Agent Addition to Coal Slurry Water Using Data-Driven Intelligent Control

Processes, Journal Year: 2025, Volume and Issue: 13(1), P. 280 - 280

Published: Jan. 20, 2025

The sedimentation process of coal slurry water is influenced by numerous factors and has complex mechanisms. Its nonlinear large hysteresis characteristics pose great challenges to optimization control, making it a current research hotspot. This paper takes the typical slime treatment preparation plant as object, and, on basis selecting raw quantity, flocculation dosage, coagulation overflow turbidity, ash content, underflow concentration, quantity key variables, establishes quality control method for detection data consisting acquisition → anomaly filling noise reduction; subsequently, different machine-learning algorithms are used predict performance coal-slurry-settling agents. It was found that Long Short-Term Memory shows highest prediction accuracy coagulants, with corresponding root mean square errors 2.72% 6.23%. Finally, using iFix software (version 5.5), an intelligent system settling constructed, which reduced usage coagulants 31.56% 37.21%.

Language: Английский

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Developing a Smart Learning System for Large Enterprises Based on Intelligent Augmented Reality

Journal of Organizational and End User Computing, Journal Year: 2025, Volume and Issue: 37(1), P. 1 - 14

Published: Jan. 23, 2025

This paper proposes a smart learning system built on deep and augmented reality (AR) to support employees with practical IoT experimentation, from components circuit board pin connections programming control. For instance, can use their mobile phones capture images of electronic access AR-enhanced instructional materials for component properties. AR-assisted offers guidance at each experimental stage hands-on practice troubleshooting. The also incorporates the pair teaching method enhance quality confidence, enabling collaborate teammates throughout process. is further equipped an online whiteboard Q&A in-depth theoretical exploration experiment. Additionally, blockchain platform records analyzes employee's progress status, providing comprehensive view development.

Language: Английский

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An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

Electronics, Journal Year: 2024, Volume and Issue: 13(13), P. 2633 - 2633

Published: July 4, 2024

Industry 4.0 integrates the intelligent networking of machines and processes through advanced information communication technologies (ICTs). Despite advancements, small mechanical manufacturing enterprises face significant challenges transitioning to ICT-supported models due a lack technical expertise infrastructure. These commonly encounter variable production volumes, differing priorities in customer orders, diverse capacities across low-, medium-, high-level outputs. Frequent issues with machine health, glitches, major breakdowns further complicate optimizing scheduling. This paper presents novel management approach that harnesses bio-inspired methods alongside Internet Things (IoT) technology address these challenges. comprehensive real-time monitoring order distribution, leveraging LoRa wireless technology. The system ensures efficient concurrent data acquisition from multiple sensors, facilitating accurate prompt capture, transmission, storage status data. experimental results demonstrate improvements collection time responsiveness, enabling timely detection resolution failures. Additionally, an enhanced genetic algorithm dynamically allocates tasks based on status, effectively reducing completion idle time. Case studies screw facility validate practical applicability effectiveness proposed system. seamless integration scheduling subsystem coordinated process, ultimately enhancing productivity resource utilization. system’s robustness efficiency highlight its potential revolutionize small-scale settings.

Language: Английский

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Insight Review on Advanced Digital Manufacturing Technology Solutions for Industry 4.0

Published: Sept. 26, 2024

Language: Английский

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The Design and Implementation of an Intelligent Carbon Data Management Platform for Digital Twin Industrial Parks

Energies, Journal Year: 2024, Volume and Issue: 17(23), P. 5972 - 5972

Published: Nov. 27, 2024

In the face of increasing environmental challenges, carbon emissions from industrial parks have become a global focal point, particularly as electricity consumption serves major source that requires effective management. Despite proactive efforts by governments and industry stakeholders to transition toward cleaner production methods, traditional energy management systems exhibit significant limitations in data collection, real-time monitoring, intelligent analysis, making it difficult meet urgent demands for reduction. To address these this study proposes approach based on digital twin technology develops an system integrates surveillance, management, emission monitoring. The supports efficient energy-saving carbon-reducing decision collection data. By incorporating Building Information Modeling (BIM) Internet Things (IoT) technologies, facilitates integration visualization multi-source data, significantly enhancing transparency results reduction validation demonstrate application platform its associated facilities can reduce park, providing robust support low-carbon sustainable development.

Language: Английский

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Analysis on Determining Factors for companies to Adopt IoT and AI Technologies

Information, Journal Year: 2024, Volume and Issue: 27(4), P. 253 - 262

Published: Dec. 15, 2024

IoT and AI technologies are gradually being adopted by more companies due to its advantages of intelligence automation, is a must in the process Industry 4.0. However, any technological investment accompanied risks challenges. When increasing or technology, it also necessary increase manpower jointly improve results innovation. Considering factors production efficiency, safety, enterprise scale, this paper introduces method for general enterprises maximize benefits their both technology manpower. It suggests implementation steps when investing technologies.

Language: Английский

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