Effects of preparation and operation conditions on precipitated iron nickel catalysts for Fischer-Tropsch synthesis

Iron nickel oxide catalysts were prepared using co-precipitation procedure and studied for the conversion of synthesis gas to light olefins.In particular,the effects of a range of preparation variables such as Fe/Ni molar ratios of the precipitation solution,precipitate aging times,calcination conditions,different supports and loading of optimum support on the structure of catalysts and their catalytic performance for the tested reaction were investigated.It was found that the catalyst containing 40%Fe/60%Ni/40wt%Al 2O3 ,which was aged for 180 min and calcined at 600 ℃ for 6 h was the optimum modified catalyst.The catalytic performance of optimal catalyst has been studied in different operation conditions such as reaction temperatures,H2 /CO molar feed ratios and reaction total pressure.Characterization of both precursors and calcined catalysts was carried out by powder X-ray diffraction（XRD）,scanning electron microscopy（SEM）,Brunauer-Emmett-Teller（BET） surface area measurements,thermal analysis methods such as thermal gravimetric analysis（TGA） and differential scanning calorimetry（DSC）.

Lifetime Evaluating and the Effects of Operation Conditions on Automotive Fuel Cells

Lifetime isone of the important indicators of automotive proton exchange membrane fuel cells. People used to evaluate the lifetime of vehicular fuel cells by laboratory tests or road tests that usually take thousands hours even years. In order to achieve a rapid evaluation technique and to seek lifetime extension methods, a lifetime calculation formation was drawn out in consideration of the vehicle driving cycle and the working condition factors. Bench experiments were individually carried out on two fuel-cell stacks same as ones applied on vehicle, and the performance decay rates of the two stacks were obtained under four operation conditions of changing load cycle, start-stop cycle, idling and heavy load. As a result, the predicted lifetimes rather conform to the actual running status in road test. And the research on the fuel cell performance decay rates under different load conditions was also done. Consequently, an unexpected finding was discovered that operating under micro-current has an effect on recovering fuel cell performance. The vehicle fuel cell rapid assessment method only requires four laboratory tests of driving cycle, load cycle, idle operating conditions and heavy load conditions, and the whole process merely lasts less than 250 h. These experimental results can be used to predict the vehicular fuel cell lifetimes on various utility models or driving cycles, therefore to optimize the application model to prolong the fuel cell lifetime. Actually in the experiment, it has already been proved successfully that the fuel cell lifetime could be extended from 1 100 h to 2 600 h by optimizing operating mode. The quick evaluation method is helpful to develop extended life fuel cell and to deplete fuel cell for a longer time.

Effects of Operation Conditions on Service Life of Ladle Lining

Effects of operation conditions such as ladle temperature, remining time of molten steel, slag basicity, slag oxidation, slag viscosity , vacuum treatment, ultra-high temperature, gas blowing and stirring, intermittent operation, and different refining equipment on service life of ladle lining refractories were researched. The following conclusions are drawn ： （1） molten steel temperature rising, remaiaing time prolonging, slag oxidation ability enhancing, slag viscosity and basicity decreasing can aecelerate the corrosion of ladle lining ; （2） ultra-high temperature and vacuum treatment of ladle not only acceler- ate the fusion corrasion of ladle lining, but also make the carbon containing refractories react forming gases leading to more corrosion, so carbon containing refractories are not stdtable.for the smelting conditions of long term vacuum treatment and ultra-high temperature, especially carbon containing refractories added with conventional additives such as A1 and Si powders ; （ 3 ） argon blowing does not accelerate the corrosion of ladle lining obvioasly, but oxygen blowing does; （4） the corrosion degree of refractories varies with the refining equipment, and the service life of ladle lining decreases according to a certain proportion with the refining ratio increasing.

In-situ study of the hydrogen peroxide photoproduction in seawater on carbon dot-based metal-free catalyst under operation condition

Hydrogen peroxide(H_(2)O_(2))photoproduction in seawater with metal-free photocatalysts derived from biomass materials is a green,sustainable,and ultra environmentally friendly way.However,most photocatalysts are always corroded or poisoned in seawater,resulting in a significantly reduced catalytic performance.Here,we report the metal-free photocatalysts(RUT-1 to RUT-5)with in-situ generated carbon dots(CDs)from biomass materials(Rutin)by a simple microwave-assisted pyrolysis method.Under visible light(λ≥420 nm,81.6 mW/cm^(2)),the optimized catalyst of RUT-4 is stable and can achieve a high H_(2)O_(2)yield of 330.36μmol/L in seawater,1.78 times higher than that in normal water.New transient potential scanning(TPS)tests are developed and operated to in-situ study the H_(2)O_(2)photoproduction of RUT-4 under operation condition.RUT-4 has strong oxygen(O_(2))absorption capacity,and the O_(2)reduction rate in seawater is higher than that in water.Metal cations in seawater further promote the photo-charge separation and facilitate the photo-reduction reaction.For RUT-4,the conduction band level under operating conditions only satisfies the requirement of O_(2)reduction but not for hydrogen(H2)evolution.This work provides new insights for the in-situ study of photocatalyst under operation condition,and gives a green and sustainable path for the H_(2)O_(2)photoproduction with metal-free catalysts in seawater.

A hybrid deep neural network based prediction of 300 MW coalfired boiler combustion operation condition

In power generation industries,boilers are required to be operated under a range of different conditions to accommodate demands for fuel randomness and energy fluctuation.Reliable prediction of the combustion operation condition is crucial for an in-depth understanding of boiler performance and maintaining high combustion efficiency.However,it is difficult to establish an accurate prediction model based on traditional data-driven methods,which requires prior expert knowledge and a large number of labeled data.To overcome these limitations,a novel prediction method for the combustion operation condition based on flame imaging and a hybrid deep neural network is proposed.The proposed hybrid model is a combination of convolutional sparse autoencoder(CSAE)and least support vector machine(LSSVM),i.e.,CSAE-LSSVM,where the convolutional sparse autoencoder with deep architectures is utilized to extract the essential features of flame image,and then essential features are input into the least support vector machine for operation condition prediction.A comprehensive investigation of optimal hyper-parameter and dropout technique is carried out to improve the performance of the CSAE-LSSVM.The effectiveness of the proposed model is evaluated by 300 MW tangential coal-fired boiler flame images.The prediction accuracy of the proposed hybrid model reaches 98.06%,and its prediction time is 3.06 ms/image.It is observed that the proposed model could present a superior performance in comparison to other existing neural network models.

Research on the Operational Conditions of Artificial Precipitation Enhancement in Liaoning Province

Taking precipitation process during May 17-18,2009 as an example,this paper analyzed and summarized the operational conditions of artificial precipitation enhancement in Liaoning Province.Operational conditions can be divided into two categories,namely,macro-weather and cloud micro-physical operational conditions,this paper described their respective indexes and criterions as well as their effect and application in formulation and command of artificial precipitation enhancement plan real-timely.

Liquid Metal-Based Self-Healable and Elastic Conductive Fiber in Complex Operating Conditions

Flexible conductive fibers are essential for wearable electronics and smart electronic textiles.However,in complex operating conditions,conductive fibers will inevitably fracture or damage.Herein,we have developed an elastic conductive self-healable fiber(C-SHF),of which the electrical and mechanical properties can efficiently heal in a wide operating range,including room temperature,underwater,and low temperature.This advantage can be owed to the combination of reversible covalent imine bond and disulfide bond,as well as the instantaneous self-healing ability of liquid metal.The C-SHF,with stretchability,conductivity stability,and universal self-healing properties,can be used as an electrical signal transmission line at high strain and under different operating conditions.Besides,C-SHF was assembled into a double-layer capacitor structure to construct a self-healable sensor,which can effectively respond to pressure as a wearable motion detector.

Health risk of whole body vibration in mining trucks during various operational conditions

Mining machineries are generally exposed to intensive vibrations in harsh mining environment. If vibrations are beyond the tolerable limit, the machine and its operator health will be under the risk. In this work, the vibration of a mining truck at different operational conditions are simulated and discussed. To achieve this aim, three haul roads with low, medium and poor qualities are considered based on the ISO standard. Accordingly, the vibration of a mining truck in different speeds, payload and distribution qualities of materials in the dump body are evaluated in each haul road quality using Trucksim software. The simulation results with statistical discussions indicate that the truck speed and the materials distribution quality have significant effects on the root mean square(RMS) of vertical vibrations. However, the effect of the payload is not considerable on the RMS. Moreover, the accumulation of materials on the rear side of the truck dump body is efficient on the vibrational health risk.

Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network

Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of civil and mechanical structures.This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network(1D-CNN).After singular value decomposition-based feature extraction processing,a temporal robust damage index(TRDI)is extracted,and the effect of EOCs is well removed.Hence,even for the signals with a very large temperature-varying range and low signal-to-noise ratios(SNRs),the final damage detection and localization accuracy retain perfect 100%.Verifications are conducted on two different experimental datasets.The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises,and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20℃to 60℃.It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly,showing great potential for application in complex and unknown EOC.

Aero-Hydro-Elastic-Servo Modeling and Dynamic Response Analysis of A Monopile Offshore Wind Turbine Under Different Operating Scenarios

This paper constructs a coupled aero-hydro-elastic-servo simulation framework for a monopile offshore wind turbine(OWT).In this framework,a detailed multi-body dynamics model of the monopile OWT including the gearbox,blades,tower and other components(nacelle,hub,bedplate,etc.)has been explicitly established.The effects of pile−soil interaction,controller and operational conditions on the turbine dynamic responses are studied systematically in time domain and frequency domain.The results show that(1)a comprehensive drivetrain model has the capability to provide a more precise representation of the complex dynamic characteristics exhibited by drivetrain components,which can be used as the basis for further study on the dynamic characteristics of the drivetrain.(2)The pile−soil interaction can influence the wind turbine dynamic responses,particularly under the parked condition.(3)The effect of the pile−soil interaction on tower responses is more significant than that on blade responses.(4)The use of the controller can substantially affect the rotor characteristics,which in turn influences the turbine dynamic responses.(5)The tower and blade displacements under the operational condition are much larger than those under the parked condition.The model and methodology presented in this study demonstrate potential for examining complex dynamic behaviors of the monopile OWTs.To ensure accuracy and precision,it is imperative to construct a detailed model of the wind turbine system,while also taking into account simulation efficiency.

Generation of input spectrum for electrolysis stack degradation test applied to wind power PEM hydrogen production

Hydrogen production by proton exchange membrane electrolysis has good fluctuation adaptability,making it suitable for hydrogen production by electrolysis in fluctuating power sources such as wind power.However,current research on the durability of proton exchange membrane electrolyzers is insufficient.Studying the typical operating conditions of wind power electrolysis for hydrogen production can provide boundary conditions for performance and degradation tests of electrolysis stacks.In this study,the operating condition spectrum of an electrolysis stack degradation test cycle was proposed.Based on the rate of change of the wind farm output power and the time-averaged peak-valley difference,a fluctuation output power sample set was formed.The characteristic quantities that played an important role in the degradation of the electrolysis stack were selected.Dimensionality reduction of the operating data was performed using principal component analysis.Clustering analysis of the data segments was completed using an improved Gaussian mixture clustering algorithm.Taking the annual output power data of wind farms in Northwest China with a sampling rate of 1 min as an example,the cyclic operating condition spectrum of the proton-exchange membrane electrolysis stack degradation test was constructed.After preliminary simulation analysis,the typical operating condition proposed in this paper effectively reflects the impact of the original curve on the performance degradation of the electrolysis stack.This study provides a method for evaluating the degradation characteristics and system efficiency of an electrolysis stack due to fluctuations in renewable energy.

Feature Extraction Method Based on Pseudo-Wigner-Ville Distribution for Rotational Machinery in Variable Operating Conditions

In the case of fault diagnosis for roller bearings, the conventional diagnosis approaches by using the time interval of energy impacts in time-frequency distribution or the pass-frequencies are based on the assumption that machinery operates under a constant rotational speed. However, when the rotational speed varies in the broader range, the pass-frequencies vary with the change of rotational speed and bearing faults cannot be identified by the interval of impacts. Researches related to automatic diagnosis for rotational machinery in variable operating conditions were quite few. A novel automatic feature extraction method is proposed based on a pseudo-Wigner-Ville distribution （PWVD） and an extraction of symptom parameter （SP）. An extraction method for instantaneous feature spectrum is presented using the relative crossing information （RCI） and sequential inference approach, by which the feature spectrum from time-frequency distribution can be automatically, sequentially extracted. The SPs are considered in the frequency domain using the extracted feature spectrum to identify among the conditions of a machine. A method to obtain the synthetic symptom parameter is also proposed by the least squares mapping （LSM） technique for increasing the diagnosis sensitivity of SP. Practical examples of diagnosis for bearings are given in order to verify the effectiveness of the proposed method. The verification results show that the features of bearing faults, such as the outer-race, inner-race and roller element defects have been effectively extracted, and the proposed method can be used for condition diagnosis of a machine under the variable rotational speed.

Optimal reaction conditions for pyridine synthesis in riser reactor

Pjridine has been generally synthesized by aldehydes and ammonia in a turbulent fluidized-bed reactor. In this paper, a novel riser reactor was proposed for pyridine synthesis. Experiment result showed that the yield of pyridine and 3-picoline decreased, but the selectivity of pyridine over 3-picoline increased compared to turbulent fluidized-bed reactor. Based on experimental data, a modified kinetic model was used for the determination of optimal operating condition for riser reactor. The optimal operating condition of riser reactor given by this modified model was as follows： The reaction temperature of 755 K, catalyst to feedstock ratio （CTFR） of 87, residence timeof3.8sandinitialacetaldehydesconcentrationof0.0029mol.L-1 （acetaldehydes to formaldehydes ratio by mole （ATFR） of 0.65 and ammonia to aldehydes ratio by mole （ATAR） of 0.9, water contention of 63wt% （formaldehyde solution））.

Influences of fluid physical properties,solid particles,and operating conditions on the hydrodynamics in slurry reactors

Slurry reactors are popular in many industrial processes,involved with numerous chemical and biological mixtures,solid particles with different concentrations and properties,and a wide range of operating conditions.These factors can significantly affect the hydrodynamic in the slurry reactors,having remarkable effects on the design,scale-up,and operation of the slurry reactors.This article reviews the influences of fluid physical properties,solid particles,and operating conditions on the hydrodynamics in slurry reactors.Firstly,the influence of fluid properties,including the density and viscosity of the individual liquid and gas phases and the interfacial tension,has been reviewed.Secondly,the solid particle properties(i.e.,concentration,density,size,wettability,and shape)on the hydrodynamics have been discussed in detail,and some vital but often ignored features,especially the influences of particle wettability and shape,as well as the variation of surface tension because of solid concentration alteration,are highlighted in this work.Thirdly,the variations of physical properties of fluids,hydrodynamics,and bubble behavior resulted from the temperature and pressure variations are also summarized,and the indirect influences of pressure on viscosity and surface tension are addressed systematically.Finally,conclusions and perspectives of these notable influences on the design and scale-up of industrial slurry reactors are presented.

Boosting lithium batteries under harsh operating conditions by a resilient ionogel with liquid-like ionic conductivity

New chemistries are being developed to increase the capacity and power of rechargeable batteries. However, the risk of safety issues increases when high-energy batteries using highly active materials encounter harsh operating conditions. Here we report on the synthesis of a unique ionogel electrolyte for abuse-tolerant lithium batteries. A hierarchically architected silica/polymer scaffold is designed and fabricated through a facile soft chemistry route, which is competent to confine ionic liquids with superior uptake ability (92.4 wt%). The monolithic ionogel exhibits high conductivity and thermal/mechanical stability, featuring high-temperature elastic modulus and dendrite-free lithium cycling. The Li/LiFePO_(4) pouch cells achieve outstanding cyclability at different temperatures up to 150 ℃, and can sustain cutting, crumpling, and even coupled thermal–mechanical abuses. Moreover, the solid-state lithium batteries with LiNi_(0.60)Co_(0.20)Mn_(0.20)O_(2), LiNi_(0.80)Co_(0.15)Al_(0.05)O_(2), and Li_(1.2)Mn_(0.54)Ni_(0.13)Co_(0.13)O_(2) cathodes demonstrate excellent cycle performances at 60 ℃. These results indicate that the resilient and high-conductivity ionogel electrolyte is promising to realize high-performance lithium batteries with high energy density and safety.

An Experimental Study on Optimization of Large-volume Airgun Source Excitation Conditions in a Reservoir

Through manual pickup of seismic phases,the number of recording stations,the farthest observation distance of station and received energy are determined,then optimal operating condition processing software is developed to evaluate the excitation effect of operating conditions. The optimal operating conditions in the Mianhuatan Reservoir are determined using this software. They are: optimal water depth 25 m,airgun array sink depth 9m and airgun array size 7m × 7m. At the same time,accumulative stacking results for every 10 times are analyzed for 300 fixed-point excitations. It is concluded that the excitation effect shows a rapidly rising trend at 10 to 90 times stacking,a slowly rising trend at 100 to 150 times stacking,a rapidly rising trend at 160 to 240 times stacking,and a slowly rising trend at 250 to 300 times stacking. As the number of stacking increases,the propagation distance and the number of recording stations also increase gradually.

Effect of Operating Conditions on Olefin Distribution in FCC Gasoline as Part of an Olefin Reduction Process

The effects of operating conditions on the distribution of olefins in the FCC gasoline, obtained during catalytic cracking reaction in the presence of the GOR-Q catalyst and conventional MLC-500 catalyst, have been studied in detail. The test results showed that the GOR-Q catalyst could obviously reduce the content of several kinds of olefins in FCC gasoline. Olefins in the FCC gasoline consist mainly of C5- C7 compounds, that are composed of C=C bond with normal or mono- branched chains. The reduction of gasoline olefin content could be achieved by decreasing the content of above-mentioned olefins. Lower reaction temperature, lower weight hourly space velocity （WHSV） and higher catalyst to oil ratio would help to reduce the content of olefins with a C = C double bond, normal olefins, mono-branched-chain olefins and diolefins. To decrease the loss of gasoline octane number, the operation for olefin reduction should be firstly focused on increasing the catalyst to oil ratio.

Effects of Operating Conditions on the Catalytic Performance of HZSM-5 Zeolites in n-Pentane Cracking

In this work,n-pentane catalytic cracking over HZSM-5 zeolites was studied at 650°C under atmosphere pressure.A particular attention was paid to the measurement of n-pentane conversion,light olefins production,product distribution,coke deposit,etc.Several indexes were defined to evaluate the effects of operating conditions on the catalytic performance of HZSM-5 zeolites.It was found that decreasing the weight hourly space velocity,increasing the reactant partial pressure,and increasing the carrier gas flow rate could inhibit C-H bond breaking and enhance the C-C bond breaking and hydride transfer reactions,leading to reduced alkenes selectivity,which suppressed the formation of external coke and alleviated the deactivation of HZSM-5 zeolites.It was deduced that the catalytic stability of HZSM-5 zeolites was improved at the cost of alkenes selectivity.Compared with decreasing the weight hourly space velocity and increasing the reactant partial pressure,increasing the carrier gas flow rate could enhance the diffusion process and protect alkenes from being consumed in coke formation in order to improve the catalytic stability of HZSM-5 zeolites with less reduction of alkenes selectivity.

Enhanced exergy cost optimization of operating conditions in FCCU main fractionator

Exergy indicates the maximal energy that can do work effectively. Different from optimization of product quality or calculation of generic energy conservation in most previous studies, the application of exergy analysis and exergy cost optimization in petrochemical industry is of great economic and environmental significance. Based on the main fractionator in Jiujiang Petrochemical Complex No. 2 FCCU, an enhanced exergy cost optimization under different operating conditions by adjusting set points of temperature and valves opening degree for flow control is studied in this paper in order to reduce exergy cost and improve the quality of energy. A steadystate optimization algorithm to enhance exergy availability and an objective function comprehensively considering exergy loss are proposed. On the basis of ensuring the quality of petroleum products, the economic benefits can be improved by optimizing the controllable variables due to the fact that exergy cost is decreased.

Fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval SVM

The fault detection and diagnosis of diesel engine valve clearance can effectively improve the availability and safety of diesel engine and have extremely important value and significance.Diesel engines generally operate in various stable operating conditions,which have important influence on the fault diagnosis.However,many fault diagnosis methods have been put forward under specific stable operating condition based on vibration signal.As the result of great impact caused by operating conditions,corresponding diagnosis models cannot deal with the fault diagnosis under different operating conditions with required accuracy.In this paper,a fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval support vector machine(SVM)is proposed.Firstly,the fault features with weak condition sensitivity have been extracted according to the influence analysis of fault on vibration signal.Moreover,soft interval constraint has been applied to SVM algorithm to reduce the random influence of vibration signal on fault features.In addition,different machine learning algorithms based on different feature sets are adopted to conduct the fault diagnosis under different operating conditions for comparison.Experimental results show that the proposed method is applicable for fault diagnosis under variable operating condition with good accuracy.