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ABSTRACT Measuring element is always accompanied by uncertainty disturbances and multiple faults during the long time operation of industrial system in complex environment such as blade and pitch system of floating wind turbines. Timely detection of the fault and fault‐tolerant control (FTC) perform a significant part in ensuring the stable operation of the system and saving maintenance fees. Sliding mode control is extensively applied to FTC because of its good robustness. Therefore, a sliding mode controller is constructed to guarantee the stability of the industrial plant which suffers multiple faults and uncertain disturbances. At the same time, most existing literature does not take into account several faults and uncertain disturbances. Firstly, employing generalized sliding mode method, we devise a sliding mode observer for evaluating state vector, actuator fault and sensor fault of the system. Secondly, according to the state estimation, we construct a sliding mode controller and prove its validity by Lyapunov's theorem. Our controller achieves satisfactory performance, and it is easier to be implemented in practical engineering than other controllers. Finally, we establish a SIMULINK model of blade and pitch system and make simulation experiments. Simulation outcomes validate the availability and practicability of our controller, which also provides a general scheme for fault estimation and FTC of other industrial plants. [ABSTRACT FROM AUTHOR]

The fractional frequency offshore wind power system (FFOWPS) is a core technology for the long‐distance transmission and integration of the large‐scale offshore wind power. However, the risk of wide‐band oscillation in the FFOWPS has become increasingly prominent due to the inevitable presence of the power electronic devices. The traditional admittance of the frequency converter, which is a crucial component in the FFOWPS, may contain right‐half‐plane (RHP) poles when operating under various control modes. To address this deficiency, this paper establishes a four‐port small‐signal admittance model of a back‐to‐back frequency converter and demonstrates its interconvertibility with the traditional model. On this basis, the wide‐band oscillation analysis method for the FFOWPS is proposed, which explicitly describes the influence of the industrial frequency system, the fractional frequency system, and their coupling interactions on system stability. Simulation results in MATLAB/Simulink verify the proposed stability analysis method, highlight stability misjudgements associated with the traditional admittance, and analyse the impact of the coupling terms on system stability. [ABSTRACT FROM AUTHOR]

Solar photovoltaics, especially rooftop systems also called distributed solar photovoltaics, are crucial in the ongoing energy transition. Modeling these systems is vital to understanding their role in a decentralized energy system. While ground‐mounted photovoltaic power plants are easier to model, generalizing yield profiles for rooftop systems is challenging. This study aims to estimate yield loss effects for rooftop solar photovoltaic systems compared to optimized ground‐mounted systems. Anticipated yield losses are 18% for residential, 7% for commercial, and 4% for industrial rooftop systems. The impact on residential prosumers' viability is assessed by comparing prosumer system optimization results with and without yield losses. Results show a non‐uniform change in installed solar photovoltaic and battery capacities, with a tendency to compensate for reduced yields by increasing photovoltaic capacity by up to 20%, given favorable cost prospects by 2050. The annualized total cost of energy for prosumer households could therefore increase by up to 20% by 2050. Despite yield reductions, installing a solar photovoltaic prosumer system remains more favorable than relying entirely on‐grid electricity. This study highlights the importance of considering yield losses in rooftop solar photovoltaics and the significant role of prosumers despite identified yield losses. [ABSTRACT FROM AUTHOR]

Uranium extraction from seawater (UES) is crucial for ensuring the sustainable development of nuclear power and has seen significant advancements in recent years. However, natural seawater is a highly complex biogeochemical system, characterized by an extremely low uranium (U) concentration (≈3.3 µg L−1), abundant competitive ions, and significant marine biological pollution, making UES a formidable challenge. This review addresses the challenges encountered in UES and explores potential methods for enhancing the industrial UES system, including membrane separation, electrochemistry, photocatalysis, and biosorption. Additionally, several representative marine tests are summarized and restrictive factors of large‐scale UES are analyzed. Finally, the further development of UES from laboratory to industry applications is promoted, with a focus on technological innovation. The goal is to stimulate innovative ideas and provide fresh insights for the future development of the UES system, bridging the gap between laboratory research and industrial implementation. [ABSTRACT FROM AUTHOR]

Carbon disclosures are essential for investors to evaluate firms' efforts to combat climate change. In this study, we focus on a specific type of carbon disclosures—namely, emission allowance disclosures—that capture how firms implement emission reductions under the European Union emission trading system (EU ETS). Given the continuously changing institutional features of the EU ETS and the lack of authoritative guidance on how firms should account for emission allowances in their financial statements, it is exceedingly difficult for investors to understand numerical emission allowance disclosures and to compare them across—and even within—firms. Motivated by this criticism, we hypothesize and find that textual disclosures complementing numerical emission allowance disclosures are associated with lower information asymmetries between firms and their investors. Further analyses show that textual disclosures that: (i) describe the accounting approach, and (ii) contain institutional information on the EU ETS, respectively, are particularly important for improving the information environment of numerical emission allowance disclosures. Overall, our findings suggest that text can improve the understanding of numbers in a carbon disclosure context. Therefore, our study not only contributes to the (industrial ecology) literature but also has important implications for regulators, policymakers, investors, financial analysts, and firms. [ABSTRACT FROM AUTHOR]

The IoT‐based monitoring system is used to collect, transmit, analyze, and monitor industrial data in real time, as well as to monitor and operate industrial field equipment. Reduced manufacturing costs, better managerial decision‐making, and increased production efficiency are all very important. The main research results are as follows: (1) The current development of the IoT technology at home and abroad is analyzed, the overall design of the laboratory equipment monitoring system with a three‐layer three‐dimensional architecture is proposed, and its working mode and functional composition are discussed. (2) A set of PLC control system with S7‐300 PLC as the core is designed by using the laboratory CS4000 process experiment device, which realizes the functional requirements of the laboratory equipment monitoring system on‐site monitoring. The intelligent IoT gateway suitable for the system is selected as the data collection and aggregation device of the system, which fully demonstrates the great role of the IoT technology in the monitoring field. (3) A laboratory remote monitoring platform that can remotely monitor experimental data is designed and its software and hardware functions are tested. The results show that all indicators have excellent performance. [ABSTRACT FROM AUTHOR]

The emerging trend in sustainable industrial processes focuses on energy‐efficient drives to enhance performance in both intermediate and continuous operating conditions. For decades, researchers have focused on permanent magnet synchronous machines (PMSM) for industrial applications in order to retain consistency in performance and efficiency under wide speed ranges. Common bus regenerative control is an energy‐efficient method used in industrial systems to manage power flow between multiple devices on a shared DC bus. It captures and reuses excess energy generated during braking/deceleration, reducing waste and improving overall system efficiency. The experimental behavior of the drive scheme has been observed as per the standards of the laboratory. In this article, regenerative control of a PMSM drive is using the voltage vector control (VVC+) technique and the SCADA based condition monitoring for the PMSM drive system is integrated to supervise the parameters including current, voltage, power, speed, torque, and temperature under dynamic operating conditions. As a primary step of implementing the drive condition monitoring is carried out through Danfoss's motion control tool software. Further, the SCADA control system is interfaced to FC302 PMSM drive through RS485 Modbus communication to extract the necessary electrical attributes and monitor the same. [ABSTRACT FROM AUTHOR]

The current lack of effective means for information technology to penetrate into the intelligent manufacturing process of products has led to its disconnection from the core links of industrial systems and low efficiency. Therefore, a nonstandard mechanical product lifecycle model and an industrial internet platform data access mechanism have been proposed using blockchain technology, and an industrial internet platform has been constructed based on the two, which has been experimentally verified. The experimental results showed that the calculation methods in the model could overall reduce the average storage of abnormal data in blockchain products by 53.6%. In addition, the maximum time cost of scalar multiplication did not exceed 20 ms, which was lower than that of bi‐linear pairing. In the comparison of different schemes, the highest encryption computation cost of the research scheme did not exceed 100 ms, and the highest decryption computation cost did not exceed 200 ms, which was generally lower than the comparison scheme. At the same time, the max, mini, and average time consumption of encrypting order data during decryption operations were 5.74, 1.10, and 2.77 s, respectively. When the simulation request reached 2000, there were only a few exceptions on the platform server, indicating that it could support at least 2000 simultaneous requests from applications. Overall, the industrial internet process management platform constructed through research has high practicality and can meet the actual needs of intelligent manufacturing enterprises. [ABSTRACT FROM AUTHOR]

ABSTRACT System reliability is a critical factor in designing complex engineering systems. Redundancy allocation problem (RAP) has attracted more interest owing to its wide scope of industrial applications for system reliability optimization. RAPs for binary‐state and multistate systems have been extensively studied. However, the redundancy allocation for continuous‐state systems composed of degrading components has not yet been fully explored. This study attempts to build the RAP for the continuous‐state parallel‐series system consisting of identical, independent, and nonrepairable components under active and cold‐standby redundancy strategies. The degradation of individual components in a system is continuous and modeled by the gamma process. The degradation at the system level is derived according to a deterministic structure function that relates the system degradation to the degradation of its components. Genetic algorithm and exhaustive search method are used to find the cost‐optimal redundancy design while satisfying a system reliability requirement. Series‐parallel lithium‐ion battery pack systems for electric vehicles (EVs) are used as an illustrative example to validate the proposed RAP and optimization model. [ABSTRACT FROM AUTHOR]

Bioactive food ingredients contribute to the promotion and maintenance of human health and wellbeing. However, these functional ingredients often exhibit low biopotency after food processing or gastrointestinal transit. Well‐designed oral delivery systems can increase the ability of bioactive food ingredients to resist harsh environments inside and outside the human body, as well as allow for controlled or triggered release of bioactives to specific sites in the gastrointestinal tract or other tissues and organs. This review presents the characteristics of common bioactive food ingredients and then highlights the barriers to their biopotency. It also discusses various oral delivery strategies and carrier types that can be used to overcome these biopotency barriers, with a focus on recent advances in the field. Additionally, the advantages and disadvantages of different delivery strategies are highlighted. Finally, the current challenges facing the development of food‐grade oral delivery systems are addressed, and areas where future research can lead to new advances and industrial applications of these systems are proposed. [ABSTRACT FROM AUTHOR]

Since the inception of the Industrial 4.0 revolution, industrial cyber‐physical systems (CPSs) have become integral to critical infrastructures and industrial sectors, including water treatment and distribution systems. Integrating physical and digital worlds has made communication systems within these plants—comprising actuators, sensors, and controllers—vulnerable to advanced cyber‐attacks. Safeguarding the nation's critical infrastructure has thus attracted significant interest from both academia and industry. This article thoroughly examines water treatment and distribution CPSs, detailing their architectural design, devices, applications, and security standards. It analyzes various cyber‐attacks and explores CPS security vulnerabilities and their detection and mitigation techniques. Additionally, it reviews the trends in machine learning (ML) and deep learning (DL) intrusion detection system (IDS) solutions, highlighting their advantages and disadvantages. The article evaluates current datasets and testbeds, identifying some of the best‐performing IDS algorithms tested on each dataset compared to previous research, which could serve as benchmarks in this field. Finally, it proposes data augmentation techniques to generate comprehensive datasets, identifies research gaps, and suggests potential improvements to enhance IDS performance. [ABSTRACT FROM AUTHOR]

The prediction of remaining useful life (RUL) is a critical component of prognostic and health management for industrial systems. In recent decades, there has been a surge of interest in RUL prediction based on degradation data of a well‐defined degradation index (DI). However, in many real‐world applications, the DI may not be readily available and must be constructed from complex source data, rendering many existing methods inapplicable. Motivated by multivariate sensor data from industrial induction motors, this paper proposes a novel prognostic framework that develops a nonlinear DI, serving as an ensemble of representative features, and employs a similarity‐based method for RUL prediction. The proposed framework enables online prediction of RUL by dynamically updating information from the in‐service unit. Simulation studies and a case study on three‐phase industrial induction motors demonstrate that the proposed framework can effectively extract reliability information from various channels and predict RUL with high accuracy. [ABSTRACT FROM AUTHOR]

The recycling of urban mining is one of the means to alleviate resource bottlenecks and build an ecological industrial system in China. To study the characteristics of high‐quality development of the urban mining industry, the high‐quality development capacity of this industry from 2006 to 2020 is evaluated based on the entropy weighting – catastrophe progression method, and its development pattern and influencing factors are discussed. The analysis results show that: ①The high‐quality development capacity of the urban mining industry declines slowly, with an average annual decline of about 0.399% from 2006 to 2020, and the overall development quality level is on average. The provinces with high‐quality development are scattered from the east region of the Hu Line to the north and south regions, and non‐high‐quality development provinces are contiguous. ②The positive spatial correlation of the urban mining industry is significant, but the agglomeration degree in high‐quality development provinces is low‐high‐low. According to the spatial agglomeration characteristics, the high‐quality development pattern is divided into five zones. ③Among the factors driving the high‐quality development of the urban mining industry, regional GDP per capita, R&D investment, the number of patent applications granted and Internet penetration rate are the core influencing factors. High‐quality sustainable zone and high‐quality restoration zone need to rely on the number of granted patent applications to achieve high‐quality maintenance and restoration, high‐quality compensation zone and high‐quality stability maintenance zone need GDP per capita and R&D investment, high‐quality development zone relies on Internet penetration rate to explore high‐quality development potential. [ABSTRACT FROM AUTHOR]

This paper presents a feedforward control algorithm that combines the benefits of optimal iterative learning control (OILC) and model‐based feedforward control (MFC) using iterative feedforward tuning and input shaping filter (IFT‐ISF) for industrial motion systems. OILC effectively compensates for tracking errors in repeating tasks under actuator constraints. However, its performance deteriorates when the trajectory changes. In contrast, MFC can achieve high performance for varying trajectory tracking tasks, but its performance may degrade for constrained systems if the control force exceeds the actuator saturation boundary. The proposed algorithm aims to overcome these limitations to achieve optimal trajectory tracking performance for varying trajectories under actuator constraints. Simulation and experimental results demonstrate that the proposed algorithm achieves optimal tracking performance while complying with the actuator constraints. The algorithm provides a data‐driven approach without requiring the tedious process of model identification. By combining the benefits of OILC and IFT‐ISF, the proposed algorithm can achieve high‐performance trajectory tracking for both repeating and varying tasks under actuator constraints, making it suitable for industrial motion systems. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing interest in employing predictive methods to forecast the remaining useful life of industrial equipment. However, the challenge lies in how to take advantage of the dynamic predictive information to facilitate the maintenance of decision‐making. This problem becomes particularly challenging for complex industrial systems consisting of multiple components with economic dependencies. This paper aims at providing an effective maintenance strategy for multi‐component systems based on predictive information, while considering economic dependencies among different system components. To this end, a dynamic predictive maintenance (PdM) strategy that minimizes the mean maintenance cost over a decision period is proposed, where both long‐term and short‐term policies are integrated into the decision‐making framework. Specifically, the long‐term policy is formulated using predictions derived from historical degradation data through a Long Short‐Term Memory (LSTM) model. Concurrently, real‐time monitoring data is employed to forecast imminent degradation in components, serving as a basis for determining the necessity of short‐term adjustments. This paper embeds the consideration of economic dependencies among components within the maintenance strategy design and employs hierarchical clustering to establish an effective and efficient maintenance grouping policy. The experimental results demonstrate that our proposed strategy significantly outperforms conventional approaches, including block‐based and age‐based maintenance, resulting in substantial cost savings. The proposed strategy is also compared with a similar version without grouping, and the results verify the added value of the optimal maintenance grouping policy in cost reduction. Moreover, a comprehensive analysis of the proposed method is provided, including the impact of different inspection costs and inspection intervals on maintenance decision‐making, which can provide insightful guidance to various PdM scenarios in practice. [ABSTRACT FROM AUTHOR]

The electrochemical regeneration of reduced nicotinamide adenine dinucleotide (NADH) using [Rh(Cp*)(bpy)Cl]+ holds significant promise for the industrial synthesis of chiral chemicals. However, challenges persist due to the high consumption of NADH and the limited efficiency of its cyclic regeneration, which currently hinder widespread application. To address these obstacles, based on in‐situ growth of 3D ordered metal‐organic framework (NU‐1000) on the surface of graphite felt, [Rh(Cp*)(bpy)Cl]+ were immobilized on the Zr6 nodes of NU‐1000 by solvent‐assisted ligand incorporation (SALI), and applied in a flow bioreactor. Moreover, we employ a gas diffusion electrode (GDE) to oxidize H2, providing a clean proton source for the electrochemical regeneration of NADH. Consequently, highly efficient enzymatic electrocatalytic synthesis of L‐lactate was achieved when coupled with L‐lactate dehydrogenases (LDH) as a model reaction, and the total turnover number (TTN) reached 19600 and 1750 for [Rh(Cp*)(bpy)Cl]+ and NAD+ after 48 h, corresponding to a high turnover frequency (TOF) of 2350 h−1 and 210 h−1 for [Rh(Cp*)(bpy)Cl]+ and NAD+, respectively. This work provides new insights for the construction of efficient enzymatic electrosynthesis systems in industrial production. [ABSTRACT FROM AUTHOR]

Value is crucial to lean product development (LPD) and set‐based design (SBD). Nevertheless, the literature needs to present more research on value definition and deployment to support exploring the design space. This scenario hinders the operationalization of lean initiatives in product development. This paper proposes a value deployment (VD) method for SBD, applying quality function deployment (QFD) matrices. We undertook a research action on the design of a TRL‐6 magnetic air conditioner. As a result, we aligned teams and development efforts toward value, building clarity and cohesion about the contribution of subsystems to product performance. Furthermore, VD enabled the integration of manufacturing in decision‐making. Our work paves the way for SBD in terms of practical relevance for industrial and systems engineering, assisting managers in adopting LPD and aligning integrated product teams toward value. [ABSTRACT FROM AUTHOR]

Ionic surfactant and similarly charged nanoparticles can co‐stabilize oil‐in‐dispersion (OID) emulsions at extremely low concentrations (0.001 cmc/0.001 wt%), in which particles do not adsorb at the oil/water interface but distribute in the aqueous phase forming a dispersion. In this paper, the effect of droplet charge density on stabilization of the n‐decane‐in‐water OID emulsion was examined by using a cetyl trimethyl ammonium bromide (CTAB)/C12B (dodecyl dimethyl carboxyl betaine) binary mixture at a low fixed total concentration (0.01 mM) with varying molar fractions of CTAB. A model based on the Derjaguin‐Landau‐Verwey‐Overbeek (DLVO) theory is proposed to calculate interaction energies between droplets and between droplets and particles. It is found that the droplet charge density can be well compensated by particle concentration along the stabilization boundary, and the OID emulsion still follows the DLVO stabilization. Particles tend to surround droplets at large distances but may form a monolayer between approaching droplets at shorter distances, which significantly reduces the van der Waals attraction between droplets. In addition, the induced auxiliary droplet–particle repulsion is proportional to the number of particles per unit area of droplet surfaces, which together with the droplet–droplet repulsion ensures a large total repulsion preventing droplets from flocculation and coalescence. This work explains quantitatively the stabilization of OID emulsions, which have potential applications in emulsion products such as foods, cosmetics, pesticides, and various industrial emulsion systems. Moreover, the development of the OID emulsions represents an important advancement in green chemistry as it substantially reduces the required amounts of emulsifiers and their environmental impact after use. [ABSTRACT FROM AUTHOR]

The rapid expansion of China's industrial sector has been accompanied by energy consumption and environmental pollution. Accurately identifying the sources of inefficiency of industrial sector is crucial for achieving sustainable development. To address this issue, this study develops a novel decomposition approach that combines the concepts of natural and managerial disposability with an aggregate directional distance function approach to measure the performance of regional industries in China from 2006 to 2019. In the proposed approach, the overall inefficiency is decomposed into technical inefficiency and structural inefficiency. The latter component, which captures inefficiency associated with resource misallocation within a country, is further divided into mix and scale elements. The results show that there remains much room for potential improvement in both the operational and environmental performance of China's industrial system, particularly in the central area. Furthermore, structural inefficiency is the primary contributor to the overall inefficiency of Chinese industry under natural disposability, whereas technical inefficiency appears as the main source of the inefficiency under managerial disposability. The results also reveal great disparities in terms of the overall inefficiency and the sources of inefficiency of Chinese industrial sector among regions between different mitigation strategies. Given the substantial structural effect on the performance of Chinese industries across regions, it is necessary to accelerate the establishment of market mechanisms to promote efficient allocation of resources. [ABSTRACT FROM AUTHOR]

The primary approach to managing biofouling in industrial water systems involves the large‐scale use of biocides. It is well‐established that biofilms are 'cell factories' that release planktonic cells even when challenged with antimicrobials. The effect of isothiazolinone on the metabolic activity and biomass of mixed Pseudomonas biofilms was monitored in real‐time using the CEMS‐BioSpec system. The exposure of biofilms to the minimum inhibitory concentration (1.25 mg L−1) of biocide did not impact planktonic cell production (log 7.5 CFU mL−1), while whole‐biofilm metabolic activity and biomass accumulation increased. Only the maximum biocide concentration (80 mg L−1) resulted in a change in planktonic cell yields and temporal inhibition of biofilm activity and biomass, a factor that needs due consideration in view of dilution in industrial settings. Interfacing the real‐time measurement of metabolic activity and biomass with dosing systems is especially relevant to optimizing the use of biocides in industrial water systems. [ABSTRACT FROM AUTHOR]

Carbon is a key element in industrial production, but CO2 is also a greenhouse gas whose release into the atmosphere must be avoided. If its generation cannot be avoided for process‐related reasons, then a comprehensive carbon management is required to enable climate‐neutral production in industry. In the Carbon2Chem® project, partners from industry and science are successfully researching technical components that enable the implementation of concepts for CO2 capture and utilization (CCU) as part of carbon management. [ABSTRACT FROM AUTHOR]

This paper presents a measurement method that utilizes object recognition technology for continuous and quantitative real‐time monitoring of water levels in industrial boilers. Real‐time videos of water levels were monitored using a small camera, and the YOLO algorithm, a single‐stage detector, was employed to use the bounding boxes of detected objects within the video as variables, directly measuring the length ratio for each frame. The method demonstrated a high level of accuracy in water‐level measurement, with an average of 99.02%, and a stable performance, with a fluctuation of 0.13% in continuous measurements. Consequently, the proposed measurement method proves feasible for quantifying continuous water levels in industrial inspection systems even in low‐resource environments. These results demonstrate a new mechanism for monitoring technology, without requiring text detection, showing the potential for improving efficiency in complex boiler systems and the feasibility of reliable water‐level measurement and control. [ABSTRACT FROM AUTHOR]

The deep integration of information technology and process industry production systems makes system failure increasingly multi‐source and multi‐scale. In contrast to conventional hazard methods, system theoretic process analysis (STPA) can analyze the hazards in system control processes from the perspective of interactions among the system components. Theoretically, this method offers advantages that are better suited for modern production systems. However, as of now, the integration between STPA and process industrial production systems is still lacking. To address this issue, this study improved the original STPA method. First, we propose the "5 flows" concept for the process industrial cyber‐physical systems. The systems are described using multilevel flow modeling (MFM). This leads to the development of the MSTPA method, which is specifically designed to analyze the cyber‐physical hazards in process industrial production systems. Subsequently, the cyber‐physical hazards of a fluidized‐bed catalytic cracking unit are analyzed in detail using the MSTPA method as an example. The results show that MSTPA can identify cyber‐physical hazards in multiple dimensions. It is proved that, compared with the original STPA and traditional hazard methods, the MSTPA method can better identify cyber‐physical hazards in process industrial production systems. [ABSTRACT FROM AUTHOR]

Most advanced hydrogen evolution reaction (HER) catalysts show high activity under alkaline conditions. However, the performance deteriorates at a natural and acidic pH, which is often problematic in practical applications. Herein, a rhenium (Re) sulfide–transition‐metal dichalcogenide heterojunction catalyst with Re‐rich vacancies (NiS2‐ReS2‐V) has been constructed. The optimized catalyst shows extraordinary electrocatalytic HER performance over a wide range of pH, with ultralow overpotentials of 42, 85, and 122 mV under alkaline, acidic, and neutral conditions, respectively. Moreover, the two‐electrode system with NiS2‐ReS2‐V1 as the cathode provides a voltage of 1.73 V at 500 mA cm−2, superior to industrial systems. Besides, the open‐circuit voltage of a single Zn–H2O cell with NiS2‐ReS2‐V1 as the cathode can reach an impressive 90.9% of the theoretical value, with a maximum power density of up to 31.6 mW cm−2. Moreover, it shows remarkable stability, with sustained discharge for approximately 120 h at 10 mA cm−2, significantly outperforming commercial Pt/C catalysts under the same conditions in all aspects. A series of systematic characterizations and theoretical calculations demonstrate that Re vacancies on the heterojunction interface would generate a stronger built‐in electric field, which profoundly affects surface charge distribution and subsequently enhances HER performance. [ABSTRACT FROM AUTHOR]

Distributed systems have been widely used in many safety‐critical areas. Any abnormalities (e.g., service interruption or service quality degradation) could lead to application crashes or decrease user satisfaction. These things may cause serious economic losses. Among the various quality assurance approaches for distributed systems, log‐based anomaly detection (LAD) has become a popular research topic. Its popularity relates to system logs being able to record and reveal important run‐time information. This paper presents a general LAD framework for distributed systems. Log grouping and feature‐pattern mining are two crucial LAD components that impact on the anomaly‐detection effectiveness. We also present a systematic survey of techniques in these two directions; propose classification frameworks for log grouping and feature patterns; and summarize four log‐grouping techniques and five feature patterns (which refer to invariant relationships among logs that can be used for anomaly detection). To evaluate their applicability, we report on the findings when applying existing techniques to Ray, a popular industrial distributed system. Based on these findings, several open issues are identified, which provide potential guidance for future research and development. [ABSTRACT FROM AUTHOR]

Although capacitive deionization (CDI) technology has been studied intensively for more than 20 years, its commercialization remains in the initial stage, which is partly caused by the insufficient knowledge exchange between academia and industry. This concept reviews multiple scaling‐up efforts in the CDI technology for treating real water streams, following a case‐by‐case fashion. While the cell architecture in pilot scales is limited to the membrane CDI, highlighting the necessary role of ion‐exchange components during scaling‐ups, different ways of electrode stacking, i. e. monopolar and bipolar, are available. The performance indicators of these CDI systems when treating real water streams are summarized to gain insights into industrial practices. Importantly, key discrepancies in cell components and performances between pilot‐scale and lab‐scale studies are emphasized. The main challenges in large‐scale CDI systems for industrial purposes are discussed, providing hints for a better integration of research and applications. [ABSTRACT FROM AUTHOR]

Generally, thermal inputs dominate the food processing industry for food preservation. Pulsed electric field (PEF) is one of the most promising nonthermal microorganism‐killing techniques. The most important factors in PEF processing are electric field strength and treatment duration. At the laboratory level, encouraging results are reported; however, industrialization raises the cost of the command charging power supply and the high‐speed electrical switch. In this review, the results of previous experimental studies on PEFs and proposed future research directions in this field are discussed. There is currently no successful PEF processing system for industrial applications. Those who wish to promote the industrial application of the PEF processing system face a significant barrier in the form of the system's high initial cost of installation. Innovative developments in high‐voltage pulse technology will reduce the cost of pulse generation and make PEF processing competitive with thermal processing methods. [ABSTRACT FROM AUTHOR]

Laser‐powder bed fusion (L‐PBF) is extensively adopted in the aerospace and automobile industries for producing metallic components. The L‐PBF process involves rapid melting and solidification of metallic powders using a laser source to produce dense parts. Most industrial L‐PBF systems use a fiber‐based laser source that emits a Gaussian beam distribution. However, the conventional Gaussian beam used in L‐PBF processes presents inherent limitations that can impact the quality and properties of the final products. This review aims to provide a theoretical background of laser beam shaping techniques and explores the application of alternative beam shapes in L‐PBF. It investigates how different beam profiles affect the melt pool geometry, process stability, productivity, mechanical properties, microstructure, and surface roughness of fabricated parts. Additionally, it discusses different types of beam shaping techniques and elements utilized to generate distinct beam shapes. Furthermore, different constraints and limitations that hinder the full exploitation of beam shaping are critically discussed. By analyzing the impact of beam shaping in L‐PBF, this review provides insight into optimizing the additive manufacturing process, enhancing part quality, and improving processing performance. It will also help readers to overview the research gaps, challenges, and promising future directions in laser beam shaping applications in L‐PBF. [ABSTRACT FROM AUTHOR]

The Induction Motor (IM) is one of the most frequently used prime movers in most industrial and transportation systems. The motor's stable and safe operation directly influences the secure and reliable operation of such prime movers. Developing an intelligent fault diagnosis system for such motors is very significant. This paper presents an intelligent fault diagnosis method based on the improved functionality of a Convolutional Neural Network (CNN) through its hyperparameter optimization using a nature‐inspired Artificial Bee Colony Optimization (ABCO) algorithm. The proposed diagnostic method introduces and analyses various possible mechanical and electrical faults in the IM. The validation of the proposed method is presented with three different modalities, including vibration, acoustic, and infrared thermography, with their comparative performance analysis. Vibration and acoustic‐based detection are done with time‐frequency scalograms using Constant Q Transform (CQT), which provides enhanced time resolution for lower and higher frequencies. The obtained result indicates that the infrared thermography‐based anomaly detection outperforms the vibration and acoustic‐based diagnosis with 100% classification accuracy. The results signify the potential to diagnose different mechanical and electrical faults in IM with substantial reliability and robustness. [ABSTRACT FROM AUTHOR]

New developments in the area of the Industrial Internet‐of‐Things (IIoT) and Industry 4.0 offer huge potential for a more efficient and flexible industrial production, but are also accompanied by rising system complexity. Consequently, to deal with the increased system complexity, novel approaches, such as reference architectures, are emerging. However, most of these approaches are not yet mature and rather theoretical than ready‐to‐use. Thus, companies need to be provided with frameworks that actively support the transformation of their systems towards Industry 4.0. One of those frameworks has been introduced with Reference Architecture Model Industry 4.0 (RAMI 4.0), which counteracts the mentioned complexity and can be used for various use cases. However, as most of its concepts are too general to be applied directly to actual systems, the need for directly applicable reference architectures emerges. Therefore, this paper proposes a method to derive more detailed reference architectures based on RAMI 4.0 by making use of model‐based systems engineering (MBSE), which target single manufacturing domains rather than the whole industry. Therefore, relevant stakeholders are analyzed and different types of reference architectures targeting their concerns are identified. The resulting reference architectures should be ready‐to‐use for interested manufacturers and thus, enhance the acceptance of RAMI 4.0 as well as improve systems engineering in industrial manufacturing. Finally, the developed reference architecture is evaluated in a proof‐of‐concept case study of a flexible production system. [ABSTRACT FROM AUTHOR]

The four Industry 4.0 design principles information transparency, technical assistance, interconnection and decentralized decisions pose challenges in integrating Information Technology (IT) and Operational Technology (OT) solutions in industrial systems. These different solutions have conflicting requirements, making interfaces between them problematic for both systems and organizations. An Industrial Business Process Twin (IBPT) entity, acting as an intermediary between the realms of IT and OT, has been proposed in a previous work, to effectively reduce the amount of required IT/OT interfaces in an attempt of overcoming this situation. In this work, we investigate the effects of this approach during the design phase. We argue that, by eliminating potentially conflicting direct interfaces between IT and OT stakeholders within the organizational structure, this approach effectively eliminates conflicting communication channels within the system design. To verify our argument, we develop a model of our IBPT concept according to the Reference Architecture Model Industrie 4.0 (RAMI4.0) using an Industry 4.0 scenario addressing the four essential Industry 4.0 design principles. Results show that the IBPT approach indeed eliminates potentially conflicting IT/OT interfaces during the system design phase. [ABSTRACT FROM AUTHOR]

Summary: Healthcare applications with the Internet of Things (IoT) are often safety‐critical, thus, require extensive testing. Such applications are often connected to smart medical devices from various vendors. System‐level testing of such applications requires test infrastructures physically integrating medical devices, which is time and monetary‐wise expensive. Moreover, applications continuously evolve, for example, introducing new devices and users and updating software. Nevertheless, a test infrastructure enabling testing with a few devices is insufficient for testing healthcare IoT systems, hence compromising their dependability. In this paper, we propose a model‐based approach for the creation and operation of digital twins (DTs) of medicine dispensers as a replacement for physical devices to support the automated testing of IoT applications at scale. We evaluate our approach with an industrial IoT system with medicine dispensers in the context of Oslo City and its industrial partners, providing healthcare services to its residents. We study the fidelity of DTs in terms of their functional similarities with their physical counterparts: medicine dispensers. Results show that the DTs behave more than 92% similar to the physical medicine dispensers, providing a faithful replacement for the dispenser. [ABSTRACT FROM AUTHOR]

Microfluidic models are proving to be powerful systems to study fundamental processes in porous media, due to their ability to replicate topologically complex environments while allowing detailed, quantitative observations at the pore scale. Yet, while porous media such as living tissues, geological substrates, or industrial systems typically display a porosity that spans multiple scales, most microfluidic models to date are limited to a single porosity or a small range of pore sizes. Here, a novel microfluidic system with multiscale porosity is presented. By embedding polyacrylamide (PAAm) hydrogel structures through in‐situ photopolymerization in a landscape of microfabricated polydimethylsiloxane (PDMS) pillars with varying spacing, micromodels with porosity spanning several orders of magnitude, from nanometers to millimeters are created. Experiments conducted at different porosity patterns demonstrate the potential of this approach to characterize fundamental and ubiquitous biological and geochemical transport processes in porous media. Accounting for multiscale porosity allows studies of the resulting heterogeneous fluid flow and concentration fields of transported chemicals, as well as the biological behaviors associated with this heterogeneity, such as bacterial chemotaxis. This approach brings laboratory studies of transport in porous media a step closer to their natural counterparts in the environment, industry, and medicine. [ABSTRACT FROM AUTHOR]

The alkaline water electrolyzer (AWE) is the earliest and most mature water‐splitting technology. However, the conventional Raney Ni electrocatalysts dominantly used in AWEs are struggling to meet current demands for higher energy efficiency and cost‐effectiveness in green hydrogen production. Although many promising electrocatalytic materials have been developed using facile preparation methods in the laboratory, they have not received much attention in commercial AWE applications. It is due to the academic negligence on specific operational conditions, critical performance metrics, and material costs associated with industrial AWEs, as well as disregarding the impact of large‐scale electrode manufacturing processes on catalytic performance. Therefore, a timely review to bridge laboratory focus with industrial requirements is essential to guide the future development of water‐splitting electrocatalysts. Here, starting from the differences of operating and testing conditions between laboratory and industrial systems, the gaps in electrocatalytic equipment, evaluation methods, and preparation principles of electrodes are outlined. To narrow these gaps, some academic efforts in advancing industrially relevant electrocatalysts are highlighted and personal perspectives on future opportunities, research focus, and challenges in this field are provided. [ABSTRACT FROM AUTHOR]

To solve the security problems of the moving robot system in the fog network of the Industrial Internet of Things (IIoT), this paper presents a privacy‐preserving data integration scheme in the moving robot system. First, a novel data collection enhancement algorithm is proposed to enhance the image effects, and a k‐anonymous location and data privacy protection protocol based on Ad hoc network (Ad hoc‐based KLDPP protocol) is designed in secure data collection phase to protect the privacy of location and network data. Second, the secure multiparty computation with verifiable key sharing is introduced to realize the valid computation against share cheating in the robot system. Third, the ciphertext classification method in a neural network is considered in the secure data storage process to realize the special application. Finally, experiments and simulations are conducted on the robot system of fog computing in the IIoT. The results demonstrate that the proposed scheme can improve the security and efficiency of the said robot system. [ABSTRACT FROM AUTHOR]

Summary: Industrial transport system refers to the movement of goods, raw materials, and finished products within and between industrial facilities such as factories, warehouses, and distribution centers. It includes a variety of modes of transport such as trucks, forklifts, conveyor belts, and automated guided vehicles (AGVs). Context‐aware route service management refers to system that can dynamically manage and optimize the routes taken by vehicles in transportation system based on contextual information. Our proposed framework for industrial transport systems and smart intelligent autonomous vehicles utilizes cluster‐based context‐aware routing (CCAR), which includes several significant contributions. Firstly, modified elephant herding optimization (MEHO) algorithm is used to efficiently group autonomous vehicles for routing. Secondly, an improved gravitational search (IGS) algorithm is used for cluster head (CH) selection responsible for transferring context information between vehicles (V2V). Thirdly, a deep hybrid multi‐graph neural network (DHMG‐NN) is designed for optimal neighboring roadside unit (RSU) node selection through different design constraints to ensure data dissemination between vehicles and infrastructure (V2I). Finally, we validate the effectiveness of our CCAR framework using various simulation scenarios. Our framework achieves a reduction of up to 78% in computation time and an improvement of up to 25% in customer satisfaction ratio and 15% higher than the overall performance ratio compared to the existing frameworks. [ABSTRACT FROM AUTHOR]

The stable operation of the process industrial system, which is integrated with various complex equipment, is the premise of production, which requires the condition monitoring and diagnosis of the system. Recently, the continuous development of deep learning (DL) has promoted the research of intelligent diagnosis in process industry systems, and the sensor system layout has provided sufficient data foundation for this task. However, these DL-driven approaches have had some shortcomings: (1) the output signals of heterogeneous sensing systems existing in process industry systems are often high-dimensional coupled and (2) the fault diagnosis model built from pure data lacks systematic process knowledge, resulting in inaccurate fitting. To solve these problems, a graph feature fusion-driven fault diagnosis of complex process industry systems is proposed in this paper. First, according to the system's prior knowledge and data characteristics, the original multisource heterogeneous data are divided into two categories. On this basis, the two kinds of data are converted to physical space graphs (PSG) and process knowledge graphs (PKG), respectively, according to the physical space layout and reaction mechanism of the system. Second, the node features and system spatial features of the subgraphs are extracted by the graph convolutional neural network at the same time, and the fault representation information of the subgraph is mined. Finally, the attention mechanism is used to fuse the learned subgraph features getting the global-graph representation for fault diagnosis. Two publicly available process chemistry datasets validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Social scientists, geographers, criminologists, and health scientists are often tasked with finding data to best capture the impact of "community context" on individual outcomes, including residential services, physical resources, and social institutions. One outlet for such data in Canada is Digital Map Technologies Inc. (DMTI) Spatial, which offers a national repository of over one million businesses and recreational points of interest. The database is generated through CanMap Streetfiles, which includes geocodes of each point's precise location. These data are available to researchers from their university data library and Esri Canada, but primarily available to private sector and government markets. That said, the goal of the current paper is to encourage researchers to access this rich yet under‐utilized data source. Each service, business, or resource in the DMTI Spatial database is assigned to a respective category using Standard Industrial Classification codes and North American Industrial Classification System codes. It is not clear, however, which is the more reliable coding criteria. We provide an overview of our review of DMTI Spatial data and take‐away suggestions for using this valuable resource for future research on meso‐level residential markers. Key messages: The goal of this paper is to outline existing data source(s) and measures from DMTI Spatial that might help capture meso‐level residential institutions.We recommend "best practices" for using DMTI Spatial data in researchers' own work to capture neighbourhood resources/amenities, or the social infrastructure of the community using either Standard Industrial Classification codes or North American Industrial Classification System codes.We conclude that Standard Industrial Classification codes in DMTI Spatial enhanced points of interest data are more complete—and more accurate—than North American Industrial Classification System codes. [ABSTRACT FROM AUTHOR]

Efficient cooling is crucial for maintaining the reliability and performance of industrial and engineering systems that generate excess heat. Entropy generation analysis, established on the second law of thermodynamics, plays a vital role in identifying inefficiencies within these systems and improving their overall efficiency. This study focuses on a theoretical investigation of the entropy generation, considering the cumulative impact of surface slipperiness, Brownian motion, applied magnetic field, thermophoresis on the nanofluid flow toward a convective heated stretching surface. The governing model partial differential equations undergo a transformation through similarity transformation, resulting in a nonlinear ordinary differential equation. This equation is subsequently solved numerically by employing the Runge–Kutta–Fehlberg integration scheme in conjunction with the shooting method. The obtained results reveal the influence of various parameters on the temperature, velocity, Nusselt number, skin friction, Sherwood number, Bejan number, nanoparticle concentration, and entropy generation. Upon analysis, it was notable that the introduction of a magnetic field, higher Biot numbers, Eckert numbers, and elevated Brownian motion led to an increase in the entropy generation within the system. Conversely, the presence of thermophoresis and reduced surface slipperiness resulted in a decrease in entropy. These results are presented through graphical representations, tables, and quantitative discussions, providing valuable insights for optimizing the cooling and performance of industrial and engineering systems. [ABSTRACT FROM AUTHOR]

Controlling coupled tank systems is challenging due to interactions between tanks, nonlinear dynamics, time delays, uncertainties, and cross‐coupling effects. The design of effective control strategies to address these complexities while ensuring stability and robust performance is difficult. Hence, this study focuses on presenting an innovative approach to enhance level control in coupled tank systems by employing a fractional‐order proportional‐integral‐derivative (FOPID) controller. The FOPID controller is designed by imposing constraints on the performance metric and closed‐loop gain. Besides, the defined optimization problem is solved by employing a tree seed algorithm. Further, the stability is analyzed graphically using the singular value analysis. The inherent complexities of coupled tank systems are effectively addressed by designing decouplers. The unique characteristics of the tree seed algorithm to navigate complex solution spaces and its effective handling of constraints offer a robust optimization framework. The validity and efficiency of the proposed method are analyzed in a range of simulation experiments conducted on distinct interconnected tank systems. Besides, the stability is verified graphically. The analysis highlights the effectiveness of the control law in handling uncertainties and disturbances. Besides, the proposed method reduces the settling time to around 20%$$ 20\% $$. Through a systematic integration of optimization and comprehensive stability analysis, the study provides a holistic solution for optimizing level control in coupled tank systems. [ABSTRACT FROM AUTHOR]

In this article, neural network backpropagation (NNBP) is used to present two‐dimensional Darcy‐Forchheimer viscous fluid (DFVF) of viscous liquid against a stretching sheet under the influence of heat sink/source and viscous dissipation. Mathematical modelling and numerical simulation are also discussed. Thermal polymer processing, engineering processes, and industrial processes all benefit from the dynamic entropy generation phenomenon. Scientists are primarily interested in finding ways to decrease entropy formation to polish up the thermal performance of industrial systems. In this continuation, the optimal frame for the Darcy‐Forchheimer flow with a curved surface has been developed. Non‐linear partial systems are converted to dimensionless differential systems through the employment of the appropriate variables. Utilizing tools from the bvp4c method, the problem's highly nonlinear equations are numerically solved. These ODEs are solved by applying the bvp4c solution approach to produce the reference dataset for NNBP. Using this reference dataset in MATLAB, the graphs for the solution and error analysis for changing various parameters are analyzed. The performance of NNBP is validated using mean squared error data, regression analysis, and error histogram. The DFVF solution is examined using the testing, validation, and training procedures. Examined through influential variables the effects of fluid flow, thermal field, Bejan number, and concentration. [ABSTRACT FROM AUTHOR]

Consuming one of the largest amount of electricity, the chlor‐alkali industry supplies basic chemicals for society, which mainly consists of two reactions, hydrogen evolution (HER) and chlorine evolution reaction (CER). Till now, the state‐of‐the‐art catalyst applied in this field is still the dimensional stable anode (DSA), which consumes a large amount of noble metal of Ru and Ir. It is thus necessary to develop new types of catalysts. In this study, an organocatalyst anchored on the single‐atom support (SAS) is put forward. It exhibits high catalytic efficiency towards both HER and CER with an overpotential of 21 mV and 20 mV at 10 mA cm−2. With this catalyst on both electrodes, the energy consumption is cut down by 1.2 % compared with the commercial system under industrial conditions. Based on this novel catalyst and the high activity, the mechanism of modifying non‐covalent interaction is demonstrated to be reliable for the catalyst's design. This work not only provides efficient catalysts for the chlor‐alkali industry but also points out that the SACs can also act as support, providing new twists for the development of SACs and organic molecules in the next step. [ABSTRACT FROM AUTHOR]

The joint optimization of various maintenance actions and the supply of spare parts has received extensive attention in recent years owing to its advantage in reducing the overall operational costs of industrial systems. In this study, a joint optimization model is proposed considering bi‐level imperfect preventive maintenance and spare part ordering. A virtual age reduction model that considers bi‐level imperfect preventive maintenance (PM) is established. Upon each system shutdown, the PM level is determined based on the cost‐effectiveness ratio. Furthermore, an improved (s, S) inventory policy is proposed utilizing the information of the remaining number of PM and accumulated consumption of spare parts. Finally, the joint optimization models consisting of different PM policies and spare parts inventory policies are solved. Case studies show that compared with the traditional (s, S) inventory policy, the proposed joint optimization model can coordinate the ordering quantity of spare parts with the PM policy, and reduce the expected maintenance cost. A sensitivity analysis of the cost parameters is performed to explore their impact on the maintenance economy. [ABSTRACT FROM AUTHOR]

The minimal inhibitory concentration of an antimicrobial required to inhibit the growth of planktonic populations (minimum inhibitory concentration [MIC]) remains the 'gold standard' even though biofilms are acknowledged to be recalcitrant to concentrations that greatly exceed the MIC. As a result, most studies focus on biofilm tolerance to high antimicrobial concentrations, whereas the effect of environmentally relevant sub‐MIC on biofilms is neglected. The effect of the MIC and sub‐MIC of an isothiazolinone biocide on a microbial community isolated from an industrial cooling system was assessed under static and flow conditions. The differential response of planktonic and sessile populations to these biocide concentrations was discerned by modifying the broth microdilution assay. However, the end‐point analysis of biofilms cultivated in static microplates obscured the effect of sub‐MIC and MIC on biofilms. A transition from batch to the continuous flow system revealed a more nuanced response of biofilms to these biocide concentrations, where biofilm‐derived planktonic cell production was maintained despite an increase in the frequency and extent of biofilm sloughing. A holistic, 'best of both worlds' approach that combines the use of static and continuous flow systems is useful to investigate the potential for the development of persistent biofilms under conditions where exposure to sub‐MIC and MIC may occur. [ABSTRACT FROM AUTHOR]

The study of microswimmers in viscoelastic fluids is important due to their essential role in many biological systems and various industrial and medical applications. This review article summarizes the behavior of microswimmers in complex viscoelastic fluids. Interestingly, both an increase or decrease in the speed and the power efficiency of microswimmers may be observed depending on the swimming pattern and the rheology of the polymeric fluid. First, it introduces the continuum frameworks for a complex fluid in which the microswimmers are present. This study briefly discusses how the reciprocal motion can propel a swimmer in a viscoelastic fluid contrary to the scallop theorem. This is followed by a section on different types of individual swimmers and their theoretical framework. A finite‐sized swimming sheet can show the resonance effect when relaxation time matches the characteristic time of the swimming. Lastly, progress on the topic of squirmers in the complex environment is presented, and it is seen that the interaction of the squirmer with the complex fluid is unpredictable. [ABSTRACT FROM AUTHOR]

This paper proposes a situational awareness‐based method of load control for safe electricity consumption by industrial users to assess the risk of regulated load equipment within industrial users and to ensure their safe use of electricity. Firstly, in the situational perception stage, collecting key parameters such as controllable equipment within industrial users, and constructing a risk indicator system for industrial users in the process of load control from four perspectives: the degree of controllability of load equipment, the correlation and mutual influence of load equipment, the uncertainty of self‐provided power and economic losses; Then, in the situational understanding and presentation stage, a risk assessment method that integrates a self‐learning weight model and a cloud model is proposed to analyze the risk of industrial users under control; Finally, in the situational guidance stage, based on the assessment results of the risk situational of industrial users, industrial users' safe electricity consumption is controlled in multiple rounds to achieve the objective of maintaining safe and reliable operation of the power grid by accepting control at a low‐risk level. The simulation of the industrial users of steel mills is used to verify the rationality and effectiveness of the method proposed here. [ABSTRACT FROM AUTHOR]

Recent decades have witnessed the proliferation of alternative food networks (AFNs) in the US and Europe. While social scientists classify heterogenous practices as AFNs, their participants share the desire to work against the existing food system, and efforts to access food from outside this system. Many analysts have captured these features of AFNs by using concepts from Karl Polanyi's The Great Transformation. Yet while Polanyi's framework has been productive, it has also contributed to binary representations of alternative food movements as either advancing non‐market values or in service to commodification and hegemony. We scrutinize San Francisco‐based independent contractors who sell food alternatives and health supplements produced by a multi‐level marketing company we call NatureRise. The marketers of NatureRise products are committed to working against an industrial food system that they consider corrupt. They promote food alternatives that they regard as more natural than food in the conventional system, and embed sales in face‐to‐face relations in which a family‐like concern for human wellbeing is expressed. Yet, they also engage in sales for what Polanyi calls "the motive of gain." Our case highlights a shortcoming in Polanyi's thought, one that characterizes many social analyses, while supporting the continued relevance of his work. [ABSTRACT FROM AUTHOR]

The current energy crisis calls for a rapid transition to renewables. Solar photovoltaics (PV) is the key component of the future energy system and a majority of PV systems are installed on rooftops in Europe. This article studies the business case of prosumers, i.e., consumers who produce part or all of their electricity with PV. The levelized cost of PV electricity is compared with the average value of PV electricity for the prosumers in residential, commercial, and industrial installations in Finland, Germany, France, Italy, and Spain with different self‐consumption (SC) and weighted average cost of capital rates. Prosumer business cases are positive in every studied market segment and country, especially for industrial PV systems with 100% SC where a payback time of less than 4 years can be achieved. The sensitivity of payback time on key input parameters is also studied. Reasonable cost of capital and adequate SC of PV generation remain the key elements for attractive prosumerism. [ABSTRACT FROM AUTHOR]