optimal air quality on profitability,production efficiency,environmental sustainability and animal welfare.Experiments were conducted to assess potential environmental improvement techniques such as installing oil-spraying systems in piggery buildings.The developed spray system worked very well and it was easy to assemble and operate.However,before selecting the most suitable spray heads,their capacity to uniformly distribute the oily mixture and the area covered by the spray heads had to be assessed.Machine vision techniques were used to evaluate the ability of different spray heads to evenly distribute the oil/water mixture.The results indicated that the best coverage was achieved by spray head No.4 and spray head No.1 which covered 79%and 67%of the target area,respectively.Spray distribution uniformity(variance)value was the lowest for spray head No.4(0.015).Spray head No.3 had the highest variance value(0.064).As the lowest variance means higher uniformity,nozzle No.4 was identified as the most suitable spray head for dust reduction in livestock buildings.

Reduction process and zinc removal from composite briquettes composed of dust and sludge from a steel enterprise

In this study, composite briquettes were prepared using gravity dust and converter sludge as the main materials; these briquettes were subsequently reduced in a tube furnace at 1000-1300℃ for 5-30 min under a nitrogen atmosphere. The effects of reaction temperature, reaction time, and carbon content on the metallization and dezincification ratios of the composite briquettes were studied. The reduced com- posite briquettes were characterized by X-ray diffraction （XRD）, scanning electron microscopy （SEM）, and energy-dispersive spectroscopy （EDS）. The results show that the gravity dust and converter sludge are combined into the composite briquettes and a reasonable combination not only improves the performance of the composite briquettes, but also leads to the reduction with no or little reductant and flux. As the re- action temperature is increased and the reaction time is extended, the metallization and dezincification ratios of the composite briquettes in- crease gradually. When the composite briquettes are roasted at 1300℃ for 30 rain, the metallization ratio and dezineification ratio reaches 91.35% and 99.25%, respectively, indicating that most of the iron oxide is reduced and the zinc is almost completely removed. The carbon content is observed to exert a lesser effect on the reduction process; as the C/O molar ratio increases, the metallization and dezincification ra- tios first increase and then decrease.

Numerical simulation of the direct reduction of pellets in a rotary hearth furnace for zinc-containing metallurgical dust treatment

A mathematical model was established to describe the direct reduction of pellets in a rotary hearth furnace （RHF）. In the model, heat transfer, mass transfer, and gas-solid chemical reactions were taken into account. The behaviors of iron metallization and dezincification were analyzed by the numerical method, which was validated by experimental data of the direct reduction of pellets in a Si-Mo furnace. The simulation results show that if the production targets of iron metallization and dezincification are up to 80% and 90%, respectively, the furnace temperature for high-temperature sections must be set higher than 1300~ C. Moreover, an undersupply of secondary air by 20% will lead to a decline in iron metallization rate of discharged pellets by 10% and a decrease in dezincing rate by 13%. In addition, if the residence time of pellets in the furnace is over 20 min, its further extension will hardly lead to an obvious increase in production indexes under the same furnace temperature curve.

Basic properties of steel plant dust and technological properties of direct reduction

Basic physicochemical properties of the dust from Laiwu Iron and Steel Co. Ltd. were studied. It is found that C, Zn, K, Na, etc. exist in the fabric filter dust, off gas （OG） sludge, fine ash in converter, and electrical field dust in sinter. Among these, OG sludge gives the finest particle, more than 90% of which is less than 2.51 mm. The dust can lead to a serious negative influence on the production of sintering and blast furnaces （BF） if it is recycled in sintering. The briquette and reduction experimental results showed that the qualified strength could be obtained in the case of 8wt% molasses or 4wt% QT-10 added as binders. Also, more than 75% of metallization ratio, more than 95% of dezincing ratio, as well as more than 80% of K and Na removal rates were achieved for the briquettes kept at 1250℃ for 15 min during the direct reduction process. SEM observation indicated that the rates of indirect reduction and carbonization became dominating when the bri-quettes were kept at 1250℃ for 6 min.

Mathematical model of the direct reduction of dust composite pellets containing zinc and iron

Direct reduction of dust composite pellets containing zinc and iron was examined by simulating the conditions of actual production process of a rotary hearth furnace （RHF） in laboratory. A mathematical model was constructed to study the reduction kinetics of iron oxides and ZnO in the dust composite pellets. It was validated by comparing the calculated values with experimental results. The effects of furnace temperature, pellet radius, and pellet porosity on the reduction were investigated by the model. It is shown that furnace temperature has obvious influence on both of the reduction of iron oxides and ZnO, but the influence of pellet radius and porosity is much smaller. Model calculations suggest that both of the reduction of iron oxides and ZnO are under mixed control with interface reactions and Boudouard reaction in the early stage, but only with interface reactions in the later stage.

Recovery of antimony from antimony-bearing dusts through reduction roasting process under CO–CO_2 mixture gas atmosphere after firstly oxidation roasted

This paper mainly investigated the antimony recovery from antimony-bearing dusts through reduction roasting process after the dust firstly oxidation roasted.CO–CO2 mixture gas was used as reducing agent,and the antimony-containing phase was reduced into Sb4O6,volatilized into smoke,and finally recovered through the cooling cylinder.The antimony recovery rate increased from 66.00 wt%to 73.81 wt%in temperature range of 650 to 800°C,and decreased with temperature increased further to 900°C due to the reduction of Sb4O6 to the nonvolatile Sb.Similarly,the CO partial pressure also played a double role in this test.Under optimized conditions of roasting temperature of 800°C,CO partial pressure of 7.5 vol%and roasting time of 120 min,98.40 wt%of arsenic removal rate and 80.40 wt%antimony recovery rate could be obtained.In addition,the“As2O3”product could be used for preparing ferric arsenate which realized the harmless treatment of it.

Integration of preparation of K,Na-embedded activated carbon and reduction of Zn-bearing dusts

Large amounts of solid wastes and flue gases are generated in iron and steel production process,probably leading to serious environmental pollution without duly handle.An innovative and green process of simultaneous reduction of zinc-bearing dusts and activation of low-rank coal was developed and its mechanism was clarified in this paper.Under the optimal conditions,the reduced zinc-bearing dusts containing low harmful elements(0.02%Zn,0.015%K and 0.03%Na)could be made as high-quality burden for blast furnace while the low-rank coal was transferred into K,Na-embedded activated carbon,which can be used as effective adsorbent for purification of SO_(2) and NO-containing flue gases.The solid wastes were successfully utilized to treat the flue gases through the process.The synergetic activation and reduction mechanism in the process was revealed.The coupling effect between reduction reactions of metal oxides in the dusts and activation reaction of carbon in the coal promoted the simultaneous reduction and activation process.In the meanwhile,part of the potassium and sodium from the zinc-bearing dusts could be adsorbed by the activated carbon and played a catalytic role in the activation process.

Lead Concentration in Hong Kong Roadside Dust after Reduction of Lead Level in Petrol

Samples of roadside dust were collected from 30 sites in Hong Kong.The total concentrations of 10 metals in the samples were analyzed,and the correlation coefficients among the metal contents and the annual average daily traffic(AADT)in 1986 were determined.Pb was found to have a significant correlation(P<0.01)with AADT.No correlation was found between Al, Ba,Cd,Cr,Cu,Fe,Mn,Ni,and Zn with respect to AADT.A general decrease in the level of Pb in roadside dust in the past few years has been observed since the reduction in the level of lead in petrol.1989 Academic Press,Inc.

A new-generation sand and dust storm forecasting system GRAPES_CUACE/Dust:Model development,verification and numerical simulation

Based on the new-generation Global/Regional Assimilation and PrEdiction System(GRAPES) developed by the Numerical Prediction Research Center,China Meteorological Administration and the Chinese Unified Atmospheric Chemistry Environment for Dust Atmospheric Chemistry Module(CUACE/Dust) developed by the Centre for Atmosphere Watch and Services(CAWAS) of the Chinese Academy of Meteorological Sciences(CAMS) ,the China sand and dust storm forecasting system GRAPES /CUACE-Dust model has been established.The latest data of land desertification,optical properties of China sand and dust aerosols,daily soil moisture and snow cover over China main land was introduced in this system.The system showed good performance in mass conservation.The comparisons of real-time prediction outputs with surface observations and aerosol indices derived from TOMS ozone spectrophotometers(TOMS AI) indicate that the model can predict the outbreak,development,transport and depletion processes of sand and dust storms accurately over China and the East Asian region.The system makes real-time quantitative prediction of a series of elements including sand and dust injection from the surface,dry and wet deposition amount,dust concentration and optical depth.We selected 7 major dust storms occurring in April 2006 and analyzed the spatiotemporal distribution patterns of dust emission,dry and wet deposition and atmospheric load in this paper.The results showed that about 225 million tons of dust were emitted into the atmosphere from the deserts over east Asia in that month.The 3 major sand and dust sources were just as the following:The deserts in northern Inner Mongolia and the boundary area around China-Mongolia were the first dust sources with a contribution of 153 million tons accounting for 68%of the total emission.The Taklimakan Desert ranked second and contributed approximately 40 million tons accounting for 17%of the dust emission.The Onqin Daga Sandland emitted about 15 million tons or 7%of the total emission.The contributions from other deserts,sandy lands and abandoned farmlands were about only 8%of the total dust emission.The total deposition over the East Asian region in April 2006 was 136 million tons.The regional distribution of dust deposition showed that the 3 major sources were also the major deposition areas.The deposition amount in the 3 major sources accounted for 78%of the total with about 135 million tons falling back to the source regions.The secondary deposition area was the China mainland downriver,where more than 2 million tons deposited accounting for 16%of the total deposition.The deposition over the region east of 120°E including China off-shore regions,Korean Peninsula,Japan and the West Pacific took only 6%of the total deposition,which was about 850000 tons.The analysis on dry and wet deposition showed that dry deposition dominated in the process,accounting for 94%of the total sand and dust depositions in the period and wet deposition only was around for 6%,since it was generally dry with less rainfall over northern China in April.

Effects and mechanisms of fluorite on the co-reduction of blast furnace dust and seaside titanomagnetite

The co-reduction roasting and grinding-magnetic separation of seaside titanomagnetite and blast furnace dust was investigated with and without fluorite addition at a reduction roasting temperature of 1250°C for 60 min, a grinding fineness of-43 μm accounting for 69.02 wt% of the total, and a low-intensity magnetic field strength of 151 kA/m. The mineral composition, microstructure, and state of the roasted products were analyzed, and the concentrations of CO and CO_2 were analyzed in the co-reduction roasting. Better results were achieved with a small fluorite dosage(≤4 wt%) in the process of co-reduction. In addition, F^- was found to reduce the melting point and viscosity of the slag phase because of the high content of aluminate and silicate minerals in the blast furnace dust. The low moisture content of the blast furnace dust and calcic minerals inhibited the hydrolysis of CaF_2 and the loss of F^-. Compared with the blast furnace dust from Chengdeng, the blast furnace dusts from Jiugang and Jinxin inhibited the diffusion of F-when used as reducing agents, leading to weaker effects of fluorite.