Fuzzy inference system using genetic algorithm and pattern search for predicting roof fall rate in underground coal mines

One of the most dangerous safety hazard in underground coal mines is roof falls during retreat mining.Roof falls may cause life-threatening and non-fatal injuries to miners and impede mining and transportation operations.As a result,a reliable roof fall prediction model is essential to tackle such challenges.Different parameters that substantially impact roof falls are ill-defined and intangible,making this an uncertain and challenging research issue.The National Institute for Occupational Safety and Health assembled a national database of roof performance from 37 coal mines to explore the factors contributing to roof falls.Data acquired for 37 mines is limited due to several restrictions,which increased the likelihood of incompleteness.Fuzzy logic is a technique for coping with ambiguity,incompleteness,and uncertainty.Therefore,In this paper,the fuzzy inference method is presented,which employs a genetic algorithm to create fuzzy rules based on 109 records of roof fall data and pattern search to refine the membership functions of parameters.The performance of the deployed model is evaluated using statistical measures such as the Root-Mean-Square Error,Mean-Absolute-Error,and coefficient of determination(R_(2)).Based on these criteria,the suggested model outperforms the existing models to precisely predict roof fall rates using fewer fuzzy rules.

Effect of Fe_(3)O_(4) nanoparticles on magnetic xerogel composites for enhanced removal of fluoride and arsenic from aqueous solution

Fe_(3)O_(4)magnetic xerogel composites were prepared by polycondensation of resorcinol(R)-formaldehyde reaction via a sol-gel process in an aqueous solution through varying the molar ratio of Fe_(3)O_(4)nanoparticles(MNPs),catalyst(C),and water(W)content.MNPs were obtained by co-precipitation(MC),oxidation of iron salts(MO),or solvothermal synthesis(MS).Both MNPs and magnetic xerogels were examined regarding the performance of arsenic and fluoride removal in a batch system.The MC-based MNPs had higher adsorption capacities for both fluoride(202.9 mg/g)and arsenic(3.2 mg/g)than other MNPs in optimum conditions.The X-ray diffraction,Fourier transform infrared spectroscopy,and energy-dispersive X-ray spectroscopy confirmed that Fe was composed into the polymeric matrix of magnetic xerogels that contained 0.59%-4.42%of Fe with a molar ratio of MNPs(M)to R between 0.01 and 0.10.With low R/C and optimum M/R ratios,an increase in the surface area of magnetic xerogels affected the fluoride and arsenic adsorption capacities.The magnetic xerogel composites with the MC-based MNPs prepared at a fixed R/C ratio(100)and at different R/W(0.05-0.06)and M/R(0.07-0.10)ratios had a high arsenic removal efficiency of 100%at an As(V)concentration of 0.1 mg/L and pH of 3.0.The maximum adsorption capacities of magnetic xerogels were approximately five times higher than those of the xerogels without MNP composites.Therefore,Fe_(3)O_(4)nanoparticles enhanced the adsorption of arsenate and fluoride.The variations of alkaline catalyst and water content significantly affected the resulting properties of textural and surface chemistry of magnetic xerogel composites.

Rain Forests in Subtropical Mountains of Dominican Republic

This article presents a study of the cloud forest dominated by the Prestoea montana (R. Graham) Nichols. This is a hyperhumid forest located in ravines and shaded areas with cloud condensation, and at heights ranging from 350 - 1250 masl. This type of forest is always found in thermal and mesotropical belts, without ever reaching the supratropical belt. This study was based on works published by other authors and on our own field sampling. The article begins by describing the geological, bioclimatic, biogeographic and floristic aspects of the areas in the study, specifically the hyperhumid areas in the central and eastern mountains. Due to the scarcity of phytosociological works on this area, we apply a statistical treatment to compare our communities with other associations described in Cuba. This comparison reveals that these forest groups are floristically quite distant from each other, both with regard to alliance and association species. We therefore propose two new communities: Cyathea furfuracea Baker and Prestoea montana (Grah.) Nichol;Ormosia krugii Urb. and Prestoea montana (Grah.) Nichol.

Remediation of amide pesticide-polluted soils by combined solarization and ozonation treatment

Agriculture has a close relationship with nature,but it can also be the source of negative and permanent environmental effects.The use of pesticides in modern agriculture is a common practice,but their side effects on the environment cannot be disregarded.In this study,we evaluated a combination of solarization and ozonation techniques for the elimination of six amide pesticides(boscalid,chlorantraniliprole,cyflufenamid,fluopyram,napropamide,and propyzamide)in soil.Initial experiments were performed with four different soils to assess the efficiency of this methodology at different soil temperatures and ozone dosages under laboratory conditions,and then a greenhouse pot experiment was conducted under controlled conditions during summer.Fifty days after the onset of the experiments,higher degradation percentages of amide pesticides were observed in ozonized soils than in other treated soils,particularly when ozone was applied at 10 cm soil depth.The results show that the utilization of ozonation,along with solarization,represents a valid method for degrading residues of the studied pesticides and suggest that this combined technology may be a promising tool for remediating pesticide-polluted soils.

Component modeling and updating method of integrated energy systems based on knowledge distillation

Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supply and demand sides. The development of artificial intelligence algorithms, has resolved issues related to model accuracy. However, under conditions of high proportion renewable energy integration, component load adjustments require increased flexibility, so the mathematical model of the component must adapt to constantly changing operating conditions. Therefore, the identification of operating condition changes and rapid model updating are pressing issues. This study proposes a modeling and updating method for IES components based on knowledge distillation. The core of this modeling method is the light weighting of the model, which is achieved through a knowledge distillation method, using a teacher-student mode to compress complex neural network models. The triggering of model updates is achieved through principal component analysis. The study also analyzes the impact of model errors caused by delayed model updates on the overall scheduling of IES. Case studies are conducted on critical components in IES, including coal-fired boilers and turbines. The results show that the time consumption for model updating is reduced by 76.67 % using the proposed method. Under changing conditions, compared with two traditional models, the average deviation of this method is reduced by 12.61 % and 3.49 %, respectively, thereby improving the model's adaptability. The necessity of updating the component model is further analyzed, as a 1.00 % mean squared error in the component model may lead to a power deviation of 0.075 MW. This method provides real-time, adaptable support for IES data modeling and updates.

Novel Porous Polyethersulfone Beads as Matrix to Immobilize Comamonas testosteroni sp. bdq06 in Quinoline Biodegradation

Novel matrix beads for the immobilization of strain Comamonas testosteroni sp. bdq06 to degrade quino- line were fabricated from polyethersulfone（PES）. The beads have an average size of 3 mm and a surface dense layer of 20 microns. To help adhesion and proliferation of bacterial cells, the surfaces of the PES beads were etched, and numerous holes about 1.5 micrometers in diameter were generated as tunnels for cell colonizing in the larger internal cavities of about 5 micrometers in diameter. The quinoline degradation was remarkably enhanced by the cells immo- bilized in PES beads compared with that by the free cells at pH 5.0 or 10.0 and a temperature of 40 ℃. The enhanced degradation of quinoline was contributed to the biofilm on the surface of PES beads, resulting in the significant re- duction of retention time from 9 h to 2 h. Furthermore, the beads remain intact after the ultrasonic treatment of them for 30 rain or recycling 50 times, indicating that they have excellent mechanical strength, flexibility and swelling ca- pacity. Thus, PES beads have great potential to be matrix for the cell immobilization in bioaugmentation.

Virtual Australia:its key elements
know,think,communicate

Virtual Australia is not a well-defined or agreed concept.This discussion paper conceptualises Virtual Australia as a‘virtual[digital]model containing and representing all non-trivial objects and their contextual environment
from blue sky to bedrock
in real world Australia’.It describes a scenario for Virtual Australia,one or two decades from now,in which the locations and conditions of non-trivial objects and their environment are updated automatically through a combination of remote sensing and wireless communication technologies in support of a‘Supranet’.It then examines the concept of the‘Supranet’
a pervasive information network based largely on wireless technology linking the physical world to a virtual model in real time
and develops and discusses three principal characteristics of the Supranet:the ability to have or collect specific information(know);the ability to process information(think);and the ability to communicate that information.If,in the near future,any or all non-trivial devices are to some extent able to know,think and communicate,the potential for object autonomy will be realised.

Biomethane use for automobiles towards a CO_(2)-neutral energy system

To pursue the goal of sustainable mobility,two main paths can be considered:the electrification of vehicles and the use of biofuels,replacing fossil fuels,in internal combustion engine(ICE)vehicles.This paper proposes an analysis of different possible scenarios for automobiles towards a CO_(2)-neutral energy system,in the path of the use of biofuels and the production,distribution and use of biomethane.The study,an update of work presented previously,focuses on different scenarios that take into account numerous parameters that affect the overall efficiency of the production-and-use process.A Well-to-Wheel analysis is used to estimate the primary energy savings and reduction in greenhouse-gas emissions compared both to the use of fossil-based methane and to other fuels and automotive technologies.In particular,the study shows that the Non-Renewable Primary Energy Consumption(NRPEC)for biomethane is slightly higher(+9%)than that of biodiesel,but significantly lower than those of all the other power trains analysed:-69%compared to the battery electric vehicle(BEV)and-55%compared to bioethanol.Compared to the use of fossil natural gas,the NRPEC is reduced to just over a third(2.81).With regard to CO_(2) emissions,biomethane has the lowest values:-69%compared to BEV,-176%compared to bioethanol and-124%with respect to biodiesel.Compared to the use of fossil natural gas,the CO_(2) emissions are reduced over a third(3.55).Moreover,the paper shows that biomethane can completely cover the consumption of fossil methane for vehicles in Italy,proposing two different hypotheses:maximum production and minimum production.It is evident,therefore,that biomethane production can completely cover the consumption of fossil methane for vehicles:this means that the use of biomethane in the car can lead to a reduction in NRPEC equal to 28.9×10^(6) GJ/year and a reduction of CO_(2) emissions equal to 1.9×10^(6) t/year.