Complex Dynamics and Intelligent Control: Advances, Challenges, and Applications in Mining and Industrial Processes
Abstract
:1. Introduction
2. Review Methodology
2.1. Information Sources and Search Strategy
- Scopus, recognized for its multidisciplinary coverage and robust indexing of scientific articles.
- Web of Science (WoS), included for its high degree of indexing in engineering and applied sciences disciplines.
- Nonlinear and chaotic dynamics: (“chaotic systems” OR “chaotic dynamics” OR “deterministic chaos” OR “nonlinear systems”).
- Industry and process optimization: (“industry” OR “process control” OR “process optimization” OR “control systems”).
- Mining and industrial scope: (“mining” OR “mineral processing” OR “mining industry” OR “ore processing”).
- Applications and case studies: (“case study” OR “applications” OR “industrial case” OR “real-world application”).
(TITLE-ABS-KEY(“chaotic systems” OR “chaotic dynamics” OR “deterministic chaos” OR “nonlinear systems”)
AND TITLE-ABS-KEY(“industry” OR “process control” OR “process optimization” OR “control systems”)
AND TITLE-ABS-KEY(“mining” OR “mineral processing” OR “mining industry” OR “ore processing”)
OR TITLE-ABS-KEY(“case study” OR “applications” OR “industrial case” OR “real-world application”))
AND PUBYEAR > 2014 AND PUBYEAR < 2026
AND (LIMIT-TO(DOCTYPE,“ar”))
AND (LIMIT-TO(LANGUAGE,“English”))
2.2. Document Extraction
- Scopus: 2628 studies, stored in CSV format (01_scopus.csv).
- Web of Science: 343 studies, downloaded in Excel format (02_Wos.xls), filtered to include those with the highest impact (most cited).
2.3. Inclusion and Exclusion Criteria
- Inclusion:
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- Articles published in English between 2015 and 2025.
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- Studies including terms related to nonlinear systems or chaotic dynamics, with direct mention of or application in industrial processes.
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- Research based on real case studies or experimental validations, preferably in mining or advanced manufacturing.
- Exclusion:
- –
- Documents in languages other than English.
- –
- Records without available title or abstract (incomplete).
- –
- Duplicates between databases.
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- Sources not corresponding to original articles (e.g., nonindexed conference proceedings, theses, or patents).
2.4. Automated Cleaning and Filtering Process
- Pandas, numpy for data manipulation.
- Scikit-learn for TF-IDF vectorization (TfidfVectorizer) and k-means clustering analysis.
- Transformers (BART-Large-CNN model) for generating thematic summaries in each cluster.
- Wordcloud for creating word clouds based on the abstracts.
- Matplotlib and seaborn for visualizing the distribution of documents across the resulting clusters.
- Data loading (Scopus, WoS): Reading of scopus.csv (2628 records) and wos.xls (343 records) using pandas, combining them into a single DataFrame.
- Cleaning and standardization: Column names were unified (Title, Abstract, Year, etc.), and duplicate or incomplete records were removed.
- Filtering by keywords (minimum of three matches): A threshold of at least three keyword matches was set (e.g., “chaotic systems”, “nonlinear systems”, “mining”). Articles with fewer than three matches were excluded.
- Clustering using k-means (): TF-IDF (TfidfVectorizer) was applied to the concatenation of Title and Abstract, followed by k-means clustering (). For each cluster, the BART-Large-CNN model was used to generate short titles or labels.
- Generating .bib file: A bibliography file in .bib format (@article{}) was created, summarizing the articles in BibTeX format.
- Exporting document lists by cluster: Separate .docx files were generated, each containing a list of articles corresponding to a specific cluster.
3. Nonlinear Dynamics in Complex Systems: Foundations and Applications
3.1. Highly Complex Nonlinear Systems
3.1.1. Strict Feedback Transformations
3.1.2. Lyapunov Stability Theory
- , (positive definiteness).
- (negative semi-definiteness).
3.1.3. Predictive and Adaptive Control
3.1.4. Applications
- Robotics: Motion control of robotic arms with dynamic uncertainties.
- Energy systems: Power grid stability and load frequency control.
- Aerospace: Flight trajectory optimization under external disturbances.
- Manufacturing: Fault-tolerant control in automated production lines.
3.2. Mathematical Modeling of Chaotic Dynamics
3.2.1. Duffing Oscillator
3.2.2. Synchronization of Chaotic Attractors
- Linear and nonlinear feedback control: Stabilizing the synchronization error by adjusting system parameters.
- Adaptive control: Dynamically updating parameters based on observed synchronization deviations.
- Sliding mode control: Ensuring robustness against system uncertainties.
- Phase-locking techniques: Aligning the phases of chaotic oscillators in coupled circuits.
3.2.3. Applications
- Secure communications: Encoding messages in chaotic signals to prevent unauthorized access.
- Biological systems: Modeling neuronal activity and heart rhythm irregularities.
- Mechanical systems: Studying chaotic vibrations in aerospace structures.
- Nonlinear circuits: Designing chaotic electronic oscillators for cryptographic applications.
3.3. Stochastic Functional Differential Equations
3.3.1. Switching Systems with Memory-Dependent Transitions
3.3.2. Semi-Markov Models
3.3.3. Applications
- Distributed control systems, including robotic networks and self-adaptive industrial processes.
- Cybersecurity, where they model attack patterns in stochastic cyber–physical systems.
- Energy networks, analyzing demand response strategies in smart grids with uncertain switching.
- Biological systems, representing neuronal signal transmission with random synaptic delays.
3.4. Nonlinear Control Methods
3.4.1. Sliding Mode Control
3.4.2. Nonlinear Feedback Control
3.4.3. Event-Triggered Control
3.4.4. Applications
- Robotics, where sliding mode control is used for motion planning under uncertainties.
- Power systems, where event-triggered control optimizes energy dispatch in smart grids.
- Aerospace, where nonlinear feedback control stabilizes aircraft and spacecraft under extreme conditions.
- Industrial automation, where adaptive nonlinear controllers improve precision in manufacturing.
3.5. Inverse Modeling and Nonlinear Optimization
3.5.1. Invertible Neural Networks
3.5.2. Bayesian Optimization
- Hyperparameter tuning for machine learning models.
- Industrial process optimization.
- Reinforcement learning and robotics.
3.6. Applications in Big Data and Machine Learning
3.6.1. Clustering Algorithms and Sparse Regression
3.6.2. Hybrid AI and Physics-Based Models
- Digital twin technology for real-time monitoring of industrial processes.
- Neural PDE solvers for fluid and structural simulations.
- Predictive analytics in smart manufacturing and energy systems.
4. Thematic Clustering for Literature Analysis
- Identification of knowledge gaps: Categorizing studies by thematic areas—such as nonlinear systems, optimization strategies, and cybersecurity—helps pinpoint underexplored topics and methodological deficiencies.
- Consolidation of methods: By analyzing studies that apply similar theoretical models or computational techniques (e.g., predictive control supported by machine learning algorithms), thematic clustering facilitates the identification of recurring methodological patterns.
- Contextualized discussion: Each cluster reveals the historical and conceptual evolution of its respective domain, ensuring a coherent literature review that avoids redundancy while enhancing the depth of the analysis.
- Focusing solutions: Companies interested in industrial cybersecurity, for example, can focus on the specific cluster that analyzes cryptographic strategies based on high nonlinear complexity, reducing research time and ensuring a more targeted approach to their business problem.
- Transferring knowledge between sectors: Topic classification reveals ways to extrapolate methods from one domain (e.g., bioconvection or aerospace control) to others (e.g., the mining industry), highlighting similarities in the challenges of robustness, scalability, and security in processes.
- Assessing technological maturity: A better thematic arrangement helps identify which clusters present standardized protocols, production-ready tools, or long-term development needs, facilitating strategic decision-making in innovation.
4.1. Use of Algorithms and Cost Functions to Define the Number of Clusters
- Elbow Method: The first indicator for determining a suitable k was obtained by plotting the Within-Cluster Sum of Squares (WCSS) against different values of k. A clear breakpoint or elbow was identified in the curve, indicating a significant decrease in intra-cluster inertia [1,2]. In our experiments, this breakpoint was observed around or , suggesting an optimal range between five and seven clusters. A qualitative inspection of the emerging topics confirmed that provided a well-balanced trade-off between granularity and coherence.
- Silhouette analysis: To complement the insights from the Elbow Method, we computed the silhouette index, which measures cluster separability. A higher silhouette score indicates well-defined clusters with minimal overlap. For , the silhouette index decreased, suggesting that some distinct topics were merged, while for , there was no significant improvement, and some well-formed clusters became fragmented. Therefore, the choice of maximized the silhouette index while preserving cluster cohesion.
- Thematic validity and expert review: Beyond numerical metrics, an expert validation process was conducted by manually reviewing the titles and abstracts of each cluster following the approach used in recent studies [14,15]. The six-cluster configuration clearly distinguished subtopics such as magnetized fluids, robust nonlinear control, stochastic switching, and big data and machine learning. Increasing the number of clusters beyond six diluted the coherence of certain groups, while reducing it to fewer than six resulted in the merging of distinct research areas (e.g., conflating chaotic cryptography with nonlinear predictive control).
- Data density considerations: The dataset, after automated and manual filtering, comprised approximately 2900 references with corresponding metadata (titles, abstracts, and keywords). After applying TF-IDF and frequency analysis, dimensionality reduction via SVD and PCA revealed cluster densities naturally concentrated in six distinct regions. This observation further supported the decision to set , minimizing the risk of merging thematically distinct studies [5,20].
- Replicability and transparency: From a systematic literature review (SLR) perspective, the selection of k was documented in Python scripts, including cross-validation results (see Section 2). This aligns with best practices for ensuring reproducibility in text mining and bibliometric studies [15,17].
4.2. Cluster 1: Advances in Magnetized Nanoparticle Fluids for Heat and Mass Transfer in Industrial Applications
4.2.1. MHD Effects, Forchheimer Influence, and Bioconvection
4.2.2. Industrial Applications and Computational Optimization
4.2.3. Impact on the Mining and Energy Industries
4.2.4. Final Reflections on the Cluster
4.3. Cluster 2: High-Complexity Nonlinear Systems in Industry
4.3.1. Adaptive Control and Nonlinear Feedback
4.3.2. Synchronization Schemes and Industrial Cybersecurity
4.3.3. Predictive Control and Machine Learning Techniques
4.3.4. Nonlinear Systems with Saturations and Intermittent Failures
4.3.5. Multidomain Applications and Industrial Perspective
4.3.6. Cluster Conclusions
- Robust control designs (sliding mode, finite-time, predefined-time, adaptive backstepping) applicable to electric machines, autonomous vehicles, and critical infrastructures.
- Synchronization methodologies and advanced encryption that, together with Industry 4.0/5.0, enhance cybersecurity and resilience against external attacks.
- The incorporation of machine learning techniques (fuzzy logic, LSTM, gray forecasting) to address the uncertain and nonlinear nature of various industrial systems, providing faster and more robust estimations.
- The resolution of real-world constraints such as saturations, intermittent failures, and delays, facilitated by model predictive control and distributed observers.
4.4. Cluster 3: Big Data Analysis and Nonlinear Modeling with External Perturbations
4.4.1. Relevance of Nonlinear Models and Big Data
4.4.2. Inverse Modeling and Estimation Under Uncertainty
4.4.3. High-Complexity Signals, Encryption, and Real-Time Analysis
4.4.4. Predictive Control, Observers, and External Perturbations
4.4.5. Applications in Mining, Energy, and Manufacturing
4.4.6. Cluster Conclusions
- Clustering and sparse regression approaches for classifying complex dynamics and synthesizing specialized controllers [1].
4.5. Cluster 4: SOEC, Hydrogen, and Energy Transition
4.5.1. Optimal Control and Participation in Demand Response
4.5.2. Just Transition in the Extractive Industry
4.5.3. Policy Support and Infrastructure Planning
4.5.4. Environmental Demands and New Business Models
4.5.5. Emerging Trends and Perspectives
- Scaling up SOEC: Strategies for large-scale design and integration with smart grids, with a focus on predictive control and deep learning algorithms to maximize efficiency and flexibility [9].
4.5.6. Cluster Conclusions
4.6. Cluster 5: Detection and Management of Stiction in Control Valves of Process Industries
4.6.1. Importance of Early Diagnosis and Analysis Phases
4.6.2. Multivariate Tools and Functional Analysis
4.6.3. Intelligent Classification and Predictive Control
4.6.4. Challenges in Complex Environments and Future Perspectives
4.6.5. Conclusions and Future Directions
4.7. Cluster 6: Stochastic Functional Differential Equations with Past-Dependent Switching
4.7.1. General Perspective and Stability Challenges
- The infinite-dimensional state space, where past trajectories influence present dynamics.
4.7.2. Applications in Industrial Networks, Distributed Control, and Cyber–Physical Systems
4.7.3. Switching and Cyberattacks
4.7.4. Optimal Control and Nonlinear Optimization Algorithms
4.7.5. Modern Control and Stability Methodologies
4.7.6. Conclusions and Future Directions
- Expanded industrial applicability: IoT environments, plants with stochastic demand, cyber–physical systems, and smart energy networks where robust and secure control is essential.
5. Technological Convergence and Multidisciplinary Perspectives
- Intelligent heat and energy management: Both the mining and petrochemical industries benefit from advances in magnetohydrodynamics and thermal control [16], with direct applications in machinery cooling and thermal storage.
5.1. Relevant Applications in Key Sectors
5.1.1. Automotive and Aerospace Industry
5.1.2. Industrial Robotics, Mechatronics, and Civil Engineering
5.1.3. Petrochemical and Manufacturing Processes
5.1.4. Energy Transition and Power Grids
5.1.5. Mining Industry
5.2. Toward an Intelligent and Sustainable Industry
- Multidisciplinary perspective: Nanotechnology, control engineering, data mining, and cryptography converge to generate high-impact solutions that can be scaled across different productive sectors.
- Gradual and validated adoption: The transition from conceptual testing to industrial application requires pilot programs, standardization, and interinstitutional collaboration between academia, government, and industry, accelerating technology transfer.
- Balance between technology and policy: Advances in energy transition, such as SOEC and green hydrogen, along with the implementation of intelligent and cybersecure networks, require appropriate regulatory frameworks and socioeconomic transition plans, especially in mining regions.
5.3. Projection Toward Smart Mining
5.3.1. Automation and Advanced Control
5.3.2. Predictive Maintenance and Intelligent Systems
5.3.3. Industrial Cybersecurity and Protection of Critical Data
5.3.4. Sustainability and Energy Transition
5.3.5. Toward Mining 4.0/5.0
5.3.6. Implementation Challenges and Opportunities
- Scalability and standardization: Transitioning from conventional mining to smart mining requires investment in digital infrastructure, strengthening communication protocols (OPC UA, MQTT), and establishing interoperability guidelines that facilitate the widespread adoption of nonlinear and cryptographic algorithms.
- Cultural change and talent development: The adoption of AI tools and advanced control systems requires trained personnel, not only in mining operations but also in data analytics, robotics, and cybersecurity.
- Validation and real-scale pilot projects: To consolidate the transition toward Mining 4.0/5.0, it is essential to develop pilot projects that demonstrate return on investment and technical feasibility in concrete operations, encouraging public–private collaboration.
6. Final Perspectives and Research Opportunities
- Deepening hybrid models: The fusion of physical methods (e.g., MHD, chemical reactions, fluid mechanics) with deep learning and Bayesian optimization approaches is emerging as one of the most promising trends [10,16]. In mining, this could lead to more precise geometallurgical models and the integration of optimized chemical reactors based on efficiency and sustainability criteria.
- Scaling cybersecurity solutions: The increasing digitalization of remote plants and SCADA networks in the mining or petrochemical sectors underscores the importance of cryptography based on nonlinear behaviors [5,6] as well as the implementation of robust synchronization mechanisms to prevent attacks such as Denial-of-Service or false data injections [8]. Strengthening this area will provide a solid defense for critical industrial infrastructure.
- Energy transition and industrial reconversion: Technologies such as SOEC and green hydrogen [4,9] offer clean production pathways, particularly relevant in regions heavily dependent on fossil fuels. The development of robust predictive control models and dynamic planning—potentially supported by AI—could accelerate the adoption of these solutions and their integration into smart grids, enhancing decarbonization and industrial competitiveness.
- Gradual validation and standardization: To transfer these methodologies to real-world environments, it is essential to establish protocols and guidelines that unify simulation, implementation, and performance evaluation criteria across different industries. This process will strengthen confidence among the productive sector, regulatory bodies, and the scientific community, fostering the adoption of disruptive technologies with reduced risk.
7. Discussion
7.1. Industrial Applications and Scope of the Reviewed Methods
7.2. Comparison Between Discrete Difference Equations and Continuous-Time Differential Equations
- Digital implementation and sampling: In most industrial control applications and computational simulations, data acquisition and system actuation occur in digital form. State variables are updated at periodic sampling intervals (e.g., every seconds), necessitating the transformation of continuous models (ordinary differential equations, ODEs) into discrete models (difference equations). Discretization methods, such as Tustin, Euler, or discrete Runge–Kutta, ensure that simulations and subsequent digital controller implementations closely reflect real-world system dynamics.
- Computational robustness and numerical stability: Continuous-time differential equations often require solving large-scale or nonlinear systems, which can present convergence and stability challenges in digital environments. Discrete formulations allow the use of well-established numerical algorithms that offer controlled truncation error at each step, improving computational efficiency and stability. This makes difference equations more suitable for discrete event-driven systems and reduces sensitivity to numerical precision limitations in computing.
- Sampling-based systems and digital control theory: Modern industrial controllers—such as those embedded in microprocessors—operate using digitized signals. As a result, digital control theory and the synthesis of predictive (MPC) or adaptive controllers are more naturally formulated in the discrete domain. This not only simplifies implementation but also enables the use of multihorizon optimization techniques that can be computed in real time, which would be significantly more complex in a strictly continuous-time framework.
- Integration of stochastic effects and disturbances: Many real-world systems experience switching dynamics, time delays, and stochastic perturbations. Difference equations facilitate the implementation of statistical estimation methods such as discrete Kalman filters and adaptive estimators. Additionally, discrete-time formulations are well suited for integrating machine learning techniques, particularly for time series data, which are naturally represented in discrete sequences, enhancing compatibility with recurrent neural networks and predictive models.
- Flexibility in multiscale and event-driven applications: Industrial simulations often involve processes occurring at multiple time scales or with asynchronous interactions, such as sensor networks or event-based actuation systems. In these cases, using difference equations within different model submodules—each with its own sampling rate—provides a modular and scalable approach that would be computationally expensive or impractical to handle using a single continuous-time formalism.
7.3. Hyperparameter Selection Strategies in Intelligent Models
- Grid search: This method systematically explores a predefined set of values for each hyperparameter. While simple and easily reproducible, grid search can be computationally expensive, especially when dealing with high-dimensional hyperparameter spaces. In industrial applications involving deep neural networks or complex nonlinear models, exhaustive search often becomes impractical without large-scale computing infrastructure.
- Random search: Instead of systematically exploring all possible combinations, random search samples hyperparameter values from predefined probability distributions (e.g., uniform, logarithmic). Studies have shown that compared to grid search, fewer random evaluations often yield comparable or even better results, particularly when only a subset of hyperparameters significantly impacts model performance. This approach is valuable in robust models where the relative importance of each hyperparameter is unknown.
- Bayesian optimization: This method builds a probabilistic model (e.g., Gaussian process regression or kernel-based regression) to approximate the cost function over the hyperparameter space. An acquisition function (e.g., Expected Improvement, Upper Confidence Bound) guides the search by balancing exploration and exploitation. The Bayesian model is updated iteratively after each evaluation. This approach is particularly useful in scenarios where each experiment is computationally expensive—such as tuning a nonlinear controller in a physical plant—where minimizing the number of evaluations is critical.
- Multiobjective optimization: In many industrial applications, hyperparameter selection must optimize multiple objectives beyond model accuracy, such as inference time or energy consumption. Methods like Pareto frontier techniques or multiobjective evolutionary algorithms (e.g., NSGA-II, SPEA2) simultaneously explore various performance metrics, identifying optimal trade-off solutions that balance different criteria.
- Cross-validation and performance metrics: A standard practice in hyperparameter tuning is splitting data into training and validation subsets (k-fold cross validation), computing performance metrics (e.g., MSE, MAE, F1-score), and selecting the configuration that optimizes these measures. In adaptive control or highly variable systems, robustness is also assessed through additional indicators such as error variance or control energy consumption. Configurations that achieve a balance between reference tracking and resource efficiency are generally preferred.
- Dynamic recalibration and adaptive learning curves: In time-varying environments (e.g., flotation processes or diesel engines with gradual component aging), some studies adopt dynamic hyperparameter recalibration techniques. This is achieved through online learning or adaptive control methods, where hyperparameters are iteratively adjusted based on real-time plant performance.
7.4. Recurrent Neural Network-Based Modeling and Control
- Temporal dynamics representation and historical dependency: RNNs retain past information through feedback loops in their architecture, making them highly effective in modeling processes with strong historical dependencies [25,55]. In complex control applications—such as internal combustion engines, chemical processes, or robotic systems—the temporal evolution and influence of previous states are critical for accurately estimating system dynamics. This provides a richer approximation than purely static models or sliding-window approaches.
- Robust learning of nonlinear models: When the system structure is unknown or highly complex (e.g., chaotic behavior in Duffing-type oscillators [27] or synchronization of uncertain systems [22,35]), RNNs can approximate the underlying dynamics without requiring an exact physical model. As a result, RNN-based modeling is increasingly integrated with adaptive or predictive control methods to enhance robustness against disturbances and nonlinearities.
- Predictive control and intelligent feedback: In modern control systems, RNNs have been utilized to develop state observers and real-time model predictive control (MPC) algorithms [7,36]. Similarly, in applications requiring robust closed-loop control—such as inverted rotation [2] or magnetohydrodynamic (MHD) fluid dynamics [16]—recurrent networks learn the underlying dynamics and compensate for modeling errors in real time, significantly reducing sensitivity to parameter uncertainties.
- Regularization strategies and stability considerations: A critical challenge in using RNNs for control is ensuring stability and mitigating issues such as gradient explosion or vanishing gradients. To address this, regularization techniques (e.g., weight decay) and architectures such as LSTM/GRU have been proposed to limit error accumulation over long simulation horizons. These approaches improve numerical convergence and practical implementation in industrial environments with stringent reliability requirements [28,38].
- Integration with global optimization and cybernetics: There is increasing interest in combining RNNs with global optimization algorithms (e.g., Bayesian optimization [10], Differential Evolution) to fine-tune neural architectures and optimize control solutions with multiobjective criteria; see Section 7.3. This has led to the development of self-tuning control systems with high adaptability to regime changes in manufacturing plants and Mining 4.0 applications [3,20].
- Industrial applications: RNNs have demonstrated effectiveness in real-time state estimation for fault detection (e.g., stiction in control valves [3], multifault classification [77]), security in chaotic cryptosystems [5,6], and even fluid-level and flow-rate prediction [25]. In the energy sector, integrating RNNs with robust control strategies can improve the efficiency of solid oxide electrolysis cells (SOEC) [9]. In mining, RNN-based models are emerging as promising tools for optimizing comminution and flotation processes by capturing the multiscale behavior of mineral processing [11,71].
7.5. Pruning and Growing Techniques for Neural Network Optimization
- Static pruning based on magnitude: This method evaluates the absolute weight of each synapse (or neuron) after training and removes those with the smallest magnitudes. Commonly referred to as magnitude-based pruning, this approach reduces network complexity while maintaining performance levels. Fañanás-Anaya et al. [55] and Arya and Nair [1] highlight that in industrial scenarios with large and noisy datasets, static pruning significantly improves inference time while preserving acceptable accuracy.
- Iterative pruning with weight feedback: This approach alternates between training and pruning over multiple cycles. The process consists of the following steps:
- Training the network until a convergence criterion is met.
- Removing synapses or neurons with weights below an adaptive threshold.
- Retraining the network to reallocate weights and mitigate capacity loss.
The final result is a lightweight model with comparable approximation capabilities to the original. Compared to static pruning, this method allows for a more gradual and robust refinement in highly nonlinear settings. According to Nguyen et al. [2] and Kang et al. [25], iterative pruning is particularly effective for recurrent neural networks (RNNs). - Structural pruning at the channel or block level: In large convolutional networks (CNNs) and deep RNN architectures, pruning can be extended beyond individual weights to entire channels or layers that contribute minimally to the model’s output. This architectural approach, often combined with quantization techniques, facilitates the deployment of models on embedded or low-power systems, which is critical in IoT and industrial applications [34,90].
- Growing strategies (network expansion): Unlike pruning, growing techniques start with small or minimal architectures and progressively add neurons or layers as needed. Common methods include the following:
- –
- Cascade Correlation: A method that sequentially adds hidden neurons to maximize residual correlation with the error [94].
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- Multiobjective optimization perspective: In industrial applications, neural model optimization often requires balancing accuracy, execution time, and energy consumption while ensuring robustness against disturbances. Consequently, pruning and growing strategies are frequently optimized using multiobjective criteria [10,37]. Techniques such as evolutionary algorithms (genetic algorithms) and Bayesian optimization are commonly applied to determine when to prune nodes or expand layers, seeking an optimal trade-off between computational efficiency and estimation/control performance.
- Applications and limitations: While pruning significantly reduces computational cost, aggressive pruning can impair the network’s ability to capture complex dynamics (e.g., chaotic attractors [6] or multigroup synchronization [22]). A more moderated approach, incorporating dynamic thresholds and weight feedback, provides finer control over the final model. On the other hand, excessive network growth without proper stopping criteria may lead to overfitting and an over-reliance on historical data, requiring rigorous cross-validation techniques [14,15].
7.6. Adaptability Statistics and Added Value in Mining
- Relationship between data requirements and implementation complexity: The mining industry, due to the multiplicity of processes (crushing, grinding, flotation, leaching), often presents partial or noisy data. Therefore, solutions that require lower data density (e.g., [2,5]) may be advantageous. However, when pursuing significant optimization benefits [10,25], it is essential to strengthen data acquisition infrastructure and ensure data quality.
- High return on investment (ROI): Methods such as stiction detection [3] or high-complexity cryptography [5] offer substantial ROI by either minimizing critical failures or preventing cyberattacks. This return is even higher in operations where downtime severely impacts productivity (e.g., unplanned shutdowns in furnaces or flotation cells).
- Scalability and adaptability: The effectiveness of approaches such as MHD with nanofluids [16] or stochastic switching [2] depends on the mining site’s ability to handle specialized fluids or implement switched controllers. While pilots in mid-scale projects serve as a reasonable starting point, large-scale mines could benefit from a progressive deployment in key subsystems.
- Need for academic–industrial collaboration: Studies suggest that co-creating knowledge—focused on machine learning, cybersecurity, and complex fluid analysis—[4,11] would accelerate the adoption of technological solutions. Establishing partnerships between universities, technology providers, and mining operators will reduce the integration gap and strengthen R&D efforts aimed at concrete results.
8. Conclusions
8.1. General Overview
8.2. Key Contributions
- Holistic approach to mining (maturity: high; urgency: medium–high). The convergence of heterogeneous geological resources, nonlinear physicochemical processes, and potential cyberattacks requires an integrated approach—encompassing geomechanics, thermohydrodynamics, and SCADA monitoring—to enhance resilience in critical scenarios [2,7]. While large-scale instrumentation and modeling frameworks exist, further standardization and cross-institutional collaboration are needed for scalability.
- Hybrid techniques (AI + control theory) (maturity: medium–high; urgency: high). Solutions that combine deep learning algorithms (e.g., LSTM–CNN) with ARX models or Bayesian methods have proven effective in noisy and data-intensive industrial environments [10,25]. The urgency of these techniques lies in the current demand for predictive maintenance and dynamic asset planning (mills, flotation cells, pumping systems), offering immediate implementation opportunities.
- Cybersecurity and nonlinear cryptography (maturity: medium; urgency: very high). The rise of memristors, digital twins, and high-complexity encryption schemes enables secure data protection in distributed and IoT networks. Recent studies [5,6] suggest that these strategies significantly increase the difficulty of external attacks, reinforcing information confidentiality and availability. Given the rapid digitalization of industrial operations, its urgency is paramount.
- Thermal management and energy transition (maturity: medium; urgency: medium). The integration of magnetized nanofluids [16] and SOEC modeling [9,23] provides energy optimization benefits aligned with sustainability policies and circular economy principles. Although preliminary successes exist, further research is required to strengthen operational stability and large-scale feasibility.
- Multiscale approaches and Industry 5.0 (maturity: incipient; urgency: medium). The convergence of nonlinear models, big data, and AI facilitates multiscale scenarios (from fracture microscopy to transport logistics), optimizing the efficiency of the value chain. If current research trends continue, Mining 5.0 could evolve into a cyber–physical environment capable of real-time adaptation [4,70], although it is still in its early stages of prototyping and extended validation.
8.3. Recommendations Based on Maturity and Urgency
- Pilot projects and progressive validation (maturity: medium–high; urgency: high). To facilitate technological transition, proof-of-concept trials in critical subsystems (e.g., crushing or transport) are recommended, with impact evaluation (return on investment, failure reduction, safety) before full-scale adoption [2,10].
- Multidisciplinary collaboration (maturity: high; urgency: medium). Complex problems—such as physicochemical interactions or large-scale simulations—demand the convergence of research centers specialized in nonlinear control, high-performance computing, and geosciences, alongside direct industrial participation [4,11].
- Cybersecurity and digital twins (maturity: medium; urgency: very high). Given the increasing exposure of mining operations to cyberattacks, the adoption of digital twins and advanced encryption architectures [5] should be prioritized. Protecting geological data confidentiality and operational continuity is essential for competitiveness and security.
- Chaos theory and human decision-making (maturity: incipient; urgency: medium). Analyzing the interaction between chaotic dynamics and human intervention in industrial processes—where operator actions can alter nonlinear trajectories—requires interdisciplinary methodologies combining technical and cognitive aspects [8,20]. Although its immediate applicability is limited, its relevance will grow as industrial autonomy increases.
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Industry/Sector | Applied Methodologies | Key Scope/Challenges | Potential Adaptation to Other Industries |
---|---|---|---|
Petrochemical (refining and processes) |
|
| Transferable to mining, food, and pharmaceutical industries, where reactions and fluid escape risks exist [5,7]. |
Aeronautics and Aerospace |
|
| Applicable to advanced robotics (mining drones, autonomous vehicles in mining sites) [78,92]. |
Electric Power (smart grids) |
|
| Relevant for mining, metallurgy, and cement given their high energy consumption and distributed infrastructure [8,34]. |
Manufacturing (Industry 4.0) |
|
| Highly compatible with the mining industry (crushing lines, transport), logistics sector, and port operations [4,25]. |
Fine Chemistry and Pharmaceuticals |
|
| Applicable to biological processes (bio-mining), hydrometallurgy, and water reuse in plants [10,26]. |
Mining and Metallurgy |
|
| Adaptable to hydrocarbon extraction (fracking), rock mechanics (tunnels), and wastewater treatment [3,5,16]. |
Reference | Data Requirements | Implementation Complexity | Potential Value | Summary Comment |
---|---|---|---|---|
Yang et al. [16] (Nanofluids + MHD) | Medium (requires fluid properties and flow configurations) | Medium–high (CFD modeling and plant calibration) | High (optimizes cooling and reduces energy consumption) | Suitable for thermal management of equipment in underground mines or intensive flotation. |
Nguyen et al. [2] (Stochastic switching) | Low–medium (transition and mode data) | High (design of switched controllers, robust PDE/ODE models) | High (versatile against ore variability, equipment failures) | Appropriate for complex loops (e.g., conveyor belts) and uncertain geomechanics. |
Demirkol et al. [5] (High-complexity cryptography) | Low (does not require large datasets, only security configurations) | Medium (hardware integration with FPGA, IoT protocols) | High (essential for protecting critical information in Mining 4.0) | Focused on SCADA cybersecurity, safeguarding topographic and production data. |
Flores-Tlacuahuac and Fuentes-Cortés [10] (Bayesian optimization) | High (continuous process with plant data) | Medium (fusion with existing control systems) | High (adaptive scheduling, resource planning) | Dynamic adjustment of crushing or dumping parameters, maximizing profitability. |
Guan et al. [3] (Stiction and phase space) | Medium (requires control log and fault records) | Low–medium (recurrence techniques, FFT) | Very high (prevents costly shutdowns due to valve failures) | Effective predictive maintenance, avoiding downtime in unit processes. |
Matouk and Botros [6] (Hypersensitive fractional attractors) | Low (circuit parameters, initial guesses) | High (theoretical complexity and coding) | Medium (applicable to encryption and extreme modeling) | Useful for industrial cryptography and catastrophic scenarios. |
Kang et al. [25] (LSTM-CNN + ARX) | High (training with historical data) | Medium–high (ML pipeline + ARX modeling) | High (precise control of tank or pool levels) | Plant optimization, reducing water and reagent consumption. |
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Rojas, L.; Yepes, V.; Garcia, J. Complex Dynamics and Intelligent Control: Advances, Challenges, and Applications in Mining and Industrial Processes. Mathematics 2025, 13, 961. https://doi.org/10.3390/math13060961
Rojas L, Yepes V, Garcia J. Complex Dynamics and Intelligent Control: Advances, Challenges, and Applications in Mining and Industrial Processes. Mathematics. 2025; 13(6):961. https://doi.org/10.3390/math13060961
Chicago/Turabian StyleRojas, Luis, Víctor Yepes, and José Garcia. 2025. "Complex Dynamics and Intelligent Control: Advances, Challenges, and Applications in Mining and Industrial Processes" Mathematics 13, no. 6: 961. https://doi.org/10.3390/math13060961
APA StyleRojas, L., Yepes, V., & Garcia, J. (2025). Complex Dynamics and Intelligent Control: Advances, Challenges, and Applications in Mining and Industrial Processes. Mathematics, 13(6), 961. https://doi.org/10.3390/math13060961