The mining industry faces increasing challenges in maintaining high production levels while minimizing unplanned failures and operational costs. Critical assets, such as crushers, conveyor belts, mills, and ventilation systems, operate under extreme conditions, leading to accelerated wear and failure risks. Traditional maintenance strategies often fail to prevent unexpected downtimes, safety hazards, and economic losses. As a response, industries are integrating predictive monitoring technologies, including machine learning, the Internet of Things, and digital twins, to enhance early fault detection and optimize maintenance strategies. This Systematic Literature Review analyzes 166 high-impact studies from Scopus and Web of Science, identifying key trends in fault detection algorithms, hybrid AI models, and real-time monitoring techniques. The findings highlight the increasing adoption of deep learning, reinforcement learning, and digital twins for anomaly detection and process optimization. Additionally, AI-driven methods are improving sensor-based data acquisition and asset management, extending equipment lifecycles while reducing failures. Despite these advancements, challenges such as data standardization, model scalability, and system interoperability persist, requiring further research. Future work should focus on real-time AI applications, explainable models, and academia-industry collaboration to accelerate the implementation of intelligent maintenance solutions, ensuring greater reliability, efficiency, and sustainability in mining operations.

Failures in critical industrial components (bearings, compressors, and conveyor belts) often lead to unplanned downtime, high costs, and safety concerns. Traditional diagnostic approaches underperform in noisy or changing environments due to heavy reliance on manual feature engineering and rule-based systems. In response, advanced machine learning, deep learning, and sophisticated signal processing techniques have emerged as transformative solutions for fault detection and predictive maintenance. To address the complexity of these advancements and their practical implications, this review combines analyses from large language models with expert validation to categorize key methodologies—spanning classical machine learning models, deep neural networks, and hybrid physics–data approaches. It also explores essential signal processing tools (e.g., Fast Fourier Transform (FFT), wavelets, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)) and methods for estimating Remaining Useful Life (RUL) while highlighting major challenges such as the scarcity of labeled data, the need for model explainability, and adaptation to evolving operational conditions. By synthesizing these insights, this article offers a path forward for the adoption of new technologies (deep learning, IoT/Industry 4.0, etc.) in complex industrial contexts, anticipating the collaborative and sustainable paradigms of Industry 5.0, where human–machine collaboration and sustainability play central roles.

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.

Physics-informed neural networks (PINNs) offer a mesh-free approach to solving partial differential equations (PDEs) with embedded physical constraints. Although PINNs have gained traction in various engineering fields, their adoption for railway bridge analysis remains under-explored. To address this gap, a systematic review was conducted across Scopus and Web of Science (2020–2025), filtering records by relevance, journal impact, and language. From an initial pool, 120 articles were selected and categorised into nine thematic clusters that encompass computational frameworks, hybrid integration with conventional solvers, and domain decomposition strategies. Through natural language processing (NLP) and trend mapping, this review evidences a growing but fragmented research landscape. PINNs demonstrate promising capabilities in load distribution modelling, structural health monitoring, and failure prediction, particularly under dynamic train loads on multi-span bridges. However, methodological gaps persist in large-scale simulations, plasticity modelling, and experimental validation. Future work should focus on scalable PINN architectures, refined modelling of inelastic behaviours, and real-time data assimilation, ensuring robustness and generalisability through interdisciplinary collaboration.

Heap leaching of copper is faced with a complex set of challenges, including mineral heterogeneity, the formation of passivating species, and the need to regulate critical variables such as pH, redox potential (Eh), oxidant concentration, and irrigation rate. If these factors are not properly managed, copper recovery is reduced, and significant environmental impacts may be generated, highlighting the urgency for systematic and sustainable approaches. To address this challenge, a systematic literature review (SLR) was conducted, screening 2344 documents and selecting 106 primary sources to analyze operational drivers and environmental considerations. Statistical methodologies (factorial designs, response surface methodology), multiscale modeling, and laboratory column tests were used to validate key variables, including pH (1.5–2.0), Eh (600–$750 \mathrm{mV}$), temperature (25–55 °C), irrigation rate (5–$15 \mathrm{L}/\left(\mathrm{h}·{\mathrm{m}}^2\right)$), acid concentration (0.5–$2.0 \mathrm{M}$), and emerging “green” reagents (e.g., glycine, organic surfactants). Precise control of these factors was found to reduce passivation, minimize fine-particle migration, and improve copper extraction up to 90%. The incorporation of oxidizing agents (e.g., Fe³⁺, H₂O₂) further accelerated mineral dissolution while preventing unwanted precipitates. In parallel, bioleaching strategies maintained high recoveries with lower chemical demand. Reviews of pilot studies confirmed the scalability of these optimized conditions, emphasizing both sustainability and cost-effectiveness.

This PRISMA 2020–compliant systematic review examines how intelligent agents, large language models (LLMs), and recurrent neural networks (RNNs) can be combined for industrial maintenance, with a sector‐specific focus on mining. Scopus and Web of Science (2018–2025) were searched using replicable queries, and a dual text‐representation pipeline (TF–IDF with bi/trigrams and sentence‐transformer embeddings) was applied. Model selection scanned k over a predefined grid with internal indices (Silhouette, Davies–Bouldin, and Calinski–Harabasz), and robustness was assessed through multiseed stability, bootstrap consensus, representation‐sensitivity checks, and a control run with HDBSCAN. Study quality and risk of bias were appraised with an AI‐and‐control–oriented matrix (ACE‐QA). Two macroclusters emerged. The first centers on distributed control, consensus and formation, fault tolerance, observers, and learning‐based designs (fuzzy/neural/RL), including finite/predefined‐time and event/dynamic event–triggered mechanisms. The second addresses secure and resilient cooperation under cyber threats (DoS, deception, and FDIA), integrating observer‐based estimation and communication‐efficient protocols. Cross‐cutting findings indicate that event‐triggered updates reduce bandwidth and compute requirements, while robust estimation and fault‐tolerant control improve availability under harsh conditions and intermittent networks—typical in mining. A maturity map suggests high technical readiness and growing adoption for RNN‐based sensing analytics, advancing readiness but emerging adoption for multiagent coordination, and early adoption of LLMs for text‐grounded maintenance intelligence. Evidence gaps persist in replicability, cross‐site transfer, uncertainty reporting, and mining‐grade validation at the edge. A design agenda is outlined that prioritizes digital‐twin stress testing, edge‐first evaluation of agent coordination, secure‐by‐design pipelines (authenticated/encrypted messaging and adversarial testing), and shift‐aware validation. In sum, a hybrid stack—RNNs for perception, LLMs for knowledge grounding, and agents for coordinated action—offers a practical route to reliable, secure, and communication‐efficient predictive maintenance in Mining 4.0.