Complex Dynamics and Intelligent Control: Advances, Challenges, and Applications in Mining and Industrial Processes
Complex dynamics and nonlinear systems play a critical role in industrial processes, where complex interactions, high uncertainty, and external disturbances can significantly impact efficiency, stability, and safety. In sectors such as mining, manufacturing, and energy networks, even small perturbat...
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MDPI AG
2025-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/6/961 |
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| author | Luis Rojas Víctor Yepes José Garcia |
| author_facet | Luis Rojas Víctor Yepes José Garcia |
| author_sort | Luis Rojas |
| collection | DOAJ |
| description | Complex dynamics and nonlinear systems play a critical role in industrial processes, where complex interactions, high uncertainty, and external disturbances can significantly impact efficiency, stability, and safety. In sectors such as mining, manufacturing, and energy networks, even small perturbations can lead to unexpected system behaviors, operational inefficiencies, or cascading failures. Understanding and controlling these dynamics is essential for developing robust, adaptive, and resilient industrial systems. This study conducts a systematic literature review covering 2015–2025 in Scopus and Web of Science, initially retrieving 2628 (Scopus) and 343 (WoS) articles. After automated filtering (Python) and applying inclusion/exclusion criteria, a refined dataset of 2900 references was obtained, from which 89 highly relevant studies were selected. The literature was categorized into six key areas: (i) heat transfer with magnetized fluids, (ii) nonlinear control, (iii) big-data-driven optimization, (iv) energy transition via SOEC, (v) fault detection in control valves, and (vi) stochastic modeling with semi-Markov switching. Findings highlight the convergence of robust control, machine learning, IoT, and Industry 4.0 methodologies in tackling industrial challenges. Cybersecurity and sustainability also emerge as critical factors in developing resilient models, alongside barriers such as limited data availability, platform heterogeneity, and interoperability gaps. Future research should integrate multiscale analysis, deterministic chaos, and deep learning to enhance the adaptability, security, and efficiency of industrial operations in high-complexity environments. |
| format | Article |
| id | doaj-art-d97c9fbcb3334d49a91c097ff3a5c1af |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-d97c9fbcb3334d49a91c097ff3a5c1af2025-08-20T03:43:37ZengMDPI AGMathematics2227-73902025-03-0113696110.3390/math13060961Complex Dynamics and Intelligent Control: Advances, Challenges, and Applications in Mining and Industrial ProcessesLuis Rojas0Víctor Yepes1José Garcia2Escuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, ChileInstitute of Concrete Science and Technology (ICITECH), Universitat Politècnica de València, 46022 Valencia, SpainEscuela de Ingeniería de Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362804, ChileComplex dynamics and nonlinear systems play a critical role in industrial processes, where complex interactions, high uncertainty, and external disturbances can significantly impact efficiency, stability, and safety. In sectors such as mining, manufacturing, and energy networks, even small perturbations can lead to unexpected system behaviors, operational inefficiencies, or cascading failures. Understanding and controlling these dynamics is essential for developing robust, adaptive, and resilient industrial systems. This study conducts a systematic literature review covering 2015–2025 in Scopus and Web of Science, initially retrieving 2628 (Scopus) and 343 (WoS) articles. After automated filtering (Python) and applying inclusion/exclusion criteria, a refined dataset of 2900 references was obtained, from which 89 highly relevant studies were selected. The literature was categorized into six key areas: (i) heat transfer with magnetized fluids, (ii) nonlinear control, (iii) big-data-driven optimization, (iv) energy transition via SOEC, (v) fault detection in control valves, and (vi) stochastic modeling with semi-Markov switching. Findings highlight the convergence of robust control, machine learning, IoT, and Industry 4.0 methodologies in tackling industrial challenges. Cybersecurity and sustainability also emerge as critical factors in developing resilient models, alongside barriers such as limited data availability, platform heterogeneity, and interoperability gaps. Future research should integrate multiscale analysis, deterministic chaos, and deep learning to enhance the adaptability, security, and efficiency of industrial operations in high-complexity environments.https://www.mdpi.com/2227-7390/13/6/961chaotic dynamicsrobust controlindustrial cybersecurityMining 4.0magnetohydrodynamicsmachine learning |
| spellingShingle | Luis Rojas Víctor Yepes José Garcia Complex Dynamics and Intelligent Control: Advances, Challenges, and Applications in Mining and Industrial Processes Mathematics chaotic dynamics robust control industrial cybersecurity Mining 4.0 magnetohydrodynamics machine learning |
| title | Complex Dynamics and Intelligent Control: Advances, Challenges, and Applications in Mining and Industrial Processes |
| title_full | Complex Dynamics and Intelligent Control: Advances, Challenges, and Applications in Mining and Industrial Processes |
| title_fullStr | Complex Dynamics and Intelligent Control: Advances, Challenges, and Applications in Mining and Industrial Processes |
| title_full_unstemmed | Complex Dynamics and Intelligent Control: Advances, Challenges, and Applications in Mining and Industrial Processes |
| title_short | Complex Dynamics and Intelligent Control: Advances, Challenges, and Applications in Mining and Industrial Processes |
| title_sort | complex dynamics and intelligent control advances challenges and applications in mining and industrial processes |
| topic | chaotic dynamics robust control industrial cybersecurity Mining 4.0 magnetohydrodynamics machine learning |
| url | https://www.mdpi.com/2227-7390/13/6/961 |
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