Industrial Production Equipment Research Articles

A novel industrial magnetically enhanced hydrogen production electrolyzer and effect of magnetic field configuration

Hydrogen energy is a clean, zero‑carbon, flexible and abundant secondary energy. The large-scale deployment of industrial hydrogen production equipment is an effective way to improve renewable energy consumption, the efficiency of hydrogen production from water electrolysis is crucial for equipment deployment. In this paper, a novel industrial magnetically enhanced AWE electrolyzer is designed to improve the efficiency of hydrogen production. The structure and assembly method of the magnetic bipolar plate are introduced, the BOP component is designed and an industrial system-level test platform is built. The results show that the magnetic enhanced electrolyzer can effectively improve the hydrogen production efficiency, however, the effects of different magnetic field configurations (Magnetic field horizontal configuration, MFHC; Magnetic field vertical configuration, MFVC; No magnetic field configuration, NMFC) are significantly different. The magnetic field of MFVC are concentrated inside the bipolar plate and perpendicular to the direction of the electric field, and there is almost no magnetic flux leakage inside the cathode and anode cells. The magnetic field and the electric field of the MFHC configuration are not completely perpendicular, the magnetic field distribution is irregular, there are more magnetic flux leakage in the cells, and the magnetic field intensity is obviously greater than that of the MFVC configuration. Compared with NMFC, MFHC and MFVC significantly improved the hydrogen production efficiency, MFHC performance is better, the current density increased by 28.48% (@50 °C-100 L/h-2 V cell voltage-30%wt KOH), DC power consumption was reduced by 6.2% (CC100A-100 L/h-30%wt KOH). The design method and configuration scheme of magnetically enhanced electrolyzer provide a new idea for the industrial application of magnetic fields in AWE hydrogen production equipment.

A Joint Scheduling Scheme for WiFi Access TSN.

In the context of Industry 4.0, industrial production equipment needs to communicate through the industrial internet to improve the intelligence of industrial production. This requires the current communication network to have the ability of large-scale equipment access, multiple communication protocols/heterogeneous systems interoperability, and end-to-end deterministic low-latency transmission. Time-sensitive network (TSN), as a new generation of deterministic Ethernet communication technology, is the main development direction of time-critical communication technology applied in industrial environments, and Wi-Fi technology has become the main way of wireless access for users due to its advantages of high portability and mobility. Therefore, accessing WiFi in the TSN is a major development direction of the current industrial internet. In this paper, we model the scheduling problem of TSN and WiFi converged networks and propose a scheme based on a greedy strategy distributed estimation algorithm (GE) to solve the scheduling problem. Compared with the integer linear programming (ILP) algorithm and the Tabu algorithm, the algorithm implemented in this paper outperforms the other algorithms in being able to adapt to a variety of different scenarios and in scheduling optimization efficiency, especially when the amount of traffic to be deployed is large.

Feasibility exploration of strain-based indicator diagram reconstruction for reciprocating compressor fault diagnosis

Reciprocating compressor is an indispensable and important production equipment in petrochemical industry. However, due to the large number of vulnerable components, thermal performance failures occur frequently. Indicator diagram has significant advantages in the diagnosis of those thermal performance failures and is therefore regarded as a reliable fault diagnosis technique for reciprocating compressor. Nevertheless, the indicator diagram needs to extract the pressure and piston position information in the cylinder, which has greatly hindered its practical application. The traditional way of extracting the pressure information may damage the cylinder wall, and the obtaining of the piston position requires key-phase sensor, both of which pose potential safety hazards. In this paper, a novel idea of obtaining cylinder pressure and piston position at the same time through cylinder strain is developed, and the feasibility of the proposed idea is verified by numerical simulation. During the simulation, this work employs ANSYS tool to extract the strain from the outer wall of the cylinder as a way of revealing the relationship between pressure and strain, and piston position and strain. Subsequently, the strain-based indicator diagram is reconstructed through the change of the strain at a certain point on the outer wall of the cylinder in a working cycle. The simulation results show that the cylinder strain can reflect cylinder internal pressure and piston position obviously, and it is feasible to construct an indicator diagram based on cylinder strain.

A multi-level modelling and fidelity evaluation method of digital twins for creating smart production equipment in Industry 4.0

Rapid advances in new-generation information technologies have been the main driving force for the transformation of manufacturing enterprises in Industry 4.0. Digital twin (DT), as a key technology to promote intelligent manufacturing, has shown great potential for manufacturing enterprises to create an industrial intelligence-driven production equipment through in-depth integration of cyber-physical systems. However, the lack of a systematic effective DT modelling method with a supporting evaluation metric is the most important factor restricting the application of DT in manufacturing enterprises. To bridge the gap, this paper proposes a novel multi-level modelling and fidelity evaluation (MLM&FE) method of DT for creating smart production equipment in manufacturing enterprises, which could help enterprises establish an industrial intelligence-driven production environment to quickly respond to changes in the customised global market, thus greatly improving competitiveness of the enterprises. Specifically, this paper firstly designs a reference framework for DT-enhanced smart production equipment, on which an MLM&FE architecture is proposed. Then, key implementation methodologies and tools for MLM&FE are introduced from the perspective of data space modelling, virtual space modelling, knowledge space modelling, model integration and evaluation. Finally, the developed smart production equipment prototype demonstrates the feasibility and effectiveness of DT MLM&FE.

Fault diagnosis of rotating machinery via multi-structure fusion discriminative projection

Rotating machinery is one of the important parts of industrial production equipment, and it is of great practical significance for fault diagnosis. Aiming at the problem of classification difficulty caused by feature interleaving in complex working conditions and high-dimension of rotating machinery fault samples, a rotating machinery fault diagnosis method based on multi-structure fusion discriminative projection (MFDP) is proposed. MFDP constructed intraclass and interclass hypergraph structures with multivariate relationships, fully revealing the higher-order association information among multiple samples. Besides, a tangential graph structure of MFDP is further constructed by combining the tangential affine of local samples to preserve the local tangential information of the manifold space. In the method, a unified objective optimization model of the discriminative hypergraph structures and local tangential graph structures is developed, and by solving the model, we can obtain fault structure features with well intraclass compactness and interclass separability. Extensive experiments on the Case Western Reserve University bearing dataset and Connecticut gear dataset show that the method has a good diagnostic accuracy of rotating machinery in different working conditions.

Research on the Multilabel Data Stream Classification Method Based on Fuzzy Complex Set-Valued Measure Learning

In the process of modern industrial production equipment developing towards the direction of structure, automation, and intelligence, motor is still the main power output equipment. If the data flow classification occurs during the operation of the motor, it will lead to problems such as the reduction of its operation efficiency and the increase in system energy consumption. In serious cases, it will even cause motor damage, and the overall system equipment will be shut down for maintenance for a long time, resulting in serious economic losses. Therefore, the research on intelligent multilabel data stream classification technology of motor is of great significance to ensure the stability and reliability of efficient operation of production equipment. In order to improve the recognition efficiency and accuracy of ms-1dcnn’s multilabel data stream classification method in the environment of variable motor conditions and strong noise interference, a multiscale feature fusion framework is constructed based on the residual network structure. The implementation principles of two kinds of attention mechanism algorithms, squeeze and excitation module and convolution attention module, are studied, respectively. The attention module suitable for one-dimensional residual network is designed and embedded into the residual module to build a multiscale attention residual network model. Finally, the effectiveness and superiority of the proposed model are verified by using the experimental platform data.

Numerical Simulation and Process Enhancement of the Hydrolysis of 2-Chlorobenzal Chloride

Hydrolysis of 2-Chlorobenzal chloride is the primary production method of 2-chlobrobenzaldehyde in industry, but the reactor technologies and reaction processes of this method are traditional and underdeveloped, in which the dichotomous leaf hydrolysis agitator is commonly used, resulting in low efficiency during the hydrolysis process. In this article, ANSYS software was utilized to enhance the hydrolytic reactors used in industrial production by simulating and analyzing the dispersion characteristics of droplets in the reactor. The particle size distribution of dispersed phase droplets and the Sauter mean diameter (D32) were used to characterize the dispersion effect, and the dispersion characteristics of the dichotomous leaf agitator and the three-bladed back-curved impeller, three-bladed propeller impeller, open turbine impeller, and crescent impeller were compared and studied. The results showed that there was a log-linear relationship between the D32 of the dispersed phase and the stirring speed in different systems; the number of tiny droplets in the three-bladed back-curved impeller system increased remarkably, and the droplets size distribution width decreased significantly, achieving mass transfer enhancement in the hydrolysis reaction system. The reliability of the simulation results was verified by the measurement experiment using the Sauter mean diameter (D32) of dispersed phase droplets. The results showed that the Sauter mean diameter (D32) variation trend is consistent with that of the simulation, and the error between them is less than 10%. A lab-scale hydrolytic reactor was designed and produced based on the scheme of an industrial hydrolytic reactor equipped with a three-bladed back-curved impeller, the effect of the hydrolytic reaction was verified, and the process conditions were strengthened under the same conditions. The results showed that, compared with the case in the reactor using a dichotomous leaf hydrolysis agitator at the same reaction temperature and catalyst conditions, the reaction time of hydrolysis completion reduced to 58.33% at the lab-scale, and to 63.89% at the industrial-scale at the stirring rate of 446 r/min, using a three-bladed back-curved impeller. These results provide a technical scheme and a basis for process strengthening for improving the production efficiency of 2-chlorobenzaldehyde production equipment in industry.

Fault Information Identification Method of Industrial Production Equipment Based on Industrial internet of things

Fault Information Identification Method of Industrial Production Equipment Based on Industrial internet of things

Adaptive Priority Adjustment Scheduling Approach With Response-Time Analysis in Time-Sensitive Networks

With the advent of Industry 4.0 and the popularization of smart terminal equipment, the interaction between industrial field information systems and production equipment has intensified. To meet the real-time transmission of time-triggered flow and the coordinated transmission of best effort flow, time-sensitive network-related technologies are used to implement flow queue forwarding by strictly following the gate control list. First, response-time analysis method is proposed to predict the upper bound of delay under a scheduling model following IEEE 802.1Qbv. Second, according to response-time analysis, a deadline monotonic scheduling algorithm with temporary priority expansion is proposed to divide the priority into more levels, which is not limited by the queue type, so as to ameliorate the transmission sequence of switch export flow. Finally, an adaptive priority adjustment scheduling algorithm with temporary priority expansion is designed to construct the optimal scheduling method further improving the scheduling success rate and reducing worst-case end-to-end delays. Compared with similar algorithms, the algorithm proposed improves the scheduling success rate by at least 30%, reduces the total delay by at least 21% and the TT flow delay by 11% in high network utilization conditions.

Methodology for Large-Scale Camera Positioning to Enable Intelligent Self-Configuration.

The development of a self-configuring method for efficiently locating moving targets indoors could enable extraordinary advances in the control of industrial automatic production equipment. Being interactively connected, cameras that constitute a network represent a promising visual system for wireless positioning, with the ultimate goal of replacing or enhancing conventional sensors. Developing a highly efficient algorithm for collaborating cameras in the network is of particular interest. This paper presents an intelligent positioning system, which is capable of integrating visual information, obtained by large quantities of cameras, through self-configuration. The use of the extended Kalman filter predicts the position, velocity, acceleration and jerk (the third derivative of position) in the moving target. As a result, the camera-network-based visual positioning system is capable of locating a moving target with high precision: relative errors for positional parameters are all smaller than 10%; relative errors for linear velocities (vx, vy) are also kept to an acceptable level, i.e., lower than 20%. This presents the outstanding potential of this visual positioning system to assist in the industry of automation, including wireless intelligent control, high-precision indoor positioning, and navigation.

Research on optimization of magnetic field sensing characteristics of PCF sensor based on SPR.

A photonic crystal fiber utilizing surface plasmon resonance (PCF-SPR) sensor based on refractive index (RI) control of magnetic fluid (MF) is designed. The air holes of the sensor are arranged in a hexagonal shape, and the optical field transmission channels on both sides of the central air hole can effectively confine the energy of the optical field. We use MF as the sensing medium, and coat the inner wall of the central air hole with gold. It can effectively stimulate the SPR effect to achieve the purpose of magneto-refractive modulation. We study the sensing characteristics of the proposed sensor by finite element analysis. The results show that the highest refractive index sensitivity reaches 19520 nm/RIU in the RI range of 1.42-1.435 and the maximum figure of merit (FOM) is 374.3 RIU-1. In addition, the magnetic field and the temperature response characteristics of the designed sensor are also investigated. In the magnetic field range of 50-130 Oe, the magnetic field sensitivity is 590 pm/Oe. In the temperature range of 24.3-144.3 °C, the temperature sensitivity is only -29.7 pm/℃. The proposed sensor has significant advantages such as stable structure, high sensitivity, easy integration, resistance to electromagnetic interference and can be used for weak magnetic magnitude detection. It has wide application prospects in industrial production, military, and medical equipment.

Guest Editorial for Artificial Intelligence for Machine Fault Diagnosis Special Section

With the rapid technological development and production requirement, machines and equipment in modern industry, such as advanced manufacturing, transportation, aerospace, and civil infrastructure, become increasingly functional and complex. Machine fault diagnosis plays a significant role for the productivity, reliability, and safety of industrial systems. In the recent decade, data-driven solutions have become more effective and promising for fault diagnosis of complex machines due to increasing data availability and processing capacity. Artificial intelligence (AI) techniques, especially deep learning approaches, are the most powerful tools in achieving accurate fault diagnosis of complex systems. However, AI-based fault diagnosis still faces great challenges in feasibility and reliability for real applications, including the lack of fault condition data, varying working conditions, insufficient generalization capability of AI models, the black-box nature of most AI methods, etc. This special section focuses on advanced and innovative solutions to address a broad view of problems about AI-based machine fault diagnosis. Six out of 18 submissions included in this special section summarize some of the research and applications in this field.

Temperature regulating fibers of high latent heat and strength: Mass production, characterization and applications

A kind of temperature regulating fibers (TRFs) with excellent mechanical and thermal properties were prepared by bi-component melt spinning technology on an industrial mass production equipment, in which the core material chips are composed of paraffin wax (PW), polyethylene (PE) and olefin block copolymers (OBC), having the preferable latent heat and thermostability at an onset volatilization temperature of 224.91 °C, and the sheath layer is polypropylene (PP). The prepared TRFs have significant heat absorption and release processes in the range of 20-35 °C with a latent heat up to 26.96 J/g. At the sheath/core ratio of 6:4, the TRFs are 2.09 cN/dtex in the breaking strength and 30.4 % in the elongation after further drawing at a winding speed of 1400 m/min, which fully meet the requirements of the fabric manufacture. After woven with cotton yarn, the TRF/cotton fabrics can effectively reduce the temperature fluctuation by 3 °C in the cooling process, displaying good thermal storage and regulating ability.

Biomimetic Design and Mass Production of Sustainable Multiscale Cellulose Fibers‐Based Hierarchical Filter Materials for Protective Clothing

AbstractAs a core part of personal protective equipment (PPE), filter materials play a key role in individual protection, especially in the fight against the COVID‐19. Here, a high‐performance multiscale cellulose fibers‐based filter material is introduced for protective clothing, which overcomes the limitation of mutual exclusion of filtration and permeability in cellulose‐based filter materials. With the hierarchical biomimetic structure design and the active surface of multiscale cellulose fibers, high PM2.5 removal efficiency of ≈92% is achieved with the high moisture transmission rate of 8 kg m−2 d−1. Through a simple and effective dip‐coating and roll‐to‐roll process, the hierarchical filter materials can be made on a large scale and further fabricated into high‐quality protective clothing by industrial production equipment.

From Waste to Resource – Utilising Residue from Ready-Made Concrete as New Aggregate

Abstract A new admixture is available, to reduce the sludge produced from the cleansing of production and transportation equipment in the fresh concrete industry. The result is agglomerations of hardening concrete, that might be utilised for aggregate. Utilisation depends on adequate properties. This paper reports from investigations on the physical and mechanical properties of the aggregate and discussions on the performance relative to natural and recycled aggregates and towards requirements for utilisation. The findings indicate substantial potential for utilisation, supporting the reduction of waste for deposit and development of the concrete industry towards a circular economy.

Research on the tension control method of lithium battery electrode mill based on GA optimized Fuzzy PID

In the industrial field, the lithium battery industry has a long history and a large market scale. Lithium battery electrode strip rolling mill belongs to the high-end production equipment in the lithium battery industry. However, due to its complex structure, the tension of lithium battery electrode mill is prone to large fluctuation. This will lead to the phenomenon of wrinkle and looseness, which will affect the quality of the electrode strip. At present, the tension control method of lithium battery electrode mill mostly adopts traditional Proportional-Integral-Differential(PID) control. Under this control mode, the production speed and precision of lithium battery electrode mill need to be improved. In this paper, the fuzzy PID tension control method of lithium battery electrode mill based on genetic optimization is studied. Based on fuzzy theory and PID control method, a tension fuzzy PID model is established for experimental verification, and the initial parameters and fuzzy rules of fuzzy PID are optimized by Genetic Algorithm(GA). This method has better stability, can improve the precision of strip tension control, make the tension more stable when the rolling mill is running, and help to improve the quality of electrode strip production.

Цифровая система мониторинга техники по анализу качества смазочных материалов

Currently, to increase the efficiency of industrial production, high-performance and expensive technological equipment is increasingly used, in which the weakest link, from the point of view of efficiency and reliability, is the components and parts of heavily loaded tribo – couplings operating both at significantly different temperatures (conditionally under lighter conditions, the temperature difference can be 100-120 degrees) and climatic conditions (high humidity, the presence of abrasives and other chemical elements in the atmosphere). As the results of the analysis of the frequency of failures of friction units and, accordingly, the cost of their restoration reach 9-20 percent of the cost of all equipment, without taking into account significant losses of income (profit) of the enterprise from downtime. The solution of this problem is based on the study of the wear rate of friction units by the wear products accumulated in working oils, cooling lubricants, and greases. A digital equipment monitoring system (DSMT) has been developed and implemented, which includes dynamic recording of the number of wear products and oil temperature by original modern recording devices, followed by the technology of their processing and use. The system also includes methods for finding the necessary information in large data sets useful and necessary in theoretical and practical terms with a similar technique controlled by a digital monitoring system. The advantages of SMT are the ability to predict the reliability of the equipment; reduce production risks and significantly reduce inefficient costs.

Design and implementation of a holistic framework for data integration in industrial machine and sensor networks

Digitalization and connectivity trends in industrial plants and production equipment create vast and heterogeneous networks of data sources, data sinks and various communication protocols. Data fusion and evaluation of these resources result in high costs for data integration and maintenance. Therefore, we propose a new framework, called MyGateway, enabling effortless integration of heterogeneous data sources, their fusion within the framework and publication to data sinks as needed. For easy integration, deployment, and expansion of the framework we provide an implementation in JAVA using open-source adapters for common industrial protocols and a simple API for usage in user specified setups.

Ultra-Strong, Ultra-Tough, Transparent, and Sustainable Nanocomposite Films for Plastic Substitute

Summary Plastics play a critical role in daily life but possess a considerably increasing negative impact on the environment and human health. Fabrication of biodegradable and eco-friendly alternatives with competitive properties for plastic substitute is urgently needed. Here, inspired by the hierarchical structure of nacre, we firstly developed a high-performance nacre-inspired composite with high transmittance (83.4% at 550 nm) and high haze (88.8% at 550 nm) via an aerosol-assisted biosynthesis process combined with the hot-press technique. The nacre-inspired composite film combines higher strength (482 MPa) and toughness (17.71 MJ m−3) than most other nacre-inspired films, and can be folded into various shapes without visible failure after unfolding. Moreover, compared with most commercial plastic films, it exhibits a lower thermal expansion coefficient (∼3 ppm K−1) and higher maximum service temperature besides better mechanical properties, which makes it a promising alternative to plastics in many technical fields.

Modelling of an industrial die casting process for the production of aluminum automotive parts

Die (permanent) casting is one of the major manufacturing processes for aluminum automotive parts thanks to its ability to provide a good balance between mechanical performance and production efficiency. In the die casting industry, computational modelling has been widely adopted to analyze, troubleshoot and optimize die design and process parameters. In this work, a computational modelling methodology including key process boundary conditions and material properties has been developed to simulate an industrial die casting process of an aluminum automotive part. It has been demonstrated that the model is accurate and robust with respect to temperature and defect prediction by comparison with a large amount of high-quality plant-trial data. The data was acquired from an in-house designed data acquisition system (DAQ) connected to an instrumented die and industrial production equipment in a production facility.