Search Results (347)

This paper reports the development of a process for leaching Pt, Pd, and Rh from simulated spent auto-catalyst material using ozone and hydrochloric acid in order to produce a pregnant leach solution that could be fed to an industrial precious metal refinery. The effects of O₃ mass flow, initial acid concentration, and temperature were investigated using a Box–Behnken experimental design with three centre-point runs and a total leach time of 6 h. Set points of 3.34, 5.01, and 6.68 g/h; 1.0 M, 3.0 M, and 5.0 M; and 30, 60, and 90 °C were used for O₃ mass flow, hydrochloric acid concentration, and temperature, respectively. The optimal extractions for Pt, Pd, and Rh were 80%, 85%, and 42%, respectively, at 5.01 g/h O₃, 5.0 M HCl, and 90 °C. Statistical analyses indicated high dependencies of Pd and Rh on hydrochloric acid concentration and temperature, with only Pt displaying a significant dependence on O₃ mass flow. Full article

Solid-state fermentation (SSF) system involves solid, liquid, and gas phases, characterized by complex mass and heat transfer mechanisms and microbial complex interactions. The SSF processes for traditional Chinese fermented foods, such as vinegar, soy sauce, and baijiu primarily rely on experience, and most of the operations are replaced by auto machine now. However, there is still a lack of engineering in-depth study of the microbial process of SSF for complete process control. To meet the demands of smart manufacturing and green production, this paper emphasizes the engineering analysis of the mechanisms behind SSF. It reviews the progress in the engineering aspects of Chinese traditional SSF, including raw material pretreatment, process parameter detection, mathematical model construction, and equipment innovation. Additionally, it summarizes the challenges faced during intelligent upgrades and the opportunities brought by scientific and technological advancements, proposing future development directions. This review provides an overview of the SSF engineering aspects, offering a reference for the intelligent transformation and sustainable development of the Chinese traditional SSF food industry. Full article

The research presented in this work explores two methods for synthesizing supported metal catalysts: wet impregnation method (IM) and sol–gel auto-combustion method (AC). These techniques were used to create a series of magnesium oxide (MgO)-based materials, including pure MgO and MgO-supported chromia catalysts, CrMgX, varying the weight percentage of chromium. The specific materials synthesized are unmodified MgO; MgO loaded with 1, 3, and 5 wt% CrO₃ via impregnation; and counterparts prepared with the same loadings using Cr (NO₃)₃ via sol–gel auto-combustion method. After synthesis, various characterization techniques were utilized to analyze the samples comprehensively. These methods encompass FTIR, Raman spectroscopy, XRD, SEM, and BET surface area analysis. The investigation revealed a clear distinction between the two synthesis methods. While the impregnation method resulted in a greater degree of interaction between the metal oxides, the sol–gel auto-combustion approach yielded materials with superior textural and morphological properties. Significantly, the BET analysis demonstrated that all the MgO and CrMgX catalysts possessed high surface areas. In particular, the CrMg 3 (AC) catalysts synthesized via sol–gel auto-combustion exhibited an exceptional surface area of 72 m² g⁻¹, which is the highest value reported for such materials in the existing literature. This remarkable surface area directly translates to enhanced catalytic activity, making these materials strong contenders for various industrial applications. The research effectively highlights the potential of sol–gel auto-combustion as a method for producing catalysts with outstanding textural properties, a crucial factor for developing high-performance catalysts for industrial processes. Full article

This research aimed to identify the probiotic features of Yarrowia lipolytica strains isolated from olive oils in Turkey. The in vitro survival capabilities of Y. lipolytica strains in gastric and pancreatic solutions were assessed. The hydrophobicity of Y. lipolytica strains was determined to be between 25.8% and 46.08% for xylene, 22.5% and 45.85% for chloroform, and 14.83% and 37.09% for ethyl acetate. In addition, auto-aggregation values were measured as 11.07–60.35%; 16.28–67.70% and 42.89–85.21% after 2, 4 and 24 h of incubation, respectively. The Y. lipolytica strains tested in this study demonstrated aggregation ability against the pathogens Escherichia coli ATCC 25922, Salmonella typhimurium ATCC 14028, Staphylococcus aureus ATCC 25923 and Listeria monocytogenes ATCC 7644. Antibiotic resistance and hemolytic activities were also checked to ensure the safety of the Y. lipolytica. Cholesterol removal by Y. lipolytica strains ranged from 12.30% to 47.42%, and their free radical scavenging activity varied between 2.85% and 39.10%. Out of 13 Y. lipolytica samples from 10 different olive oil sources, Y. lipolytica Y6, Y7, and Y11 exhibited the best strains with probiotic potential properties. This study discovered that Y. lipolytica with probiotic properties can be isolated in olive oil samples, a finding that has not been previously documented in the literature and may have potential industrial applications Full article

High-speed dual-motor electric drive tracked vehicles (DDTVs) have emerged as a research hotspot in the field of tracked vehicles in recent years due to their advantages in fuel economy and the scalability of electrical equipment. The emergency braking of a DDTV at high speed can lead to slipping or even yawing (which is caused by a large deviation of forces at each track directly), posing significant challenges to the vehicle’s stability and safety. Therefore, the accurate real-time acquisition of critical driving parameters, such as the longitudinal force and vehicle speed, is crucial for the stability control of a DDTV. This paper developed a novel estimating algorithm of critical driving parameters for DDTVs equipped with conventional sensors such as rotary transformers at PMSMs and onboard accelerometers on the basis of their dynamics models. The algorithm includes a sensor signal preprocessing module, a longitudinal force estimation method based on a nonlinear observer, and a speed estimation method based on an adaptive Kalman filter. Through hardware-in-loop experiments based on a Speedgoat real-time target machine, the proposed algorithm is proven to estimate the longitudinal force of the track and vehicle speed accurately, whether the vehicle has stability control functions or not, providing a foundation for the further development of vehicle stability control algorithms. Full article

Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through the accurate diagnosis and classification of faults in three-phase induction motors using artificial intelligence techniques by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage, and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network by using the scanning method with multiple training and validation iterations with the introduction of new data. The results of these tests showed that the network exhibited excellent generalization across all evaluated situations, achieving the following accuracy rates: motor without fault = $94.2$%, unbalanced fault = 95%, bearings with fault = 98%, and stator with fault = 95%. Full article

The wide use of telecommunications, computers and the internet, especially over the last four decades, has formed a new economic phenomenon, the “Digital Economy”. As a matter of facts, the development of digitalization has raised questions about its contribution to official economic indicators. This research examines the evolution of the information and communication industry (ICI) and its contribution to the national Gross Domestic Product (GDP) of six European entities. Time series and auto-ARIMA models are employed to process the data. Moreover, this study forecasts the development of the ICI in the future. The results demonstrate a clear stable growth in the variable under examination over time, showing an increasingly greater contribution of the ICI to the national GDP in most cases with the exception of Greece, which has a high provisional risk. Full article

Acetobacter syzygii CCTCC M 2022983 was isolated and characterized from Tibetan kefir grains, which is utilized as a functional food with diverse bioactive properties. After 6 days of fermentation by A. syzygii, Acetobacter fermented extract (AFE) showed significantly higher antioxidant, anti-tyrosinase, and anti-melanin effects compared to the unfermented yeast extract (UFY). Western blotting confirmed that AFE reduced melanogenesis-related proteins (MITF, TYR, TRP-1, TRP-2). LC-MS/MS analysis identified 4-hydroxybenzoic acid as abundant in AFE, contributing to its antioxidant capacity. Succinic acid and citric acid emerged as the major compound and a type of mixed inhibitor against mushroom tyrosinase, with IC50 values of 2.943 mM and 1.615 mM, respectively. Fluorescence spectra analysis revealed that these acids caused conformational changes in tyrosinase. Moreover, succinic acid and citric acid prevented L-DOPA from auto-oxidation with IC50 values of 0.355 mM and 0.261 mM, respectively. Molecular docking analysis suggested that these acids interacted with the association of the H and L subunits of tyrosinase, thereby reducing its stability. In B16-F10 cells, succinic and citric acids significantly reduced melanin production in a dose-dependent manner. Thus, succinic acid and citric acid revealed promising potential for applications in the food and medicine industries as melanogenesis inhibitors due to their safety. Full article

Underwater methane (CH₄) detection technology is of great significance to the leakage monitoring and location of marine natural gas transportation pipelines, the exploration of submarine hydrothermal activity, and the monitoring of submarine volcanic activity. In order to improve the safety of underwater CH₄ detection mission, it is necessary to study the effect of hydrogen sulfide (H₂S) in leaking CH₄ gas on sensor performance and harmful influence, so as to evaluate the health status and life prediction of underwater CH₄ sensor arrays. In the process of detecting CH₄, the accuracy decreases when H₂S is found in the ocean water. In this study, we proposed an explainable sorted-sparse (ESS) transformer model for concentration interval detection under industrial conditions. The time complexity was decreased to O (n log_n) using an explainable sorted-sparse block. Additionally, we proposed the Ocean X generative pre-trained transformer (GPT) model to achieve the online monitoring of the health of the sensors. The ESS transformer model was embedded in the Ocean X GPT model. When the program satisfied the special instructions, it would jump between models, and the online-monitoring question-answering session would be completed. The accuracy of the online monitoring of system health is equal to that of the ESS transformer model. This Ocean-X-generated model can provide a lot of expert information about sensor array failures and electronic noses by text and speech alone. This model had an accuracy of 0.99, which was superior to related models, including transformer encoder (0.98) and convolutional neural networks (CNN) + support vector machine (SVM) (0.97). The Ocean X GPT model for offline question-and-answer tasks had a high mean accuracy (0.99), which was superior to the related models, including long short-term memory–auto encoder (LSTM–AE) (0.96) and GPT decoder (0.98). Full article

This article explores the evolution of road freight transport in the context of Industry 4.0, focusing on management practices and technological advancements in transport and logistics companies’ management information systems. By analyzing the latest international practices in road freight quality management within Industry 4.0 through regression and correlation methods, a model highlighting the significant influence of quality over price and institutional factors on the development of information and communication technology (ICT) goods exports was developed. It showcases how digital frameworks, alongside AI and big data, can enhance road freight quality in Industry 4.0, establishing a digital ecosystem for transport and logistics quality management. This study introduces a novel perspective on managing road freight transport as a digital ecosystem, offering insights into improving ICT goods exports in Kazakhstan by enhancing management information systems. It suggests a new management information system organization scheme to increase road freight quality management efficiency and ensure quality in Industry 4.0 settings. Full article

Big data analytics capabilities (BDACs) are strategic capabilities that expedite decision-making processes, empowering organizations to mitigate the impacts of supply chain disruptions. These capabilities enhance the ability of companies to be more proactive in detecting and predicting disruptive events, increasing their resilience. This study analyzed the effects BDACs have on firms’ reaction time and the effects companies’ reaction time has on their resilience. The research model was assessed with 263 responses from a survey with professionals of auto-parts companies in Brazil. Data were analyzed with the Partial-Least-Squares—Structural Equation Modeling method. Cluster analysis techniques were also applied. This study found that BDACs reduce reaction time, which, in turn, improves firms’ resilience. We also observed greater effects in first-tier and in companies with longer Industry 4.0 journeys, opening further perspectives to investigate the complex mediations of digital readiness, reaction time, and organizational resilience performance of firms and supply chains. Our research builds upon the dynamic capabilities theory and identifies BDACs as dynamic capabilities with the potential to enhance resilience by reducing data, analytical, and decision latencies, which are recognized as core elements of the reaction time concept, which is particularly crucial during disruptive supply chain events. Full article

Surface defect inspection based on deep learning has demonstrated outstanding performance in improving detection accuracy and model generalization. However, the small scale of defect datasets always limits the application of deep models in industry. Generative models can obtain realistic samples in a very cheap way, which can effectively solve this problem and thus has received widespread attention in recent years. This paper provides a comprehensive analysis and summary of the current studies of surface defect inspection methods proposed between 2022 and 2024. First, according to the use of generative models, these methods are classified into four categories: Variational Auto-Encoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models (DMs), and multi-models. Second, the research status of surface defect inspection based on generative models in recent years is discussed from four aspects: sample generation, detection objective, inspection task, and learning model. Then, the public datasets and evaluation metrics that are commonly used for surface defect inspection are discussed, and a comparative evaluation of defect inspection methods based on generative models is provided. Finally, this study discusses the existing challenges for the defect inspection methods based on generative models, providing insights for future research. Full article

The auto-balancing mechanism is used in the spin-dry regime of the laundry machine. The high rotating speed and unbalanced mass inside the drum create centrifugal force, which is the cause of vibration. The auto-balancing mechanism consists of a set of balls in the circular guiding track. During the spin-dry process, the balls settle in the opposite position to an unbalanced mass. The centrifugal force of the balls compensates the one of the unbalanced masses. The paper deals with the non-linear numerical simulation of the imbalance settlement and the following parametric study. The solution to the problem is demonstrated on an industrial laundry machine with a maximum capacity of 7 kg of dry laundry and a maximum rotating speed of 930 rpm. The proposed numerical model allows us to investigate the behavior of the auto-balancing mechanism and predict the vibration amplitudes of the system. Full article

Biogas has emerged as a valid feedstock for biomethanol production from steam reforming. This study investigates an alternative layout based on an auto-thermal electrified reforming assuming a 1 MW equivalent anaerobic digestion plant as a source for methanol synthesis. The process considers an oxy-steam combustion of biogas and direct carbon sequestration with the presence of a reverse water–gas shift reactor to convert CO₂ and H₂ produced by a solid oxide electrolyzer cell to syngas. Thermal auto-sufficiency is ensured for the reverse water–gas shift reaction through the biogas oxy-combustion, and steam production is met with the integration of heat network recovery, with an overall process total electrical demand. This work compares the proposed process of electrification with standard biogas reforming and data available from the literature. To compare the results, some key performance indicators have been introduced, showing a carbon impact of only 0.04 kgCO₂/kgMeOH for the electrified process compared to 1.38 kgCO₂/kgMeOH in the case of biogas reforming technology. The auto-thermal electrified design allows for the recovery of 66.32% of the carbon available in the biogas, while a similar electrified process for syngas production reported in literature reaches only 15.34%. The overall energy impact of the simulated scenarios shows 94% of the total energy demand for the auto-thermal scenario associated with the electrolyzer. Finally, the introduction of the new layout is taken into consideration based on the country’s carbon intensity, proving carbon neutrality for values lower than 75 gCO₂/kWh and demonstrating the role of renewable energies in the industrial application of the process. Full article

Estimating line impedance is relevant in transmission and distribution networks, in particular for planning and control. The large number of deployed PMUs has fostered the use of passive indirect methods based on network model identification. Electrified railways are a particular example of a distribution network, with moving highly dynamic loads, that would benefit from line impedance information for energy efficiency and optimization purposes, but for which many of the methods used in industrial applications cannot be directly applied. The estimate is carried out onboard using a passive method in a single-point perspective, suitable for implementation with energy metering onboard equipment. A comparison of two methods is carried out based on the non-linear least mean squares (LMS) optimization of an over-determined system of equations and on the auto- and cross-spectra of the pantograph voltage and current. The methods are checked preliminarily with a simulated synthetic network, showing good accuracy, within 5%. They are then applied to measured data over a 20 min run over the Swiss $16.7$ Hz railway network. Both methods are suitable to track network impedance in real time during the train journey; but with suitable checks on the significance of the pantograph current and on the values of the coefficient of determination, the LMS method seems more reliable with predictable behavior. Full article