Operation Problems Research Articles

Application Progress of New Adsorption Materials for Removing Fluorine from Water

A large amount of fluorine−containing wastewater was produced with the rapid development of the industry. Excessive fluoride content in water will not only endanger ecological security but also pose threat to human health. In this study, common new adsorbents for defluorination, such as metal−based adsorbents, natural adsorbents, and nanomaterial adsorbents were reviewed for its physicochemical properties and fluorine removal efficiency. The fluorine removal mechanism of different adsorbents was introduced in detail, and the future work of the removal of fluorine using novel adsorbents was proposed. This research also introduces the application of the coupling of the adsorption method with the technology of chemical precipitation, filtration, and super−magnetic separation to treat high concentration of fluoride wastewater. A good selection of process combinations according to different needs can achieve high−efficiency defluorination in water. Finally, some existing problems of practical operation of fluorine using removal materials in the environment are summarized, hoping to contribute to the future research of fluorine removal materials.

Research on the Application of Electromagnetic Navigation Technology in Surgical Robots

Due to the need to achieve precise operations during surgery, in order to prevent hand tremors and poor surgical field of view, more and more surgical robots are used in surgical operations combined with navigation technology to meet the requirements for surgical accuracy. Open surgery such as orthopaedics, joint replacement and neurosurgery on the market generally use optical navigation systems to guide robots to achieve precise positioning, but optical navigation systems cannot be used for operations in areas with small surgical space. Therefore, a robotic surgical system based on electromagnetic navigation technology that can be applied to the craniofacial area was proposed. By using this robot, the problems of difficult operation and low precision caused by the narrow craniofacial space can be solved. Key techniques and considerations are studied. The function of the developed prototype is verified through model tests. The test results show that the surgical robot under the electromagnetic navigation technology can achieve precise surgical operations improve the success rate of the doctor's surgery and reduce postoperative complications.

Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals

Kinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance because this machine is the core that provides the power and generates the motion of the industrial process. However, also, it is possible to diagnose other types of faults that occur in other elements in the kinematic chain, which are reflected as problems in the motor operation. With this purpose, the coupling between the motor and the final load in the chain requires, in many situations, the use of a gearbox that balances the torque–velocity relationship. Thus, the gear wear in this component is addressed in many works, but the study of gradual wear has not been completely covered yet at different operating frequencies. Therefore, in this work, a methodology is proposed based on statistical features and genetic algorithms to find out those features that can best be used for detecting the gradual gear wear of a gearbox by using the signals, measured directly in the motor, from current and vibration sensors at different frequencies. The methodology also makes use of linear discriminant analysis to generate a bidimensional representation of the system conditions that are fed to a neural network with a simple structure for performing the classification of the condition. Four uniform gear wear conditions were tested, including the healthy state and three gradual conditions: 25%, 50%, and 75% wear in the gear teeth. Because of the sampling frequency, the number of sensors, the time for data acquisition, the different operation frequencies analyzed, and the computation of the different statistical features, meant that a large amount of data were generated that needed to be fused and reduced. Therefore, the proposed methodology provides an excellent generalized solution for data fusion and for minimizing the computational burden required. The obtained results demonstrate the effectiveness of fault gradualism detection for the proposed approach.

A framework to implement augmented reality based on BIM to improve operation and maintenance of mechanical facilities of commercial complexes

PurposeThe mechanical facilities of large-scale buildings have many complexities, so facility management (FM) encounters a massive amount of information during the operation, and inspection is not implemented efficiently. This study aims to integrate building information modeling (BIM), augmented reality (AR) and sensors to mitigate the operation and maintenance (O&M) problems of mechanical facilities by establishing intelligent management.Design/methodology/approachThe component-level data of an under-construction commercial complex were implemented in a 3D model in Autodesk Revit and Navisworks. Then, sensors were installed in mechanical facilities to provide vital online information for the model. Moreover, Unity was used on Vuforia for the AR visualization of the devices.FindingsIt was found that FM not only could obtain building information but can also visualize the functioning of mechanical facilities online through a 3D model in BIM. Thus, in the case of a failure, the AR platform is used on tablets, smartphones and smart glasses to show the technician how mechanical facilities can be repaired and maintained.Originality/valuePrevious studies focused on some factors and processes to improve mechanical FM and did not cover the entire aspect. However, this method reduces the average maintenance time, extends the lives of mechanical devices and prevents unpredicted failures.

Effect of Post-Polyketide Synthase Modification Groups on Property and Activity of Polyene Macrolides.

The biosynthesis of polyene macrolides, which are natural products produced by soil actinomycetes, have been extensively explored, and recent studies have focused on the effects of post-polyketide synthase (PKS) modifications to polyene macrolides on toxicity, water solubility, and antifungal activity. For example, there are interactions between glycosyl, carboxyl, and hydroxyl or epoxy groups generated in the post-PKS modification steps; salt bridges will be formed between carboxylate and ammonium on the mycosamine; and water bridges will be formed between hydroxy and hydroxyl on mycosamine. These interactions will affect their water solubility and substrate-recognition specificity. This review summarizes research related to these post-PKS modification groups and discusses some genetic engineering operation problems and solutions that may be encountered when modifying these post-PKS modification groups. In addition, this review provides a basis for the structural research of polyene macrolide antibiotics and contributes to comprehensive and systematic knowledge, and it may thus encourage researchers to develop novel antifungal drugs with higher therapeutic indexes and medical values.

Collaborative Determination Method of Metro Train Plan Adjustment and Passenger Flow Control under the Impact of COVID-19

Aiming at the problem of metro operation and passenger transport organization under the impact of the novel coronavirus (COVID-19), a collaborative determination method of train planning and passenger flow control is proposed to reduce the train load rate in each section and decrease the risk of spreading COVID-19. The Fisher optimal division method is used to determine reasonable passenger flow control periods, and based on this, different flow control rates are adopted for each control period to reduce the difficulty of implementing flow control at stations. According to the actual operation and passenger flow changes, a mathematical optimization model is established. Epidemic prevention risk values (EPRVs) are defined based on the standing density criteria for trains to measure travel safety. The optimization objectives of the model are to minimize the EPRV of trains in each interval, the passenger waiting time and the operating cost of the corporation. The decision variables are the number of running trains during the study period and the flow control rate at each station. The original model is transformed into a single-objective model by the linear weighting of the target, and the model is solved by designing a particle swarm optimization and genetic algorithm (PSO-GA). The validity of the method and the model is verified by actual metro line data. The results of the case study show that when a line is in the moderate-risk area of COVID-19, two more trains should be added to the full-length and short-turn routes after optimization. Combined with the flow control measures for large passenger flow stations, the maximum train load rate is reduced by 35.18%, and the load rate of each section of trains is less than 70%, which meets the requirements of COVID-19 prevention and control. The method can provide a theoretical basis for related research on ensuring the safety of metro operation during COVID-19.

Application of HSMAAOA Algorithm in Flood Control Optimal Operation of Reservoir Groups

The joint flood control operation of reservoir groups is a complex engineering problem with a large number of constraints and interdependent decision variables. Its solution has the characteristics of strong constraint, multi-stage, nonlinearity, and high dimension. In order to solve this problem, this paper proposes a hybrid slime mold and arithmetic optimization algorithm (HSMAAOA) combining stochastic reverse learning. Since ancient times, harnessing the Yellow River has been a major event for the Chinese nation to rejuvenate the country and secure the country. Today, flood risk is still the greatest threat to the Yellow River basin. This paper chooses five reservoirs in the middle and lower reaches of the Yellow River as the research object, takes the water level of each reservoir in each period as the decision variable, and takes the peak clipping of Huayuankou control point as the objective to build an optimization model. Then, HSMAAOA is used to solve the problem, and the results are compared with those of the slime mold algorithm (SMA) and particle swarm optimization (PSO). The peak clipping rates of the three algorithms are 52.9% (HSMAAOA), 48.69% (SMA), and 47.55% (PSO), respectively. The results show that the HSMAAOA algorithm is better than other algorithms. This paper provides a new idea to solve the problem of the optimal operation of reservoir flood controls.

Функционирование релейной защиты фидеров контактной сети в условиях изменения инфраструктуры

The problem of ensuring stability of functioning of contact network feeders is an important aspect of the railway traction power supply system operation. The ability of the protection to function correctly in emergency and normal operating modes of the AC traction network operation directly affects the efficiency and continuity of a transportation process. The paper is devoted to assessing the impact of changes in the infrastructure of railway sections on functioning of the protectors of the contact network feeders. Regular circulation of trains of increased tonnage and length on electrified sections of AC railways involves a number of problems in operation of traction network protection devices. The article summarizes long-term observations of functioning of the feeder protectors of the contact network of operating sections of the AC railway. The results of field measurements at traction substations of the Far Eastern Railway are presented. The analysis of angular characteristics of operation of remote protectors of the contact network feeders is carried out. The efficiency of functioning of protectors with angular characteristics of operation of remote protectors of various forms in driving modes of trains of increased tonnage and length is estimated. The measures aimed at increasing the stability of functioning of the contact network feeders in various modes of operation of the traction network are evaluated.

Comparing model predictive control and reinforcement learning for the optimal operation of building-PV-battery systems

The integration of renewable energy, such as solar photovoltaics (PV), is critical to reducing carbon emissions but has exerted pressure on power grid operations. Microgrids with buildings, distributed energy resources, and energy storage systems are introduced to alleviate these issues, where optimal operation is necessary to coordinate different components on the grid. Model predictive control (MPC) and reinforcement learning (RL) have been proven capable of solving such operation problems in proof-of-concept studies. However, their applications in real-world buildings are limited by the low reproducibility and the high implementation costs. There is a lack of systematic and quantitative understanding of their strength and weakness in actual applications. Hence, this study aims to improve the scalability of optimal control solutions for smart grid operations by comparing MPC and RL regarding their requirements and control performance. We leveraged the CityLearn simulation framework to implement and compare alternative control solutions based on MPC and RL for the energy management of microgrids. In addition to the control performance of cost saving and carbon reduction, other factors such as robustness and transferability were also examined. While both methods achieved promising results, MPC had slightly better performance and could be transferred more smoothly. Given the standardized framework, MPC is more suitable in most cases for the purpose of microgrid operations. However, RL could be preferable for its quickness in making decisions if a large number of energy systems are involved.

Nano-silica anti-icing coatings for protecting wind-power turbine fan blades.

Wind power is a promising electricity source. Nevertheless, wind turbine blade icing can cause severe problems in turbine operation. In this study, SiO2 spherical nanoparticles (∼90nm), produced by RF (radio frequency) plasma spheroidization, were mixed with E51, PDMS, and ethyl acetate, and sprayed on the surface of aluminum plates and regular power windmill fan blades which were already coated with polyurethane primer. XPS and IR spectroscopies revealed the development of SiC and SiPh (Ph=phenolic ring) bonds, whose formation should be favored by the ultrasound and curing processes at 50°C. The integrity of the coating/substrate interface, whose strength is ascribed to hydrogen bonds, was maintained after 100 icing-melting cycles. The coatings display superhydrophobic behavior and excellent anti-icing performance, along with stability in abrasion, sunlight and self-cleaning ability towards solid pollutants.

On Warehouse and distribution operation optimization of electric power materials

With the wide application of emerging technologies in the supply chain, logistics, as a part of the supply chain, faces both opportunities and challenges of intelligent transformation. With the rapid development of electric power logistics industry, logistics distribution business is an important part to ensure the operation of electric power enterprises. With the continuous expansion of the scale of the power grid, the rapid growth of the types and quantities of power engineering materials has brought great challenges to warehouse management and distribution, increased the operating costs of the power grid companies, and deviated from the requirements of lean development of the logistics industry. Combined with advanced technology and intelligent concept, this paper puts forward suggestions to optimize and solve the problems of warehousing and distribution operation of power grid materials, promote the key management and control of warehousing and distribution links of power grid companies, and promote the digital operation of intelligent logistics warehousing and distribution mode.

Research Results of the Operating Efficiency of the Electric Train Brake Air System

This document discusses the identified problems of the electric train braking system operation in the autumn-winter period. The results of observations are presented. The results of calculating the amount of condensate released during the 1 hour of compressor operation at a certain ambient air temperature are presented. Engineering calculations were performed. A technical solution is proposed to improve the efficiency of pneumatic equipment by installing heated purge valves on the main instead of conventional valves to prevent freezing.

ІНДУСТРІАЛЬНІ ПАРКИ ЯК МЕХАНІЗМ ПОСТКОНФЛІКТНОГО ВІДНОВЛЕННЯ УКРАЇНИ

The article examines the essence of the concepts of special economic zones such as "industrial parks". The world experience of creation of industrial parks in the Czech Republic, Poland, South Korea and Turkey is studied. The conditions and the list of benefits that facilitated the attraction of foreign investment in the creation of new industrial parks are determined. The value and impact on the economic situation of the countries where special economic zones have been implemented are determined. Industrial parks have brought significant economic success, which has become a major factor in accelerated economic development, in particular, the growth of real gross domestic product and exports. The article analyses the state of development of industrial parks in Ukraine after the beginning of the full-scale armed aggression of the Russian Federation. The problems of creation, operation and expansion of industrial parks during the martial law in Ukraine are identified. In order to avoid the complete destruction of enterprises and industry, the Cabinet of Ministers of Ukraine approved the Action Plan for the relocation of production facilities from the areas of active hostilities to safer central and western regions of the country. The largest share of relocated enterprises is made up of wholesale and retail trade, processing industry, motor vehicle repair, professional, scientific, technical activities, and construction enterprises. This article identifies the institutional problems of creating a favourable investment climate in Ukraine before the war, including political, economic and legal instability. The war and attacks on the energy system have had a negative impact on the investment climate in Ukraine since the start of Russia's full-scale armed aggression. The activity of industrial parks in Ukraine remains insufficient due to a number of systemic shortcomings in the preparation and implementation of initiatives to create industrial parks. Insufficient experience of the initiators and imperfect legislative regulation of certain aspects have a negative impact on the investment climate, as potential participants in special economic zones do not receive offers to locate production facilities.

Optimization design of real-time scheduling scheme for city bus vehicles based on BP neural network

Urban public transportation is inseparably related to people’s travel and life. Prioritizing the development of public transportation is a major national policy proposed by the Chinese government, especially to actively promote the development of intelligent public transportation systems. Developing and building advanced intelligent public transport operation and scheduling management system, designing efficient, flexible and low-cost operation and scheduling mode, improving the management level and service quality of public transport enterprises, and thus improving the road traffic condition of the whole city, is undoubtedly the future development direction of public transport enterprises. This paper combines the problems of bus operation and scheduling in China at the present stage, and proposes the operation and scheduling model and method of the intelligent urban public transportation planning system, with a view to realizing a flexible public transportation operation model suitable for different service situations and providing systematic theoretical support for solving the imbalance between supply and demand of public transportation.

Temporal Dynamic Network With Learnable Coupled Adjacent Matrix for Wind Forecasting

Wind forecasting is a typical and fundamental problem in efficient operation of wind power generation systems. Despite of the developed brilliant techniques, current methods for wind forecasting still rely heavily on numerical weather prediction. To investigate the huge amount of meteorological data and to improve the forecasting ability, this paper proposes a temporal dynamic graph convolutional network with learnable coupled adjacent matrix (TCNet), which takes observations of multiple meteorological elements as predictors for wind forecasting. Specifically, for better illustrating the inner property of wind components, a learnable coupled adjacent matrix (CAM) module is introduced. A spatially stable adjacent matrix is built to model the low-frequency part while a temporally dynamic adjacent matrix is developed to extract the high-frequency one. Casting on this mechanism, the CAM can be embedded to vanilla graph convolution network. Apart from that, a simple but effective temporal weights allocating strategy, named temporal dynamic (TED) module, is proposed to depict the cyclicity. By integrating TED with CAM, TCNet can effectively extract the spatio-temporal feature of wind speed and its correlation with other meteorological elements. Using observed datasets of meteorological elements in Denmark and Netherland, we conduct experiments to validate the performance and efficiency of our proposed model. The results indicate the proposed TCNet outperforms the state-of-the-art Graph Convolutional Networks methods and wind forecasting methods.

A NEW METHOD FOR PREDICTION OF THE CONDENSATE BANKING SIZE AROUND A GAS CONDENSATE WELL

The main problem in the production operation from gas condensate reservoirs is well deliverability loss due to condensate formation and banking around the well as pressure falls below the dew point pressure. It is a common practice to calculate gas condensate well production performance based on the three flow regions. The size of Region 1 (a near wellbore region with high condensate saturation) plays an essential role in the productivity loss of gas condensate wells. In this study, a radial reservoir model is constructed using a compositional model to perform a number of analyses on a single well model. Real data from a large gas condensate reservoir located in the Middle East is applied to the reservoir model. Then, the impacts of affecting parameters are studied using the validated reservoir model. The results of this study show that key parameters, which have a significant effect on the size of condensate banking around gas condensate wells, are rock permeability, rock porosity, reservoir thickness, cumulative gas production, and pressure-dependent fluid parameters such as interfacial tension, viscosity, and density. Finally, using dimensionless analysis, a new relation is proposed to predict the radius of condensate banking (Region 1). This procedure is applicable in similar cases.

Assessment of quality of signal transmission process in focs with DWDM

The power of a group DWDM signal increases nonlinear interference in the optical path, especially the value of the interference of the FWM. These interfering factors degrade signal transmission quality and can completely disable the WDM system. Therefore, when designing a DWDM system, it is necessary to optimize the power of the transmitted signal to minimize the probability of error in the optical channel. The article presents the research results and analysis of factors affecting the quality of fiber-optic communication systems with wave separation of channels. Selecting the optimal power level of the group signal and evaluating the transmission quality of optical communication channels in WDM allows us to solve the problem of science-based design, implementation, and effective operation of advanced optical communication systems with wave division of channels.

ConvOPF-DOP: A Data-Driven Method for Solving AC-OPF Based on CNN Considering Different Operation Patterns

AC optimal power flow (AC-OPF) is a significant problem in the economic operation of power systems. Traditional AC-OPF calculation methods only consider a specific operation pattern, which has limitations. We propose ConvOPF-DOP, a novel data-driven approach based on Convolutional Neural Network (CNN), to solve the AC-OPF problem in different operation patterns. This study uses the cluster analysis method to obtain the network structure and operating condition information from historical data. In this way, even when the network structure is unknown, the corresponding network topology label can also be identified from the collected data. Then we use the network topology label and load data as input data, optimize hyperparameters of the CNN model by the Bayesian optimization method, and train the CNN model to learn the relationship between the input data and the voltage output. Finally, the Power Flow (PF) calculation method is used to get the remaining optimal solutions. The effectiveness and superiority of ConvOPF-DOP are verified through 30-bus systems in four different operation patterns. ConvOPF-DOP brings 350× speed increase compared with the traditional method while ensuring high accuracy of generated optimal solutions.

Research and Development of Intelligent Cleaning and Mud Content Detection Technology for Machine-Made Sand

In this paper, an improved method of sand and mud content detection for concrete is proposed, and a set of sand and mud content detection device for concrete is designed. In this paper, a concrete gas content meter and a bubble parameter analysis system were used to study the effect of sand clay content on the gas content of commercial concrete, the loss rate of gas content in warp time, the bubble spacing coefficient and the distribution of hardened appearance pores. The pore structure test shows that the increase of mud content will appropriately reduce the proportion of harmless pores in machine-made sand concrete, and play a role in refining the pore structure in machine-made sand concrete. By using PLC controller and automatic detection device of liquid level and turbidity, the problems of complicated operation, inconvenient carrying and poor repeatability of test data are solved. It reduces the influence of human operation error and improves the accuracy of test data.

Analysis of Inherent Characteristics of Two‐Speed Planetary Gear Transmission System

In the helicopter transmission system, because the two‐speed gear planetary transmission system without power interruption adopts the design of a double planetary gear system, the complexity of the structure leads to the time‐varying meshing stiffness, tooth surface friction and the interaction between components and other nonlinear factors are uncontrollable, and causes the helicopter transmission system vibration noise, unstable operation, and other problems. To solve the above problems, the inherent characteristics of the two‐speed planetary gear transmission system without power interruption were studied and analyzed to find out the influence law of its inherent characteristics. The inherent characteristics of a planetary gear drive system are mainly related to the integrated stiffness such as component mass, support stiffness, and meshing stiffness in the system. Based on the centralized mass method, the translation‐torsional coupling dynamics model of the double planetary gear drive system was established. According to the displacement coordination relationship between the components of the system, the dynamics equation of the double planetary gear drive system was constructed. The mass matrix and stiffness matrix were obtained using MATLAB, and the system’s natural frequency was obtained by solving the dynamics equation. The results show that the low‐order natural frequency is mainly the torsional vibration mode of the center component and the torsional vibration mode of the center component. The influence of the mass of the component on the natural frequency is more complex and has a certain influence on both the high‐ and low‐order natural frequency. The support stiffness in the system mainly affects the low‐order natural frequency and the meshing stiffness in the system mainly affects the high‐order natural frequency.