Industrial Systems Research Articles

The formation and post-formation processes of vortex rings discharged from laminar starting jets

Motivated by biological systems, vortex rings can enhance the propulsive efficiency in industrial systems. To study the vortex properties during the formation and post-formation stages, impulsively starting jets are investigated by simulations. The effects of the stroke ratio and nozzle geometry are studied at a fixed jet Reynolds number of 2500. The stroke ratio at the formation number is found to be not enough to produce a vortex ring with maximum circulation. The stroke ratio is suggested to be about twice as large as the formation number. An alternative criterion based on the circulation ratio is proposed to describe the onset of pinch-off. This criterion states that the pinch-off would start when the vortex ring attains about 80% of the total jet circulation. During the formation stage, the scaling laws for vortex trajectories and circulation are proposed for continuous formation. By combining the suggested scaling relations and Saffman's velocity formula, the evolution of non-dimensional energy can be predicted. During the post-formation stage, the scaling laws for vortex properties (e.g., the vortex ring diameter, translational velocity, and circulation) are found to be independent of both the nozzle configuration and the vortex Reynolds number. On the grounds of the invariance of impulse in vortex decay, the scaling laws of vortex motion are derived for the non-dimensional energy, circulation, and diffusivity scale of the vortex core. In consequence, the normalized energy and circulation found in experiments can be successfully derived from the similarity model for both nozzle configurations.

A smart agriculture information system delivering research data for adoption by the Australian grains industry

Online Farm Trials (OFT) is a bespoke online information system providing current and historical grains trials research data and information in Australia. It represents a technology that supports smart agriculture by enabling discoveries in the data. Its impact on users has been established, supporting industry knowledge and decision making and leading to improved work practices. The aim of this research is to identify the contribution of OFT, as a tool that supports smart agriculture, and to explore current adoption, barriers and the opportunities for expansion of the system in the Australian grains industry, as perceived by industry experts. In-depth, semi-structured interviews were conducted with leaders and innovators from the agriculture industry (N = 16). Thematic analysis of the qualitative data reveals widespread value of OFT as an important resource for accessing industry-dedicated, current and historical research trials data. Barriers to adoption were identified which included incomplete data, and a lack of industry-wide awareness. Opportunities to consolidate and expand the data, together with improved industry awareness, are essential to improving industry-wide adoption. Key learnings were identified in the research that has benefit for the industry, and consideration for the adoption of technology in smart agriculture. This includes functionality, audience, completeness of information/data, consideration of new technologies and the promotion of the system supported by real world examples to encourage industry adoption. Recommendations to support the design and development of digital platforms in agriculture, in the future, are also offered.

Implementation of a performance management system for environmental sustainability in an industrial organization

Abstract The paper explores the implementation of process performance management systems in the automotive industry, specifically focusing on improving the assembly line of an organization within this sector thanks to the introduction of cobots in the line of manufacturing electronic modules used in car seat control systems. With an emphasis on environmental sustainability and circular economy (CE) concepts, the study investigates the positive effects of optimizing mechanical and electromechanical systems within the assembly line. Leveraging concepts such as KAIZEN and Gemba Kaizen, along with the guiding principles of Just-In-Time (JIT), Continuous Improvement and Automation with a Human Touch (JIDOKA), the research aims to elucidate the potential benefits of such enhancements. Key performance indicators (KPIs) including Overall Equipment Effectiveness (OEE), First Pass Yield (FPY), and scrap rates were considered. Rejects, primarily stemming from human error in part handling, emerged as the major challenge facing this assembly line. To address this issue, collaborative robots were introduced to automate part handling, replacing manual processes with precise and reliable automation. Before the implementation, the cycle time stood at 13.6 units, which was reduced to 12.5 units post-implementation, resulting in a significant increase in productivity equating to 3000 more parts produced per week. OEE increasing from 80% to 87.74%, FPY rising from 96.42% to 98.48%, and the scrap rate decreasing from 0.06% to 0.02%. By addressing inefficiencies at the assembly line level, organizations can achieve significant improvements in performance metrics while concurrently contributing to a greener and more sustainable future.

Wastewater monitoring system in the textile industry

Wastewater monitoring system in the textile industry

Studies on the complex interactions of different egg yolk proteins and their roles on rheological properties and stability of high internal phase Pickering emulsions

Egg yolks (EY) are widely used in food emulsions due to their high contents of amphiphilic proteins. This study investigated the different characterization and interactions among low-density lipoproteins (LDL), high-density lipoproteins (HDL) and phosvitins (Pv) using fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), scanning electron microscopy (SEM). The high internal-phase Pickering emulsions (HIPPEs) prepared by those protein complexes were analyzed on the basis of droplet size, optical microstructure, confocal microscopy images, interfacial protein compositions and rheological behavior. Results showed that LDL had a smaller particle size (56.44 ± 1.27 nm) and a greater ability to reduce interfacial tension, resulting in smaller droplet sizes (7.09 ± 0.25 μm) in LDL-stabilized HIPPEs. In contrast, HDL possessed higher hydrophobicity and a greater number of free sulfhydryl groups. The droplets in HDL-stabilized HIPPEs were larger, more compact, and exhibited lower solid lipid balance values in rheological tests. The competing adsorption of HDL and LDL at the interface influenced the properties of HDL-LDL prepared emulsions. However, an appropriate amount of Pv promoted the formation of an electrical double layer between LDL and HDL, further reducing interfacial tension. LDL-HDL-Pv complex stabilized HIPPEs displayed more gel-like rheological properties and the highest stability during the storage. This study provides insights for the development of EY protein-based emulsification systems in food industry.

Wave-transparent and eco-friendly flame-retardant phosphorus-based phthalonitrile/quartz composites by a synchronized self-curing strategy with cyano and alkynyl groups

The robust human–machine interaction systems of the aerospace industry necessitate the development of thermal resistant wave-transparent composites, with a pivotal focus on organic resin matrix. However, enhancing the heat resistance of such materials while preserving other critical attributes has been a formidable challenge. In this study, a novel designed strategy of a dual-curing functional phosphorus-based phthalonitrile monomer (P-ALK-PN) has been proposed. The optimized curing temperatures for intra- or intermolecular Diels-Alder reactions of alkynyl groups and polyaddition reaction of cyano groups resulted in a homogeneous and highly crosslinked N-enriched phthalonitrile system. After curing at 450 °C, the resin, namely P-ALK-PN-450 °C, demonstrated exceptional thermal stability (T5% = 644 °C), thermo-oxidative stability (T5% = 554 °C), and char yield at 800 °C (90.7 % in N2 and 67.5 % in air). Their quartz fiber reinforced composites (P-ALK-PN/QFs) were subsequently fabricated by the hot-pressing process. Upon post-curing at 420 °C, P-ALK-PN-420 °C/QF displayed elevated glass transition temperature (550 °C), flexural strength (319 MPa), and relatively low dielectric properties with a dielectric constant (Dk) of approximately 4.2 and a dissipation factor (Df) less than 0.02 across a wide temperature ranging from 25 °C to 600 °C. Especially, it exhibited excellent flame retardancy (only a 6.7 % weight loss at ambient temperatures exceeding 1300 °C for 200 s) as well, stemming from release of eco-friendly inert gases (NH3) and formation of a graphitized protective layer during pyrolysis. These promising results highlight the potential applications of P-ALK-PN resins in aerospace and high-performance engineering fields.

SIRT: A distinctive and smart invasion recognition tool (SIRT) for defending IoT integrated ICS from cyber-attacks

With the rise of smart industries, Industrial Control Systems (ICS) has to move from isolated settings to networked environments to meet the objectives of Industry 4.0. Because of the inherent interconnection of these services, systems of this type are more vulnerable to cybersecurity breaches. To protect ICSs from cyberattacks, intrusion detection systems equipped with Artificial Intelligence characteristics have been used to spot unusual system behavior. The main research problem focused on this work is to guarantee ICS security, a variety of security strategies and automated technologies have been established in past literary works. However, the main problems they face include a high proportion of incorrect predictions, longer execution times, more complex system designs, and decreased efficiency. Thus, developing and putting in place a Smart Invasion Recognition Tool (SIRT) to defend critical infrastructure systems against new cyberattacks is the main goal of this project. This system cleans and normalizes the supplied ICS data using a unique preprocessing technique called Variational Data Normalization (VDN). Furthermore, a novel hybrid technique called Frog Leap-based Ant Movement Optimization (FLAMO) is applied to choose the most important and necessary features from normalized industrial data. Furthermore, the methodology of Weighted Bi-directional Gated Recurrent Network (WeBi-GRN) is utilized to precisely distinguish between genuine and malicious samples from information collected by ICS. This work validates and evaluates the performance findings using many assessment indicators and a range of open-source ICS data. According to the study's findings, the proposed SIRT model accurately classifies the different types of assaults from the industrial data with 99 % accuracy.

Full-spectral response of mesoporous Z-scheme nanoheterojunction NaYF4:Yb,Tm@ZnO/BiOI photocatalysts for simultaneous contaminant and pathogen elimination in wastewater

The increasing contamination of water bodies demands effective and economical solutions for wastewater treatment. This study introduces a novel Z-scheme photocatalyst, NaYF4:Yb,Tm@ZnO/BiOI (UZB), synthesized via a simple chemical route. UZB exhibits exceptional photocatalytic degradation efficiencies of 95.1%, 88.7%, and 56.3% for methyl orange under full spectrum, visible, and near-infrared light, respectively. The integration of heterostructures within UZB promotes efficient charge carrier separation, significantly boosting its photocatalytic activity. When applied to actual wastewater, UZB markedly reduces chemical oxygen demand (COD) and total organic carbon (TOC) by 94.1% and 53.2%, respectively, and also effectively decreases turbidity and ammonia concentrations. Additionally, UZB shows strong antibacterial effects against S. aureus and E. coli, with inhibition rates of 93.9% and 92.0%, though some cytotoxicity is noted. These results underscore UZB's potential as a highly efficient photocatalyst for the simultaneous removal of organic pollutants and pathogens from wastewater, supporting its application in industrial water treatment systems.

Analysing the Performance of Large-Scale Rooftop Solar PV System Using Helioscope, PVsyst and PV*SOL Photovoltaic Simulation Software: As a Renewable Energy Strategy

In Malaysia, government initiatives and policymakers play significant role in creating effective strategies aiming at enhancing the usage of residential, industrial and commercial solar PV systems. However, the most significant barrier to the widespread adoption of photovoltaic (PV) systems is their high installation costs; as a result, numerous studies on PV system modification are now being conducted. Several simulation tools are available for the efficient and extensive integration of solar energy. To predict energy output using climate data and assess performance ration in accordance, such software can be optimized in terms of parameters like PV module number, inverter, tilt angle, and module design structure. The accuracy of these figures fluctuates, though, because climate factors affect how productive photovoltaic systems are. In this current paper, the simulation and design of a 6.9 MW solar power plant in University Tun Hussein Onn Malaysia, have been investigated using three popular software PVsyst v5.06, Helioscope and PV*SOL. This study's main objective is to assess the potential of solar renewable energy sources to produce electrical energy under the Supply Agreement of Renewable Energy (SARE) program using flexible solar system design modelling tools, PVsyst, Helioscope, and PV *SOL. The comparison results showed that Helioscope and PVsyst are considered to be the most dependable software. PVsyst appraised the definite energy production cost with lowermost relative error of 0.12 % and Helioscope estimated performance ratio with lowest relative error of 4.74%. In terms of energy losses, environmental element contributing to the most energy losses where PVsyst recorded 11.1% photovoltaic (PV) loss caused by irradiation and temperature changes. Whereby Helioscope clearly shows that environmental power loss of 8.4% represents the largest portion caused by irradiation and temperature changes. PV*SOL recorded an energy loss of 7.3% due to shading, temperature and reflection on module interface.

A review of ice cream manufacturing process and system improvement strategies

The food industry faces several challenges, including intricate supply chains, compliance with food safety regulations, sustainability concerns, and the rising demand for high-quality products. Furthermore, consumers increasingly seek personalized food products with specific fat, sugar, and micronutrient levels. The ice cream industry is no exception in facing these challenges. Fortunately, Industry 4.0 technologies, such as smart manufacturing, data analytics, and the Industrial Internet of Things (IIoT), offer viable solutions to many of the aforementioned challenges. However, a deeper understanding of industrial ice cream manufacturing processes and systems is essential to apply these technologies effectively. While the related literature has often focused on ingredient selection to achieve the desired ice cream flavor and texture, there is a noticeable absence of comprehensive efforts to evaluate the impact of process- and systems-related aspects in ice cream manufacturing. This study employs a semi-systematic literature review approach to compile recent research that examines the influence of process- and system-level factors on ice cream product quality and production processes, focusing on the aspects that can benefit from implementing Industry 4.0 technologies. The literature review reveals that 1) at the process level, researchers have focused on three key processes (i.e., pasteurization, homogenization, and dynamic freezing) and their impact on the quality of the ice cream; 2) at the system level, researchers have concentrated their efforts on techno-economic factors, process scheduling, productivity, and sustainability.

Fixed-bed natural gas CO2 adsorption performance of ZSM-5 zeolites impregnated with amines

This research studies the fixed-bed natural gas carbon dioxide (CO2) adsorption performance of ZSM-5 zeolites impregnation with different amines: monoethanolamine (MEA), ethylenediamine (ED), ethylenediaminetetraacetic acid (EDTA), and 4-aminophenol. The structural, morphological, and performance characteristics of the modified zeolites were examined through the use of several analytical techniques. These techniques featured Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). The thermogravimetric analysis revealed weight losses in two stages linked to the removal of bound water molecules and the breakdown of the hydroxyl structure. Through fixed bed adsorption experiments, it was discovered that ZSM-5 zeolites functionalized with MEA/ED showed the CO2 adsorption capacity. This study investigated how well four different mathematical models—Clark, Yoon-Nelson, Thomas, and Adams-Bohart—could predict CO2 adsorption breakthrough curves from natural gas using non-linear regression methods. The results showed that all models had very high adjusted determination coefficients (Adj. R²) of over 0.99, indicating they matched the experimental data really well for various adsorbents. The analysis highlighted the models' effectiveness in reflecting adsorption kinetics and capacities, particularly noting the superior performance of the Thomas, Adams-Bohart, and Yoon-Nelson models in capturing uniform breakthrough behaviors. These findings underscore the models' utility in enhancing CO2 removal processes in natural gas purification, which is pivotal for optimizing industrial gas processing systems. These results highlight the efficiency of amine-functionalized ZSM 5 zeolites in capturing CO2 from natural gas streams.

Day-Ahead Nonlinear Optimization Scheduling for Industrial Park Energy Systems with Hybrid Energy Storage

Hybrid energy storage can enhance the economic performance and reliability of energy systems in industrial parks, while lowering the industrial parks’ carbon emissions and accommodating diverse load demands from users. However, most optimization research on hybrid energy storage has adopted rule-based passive-control principles, failing to fully leverage the advantages of active energy storage. To address this gap in the literature, this study develops a detailed model for an industrial park energy system with hybrid energy storage (IPES-HES), taking into account the operational characteristics of energy devices such as lithium batteries and thermal storage tanks. An active operation strategy for hybrid energy storage is proposed that uses decision variables based on hourly power outputs from the energy storage of the subsequent day. An optimization configuration model for an IPES-HES is formulated with the goals of reducing costs and lowering carbon emissions and is solved using the non-dominated sorting genetic algorithm II (NSGA-II). A method using the improved NSGA-II is developed for day-ahead nonlinear scheduling, based on configuration optimization. The research findings indicate that the system energy bill and the peak power of the IPES-HES under the optimization-based operational strategy are reduced by 181.4 USD (5.5%) and 1600.3 kW (43.7%), respectively, compared with an operation strategy based on proportional electricity storage on a typical summer day. Overall, the day-ahead nonlinear optimal scheduling method developed in this study offers guidance to fully harness the advantages of active energy storage.

Intelligent Financial Management Systems Innovations and Applications in the Digital Era

With the advancement of digital transformation, intelligent financial management systems have played a significant role in improving the efficiency, accuracy, and decision-making capabilities of corporate financial management. This study explores the performance and impact of three intelligent financial management systems — Digital Processing Methods and Financial Reimbursement Document System (System A), Industrial Digitalization Industry Enterprise Growth Evaluation System (System B), and Cloud-based Intelligent Financial Management Tools (System C) — in practical applications through case study methods. The results show that these systems have significantly improved the efficiency and accuracy of financial management, enhanced decision-making capabilities, and reduced operational costs through automated processes, data mining, and machine learning techniques. The study also points out challenges in the implementation process, such as data quality, user acceptance, and system integration issues, and predicts future development trends. Finally, the paper discusses the limitations of the research and proposes directions for future research.

The use of digital twin for mobile robot swarm task allocation

Cyber-physical systems, with the advent of the Internet of Things (IoT), have paved the way for the rise of the Industrial Internet of Things (IIoT) in industrial environments. A significant manifestation of this synergy is the Digital Twin—a virtual representation of industrial entities, including robots. This paper examines the fusion of digital twins in swarm robotics, emphasizing communication, and task allocation among robots. This study employs several mobile robots interconnected via a wireless network, each equipped with a Raspberry Pi and specific sensors. A centralized server, acting as both a Docker host and a Docker registry, facilitates task allocation. The results indicate effective communication among robots, with the digital twin playing an instrumental role in safety assessment and task allocation, as well as deploying Docker containers using Portainer and GitLab APIs. Unity3D aids in visualizing IMU motion and orientation. The motivation behind this study stems from the need to enhance efficiency and safety in industrial settings through advanced automation and coordination in swarm robotics. This research not only demonstrates the feasibility of integrating digital twins with IoT-enabled robots but also sets the stage for future investigations into more complex task allocation and safety protocols in industrial environments. By leveraging digital twins in swarm robotics, we open new pathways for research into more adaptive, responsive, and efficient industrial systems.

Enhanced Heat Transfer in Ternary Tangent Hyperbolic Nanofluids through Non-Darcy Porous Media

The effectiveness of heat transfer fluids (HTFs) is pivotal in maximizing the efficiency and longevity of various devices, from small-scale applications to large industrial systems. A comprehensive understanding of the properties of innovative ternary heat transfer nanofluids (TNFs) is essential, particularly when utilized over a Darcy-Forchheimer porous medium. This study explores tangent hyperbolic thermal nanofluids (TNFs) made up of nanoparticles such as graphene, zirconium oxide and magnesium oxide and, suspended in an ethylene glycol base fluid, within a non-Darcy porous medium. Similarity variables are used to streamline the mathematical representation of fluid flow and heat transmission in TNFs. Then, semi-analytical solutions to the reduced governing equations are obtained using the homotopy analysis method. The influence of tri-nanoparticles, porosity, and the Forchheimer parameter on skin friction, fluid flow dynamics, heat transfer rates, and the Nusselt number is investigated. The Forchheimer parameter lowers the Nusselt number by 27.80% for TNFs, 21.27% for the hybrid nanofluid, and 21.08% for the nanofluid. As a result, the temperature within TNFs is more evenly distributed. TNFs can transfer more heat by raising the medium’s porosity and the tri-nanoparticle volume fraction. These results unveil groundbreaking insights into enhancing the efficiency of heat transfer fluids. The introduction of a porous medium emerges as an alternate strategy to boost the performance of TNFs.

The Defect Identification System of Electromechanical Equipment on the Edge Side of the Power Grid under Edge Computing

With the development of the industrial Internet of Things, modern industrial systems have developed towards intelligence. Electromechanical Equipment (EE) is essential, and its defect identification is fundamental. Firstly, this research introduces the basic content of Gated Recurrent Unit (GRU), Variational Auto-Encoder (VAE) in Deep Learning (DL), and Edge Computing (EC) to explore the construction of a defect identification system for EE on the edge side of the power grid. Secondly, combined with the advantages of GRU and VAE, a GRU-VAE defect recognition model is proposed. Then, the EC architecture is introduced, and the EE defect identification system based on the GRU-VAE algorithm is constructed. The EC intelligent EE defect identification service system is designed with this as the core. Finally, simulation experiments are carried out using different data sets to verify the performance of the GRU-VAE model. The results show that the GRU-VAE model has higher precision and recall than the separate GRU model and VAE model, and the corresponding F1 value is also higher. The F1 value can reach 0.997 on aperiodic data and 0.966 on periodic data. In addition, the optimal thresholds of different datasets are analyzed, and the relationship between the length of the time window and the model’s performance is studied. When the time window length is 15, the model performance is optimal. This research on the defect identification system of EE on the edge side of the power grid based on EC and DL can provide a new path and inject new vitality into the defect detection of EE.

A new low NOx emission technique for NH3/H2 blends in a flameless combustor through offset injection

The application of ammonia (NH3) as a possible future fuel presents a plausible solution for green energy storage. It helps provide a carbon-neutral fuel alternative for industrial power generation and transportation. However, the combustion of NH3 presents a formidable challenge due to its low reactivity, inadequate flame stability, sluggish flame propagation, and high NOx emissions. Consequently, its integration into combustion systems necessitates substantial system and strategy modification to enable its deployment to industrial systems. The current study presents a novel fuel/air injection technique, which emphasizes the high recirculation of hot combustion products and the extended residence time of fuel/air mixtures. A comprehensive experimental and numerical investigation is conducted using a swirl air injection and offset fuel injection to achieve the flameless combustion mode for optimized NH3/H2 fuel blends. A range of mixture conditions (ϕ = 0.5–1.2) and NH3/H2 compositions (50/50–70/30) are experimentally examined. The investigations helped elucidate the effect of residence time and recirculation on NOx emissions through kinetic simulations using a reactor network model. Subsequently, 3-D numerical simulations helped identify regions of high recirculation, quantified through reactant dilution ratios and uniform temperature distribution. These aspects are determined using a new parameter, the temperature uniformity index along the axial direction of the combustor. The emissions of NOx, unburnt NH3, and unburnt H2 are quantified for different equivalence ratios and NH3 mole fractions in the fuel mixture. The investigations reveal that NOx emissions reached their minimum (450–654 ppm) and (344-211 ppm), when the burner operated at lean (ϕ = 0.5–0.8) and rich (ϕ = 1.0–1.2) conditions, respectively, for 70/30 NH3/H2 blend. The emissions of unburnt NH3 and H2 species remain minimal for lean conditions. Both lean and rich operational regimes demonstrated similar or superior emission characteristics in flameless combustion mode when compared to the conventional combustion mode.

Wavelet-based spatiotemporal sparse quaternion dictionary learning for reconstruction of multi-channel vibration data

Accurate reconstruction of multi-source data information plays an essential role in remote intelligent operation, and prognostic and health maintenance (RIO-PHM) of industrial systems. However, traditional signal reconstruction methods such as compressed sensing fail to capture the spatio-temporal characteristics and coupling nature of the multi-source or multi-channel data. To alleviate this bottleneck, in this paper, a new wavelet-based spatiotemporal sparse quaternion dictionary learning (WSTS-QDL) algorithm is formulated for the reconstruction of multi-channel vibration data for the first time. Specifically, the wavelet-based spatiotemporal sparse low-rank matrix (SLRM) algorithm is elaborated and the sparse coefficient matrix of the multi-channel signals can be estimated using the split Bregman iteration (SBI) technique. Then, quaternion-based dictionary learning is introduced for multi-channel data reconstruction according to the updated sparse coefficient matrix during quaternion dictionary learning. Eventually, two equipmental scenarios including run-to-failure data of rolling bearing in bearing test bench and vibration signal of the failured bearing in corn thresher, are utilized for verification. The experimental results demonstrate that the highest recovery accuracy performance with scoring function and the lowest errors are achieved by the proposed method in contrast with the state-of-the-art benchmarks such as orthogonal matching pursuit (OMP) method and spatiotemporal sparse Bayesian learning (SSBL), and the waveform of phase space trajectory and points cloud distribution of the Poincare section in the original signal are similar/same to that obtained by the proposed method.

Is industrial structure determined on water endowment in China? An empirical study of 31 provinces between 2011 and 2021

The interaction between water resources and industrial development remains inadequately comprehended in China. This study aims to explore the influence of water resources on industrial structure and its underlying mechanisms. The study establishes a theoretical mechanism framework based on the resource curse and blessing theory, examines the influence of water endowment on industrial structure, and investigates its influencing mechanism by using panel data from 31 provinces in China between 2011 and 2021. The study finds that, between 2011 and 2021, the national water-industry coordination gradually increased and remained uncoordinated. Water endowment and industrial structure exhibit a U-shaped relationship with spatial spillover effects. Water endowment reverses the industrial structure in water-scarce regions, while it promotes it in water-abundant regions. Both the curse and blessing of water resources are identified in China, and 30 provinces are “cursed”. The water-industry coordination plays a moderating role in the influence of water endowment on industrial structure, mitigating the effects of both the water resource curse and blessing. It expands the range of influence of the reverse effect and can shift the water resource curse to the water resource blessing. It is therefore proposed to employ water resources as an effective instrument to facilitate regional industrial upgrading, reinforce advanced agglomeration industry system, promote industrial structure adaptation to water resources, and strengthen the implementation of “Determining Industry on Water”. The paper illustrates the different water-industry interactions in water-scarce and water-abundant regions and the role of water-industry coordination in transforming the resource curse into the resource blessing.

SIMTSeg: A self-supervised multivariate time series segmentation method with periodic subspace projection and reverse diffusion for industrial process

Subsequences with varied regimes in the industrial multivariate time series (MTS) are closely associated with the dynamic status of the multi-phased industrial process. Time series segmentation (TSS) provides insights into the underlying behavior of industrial systems. However, the complexity of industrial data poses significant challenges to the conventional TSS methods. Motivated by this, a Self-supervised Industrial Multivariate Time-series Segmentation method (SIMTSeg) is presented in this work. An MTS folding module based on Ramanujan periodic subspace projection is first proposed, where the MTS is reshaped into the 3D feature map to realize the compact representation of the intricate data dependencies. Subsequently, a self-supervised module based on the encoder-decoder architecture is adopted to address the problem of deficient and task-specific annotations in industrial data. The folded feature map is denoised step by step following the reverse diffusion process, and finally turns into the segmentation mask without redundant details. The proposed SIMTSeg has been validated by a popular industrial benchmark, the Tennessee Eastman Process, and outperforms the unsupervised data-driven baselines in terms of various performance metrics. SIMTSeg has no prerequisite on the number of segmentation points or regime types, and is capable of giving more meaningful segmentation results that are in line with the high-level semantics.