Large-scale Equipment Research Articles

Lipid extraction in the primary sludge generated from urban wastewater treatment: characteristics and seasonal composition analysis.

A seasonal study of the lipid composition of a primary sludge (dry and dewatered base) obtained from an urban wastewater treatment plant located in Aguascalientes (Mexico) is reported. This study assessed the variability in sludge composition to establish its potential as a raw material for biodiesel production. Lipid recovery was achieved by extraction using two solvents. Hexane was employed for lipid extraction from dry sludge, whereas hexane and ethyl butyrate were used for comparison with dewatered sludge. The formation (%) of fatty acid methyl esters (biodiesel) was determined using extracted lipids. The extraction results from the dry sludge showed 14 and 6% of recovered lipids and their conversion to biodiesel, respectively. For the dewatered sludge, the lipid recovery and biodiesel formation were 17.4 and 60% using hexane, and 23 and 77% for ethyl butyrate, respectively, on a dry basis. Statistical data indicated that lipid recovery depended on the physicochemical characteristics of sewage sludge, which were related to seasonal changes, population activities, and changes in plant configuration, among other factors. These variables must be considered in the design of large-scale extraction equipment for the application and commercial exploitation of biomass waste in biofuel production.

Scaling-Up of the Production of Biochar from Olive Tree Pruning for Agricultural Use: Evaluation of Biochar Characteristics and Phytotoxicity

Due to the large acreage of olive trees in the Mediterranean basin, the biochar from olive tree pruning may become an important resource as part of circular economy strategies. However, so far, there is not much knowledge on whether the same characteristics are repeated in biochar once production is up-scaled to an industrial level. Accordingly, this study aimed to scale up the production of olive tree pruning biochar with three reactors (semi-pilot, pilot and industrial) to ascertain the production parameters that determine the characteristics of the obtained biochar and its possible toxicity to use in agriculture or environmental applications. First, the production conditions in the semi-pilot reactor were optimised by testing three temperatures (400, 500 and 600 °C), with the result that 600 °C was the optimal production temperature because of a high carbon content (70.88%), moderate pH (8.1), good carbon sink (R50 > 0.5) and low contents of PAHs (<6 mg/kg) and heavy metals, resulting in a phytostimulanting effect for all the crops studied. Then, the production was upscaled, using 600 °C as pyrolysing temperature. At the industrial scale, accurate temperature control is essential because when temperatures above 650 °C were reached, the biochar showed a pH above 11, resulting in severe phytotoxicity. The longer retention time of the material in the industrial pyrolysers improved the carbon stability and, therefore, the biochar’s role as a carbon sink. Consequently, it was proven that it is possible to produce olive tree pruning biochar adequate for agriculture and environmental applications with large-scale equipment, and the two most important factors needing control are the temperature and retention time.

Four thermostatic steps: A novel CRISPR-Cas12-based system for the rapid at-home detection of respiratory pathogens.

The outbreak of coronavirus disease 2019 (COVID-19) in 2019 has severely damaged the world's economy and public health and made people pay more attention to respiratory infectious diseases. However, traditional quantitative real-time polymerase chain reaction (qRT-PCR) nucleic acid detection kits require RNA extraction, reverse transcription, and amplification, as well as the support of large-scale equipment to enrich and purify nucleic acids and precise temperature control. Therefore, novel, fast, convenient, sensitive and specific detection methods are urgently being developed and moving to proof of concept test. In this study, we developed a new nucleic acid detection system, referred to as 4 Thermostatic steps (4TS), which innovatively allows all the detection processes to be completed in a constant temperature device, which performs extraction, amplification, cutting of targets, and detection within 40min. The assay can specifically and sensitively detect five respiratory pathogens, namely SARS-CoV-2, Mycoplasma felis (MF), Chlamydia felis (CF), Feline calicivirus (FCV), and Feline herpes virus (FHV). In addition, a cost-effective and practical small-scale reaction device was designed and developed to maintain stable reaction conditions. The results of the detection of the five viruses show that the sensitivity of the system is greater than 94%, and specificity is 100%. The 4TS system does not require complex equipment, which makes it convenient and fast to operate, and allows immediate testing for suspected infectious agents at home or in small clinics. Therefore, the assay system has diagnostic value and significant potential for further reducing the cost of early screening of infectious diseases and expanding its application. KEY POINTS: • The 4TS system enables the accurate and specific detection of nucleic acid of pathogens at 37°C in four simple steps, and the whole process only takes 40min. •A simple alkali solution can be used to extract nucleic acid. • A small portable device simple to operate is developed for home diagnosis and detection of respiratory pathogens.

Precise Configuring of Actuators/Sensors for Active Control of Sound Quality in Cabs with Modal Vibration Energy and LA-PSO

Active control of structural modal vibration is an effective strategy to enhance the sound quality of cabs in commercial vehicles. However, accurate determination of the positioning and quantity of modal active control sensors and actuators is crucial for cabs with intricate structures, owing to the presence of multiorder modes and their coupling. The study presented herein focuses on the cab of a commercial vehicle and contemplates the features of the irregular large-space structure of the cab. By capitalizing on the modal frequency and mode shape of the cab, utilizing the piezoelectric control principle and modal vibration energy as the assessment index, an advanced multimode composite control criterion is postulated to ascertain the configuration of primary sensors and actuators. The particle swarm optimization (PSO) objective function is constructed to accomplish the optimal position matching of the actuator/sensor, using the multimodal surface velocity vector of the vibration sensor as the core parameter. Furthermore, an improved linear adaptive particle swarm optimization (LA-PSO) technique is advanced to satisfy the requirements of optimal convergence performance and accuracy of the complex cab structure. The optimization culminates in a 9 × 9 multichannel active control scheme for determining the optimal position of the actuators/sensors. This investigation provides a technical foundation for the active control of sound quality in automotive cabs and presents an innovative method for implementing effective noise control systems in large-scale machinery and equipment.

Ultra-sensitive and specific detection of pathogenic nucleic acids using composite-excited hyperfine plasma spectroscopy combs sensitized by Au nanoarrays functionalized with 2D Ta2C-MXene

Accurate and rapid screening techniques on a population scale are crucial for preventing and managing epidemics like COVID-19. The standard gold test for nucleic acids in pathogenic infections is primarily the reverse transcription polymerase chain reaction (RT-PCR). However, this method is not suitable for widespread screening due to its reliance on large-scale equipment and time-consuming extraction and amplification processes. Here, we developed a collaborative system that combines high-load hybridization probes targeting N and OFR1a with Au NPs@Ta2C-M modified gold-coated tilted fiber Bragg grating (TFBG) sensors to enable direct nucleic acid detection. Multiple activation sites of SARS-CoV-2 were saturable modified on the surface of a homogeneous arrayed AuNPs@Ta2C-M/Au structure based on a segmental modification approach. The combination of hybrid probe synergy and composite polarisation response in the excitation structure results in highly specific hybridization analysis and excellent signal transduction of trace target sequences. The system demonstrates excellent trace specificity, with a limit of detection of 0.2 pg/mL, and achieves a rapid response time of 1.5 min for clinical samples without amplification. The results showed high agreement with the RT-PCR test (Kappa index = 1). And the gradient-based detection of 10-in-1 mixed samples exhibits high-intensity interference immunity and excellent trace identification. Therefore, the proposed synergistic detection platform has a good tendency to curb the global spread of epidemics such as COVID-19.

Analytical Method for Nonlinear Vibration of PLL-type Grid-connected Converter

In recent years, large-scale renewable energy equipment has promoted the development of grid-connected converters. Among the existing grid-connected converters, PLL-type grid-connected converters are in an unshakable position in the business. As a key device, PLL also plays an irreplaceable role. However, due to the nonlinear characteristics of PLL itself, traditional linear methods cannot be used for more in-depth research, and the existing methods do not systematically obtain the approximate analytical solution of this kind of nonlinear model. Therefore, based on the PLL-type grid-connected converter model, this paper introduces the nonlinear vibration theory to obtain the approximate analytical solution of the model. Specifically, the dynamic model of a PLL-type grid-connected converter system is obtained through the inherent characteristics of the components. Then the normal perturbation and the averaging method are introduced to derive the approximate analytical solution of the model. Finally, the correctness of approximate analytical solutions is verified through experiments, and the application scenarios of the method are summarized.

A multimode structured prediction model based on dynamic attribution graph attention network for complex industrial processes

Complex industrial processes with dynamic and time-varying characteristics, as well as diverse operating conditions, pose challenges in developing accurate real-time online prediction models for the key performance indicator (KPI). To address these challenges, a KPI prediction framework is proposed in this study, which utilizes a multimode structured prediction model based on a dynamic-attribution graph attention network (DAGAT). First, a multimode clustering method based on a Gaussian mixture model is designed to divide and locate industrial multimode operational data. Second, to address the limitations of traditional deep network models in accurately capturing the complex relationships between process variables and KPI, a graph structure is used to model multi-sensor time series data. Subsequently, a novel DAGAT is proposed to improve node prediction performance and interpretation of industrial graph data. Considering the cumulative effect of large-scale equipment, the DAGAT incorporates knowledge of the industrial process, enabling it to capture the influence of chemical-reaction variables. Simultaneously, the Huber loss function is used to further optimize the prediction results. Moreover, the model can calculate in real-time the contribution of each individual process variable to KPI, providing guidance for optimal control. Finally, the effectiveness and superiority of the proposed method are validated through an industrial case.

A Review of Fault Diagnosis Methods for Key Systems of the High-Speed Train

High-speed train is a large-scale electromechanical coupling equipment with a complex structure, where the coupling is interlaced between various system components, and the excitation sources are complex and diverse. Therefore, reliability has become the top priority for the safe operation of high-speed trains. As the operating mileage of high-speed trains increases, various key systems experience various degrees of performance degradation and damage failures. Moreover, it is accompanied by the influence of external environmental high interference noise and weak early fault information. Thus, those factors are serious challenges for the condition monitoring and fault diagnosis of high-speed trains. Therefore, this paper summarizes the research progress and theoretical results of the fault detection, fault isolation, and fault diagnosis methods of the key systems of high-speed trains. Finally, the paper summarizes the applicability of the main methods, discusses the challenges and opportunities of condition monitoring and fault diagnosis of high-speed trains, and looks forward to improving its diagnosis level.

Multi-IRS and Multi-UAV-Assisted MEC System for 5G/6G Networks: Efficient Joint Trajectory Optimization and Passive Beamforming Framework

This article presents a multi-intelligent reflecting surface (IRS)- and multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system for 5G/6G networks. In the studied system, multiple UAVs are integrated for providing services to large-scale user equipment (UEs) with the help of multiple IRSs. This article aims to minimize the overall cost including energy consumption, completion time, and maintenance cost of UAVs by jointly optimizing the trajectories of UAVs and phase shifts of IRSs. When solving this problem, one has to count in mind the deployment of stop points (SPs) of UAVs, and consider the association among UEs, and UAVs (i.e., which UE will send data to which UAV at which SP), the order of SPs, and the phase shifts of IRSs. Therefore, traditional optimization techniques may not solve the above-mentioned problem in an efficient way. To tackle the above-mentioned problem, this article proposes an algorithm called TPaPBA that consists of four phases. The first phase optimizes the SPs’ deployment via using a differential evolution algorithm having variable population size. As a result, all the SPs of UAVs can be obtained. Then, the second phase optimizes the association among UEs, SPs, and UAVs. Specifically, TPaPBA first adopts a clustering algorithm to optimize the SPs-UAVs association, and then a close criterion is introduced to optimize UEs-SPs association. Subsequently, third phase adopts a low-complexity greedy algorithm to optimize the order of SPs for UAVs. Finally, the phase shifts of IRSs are optimized to enhance the data rate between UEs and UAVs. The simulation results of TPaPBA on ten instances having UEs ranging from 100 to 1000, reveals that TPaPBA has significantly improved the system performance contribution and outperforms other approaches in terms of reducing the overall cost of UAVs.

The Promise of Gold. Gold and Governance in China’s Borderlands, Then and Now

Eagleback Village is located in Eya Naxi Autonomous Township, deep in the mountains of southern Sichuan Province, an area whose history has been intimately bound up in the search for gold since at least the Ming dynasty. In the People’s Republic of China’s Reform era, efforts to extract Eagleback’s remaining gold resources have been fraught with controversy. Since 1984, reforms allowing individuals to pursue their own economic interests, combined with the difficulty of monitoring gold digging in such a remote area, have created a ‘grey area’ regarding ownership of the valley’s gold resources. A gold rush began when speculators, villagers and township cadres alike began engaging in artisanal and small-scale mining for gold in the hope of being able to participate in China’s growing economy and improve the standard of living of their families. At the county level in the early 2000s, efforts began to act on development and poverty alleviation policies, in particular the ‘Three Connects’ (三通) policy which aimed to connect every settlement in the county with roads, electricity, and water. County officials looking for contractors able to build roads made a deal with a Hunan gold mining multinational corporation (MNC) for mining rights throughout the county. However, when the project reached Eya Naxi Autonomous Township the damage done to the Longda River by the use of large-scale mining equipment led to a civil uprising in which villagers protested, eventually succeeding in bringing the mining to a halt in 2010. This chapter takes a historical approach while drawing on ethnographic fieldwork to present an example of ‘rightful resistance’ that offers a more nuanced understanding than that frequently offered with regard to state–society relations in China.

Machine-learning-aided application of high-gravity technology to enhance ammonia recovery of fresh waste leachate

Stripping is widely applied for the removal of ammonia from fresh waste leachate. However, the development of air stripping technology is restricted by the requirements for large-scale equipment and long operation periods. This paper describes a high-gravity technology that improves ammonia stripping from actual fresh waste leachate and a machine learning approach that predicts the stripping performance under different operational parameters. The high-gravity field is implemented in a co-current-flow rotating packed bed in multi-stage cycle series mode. The eXtreme Gradient Boosting algorithm is applied to the experimental data to predict the liquid volumetric mass transfer coefficient (KLa) and removal efficiency (η) for various rotation speeds, numbers of stripping stages, gas flow rates, and liquid flow rates. Ammonia stripping under a high-gravity field achieves η = 82.73% and KLa = 5.551 × 10−4 s−1 at a pH value of 10 and ambient temperature. The results suggest that the eXtreme Gradient Boosting model provides good accuracy and predictive performance, with R2 values of 0.9923 and 0.9783 for KLa and η, respectively. The machine learning models developed in this study are combined with experimental results to provide more comprehensive information on rotating packed bed operations and more accurate predictions of KLa and η. The information mining behind the model is an important reference for the rational design of high-gravity-field-coupled ammonia stripping projects.

Digital twin for zinc roaster furnace based on knowledge-guided variable-mass thermodynamics: Modeling and application

Roaster furnace is a large-scale equipment in zinc smelting process, which plays an significant role in safety production and enviromental protection. An accurate digital twin of roaster furnace can help to explore the influence of control parameters and serve as a test platform to verify the effectiveness of control strategies. However, the traditional thermodynamics cannot be used for dynamic modeling, and inability to measure key data greatly affects the accuracy of kinetic modeling. Therefore, this paper establishes a novel digital twin of zinc roaster furnace based on knowledge-guided variable-mass thermodynamics. First, based on the integration of mechanism analysis for mass balance and energy balance, a dynamic modeling method is proposed. Then, particle swarm optimization (PSO) algorithm is introduced to optimize the parameters of conversion rates guided by knowledge. Finally, by connecting the dynamic model with distributed control system (DCS) through OPC communication protocol, a digital twin of roaster furnace is constructed. Extensive experiments show that the simulation results of the digital twin roughly agree with the actual industrial data under steady and dynamic working conditions, and the application of the digital twin on the performance analysis of control parameters and testing of control strategies can provide guidance for the optimal control of roaster furnace.

Magnetic field Pre-polarization enhances the efficiency of alkaline water electrolysis for hydrogen production

Water electrolysis can become a promising mean to produce clean hydrogen. Improving the efficiency of water electrolysis is of great significance for accelerating the development of large-scale hydrogen production equipment and low-carbon economy. This paper presents a novel method for enhancing the alkaline water electrolysis (AWE) efficiency by Magnetic Field Pre-polarization (MFPP), which is easily matched to high-power industrial AWE system. Firstly, compactly-assembled structure and energy consumption of industrial AWE electrolyzer are analyzed. Secondly, the MFPP method was proposed to enhance the efficiency of AWE. The structure parameters of MFPP equipment are optimized by simulation and the physical model is designed. Finally, a system-level equipment was developed to evaluate the effect of MFPP on the performance of the alkaline water electrolysis (AWE) system. Repeatability measurement, I-V characteristic analysis and error statistics were carried out under different working conditions. The effectiveness of MFPP method has been proved on industrial electrolyzer (with 22 cells). Compared with the electrolysis voltage of 40 V-42 V@22_cell, the efficiency improvement is more significant when the voltage is in the range of 42–46 V@22_cell. Under the 0.75 T MFPP, the maximum hydrogen production efficiency increased by 9.2 %. The mean absolute deviation (MAD) of test results is less than 1.88 (with a proportion of 0.11 %) and the mean squared error (MSE) is less than 0.49.

Preemptive multi-skill resource-constrained project scheduling of marine power equipment maintenance tasks1

The shipbuilding industry, characterized by its high complexity and remarkable comprehensiveness, deals with large-scale equipment construction, conversion, and maintenance. It contributes significantly to the development and national security of countries. The maintenance of large vessels is a complex management engineering project that presents a challenge in lowering maintenance time and enhancing maintenance efficiency during task scheduling. This paper investigates a preemptive multi-skill resource-constrained project scheduling problem and a task-oriented scheduling model for marine power equipment maintenance to address this challenge. Each task has a minimum capability level restriction during the scheduling process and can be preempted at discrete time instants. Each resource is multi-skilled, and only those who meet the required skill level can be assigned tasks. Based on the structural properties of the studied problem, we propose an improved Moth-flame optimization algorithm that integrates the opposition-based learning strategy and the mixed mutation operators. The Taguchi design of experiments (DOE) approach is used to calibrate the algorithm parameters. A series of computational experiments are carried out to validate the performance of the proposed algorithm. The experimental results demonstrate the effectiveness and validity of the proposed algorithm.

Identification of Antibiotic Resistance in ESKAPE Pathogens through Plasmonic Nanosensors and Machine Learning.

Antibiotic-resistant ESKAPE pathogens cause nosocomial infections that lead to huge morbidity and mortality worldwide. Rapid identification of antibiotic resistance is vital for the prevention and control of nosocomial infections. However, current techniques like genotype identification and antibiotic susceptibility testing are generally time-consuming and require large-scale equipment. Herein, we develop a rapid, facile, and sensitive technique to determine the antibiotic resistance phenotype among ESKAPE pathogens through plasmonic nanosensors and machine learning. Key to this technique is the plasmonic sensor array that contains gold nanoparticles functionalized with peptides differing in hydrophobicity and surface charge. The plasmonic nanosensors can interact with pathogens to generate bacterial fingerprints that alter the surface plasmon resonance (SPR) spectra of nanoparticles. In combination with machine learning, it enables the identification of antibiotic resistance among 12 ESKAPE pathogens in less than 20 min with an overall accuracy of 89.74%. This machine-learning-based approach allows for the identification of antibiotic-resistant pathogens from patients and holds great promise as a clinical tool for biomedical diagnosis.

The research about application of quasi-zero stiffness vibration isolation technology in a large vehicle-mounted optic-electronic equipment

The large vehicle-borne optic-electronic tracking equipment has the characteristics of heavy inertia, complex road condition and wide vibration spectrum. In order to solve the problem of passive broadband vibration isolation for vehicle-mounted moving platform optic-electronic equipment, a combined quasi-zero stiffness vibration isolation scheme is proposed. The vibration of the engine, generator, water cooler and the remaining vibration amplitude, frequency and Yaw of the road after passing the self-vibration isolation system of the vehicle are collected and confirmed by means of calculation simulation and field test. At the same time, the effective load of photoelectric equipment is analyzed by frequency sweeping test, and the corresponding multi-mode frequency points are obtained. According to the characteristics of the special equipment, a quasi-zero-stiffness vibration isolation system is designed to suppress the wide-frequency and high-amplitude vibration and Yaw caused by vehicle-borne vibration source, ground vibration source and resonance point of the equipment, the foundation stability of load equipment under dynamic condition is realized, which provides a condition for solving the problem of high precision and stability tracking which is greatly influenced by vibration. The vibration isolation system can realize the vibration isolation efficiency of 90% in the range of 20 Hz to 2000 Hz under the condition of 60 Km/h three-class highway with 1.5 t photoelectric load, and restrain the deflection caused by unbalance torque and vibration source offset effectively. It can be concluded that the quasi-zero stiffness vibration reduction and isolation combination design can effectively suppress the vibration of the large-scale vehicle-mounted optic-electronic equipment under the condition of moving platform.

State Recognition of Multi-Nozzle Electrospinning Based on Image Processing

The online monitoring of a multi-jet electrospinning process is critical to the achievement of stable mass electrospinning for industrial applications. In this study, the construction of an ejection state recognition system of a multi-jet electrospinning process based on image processing is reported. The ejection behaviors regarding multi-nozzle electrospinning were recorded by CMOS industrial cameras in real time. The characteristic information regarding the multi-jet cone tip was obtained by processing the images regarding Roberts operator edge detection, Hough transform line detection, and mask histogram analysis. The jet anomalies of the hanging droplets in the nozzle outlet area could be obtained and identified by the vision. The identification accuracy towards the target hanging droplets was more than 85%. This work reports the intelligent control of large-scale multi-nozzle electrospinning equipment.

Additive Manufactured Strain Sensor Using Stereolithography Method with Photopolymer Material.

As a result of the developments in additive manufacturing (AM) technology, 3D printing is transforming from a method used only in rapid prototyping to a technique used to produce large-scale equipment. This study presents the fabrication and experimental studies of a 3D-printed strain sensor that can be used directly in soft applications. Photopolymer-based conductive and flexible ultraviolet (UV) resin materials are used in the fabrication of the sensor. A Stereolithography (SLA)-based printer is preferred for 3D fabrication. The bottom base of the sensor, which consists of two parts, is produced from flexible UV resin, while the channels that should be conductive are produced from conductive UV resin. In total, a strain sensor with a thickness of 2 mm was produced. Experimental studies were carried out under loading and unloading conditions to observe the hysteresis effect of the sensor. The results showed a close linear relationship between the strain sensor and the measured resistance value. In addition, tensile test specimens were produced to observe the behavior of conductive and non-conductive materials. The tensile strength values obtained from the test results will provide information about the sensor placement. In addition, the flexible structure of the strain sensor will ensure its usability in many soft applications.

Modular Design of Micro Pile Drilling Rig for Cableway Transportation

At present, the construction of UHV ±800kV and above DC power is in a period of rapid development, and the UHV field will usher in a market of hundreds of billions of dollars. The construction period in the construction project is very important for the early investment of the project to generate economic benefits. The construction time of the tower foundation platform in the project is the key point. The mountainous terrain is numerous and complex, and the geology is also very different, which brings great difficulties to the construction schedule. The related micro-pile professional large-scale mechanical equipment has just started in China, and the design of professional micro-pile that is in line with the tower foundation platform construction in the power industry is accelerated. Drilling construction equipment is very necessary; considering the construction cost of construction roads and the increasing domestic requirements for green environmental protection, the designed professional drilling construction equipment must meet the conditions of cableway transportation to reduce construction costs and improve the minimum damage to the environment, among which The load-bearing limit of ropeway transportation is generally not more than 3.5t. Jiangxi Dongrui Machinery Co., Ltd. has comprehensively designed and manufactured relevant professional micro-pile construction drilling equipment, realized the modular design of micro-pile drilling rigs, and disassembled related drilling rigs. Compared with the traditional integral drilling rig, the designed modular micro pile drilling rig WZFT300 has the characteristics of stronger terrain application, wider application scenarios, stronger functions, high economic benefits and high degree of digitization.

An innovative design of mega shaft boring machine (SBM) cutterhead based on TRIZ and AD theory

Cutterhead is the key component of the mega shaft boring machine (SBM), whose complex structure and huge size lead to high manufacturing difficulty and cost, and its reliability is difficult to guarantee under huge loads. It is difficult to design the cutterhead only referring to the existing small horizontal tunnel boring machine cutterhead. In order to brainstorm innovative ideas, the paper incorporates the TRIZ into AD to address the following issues. Firstly, engineering conflicts in designing process are eliminated by TRIZ tools to generating innovative ideas. Then, the decomposition of functional requirements and design parameters are employed based on independence axiom of AD, transferring the abstract ideas into concrete design solutions. Moreover, fuzzy comprehensive evaluation is adopted to choose a better design scheme from alternatives of critical component. Finally, the feasibility and practicality of the proposed approach has been demonstrated through case study of designing a novel mega SBM cutterhead. The cutterhead structure is significantly improved with embedded movable cutter boxes, which can excavate step by step to reduce the driving torque, and the manufacturing and transportation cost has been greatly reduced. The method can help designer to innovative design the key components of large-scale underground tunneling equipment.