This paper proposes a multi-objective critical component group identification method for automated container terminals logistics systems based on a physical-electrical-information interdependent network. System components are mapped into physical-execution, electrical-support, and information-control layers, connected by intra-layer homogeneous edges and inter-layer dependency edges. Layer-specific cascading-failure mechanisms are developed, including functional-dependency-induced failure in the physical layer, Kirchhoff-law-based load-capacity failure in the electrical layer, and shortest-path-based load redistribution in the information layer. By integrating topological and functional resilience, this study formulates a multi-objective optimization model to identify minimal critical component groups that maximize resilience degradation and solves it using an NSGA-II-PSO-KCAD hybrid algorithm. A case study of Qingdao Port Automated Terminal shows scale-dependent effects of critical component groups, substitutability among Pareto-optimal component combinations, higher cascading impacts of information-layer nodes under the adopted inter-layer modeling approach, load definitions, and parameter settings, and the importance of bridge roles and cross-layer coupling strength for critical edges. The method supports resilience-oriented maintenance decision-making in complex logistics systems.

This study proposes a particle-age-based bandwidth calculation method for the Uniform kernel, an efficient kernel density estimator (KDE) in Lagrangian Particle Dispersion Model (LPDM), to mitigate excessive smoothing of concentrations near release points. The proposed method, along with Parabolic and Gaussian kernels, was jointly validated against wind tunnel experiments of a representative AP1000 nuclear power plant site with complex topography and irregular building layouts. Sensitivity analyses were also performed to quantify the performance of three KDEs with different LPDM parameters. Results show that proposed method improves Uniform kernel's accuracy and meets acceptable criteria, achieving similar performances as the other two KDEs. Sensitivity analyses illustrate that three KDEs can suppress increasing stochastic errors with high-resolution grid settings. Uniform kernel's bandwidths near release point should vary sharply horizontally and smoothly vertically, with appropriate bandwidth multiplication factors from 0.3 to 0.5. Parabolic and Gaussian kernels show less sensitivity to maximum horizontal bandwidth. The integer governing the Gaussian kernel's cut-off mechanism should be at least 2. Uniform and Parabolic kernels may lead to partial loss of particle mass contribution with coarser grid sizes. Gaussian kernel demonstrates more robust performance with lower numbers of released particles.

FLEXPART, a Lagrangian particle dispersion model, is widely used in atmospheric dispersion and nuclear emergency response studies. However, a systematic sensitivity analysis of this model is still lacking. This study, which utilizes meteorological data output from the WRF model (with 1 km spatial resolution and 1 h temporal resolution) to drive the FLEXPART model, validates FLEXPART using the Kincaid experiment data and assesses its sensitivity to key parameters: particle number, lower bound of the turbulence intensity, and grid size. The simulation results demonstrate that the model adequately reproduces the plume dispersion pattern and covers most monitoring stations. Furthermore, the numerical results show high consistency with the observational data (FAC2: 0.50; FB: −0.22; NMSE: 1.01). The sensitivity results indicate that the particle number and the grid size significantly influence the model performance. It is recommended to use a particle number of 1000 particles/min, a lower bound of the turbulence intensity of (0.6, 0.6, 0.3) m/s, a horizontal grid size of 100 m, and a vertical grid size of 10 m for similar scenarios. These findings offer actionable guidance for parameter selection in nuclear emergency scenarios and similar dispersion studies, supporting improved model reliability and applicability.

CCUS has been positioned as an indispensable technology to achieve carbon neutrality. Previous studies on CCUS techno-economic analysis mainly focused on time effect on economic value and neglected that on physical emission reduction value, thus unable to accurately appraise CCUS projects economics in their lifetime. In this study, a concept of compound climate change mitigation value (CCMV) is proposed and a techno-economic assessment model is developed with present value of net cost (PVNC) divided by CCMV over CCUS projects lifetime. PVNC can reflect time effect on monetary-inflation by an economic discount rate (DR), while CCMV can manifest the relationship between CO2 mitigation and global temperature rise by a compound mitigation rate (CMR). The method was applied to four CCUS cases representing combinations of different CO2 capture sources and storage options within China's engineering context. Results suggest that this method is applicable in reflecting cumulative reduction value of CCUS, and capable of assessing economic-performance of CCUS projects more comprehensively considering CCMV toward a target carbon neutrality year. The obtained solutions indicate that the economics of a given CCUS project is related to both DR and CMR. Specifically, within a certain scenario, at a low DR (CMR), profitable ones should be deployed even more proactively and economically infeasible ones should be further postponed compared to that at a low DR. This work can provide decision-bases for determining optimal deployment window of large-scale CCUS projects.

Release rate estimation is crucial for the consequence assessment and emergency decision-making in nuclear accidents. However, inevitable model biases can lead to significant deviations. This study proposes an Adaptive Center Constraint for joint release rate estimation and model correction (ACC joint) for improved robustness and accuracy. It uses a tailored cost function to determine the optimal center constraint, which can automatically adapt to different cases. It was validated against four wind tunnel experiments, which simulated complex dispersion scenarios with densely built-up and highly heterogeneous terrains. The ACC joint method was compared with the Tikhonov and joint correction. The results indicate that the proposed method significantly improves the accuracy of release rate estimation. Compared to the joint correction method, the mean relative error is reduced by 38.6% and 31.4% in the all-measurement and independent validation, respectively. Furthermore, sensitivity analysis reveals that the ACC joint method provides lower mean relative error with different numbers of measurements and shows ultimate stability in all scenarios. It also suggests that measurement sites should be positioned in downwind high-concentration areas and the foot of mountainous areas for reliable estimation. The results from different cost functions verify the scalability of the proposed method, providing potential applications to other complex scenarios.

In the existing researches about partial disassembly line balancing problem (PDLBP), all workstations are assumed always be available. In the real world, however, aging and failure of machines are inevitable, which may result in the shutdown of the whole disassembly line. To solve this problem, this study introduces preventive maintenance scenarios into PDLBP to prevent unexpected breakdowns and promote the productivity and smoothness of the disassembly line. Then, considering the substantial level of uncertainty in the state of end-oflife products, a multi-objective fuzzy mathematical model is established to minimize the cycle time, disassembly profit, and total assignment plan alteration simultaneously. And an enhanced hybrid artificial bee colony algorithm is developed to solve it. The attribute-driven heuristic strategy is applied to improve the quality of initial food sources, and the adaptive neighborhood search is designed to improve the exploitation efficiency of the employed bees and onlooker bees. Additionally, a hybrid global learning strategy is developed to guide the scout bees to raise the diversity of food sources. Finally, three case studies are conducted to validate the proposed algorithm. The results indicate that the proposed algorithm can achieve superior performance.

Existing literature on the robotic disassembly line balancing problem often assume that robots are always in good working condition. But in fact, due to the inevitable aging of the machines, robots may break down unexpectedly. And the original task assignment would become infeasible, which may lead to unplanned shutdowns of the disassembly line. To tackle this situation, a partial multi-robotic disassembly line balancing problem considering preventive maintenance scenarios (PMRDLBP-PM) is proposed. The objectives of the PMRDLBP-PM not only encompass the traditional goals of robotic disassembly line balancing problem, such as cycle time and disassembly profitability, but also take into account the potential additional time and cost incurred from the reconfiguration of workstations necessitated by changes in task allocation. Then, a multi-population cooperative coevolution artificial bee colony (MPCCABC) algorithm is developed. Specifically, to enhance the quality of the initial population and ensure population diversity, the population is divided into four subpopulations, including three high-quality subpopulations generated by heuristic rules based on optimization objectives, and one randomly generated subpopulation. And an adaptive progressive neighborhood search strategy is proposed to improve search efficiency by adjusting the complexity of neighborhood operations based on search feedback. Moreover, a cooperative co-evolution strategy with historical information is adopted to supplement historical optimal information in subpopulation information exchange, increasing computing resource utilization and accelerating convergence speed. Finally, three instances are conducted to test the validity of the proposed model and algorithm. The results demonstrate that the MPCCABC can achieve superior performance for the considered PMRDLBP-PM.

ObjectiveLocal-scale atmospheric dispersion modeling of radionuclides is crucial for nuclear emergency response during the early phase. The Lagrangian puff dispersion model excels in accurately and rapidly reproducing radioactive fields at this scale by accounting for natural turbulence and integrating wind fields with spatial and temporal variations. Given that nuclear power plants (NPPs), especially Chinese NPPs, are often located in heterogeneous terrains, which lead to channeling and slope flows, puff splitting in the puff dispersion model is necessary to accurately represent the phenomenon of plume splitting and layer decoupling phenomena. Despite its importance, the threshold values for puff splitting have not been adequately studied. In addition, the complex terrain around NPP sites generates highly complicated flows, necessitating the use of a diagnostic wind field model coupled with the atmospheric dispersion model to improve the accuracy of dispersion simulations.MethodsTo further provide an effective atmospheric dispersion modeling and establish threshold values of puff splitting for the Lagrangian puff dispersion model, the local-scale Lagrangian splitting puff dispersion model (SPUFF) was developed and fully integrated with the California meteorological model (CALMET). Two local-scale dispersion simulations were conducted using the CALMET to drive the SPUFF: one against the Sanmen NPP wind tunnel experiments with east (E) and northeast (NE) wind directions and another to simulate the Fukushima Daiichi nuclear accident. These simulations aimed to validate SPUFF's performance and practicality. Furthermore, a comprehensive sensitivity analysis was performed to determine the credible range of horizontal threshold values for puff splitting. The dispersion results were evaluated using multiple statistical metrics: the fraction of simulations within a factor of two/five/ten of the observations (FAC2/5/10), fractional mean bias (FB), normalized mean-square error (NMSE), normalized absolute difference (NAD), and geometric mean bias (MG).ResultsValidation results indicated that plumes generated by SPUFF effectively covered the majority of measurement sites, with coverage rates reaching 99.60% and 97.54% in the E and NE directions, respectively. All four crucial statistical metrics for SPUFF met acceptable criteria (FAC2: 0.52, FB: －0.17; NMSE: 0.75, NAD: 0.31 in the E direction; FAC2: 0.48, FB: 0.37; NMSE: 1.28, NAD: 0.39 in the NE direction), indicating remarkable performance. Practical evaluations demonstrated that SPUFF can reproduce more measurements in the Futaba station compared to the Lagrangian particle model (LAPMOD). SPUFF also successfully captured the concentration peak effects resulting from the reactor events during the Fukushima nuclear accident. Sensitivity analysis suggested that applying no puff splitting module might be sufficient for complex terrains with constant meteorological conditions (constant wind fields). However, puff splitting becomes crucial in complex terrains with variable meteorological conditions. For local-scale dispersion scenarios involving NPPs, the recommended threshold values for puff splitting range between 700 m and 1100 m.ConclusionsThis paper provides a comprehensive evaluation of the Lagrangian splitting puff dispersion model (SPUFF) and demonstrates its practical application. The results strongly indicate that SPUFF is a valuable tool for future nuclear emergency responses. Additionally, this paper proposes a credible range of threshold values for puff splitting, offering guidelines for applying the puff model in local-scale dispersion scenarios at NPP sites.

Material perception is one of the several essential abilities required by robots, and it also poses significant challenges in real-world field applications. In harsh environments, the existing material recognition methods have the problem of low accuracy. In this paper, ultrasonic technology is applied to the field of material recognition, which is derived from the predatory strategy of echolocation animals. We propose a novel noncontact material recognition method of surrounding objects for robots based on ultrasonic echo signals, which can be used in external extreme environments. This method primarily adopts the 16-dimensional feature vector extracted from intrinsic mode functions that we gain from empirical mode decomposition as inputs of machine learning algorithms to recognize different materials, and we use K-nearest neighbor, decision tree, and support vector machine algorithms on the feature vector set to decide the best classifier, and its acoustic theoretical model is established additionally. The experimental results validate the accuracy of the acoustic theoretical model and the effectiveness of the proposed method. Compared with the existing methods, the proposed method improves the low accuracy and the poor recognition effect in ultrasonic material recognition. This method provides a new idea for robots to recognize and perceive materials in extreme environments.

Purpose This paper aims to address the issue of model discontinuity typically encountered in traditional Denavit-Hartenberg (DH) models. To achieve this, we propose the use of a local Product of Exponentials (POE) approach. Additionally, a modified calibration model is presented which takes into account both kinematic errors and high-order joint-dependent kinematic errors. Both kinematic errors and high-order joint-dependent kinematic errors are analyzed to modify the model. Design/methodology/approach Robot positioning accuracy is critically important in high-speed and heavy-load manufacturing applications. One essential problem encountered in calibration of series robot is that the traditional methods only consider fitting kinematic errors, while ignoring joint-dependent kinematic errors. Findings Laguerre polynomials are chosen to fitting kinematic errors and high-order joint-dependent kinematic errors which can avoid the Runge phenomenon of curve fitting to a great extent. Levenberg–Marquard algorithm, which is insensitive to overparameterization and can effectively deal with redundant parameters, is used to quickly calibrate the modified model. Experiments on an EFFORT ER50 robot are implemented to validate the efficiency of the proposed method; compared with the Chebyshev polynomial calibration methods, the positioning accuracy is improved from 0.2301 to 0.2224 mm. Originality/value The results demonstrate the substantial improvement in the absolute positioning accuracy achieved by the proposed calibration methods on an industrial serial robot.

Manufacturers are pursuing energy-efficient production in response to the fluctuating energy price, growing global competition, rigorous international laws, and severe environmental crisis. This article proposes to boost the energy efficiency of production systems by controlling the production. It extends the existing energy-saving control research by presenting integrated modeling, analyzing, and controlling approaches. The work starts from the modeling of the production systems and establishes an analytical model to systematically quantify the production loss resulted from energy-saving control and the various disruptions. A dynamic control algorithm is proposed to reduce energy consumption and maintain desirable productivity. Simulation studies are utilized to demonstrate the application of the proposed method and verify its effectiveness. Note to Practitioners—The previous research indicates that it is possible to strategically turn off stations during production for energy saving. However, production systems are complex dynamic systems consisting of interconnected stations and supporting subsystems. Similar to station random failures, turning off stations for energy saving can severely jeopardize the production, and deviate the production from the desired target. Therefore, a quantitative method is established to calculate the production loss resulted from the energy-saving control and disruptions. The method is important to understand the real control cost. Based on the analysis, a dynamic control algorithm is formulated to balance the achieved control benefit and the production loss. It provides plant managers a useful tool to make energy-saving control decisions with a thorough understanding of production system dynamics. The presented research is established for serial batch production systems. The examples of batch stations include the refrigerator foaming equipment in refrigerator assembly lines and the vacuum oven in battery assembly lines. The model cannot be directly applied in serial-parallel production systems.

Predictive maintenance (PM) and quality management help to improve the business bottom line by alleviating the system performance degradation caused by unscheduled machine breakdown and product quality problems. In modern production systems, the wide application of new IT technology results in data-rich environments. However, it is not clear how to take advantage of the data to facilitate maintenance decision-making and production performance improvement. Aiming at multistage production systems with batching machines and finite buffers, this research studies data-driven modelling, analysis and improvement of production systems with predictive maintenance and product quality. First, a data-driven quantitative method is proposed to analyze the impact of machine breakdowns, predictive maintenance and product quality failure on system performance. Then, based on the obtained system production loss, a PM decision model is established to minimise the maintenance and production costs, and the optimal maintenance policy is exploited based on an approximate dynamic programming algorithm. In addition, downtime bottleneck (DT-BN) is defined, and a data-driven bottleneck indicator is derived. A continuous improvement method is established through the identification and mitigation of the bottlenecks. Finally, numerical case studies are performed to validate the effectiveness of the proposed PM decision model and continuous improvement method.

Predictive maintenance has become increasingly prevalent in modern production systems that are challenged by high-mix low-volume production and short production life cycle. It is very helpful to prevent costly equipment failures, and reduce significant production loss caused by unscheduled machine breakdown. Although important, decision models for joint predictive maintenance and production in manufacturing systems have not been fully explored. Therefore, we propose a reinforcement learning based decision model, that brings together production system modeling and approximate dynamic programming. We start from the development of a state-based model by analyzing the dynamics of a multistage production system with predictive maintenance. It provides an approach to quantitatively evaluate the various disruptions as well as the maintenance decision's impact on production. Then a reinforcement learning method is proposed to explore optimal maintenance policies, that optimize the production and maintenance cost. To further improve the performance of the production system, machine stoppage bottlenecks are defined. An event-based indicator is proved to identify bottlenecks with production data. We test the proposed models in simulation case studies. The proposed predictive maintenance decision model is compared with three policies, which are state-based policy (SBP), time-based policy (TBP) and greedy policy (GP). The numerical studies show that the proposed decision model outperforms the policies, and it has the lowest system cost that is 9.68%, 39.07%, and 39.56% lower than SBP, TBP, and GP, respectively. In addition, the research shows that bottleneck identification and mitigation could help manufacturing systems to achieve more than 9.00% throughput improvement.

Reclaimed water is an important alternative water supply because it solves the water shortage problem. This manuscript is intended to provide a critical review of recent publications that address future reclaimed water requirements and analyze and visualize historical trends, research hot topics and promising future research directions. The results show that treatment technologies and optimized system designs for reclaimed water were early topics of interest. However, in the current era, "climate change," "sustainability," "technology," "impact" and other keywords appear frequently as the hot topics. Specifically, emerging research topics include (1) the influence of climate change on water quality and water supply system optimization under uncertainty, (2) improving public acceptance and strengthening water management and policy implementation, (3) developing and applying cost-effective treatment technologies for the removal of trace pollutants and (4) more comprehensive health risk assessment and online detection technology. This analysis accurately reflects historical trends in the field and will help researchers choose future research topics.

The use of ocean bottom seismometer (OBS) is of great significance to explore the tectonic evolution of the East China Sea. To study the characteristics of the deep geological structure from the East China Sea shelf basin to the Ryukyu trench uplifting fold belt, the NW-SE active source deep seismic experiment (OBS-2015) was carried out in the East China Sea. By using the delayed fire source of multilayer gun array, wide-angle seismic reflection and refraction signals up to the Moho can be obtained in the continental shelf and Okinawa Trough, and various seismic phases such as Ps, Pg and PmP can be recognized. The data acquisition results of OBS deep geoscience seismic exploration in the East China Sea show that the effective seismic phase up to Moho surface can also be obtained by using the three-dimensional array source composed of medium and small capacity air guns, which provides a new way for the selection of excitation source for OBS deep exploration. After data preprocessing, identifying and picking up seismic phases, a 2D velocity structure profile was obtained by ray tracing and travel time forward and inversion modeling. Geological interpretation and comprehensive study show that the East China Sea shelf basin is a Mesozoic-Cenozoic sedimentary basin with the maximum superimposed Mesozoic-Cenozoic thickness of 13 km. The crust of the East China Sea shelf area is the extension of the continental crust to the sea. The Moho surface has small fluctuation and the buried depth is about 30 km. From the Diaoyu Island fold to the Ryukyu Island Arc, the Moho Surface fluctuates sharply, rising rapidly from west to East. The crustal thickness of the Okinawa Trough area is only about half the thickness of in the East China Sea shelf basin. It is a high velocity geological body with the thinnest central axis of approximately 13 km and rapid thickening to both sides (approximately 19 km). The high velocity (up to 7.1 km/s) abnormal body develops above the Moho surface in the Okinawa Trough area. The Moho surface in the eastern depression has uplifted significantly. This shows an obvious mirror image relationship with the extensional basin. The vertical velocity structure of the Okinawa Trough is quite different from that of the continental crust and is close to that of the oceanic crust. It is speculated that the thick high-speed body developed on the Moho surface is the manifestation of a large amount of upwelling of mantle derived materials during the back arc stretching process. Combined with the phenomena of low-speed abnormal magma chamber in the shallow part and the penetration of magmatic rocks into the sedimentary basement layer, it is believed that the crust in the southern Okinawa Trough along the survey line has ruptured and entered the initial stage of sea-floor spreading, and the initial oceanic crust has appeared in the southern Okinawa Trough and the crust has atypical oceanic crust characteristics. This experiment provides important basic data for studying the deep crustal structure and regional geological evolution of the East China Sea.

传统的干涉仪采用激光光源,由于激光的高相干性,在平行平板、棱镜等特殊光学元件测量时存在严重的串扰;LED、钨灯等光源因准直性较差、光强稳定性差和相干长度较短等特征,同样不适合作为特殊光学元件检测干涉仪的光源.短相干激光光源具有方向性好、亮度高和相干长度适中等优点,是特殊元件检测干涉仪的理想光源.针对短相干干涉仪对光源的需求,对通过电流调制半导体激光器光源的相干性进行了理论研究.研究结果表明,调制频率越高、调制强度越大,光源的相干长度越短;偏置电流越大,输出功率越高,相干性也会变好,不利于产生短相干光源.在此基础上,搭建并研制了一款短相干激光光源,该光源相干长度为80μm,光源的旁瓣抑制比下降为0.31,输出光功率可达30 mW,能够满足短相干测量对光源的要求.

Aggregation method has been widely utilized to evaluate the performance measures of production lines. However, traditional aggregation method (TAM) has low prediction accuracy in production lines with multiple bottlenecks, such as “inverted bowl” lines and “oscillatory” lines. Therefore, the root causes of the low prediction accuracy are first investigated. Extensive numerical studies indicate that A-units are one of the major causes, where a A-unit is defined as the subsystem between two consecutive bottlenecks. Then, an improved aggregation method (IAM) is proposed to improve the prediction accuracy of TAM. IAM is established by extending the traditional two-machine aggregation building blocks to general multimachine aggregation building blocks in A-units. Specifically, for a small-scale A-unit, an aggregation building block is established for all the machines and buffers in the A-unit. For a large-scale A-unit, a heuristic rule is proposed to divide the A-unit into several small-scale production line segments, where an aggregation building block is established for each segment. Numerical studies indicate that IAM can effectively improve the estimation accuracy of the aggregation method while maintaining a reasonable computational efficiency.

The advance of new generation of IT and sensor technologies results in data enriched production environment. However, there is a lack of an effective utilization of the data to improve productivity while reducing quality management cost. Therefore, this paper proposes a systematic method to analyze the production dynamics, and presents an event-based method to quantitatively evaluate the impact of various disruptions on system throughput, including machine breakdown and quality failure. It is proved that the impact of the events can be measured with system loss which is the summation of the production loss of the slowest machine and the overall number of defective parts produced in the subsystem where the slowest machine locates in. The data-driven method is integrated into an optimization method to exploit the optimal quality inspection allocations. In the method, a non-linear optimization problem is formulated and solved with an adaptive genetic algorithm to trade off the penalty cost of production loss and the investment cost of quality inspection. The research results in a comprehensive understanding of production dynamics subject to quality inspection and rework. It is of critical importance to boost productivity with better quality inspection allocations. Simulation studies are performed to validate the proposed methods.

Control of production operations is one of the most cost-effective methods to improve energy efficiency in production lines. Although many research efforts have been devoted to utilizing production control to boost production energy efficiency, it is not clear how production dynamics change in reaction to the energy saving operations, which is important to capture energy saving opportunities. In this paper, an optimal energy saving control method based on N-policy is introduced to reduce the system energy consumption while only slightly decreasing productivity. A Markov chain model is established to interpret the dynamics of serial production lines with N-policy. The analytical formulas of production rate and energy consumption are derived. Then, an energy-efficient control model is formulated to balance the productivity and energy consumption. For two-machine production lines, the monotonicity property of the model is proved, and the analytical N-policy is derived. For longer production lines, an adaptive genetic algorithm is utilized to solve the energy-efficient control model. Case studies validate the effectiveness of the proposed optimal energy saving control method.

CO2驱技术可以提高低渗透油藏采收率和实现CO2地质封存,在中国具有广阔的应用前景.针对中国陆相低渗透油藏复杂的地质和开发特征,系统阐述了中国石化CO2驱油理论、油藏工程优化设计、注采工程及防腐工艺、产出CO2回收利用等技术进展及矿场试验情况.分析了低渗透油藏CO2驱技术面临的挑战和技术需求,从低成本CO2捕集、输送、降低混相压力,改善CO2驱开发效果、埋存优化及监测技术和超临界CO2压裂技术等方面提出了加快CO2驱技术发展的建议.对于推动CO2驱油和埋存技术发展,提高低渗透油藏储量动用率和采收率、保障国家能源供应和低碳减排具有重要意义.