Solar-assisted two-stage catalytic membrane reactor for coupling CO_(2) splitting with methane oxidation reaction

A two-stage catalytic membrane reactor(CMR)that couples CO_(2) splitting with methane oxidation reactions was constructed based on an oxygen-permeable perovskite asymmetric membrane.The asymmetric membrane comprises a dense SrFe_(0.9)Ta_(0.1)O_(3-σ)(SFT)separation layer and a porous Sr_(0.9)(Fe_(0.9)Ta_(0.1))_(0.9)Cu_(0.1)O_(3-σ)(SFTC)catalytic layer.In thefirst stage reactor,a CO_(2) splitting reaction(CDS:2CO_(2)→2CO+O_(2))occurs at the SFTC catalytic layer.Subsequently,the O_(2) product is selectively extracted through the SFT separation layer to the permeated side for the methane combustion reaction(MCR),which provides an extremely low oxygen partial pressure to enhance the oxygen extraction.In the second stage,a Sr_(0.9)(Fe_(0.9)Ta_(0.1))_(0.9)Ni_(0.1)O_(3-σ)(SFTN)catalyst is employed to reform the products derived from MCR.The two-stage CMR design results in a remarkable 35.4%CO_(2) conversion for CDS at 900℃.The two-stage CMR was extended to a hollowfiber configuration combining with solar irradiation.The solar-assisted two-stage CMR can operate stably for over 50 h with a high hydrogen yield of 18.1 mL min^(-1) cm^(-2).These results provide a novel strategy for reducing CO_(2) emissions,suggesting potential avenues for the design of the high-performance CMRs and catalysts based on perovskite oxides in the future.

Two-Stage Planning of Distributed Power Supply and Energy Storage Capacity Considering Hierarchical Partition Control of Distribution Network with Source-Load-Storage

Aiming at the consumption problems caused by the high proportion of renewable energy being connected to the distribution network,it also aims to improve the power supply reliability of the power system and reduce the operating costs of the power system.This paper proposes a two-stage planning method for distributed generation and energy storage systems that considers the hierarchical partitioning of source-storage-load.Firstly,an electrical distance structural index that comprehensively considers active power output and reactive power output is proposed to divide the distributed generation voltage regulation domain and determine the access location and number of distributed power sources.Secondly,a two-stage planning is carried out based on the zoning results.In the phase 1 distribution network-zoning optimization layer,the network loss is minimized so that the node voltage in the area does not exceed the limit,and the distributed generation configuration results are initially determined;in phase 2,the partition-node optimization layer is planned with the goal of economic optimization,and the distance-based improved ant lion algorithm is used to solve the problem to obtain the optimal distributed generation and energy storage systemconfiguration.Finally,the IEEE33 node systemwas used for simulation.The results showed that the voltage quality was significantly improved after optimization,and the overall revenue increased by about 20.6%,verifying the effectiveness of the two-stage planning.

Two-Stage Optimal Scheduling of Community Integrated Energy System

From the perspective of a community energy operator,a two-stage optimal scheduling model of a community integrated energy system is proposed by integrating information on controllable loads.The day-ahead scheduling analyzes whether various controllable loads participate in the optimization and investigates the impact of their responses on the operating economy of the community integrated energy system(IES)before and after;the intra-day scheduling proposes a two-stage rolling optimization model based on the day-ahead scheduling scheme,taking into account the fluctuation of wind turbine output and load within a short period of time and according to the different response rates of heat and cooling power,and solves the adjusted output of each controllable device.The simulation results show that the optimal scheduling of controllable loads effectively reduces the comprehensive operating costs of community IES;the two-stage optimal scheduling model can meet the energy demand of customers while effectively and timely suppressing the random fluctuations on both sides of the source and load during the intra-day stage,realizing the economic and smooth operation of IES.

Influence of two-stage harvesting on the properties of cold-pressed rapeseed(Brassica napus L.)oils

Rapeseed(Brassica napus L.)harvesting method is critical since it significantly determines the seed yield,oil quality,and industrial efficiency.This study investigated the influences of harvesting methods on the quality of cold-pressed rapeseed oil of two varieties.Oil color,peroxide value(POV),tocopherol content,fatty acid composition,and polarity of total polyphenols(PTP)contents of two rapeseed varieties in Huanggang and Xiangyang were compared through artificially simulated combined harvesting and two-stage harvesting.Results showed significant differences in the quality of rapeseed oil between the two harvesting methods.The red value(R-value),POV,total tocopherol contents,linoleic and linolenic acid content,and PTP content of the pressed rapeseed oil prepared by the combined harvesting method were about 27.6,5.7,15.8,2.0,0.5,and 28.6%lower than those of the oil produced from the two-stage harvesting method,respectively.Xiangyang and Huayouza62 performed better in the two regions and two varieties,respectively.To sum up,the rapeseed oil obtained 41–44 days after final flowering of combined harvesting,35 days after final flowering,and six days of post-ripening of the two-stage harvesting had the best quality.

Tourism Route Recommendation Based on A Multi-Objective Evolutionary Algorithm Using Two-Stage Decomposition and Pareto Layering

Tourism route planning is widely applied in the smart tourism field.The Pareto-optimal front obtained by the traditional multi-objective evolutionary algorithm exhibits long tails,sharp peaks and disconnected regions problems,which leads to uneven distribution and weak diversity of optimization solutions of tourism routes.Inspired by these limitations,we propose a multi-objective evolutionary algorithm for tourism route recommendation(MOTRR)with two-stage and Pareto layering based on decomposition.The method decomposes the multiobjective problem into several subproblems,and improves the distribution of solutions through a two-stage method.The crowding degree mechanism between extreme and intermediate populations is used in the two-stage method.The neighborhood is determined according to the weight of the subproblem for crossover mutation.Finally,Pareto layering is used to improve the updating efficiency and population diversity of the solution.The two-stage method is combined with the Pareto layering structure,which not only maintains the distribution and diversity of the algorithm,but also avoids the same solutions.Compared with several classical benchmark algorithms,the experimental results demonstrate competitive advantages on five test functions,hypervolume(HV)and inverted generational distance(IGD)metrics.Using the experimental results of real scenic spot datasets from two famous tourism social networking sites with vast amounts of users and large-scale online comments in Beijing,our proposed algorithm shows better distribution.It proves that the tourism routes recommended by our proposed algorithm have better distribution and diversity,so that the recommended routes can better meet the personalized needs of tourists.

Multi-source coordinated stochastic restoration for SOP in distribution networks with a two-stage algorithm

After suffering from a grid blackout, distributed energy resources(DERs), such as local renewable energy and controllable distributed generators and energy storage can be used to restore loads enhancing the system’s resilience. In this study, a multi-source coordinated load restoration strategy was investigated for a distribution network with soft open points(SOPs). Here, the flexible regulation ability of the SOPs is fully utilized to improve the load restoration level while mitigating voltage deviations. Owing to the uncertainty, a scenario-based stochastic optimization approach was employed,and the load restoration problem was formulated as a mixed-integer nonlinear programming model. A computationally efficient solution algorithm was developed for the model using convex relaxation and linearization methods. The algorithm is organized into a two-stage structure, in which the energy storage system is dispatched in the first stage by solving a relaxed convex problem. In the second stage, an integer programming problem is calculated to acquire the outputs of both SOPs and power resources. A numerical test was conducted on both IEEE 33-bus and IEEE 123-bus systems to validate the effectiveness of the proposed strategy.

Two-stage revision in periprosthetic knee joint infections

Periprosthetic joint infection(PJI)following total knee arthroplasty is one of the most catastrophic and costly complications that carries significant patient wellness as well as economic burdens.The road to efficiently diagnosing and treating PJI is challenging,as there is still no gold standard method to reach the diagnosis as early as desired.There are also international controversies with respect to the best approach to manage PJI cases.In this review,we highlight recent advances in managing PJI following knee arthroplasty surgery and discuss in depth the two-stage revision method.

A New Two-Stage Tunable Space-Time Adaptive Detector

In order to improve the rejection capability of mismatched interferer signals,a new two-stage detector is proposed under homogeneous scenarios with unknown covariance matrix,which is obtained by cascading the adaptive matched filter(AMF)detector and the enhanced RAO(EnRAO)detector.The new detector has constant false alarm performance,and the closed-form expression of probability of false alarm and probability of detection is derived.The performance of the new detector is assessed,and analyzed in comparison with other detectors.The results show that,the proposed detector can provide enhanced rejection capability in the case of mismatch,but the performance of the detector is slightly lost under the condition of matching.

A two-stage but time-saving reconstructive scheme to treat severe lower limb length discrepancy:Two case reports

Limb length discrepancy(LLD)is a common orthopedic condition that can result in significant functional impairment,pain,and cosmetic deformities.Current reconstructive techniques for severe LLD are primarily based on callus distraction,which is a time-consuming process that can lead to complications,such as significant infection,joint stiffness,and stress fractures.To reduce the therapeutic time and minimize the risk of complications,we investigated the use of vascularized bone flaps as a technical supplement to callus distraction in the reconstruction of short limbs.We present two cases of severe LLD in the upper and lower legs,in which a twostage reconstruction approach was used.In the first stage,external fixation was applied to the affected limb to correct the soft tissue length and convert the short deformity into a bone defect.In the second stage,the bone defect was reconstructed using bilateral(patient A)or unilateral(patient B)free vascularized fibula bone grafts.Both patients had complete survival of the fibular grafts without stress fractures,and bone consolidation took 8 months(patient A)and 4 months(patient B).Compared to the traditional callus distraction,the two-stage approach was found to be more time-saving and reliable.The entire reconstructive scheme required 18 and 4 months for patients A and B,respectively,whereas the traditional callus distraction required 41 and 17 months,respectively.These findings suggest that the use of vascularized bone flaps as a technical supplement for callus distraction may provide an effective and efficient alternative for the treatment of severe LLD.Further studies are needed to validate these results and assess the long-term outcomes of this approach.

An interacting multiple model-based two-stage Kalman filter for vehicle positioning

To address the problem that a general augmented state Kalman filter or a two-stage Kalman filter cannot achieve satisfactory positioning performance when facing uncertain noise of the micro-electro-mechanical system（MEMS） inertial sensors, a novel interacting multiple model-based two-stage Kalman filter（IMM-TSKF） is proposed to adapt to the uncertain inertial sensor noise. Three bias filters are developed based on different noise characteristics to cover a wide range of noise levels. Then, an accurate estimation of biases is calculated by the interacting multiple model algorithm to correct the bias-free filter. Thus, the vehicle positioning system can achieve good performance when suffering from uncertain inertial sensor noise. The experimental results indicate that the average position error of the proposed IMMTSKF is 25% lower than that of the general TSKF.

Production of Light Olefins from Biosyngas by Two-stage Catalytic Conversion Process via Dimethyl Ether

NiSAPO-34 and NiSAPO-34/HZSM-5 were prepared and evaluated for the performance of dimethyl ether （DME） conversion to light olefins （DTO）. The processes of two-stage light olefin production, DME synthesis and the following DTO, were also investigated using biosyngas as feed gas over Cu/Zn/A1/HZSM-5 and the optimized 2%NiSAPO-34/HZSM- 5. The results indicated that adding 2%Ni to SAPO-34 did not change its topology structure, but resulted in the forming of the moderately strong acidity with decreasing acid amounts, which slightly enhanced DME conversion activity and C2=-C3= selectiw ity. Mechanically mixing 2%NiSAPO-34 with HZSM-5 at the weight ratio of 3.0 further prolonged DME conversion activity to be more than 3 h, which was due to the stable acid sites from HZSM-5. The highest selectivity to light olefins of 90.8% was achieved at 2 h time on stream. The application of the optimized 2%NiSAPO-34/HZSM-5 in the second-stage reactor for DTO reaction showed that the catalytic activity was steady for more than 5 h and light olefin yield was as high as 84.6 g/m3syngas when the biosyngas （H2/CO/CO2/N2/CH4=41.5/26.9/14.2/14.6/2.89, vol%） with low H/C ratio of 1.0 was used as feed gas.

基于Two-stage DEA-Tobit模型的中国省际竞技体育效率研究

基于Two-stage DEA的网络结构DEA方法,对我国31个省(直辖市、自治区)在第十二届全运会上的表现进行更深入的效率评价,并结合Tobit回归模型对影响竞技体育效率的社会环境因素进行分析.结果表明,全运会两个阶段中,仅山东省和西藏自治区为DEA有效的,AP阶段和AC阶段为DEA有效的地区分别为10个和3个,且提高各地区总效率的关键在于提高其AC阶段效率.从影响因素来看,地区经济水平与效率间存在负向影响,而体育场地资源配置及体育类支出是影响效率的主要因素,国民体质综合指数和体育投入规模对其无显著影响.

Advanced landfill leachate treatment using a two-stage UASB-SBR system at low temperature

A two-stage upflow anaerobic sludge blanket （UASB） and sequencing batch reactor （SBR） system was introduced to treat landfill leachate for advanced removal of COD and nitrogen at low temperature. In order to improve the total nitrogen （TN） removal efficiency and to reduce the COD requirement for denitrification, the raw leachate with recycled SBR nitrification supematant was pumped into the first-stage UASB （UASB1） to achieve simultaneous denitrification and methanogenesis. The results showed that UASB1 played an important role in COD removal and UASB2 and SBR further enhanced the nutrient removal efficiency. When the organic loading rates of UASB1, UASB2 and SBR were 11.95, 1.63 and 1.29 kg COD/（m^3.day）, respectively, the total COD removal efficiency of the whole system reached 96.7%. The SBR acted as the real undertaker for NH4^＋-N removal due to aerobic nitrification. The system obtained about 99.7% of NH4^＋-N removal efficiency at relatively low temperature （14.9-10.9℃）. More than 98.3% TN was removed through complete denitrification in UASB 1 and SBR. In addition, temperature had a significant effect on the rates of nitrification and denitrification rather than the removal of TN and NH4^＋-N once the complete nitrification and denitrification were achieved.

Cost-effectiveness analysis of population-based screening of hepatocellular carcinoma: Comparing ultrasonography with two-stage screening

AIM: To assess the cost-effectiveness of two populationbased hepatocellular carcinoma(HCC) screening programs, two-stage biomarker-ultrasound method and mass screening using abdominal ultrasonography(AUS).METHODS: In this study, we applied a Markov decision model with a societal perspective and a lifetime horizon for the general population-based cohorts in an area with high HCC incidence, such as Taiwan. The accuracy of biomarkers and ultrasonography was estimated from published meta-analyses. The costs of surveillance, diagnosis, and treatment were based on a combination of published literature, Medicare payments, and medical expenditure at the National Taiwan University Hospital. The main outcome measure was cost per lifeyear gained with a 3% annual discount rate. RESULTS: The results show that the mass screening using AUS was associated with an incremental costeffectiveness ratio of USD39825 per life-year gained, whereas two-stage screening was associated with an incremental cost-effectiveness ratio of USD49733 per life-year gained, as compared with no screening. Screening programs with an initial screening age of 50 years old and biennial screening interval were the most cost-effective. These findings were sensitive to the costs of screening tools and the specificity of biomarker screening.CONCLUSION: Mass screening using AUS is more cost effective than two-stage biomarker-ultrasound screening. The most optimal strategy is an initial screening age at 50 years old with a 2-year inter-screening interval.

Current status of associating liver partition with portal vein ligation for staged hepatectomy: Comparison with two-stage hepatectomy and strategies for better outcomes

Since its introduction in 2012,associating liver partition with portal vein ligation for staged hepatectomy(ALPPS)has significantly expanded the pool of candidates for liver resection.It offers patients with insufficient liver function a chance of a cure.ALPPS is most controversial when its high morbidity and mortality is concerned.Operative mortality is usually a result of posthepatectomy liver failure and can be minimized with careful patient selection.Elderly patients have limited reserve for tolerating the demanding operation.Patients with colorectal liver metastasis have normal liver and are ideal candidates.ALPPS for cholangiocarcinoma is technically challenging and associated with fair outcomes.Patients with hepatocellular carcinoma have chronic liver disease and limited parenchymal hypertrophy.However,in selected patients with limited hepatic fibrosis satisfactory outcomes have been produced.During the inter-stage period,serum bilirubin and creatinine level and presence of surgical complication predict mortality after stage II.Kinetic growth rate and hepatobiliary scintigraphy also guide the decision whether to postpone or omit stage II surgery.The outcomes of ALPPS have been improved by a combination of technical modifications.In patients with challenging anatomy,partial ALPPS potentially reduces morbidity,but remnant hypertrophy may compare unfavorably to a complete split.When compared to conventional two-stage hepatectomy with portal vein embolization or portal vein ligation,ALPPS offers a higher resection rate for colorectal liver metastasis without increased morbidity or mortality.While ALPPS has obvious theoretical oncological advantages over two-stage hepatectomy,the long-term outcomes are yet to be determined.

Multi-objective optimization for leaching process using improved two-stage guide PSO algorithm

A mathematical mechanism model was proposed for the description and analysis of the heat-stirring-acid leaching process.The model is proved to be effective by experiment.Afterwards,the leaching problem was formulated as a constrained multi-objective optimization problem based on the mechanism model.A two-stage guide multi-objective particle swarm optimization(TSG-MOPSO) algorithm was proposed to solve this optimization problem,which can accelerate the convergence and guarantee the diversity of pareto-optimal front set as well.Computational experiment was conducted to compare the solution by the proposed algorithm with SIGMA-MOPSO by solving the model and with the manual solution in practice.The results indicate that the proposed algorithm shows better performance than SIGMA-MOPSO,and can improve the current manual solutions significantly.The improvements of production time and economic benefit compared with manual solutions are 10.5% and 7.3%,respectively.

A two-stage optimization method for unmanned aerial vehicle inspection of an oil and gas pipeline network

Oil and gas pipeline networks are a key link in the coordinated development of oil and gas both upstream and downstream.To improve the reliability and safety of the oil and gas pipeline network, inspections are implemented to minimize the risk of leakage, spill and theft, as well as documenting actual incidents. In recent years, unmanned aerial vehicles have been recognized as a promising option for inspection due to their high efficiency. However, the integrated optimization of unmanned aerial vehicle inspection for oil and gas pipeline networks, including physical feasibility, the performance of mission, cooperation, real-time implementation and three-dimensional(3-D) space, is a strategic problem due to its large-scale,complexity as well as the need for efficiency. In this work, a novel mixed-integer nonlinear programming model is proposed that takes into account the constraints of the mission scenario and the safety performance of unmanned aerial vehicles. To minimize the total length of the inspection path, the model is solved by a two-stage solution method. Finally, a virtual pipeline network and a practical pipeline network are set as two examples to demonstrate the performance of the optimization schemes. Moreover, compared with the traditional genetic algorithm and simulated annealing algorithm, the self-adaptive genetic simulated annealing algorithm proposed in this paper provides strong stability.

CO removal by two-stage methanation for polymer electrolyte fuel cell

In order to remove CO to achieve lower CO content of below 10 ppm in the CO removal step of reformer for polymer electrolyte fuel cell （PEFC） co-generation systems, CO preferential methanation under various conditions were studied in this paper. Results showed that, with a single kind of catalyst, it was difficult to reach both CO removal depth and CO2 conversion ratio of below 5%. Thus, a two-stage methanation process applying two kinds of catalysts is proposed in this study, that is, one kind of catalyst with relatively low activity and high selectivity for the first stage at higher temperature, and another kind of catalyst with relatively high activity and high selectivity for the second stage at lower temperature. Experimental results showed that at the first stage CO content was decreased from 1% to below 0.1% at 250-300 ℃, and at the second stage to below 10 ppm at 150-185 ℃. CO2 conversion was kept less than 5%, At the same time, influence of inlet CO content and GHSV on CO removal depth was also discussed in this paper.

A simulation-based two-stage interval-stochastic programming model for water resources management in Kaidu-Konqi watershed,China

This study presented a simulation-based two-stage interval-stochastic programming （STIP） model to support water resources management in the Kaidu-Konqi watershed in Northwest China. The modeling system coupled a distributed hydrological model with an interval two-stage stochastic programing （ITSP）. The distributed hydrological model was used for establishing a rainfall-runoff forecast system, while random parameters were pro- vided by the statistical analysis of simulation outcomes water resources management planning in Kaidu-Konqi The developed STIP model was applied to a real case of watershed, where three scenarios with different water re- sources management policies were analyzed. The results indicated that water shortage mainly occurred in agri- culture, ecology and forestry sectors. In comparison, the water demand from municipality, industry and stock- breeding sectors can be satisfied due to their lower consumptions and higher economic values. Different policies for ecological water allocation can result in varied system benefits, and can help to identify desired water allocation plans with a maximum economic benefit and a minimum risk of system disruption under uncertainty.

Method of oxygen-enriched two-stage underground coal gasification

Two-stage underground coal gasification was studied to improve the caloric value of the syngas and to extend gas production times.A model test using the oxygen-enriched two-stage coal gasification method was carried out.The composition of the gas produced,the time ratio of the two stages,and the role of the temperature field were analysed.The results show that oxygen-enriched two-stage gasification shortens the time of the first stage and prolongs the time of the second stage.Feed oxygen concentrations of 30%, 35%,40%,45%.60%,or 80%gave time ratios（first stage to second stage） of 1：0.12,1：0.21.1：0.51,1：0.64, 1：0.90.and 1：4.0 respectively.Cooling rates of the temperature field after steam injection decreased with time from about 19.1-27.4℃/min to 2.3-6.8℃/min.But this rate increased with increasing oxygen concentrations in the first stage.The caloric value of the syngas improves with increased oxygen concentration in the first stage.Injection of 80%oxygen-enriched air gave gas with the highest caloric value and also gave the longest production time.The caloric value of the gas obtained from the oxygenenriched two-stage gasification method lies in the range from 5.31 MJ/Nm^3 to 10.54 MJ/Nm^3.