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Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 3rd...
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Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 3rd Edition

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2877

Special Issue Editors

Prof. Dr. Xiongbo Wan

E-Mail Website
Guest Editor
1. School of Automation, China University of Geosciences, Wuhan 430074, China
2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
3. Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China
Interests: artificial intelligence; robust control of time-delay systems
Special Issues, Collections and Topics in MDPI journals
Dr. Zixin Huang

E-Mail Website
Guest Editor
School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
Interests: underdrive system control; intelligent control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sheng Du

E-Mail Website
Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: process control; intelligent control; intelligent optimization; computational intelligence; artificial intelligence
Prof. Dr. Li Jin

E-Mail Website
Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: power system stability analysis and control; time-delay system; robust theory and application

Special Issue Information

Dear Colleagues,

In the ever-evolving landscape of industrial process modeling and optimization, data-driven intelligent algorithms have emerged as a transformative force. This Special Issue aims to explore the intersection between data-driven approaches, intelligent modeling, and optimization algorithms in the context of industrial processes. With the relentless growth of Industry 4.0, the integration of advanced data analytics, machine learning, and artificial intelligence has become imperative to opening up new possibilities in production efficiency, sustainability, and quality assurance in industrial processes.

Scope and objectives:

This Special Issue aims to explore the multifaceted aspects of data-driven intelligent modeling and optimization algorithms for industrial processes. Our main objectives are to harness the power of data to improve control, decision making, and parameter optimization, and to drive industrial systems to unprecedented levels of efficiency, reliability, and adaptability.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Data-driven modeling;
  • Intelligent data representation;
  • Integration/hybrid modeling;
  • Machine learning and optimization;
  • Advanced machine learning algorithms;
  • Hybrid models with optimization algorithms;
  • Adaptive learning algorithms;
  • Intelligent process monitoring;
  • Real-time data monitoring and analysis;
  • Soft sensing technologies;
  • Operating mode perception and recognition;
  • Decision support systems;
  • Intelligent decision support systems;
  • Integration of optimization algorithms;
  • Human–machine collaboration for enhanced decision making.

Prof. Dr. Xiongbo Wan
Dr. Zixin Huang
Prof. Dr. Sheng Du
Prof. Dr. Li Jin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • data-driven modeling
  • industrial processes
  • machine learning algorithms
  • optimization algorithms
  • adaptive learning

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Related Special Issues

Published Papers (4 papers)

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Research

23 pages, 3619 KB  
Article
Unbalanced Data Mining Algorithms from IoT Sensors for Early Cockroach Infestation Prediction in Sewer Systems
by Joaquín Aguilar, Cristóbal Romero, Carlos de Castro Lozano and Enrique García
Algorithms 2026, 19(2), 152; https://doi.org/10.3390/a19020152 - 14 Feb 2026
Viewed by 303
Abstract
Predictive pest management in urban sewer networks represents a sustainable alternative to reactive, biocide-based methods. Using data collected through an IoT architecture and validated with manual inspections across eight manholes over 113 days, we implemented a rigorous comparative framework evaluating eleven data mining [...] Read more.
Predictive pest management in urban sewer networks represents a sustainable alternative to reactive, biocide-based methods. Using data collected through an IoT architecture and validated with manual inspections across eight manholes over 113 days, we implemented a rigorous comparative framework evaluating eleven data mining algorithms, including classical methods (KNN, SVM, decision trees) and advanced ensemble techniques (XGBoost, LightGBM, CatBoost) optimized for unbalanced datasets. Gradient boosting models with explicit handling of class imbalance—where the absence of pests exceeds 77% of observations—showed exceptional performance, achieving a Macro-F1 score above 0.92 and high precision in identifying the minority high-risk class. Explainability analysis using SHAP consistently revealed that elevated CO2 concentrations are the primary predictor of infestation, enabling early identification of critical zones. This study demonstrates that carbon dioxide (CO2) acts as the most robust bioindicator for predicting severe infestations of Periplaneta americana, significantly outperforming conventional environmental variables such as temperature and humidity. The implementation of the model in a real-time monitoring platform generates interpretable heat maps that support proactive and localized interventions, optimizing resource use and reducing dependence on biocides. This study presents a scalable, operationally viable predictive system designed for direct integration into municipal asset management workflows, offering a concrete, industry-ready solution to transform pest control from a reactive, labor-intensive process into a data-driven, proactive operational paradigm. This approach not only transforms pest management from reactive to predictive but also aligns with the Sustainable Development Goals, offering a scalable, interpretable, and operationally viable system for smart cities. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 3rd Edition)
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30 pages, 15770 KB  
Article
A Hybrid Deep Learning Framework for Enhanced Fault Diagnosis in Industrial Robots
by Jun Wu, Yuepeng Zhang, Bo Gao, Linzhong Xia, Xueli Zhu, Hui Wang and Xiongbo Wan
Algorithms 2025, 18(12), 779; https://doi.org/10.3390/a18120779 - 10 Dec 2025
Viewed by 715
Abstract
Predominant fault diagnosis in industrial robots depends on dedicated vibration or acoustics sensors. However, their practical deployment is often limited by installation constraints, susceptibility to environmental noise, and cost considerations. Applying Energy-Based Maintenance (EBM) principles to achieve enhanced fault diagnosis under practical industrial [...] Read more.
Predominant fault diagnosis in industrial robots depends on dedicated vibration or acoustics sensors. However, their practical deployment is often limited by installation constraints, susceptibility to environmental noise, and cost considerations. Applying Energy-Based Maintenance (EBM) principles to achieve enhanced fault diagnosis under practical industrial conditions, we propose a hybrid deep learning framework, the Multi-head Graph Attention Network (MGAT) with Multi-scale CNNBiLSTM Fusion (MGAT-MCNNBiLSTM) for industrial robots. This approach obviates the need for additional dedicated sensors, effectively mitigating associated deployment complexities. The framework embodies four core innovations: (1) Based on the EBM paradigm, motor current is established as the most effective and practical choice for enabling cost-efficient and scalable industrial robot fault diagnosis. A corresponding dataset of motor current has been acquired from industrial robots operating under diverse fault scenarios. (2) An integrated MGAT-MCNNBiLSTM architecture that synergistically models multiscale local features and complex dynamics through its MCNNBiLSTM module while capturing nonlinear interdependencies via MGAT. This comprehensive feature representation enables robust and highly accurate fault detection. (3) The study found that the application of spectral preprocessing techniques yields a marked and statistically significant enhancement in diagnostic performance. A comprehensive and systematic analysis was undertaken to uncover the underlying reasons for this observed performance improvement. (4) To emulate challenging industrial settings and cost-sensitive implementations, noise signal injection was employed to evaluate model robustness in high-electromagnetic-interference environments and low-cost, low-resolution ADC implementations. Experimental validation on real-world industrial robot datasets demonstrates that MGAT-MCNNBiLSTM achieves a superior diagnostic accuracy of 90.7560%. This performance marks a significant absolute improvement of 1.51–8.55% over competing models, including LCNNBiLSTM, SCNNBiLSTM, MCCBiLSTM, GAT, and MGAT. Under challenging noise and low-resolution conditions, the proposed model consistently outperforms CNNBiLSTM variants, GAT, and MGAT with an improvement of 1.37–10.26% and enhanced industrial utility and deployment potential. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 3rd Edition)
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21 pages, 1053 KB  
Article
Machine Learning Systems Tuned by Bayesian Optimization to Forecast Electricity Demand and Production
by Zhen Wang, Salim Lahmiri and Stelios Bekiros
Algorithms 2025, 18(11), 695; https://doi.org/10.3390/a18110695 - 3 Nov 2025
Cited by 2 | Viewed by 919
Abstract
Given the critical importance of accurate energy demand and production forecasting in managing power grids and integrating renewable energy sources, this study explores the application of advanced machine learning techniques to forecast electricity load and wind generation data in Austria, Germany, and the [...] Read more.
Given the critical importance of accurate energy demand and production forecasting in managing power grids and integrating renewable energy sources, this study explores the application of advanced machine learning techniques to forecast electricity load and wind generation data in Austria, Germany, and the Netherlands at different sampling frequencies: 15 min and 60 min. Specifically, we assess the performance of the convolutional neural networks (CNNs), temporal CNN (TCNN), Long Short-Term Memory (LSTM), bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), bidirectional GRU (BiGRU), and the deep neural network (DNN). In addition, the standard machine learning models, namely the k-nearest neighbors (kNN) algorithm and decision trees (DTs), are adopted as baseline predictive models. Bayesian optimization is applied for hyperparameter tuning across multiple models. In total, 54 experimental tasks were performed. For the electricity load at 15 min intervals, the DT shows exceptional performance, while for the electricity load at 60 min intervals, DNN performs the best, in general. For wind generation at 15 min intervals, DT is the best performer, while for wind generation at 60 min intervals, both DT and TCNN provide good results, in general. The insights derived from this study not only advance the field of energy forecasting but also offer practical implications for energy policymakers and stakeholders in optimizing grid performance and renewable energy integration. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 3rd Edition)
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16 pages, 995 KB  
Article
An Information Granulation-Enhanced Kernel Principal Component Analysis Method for Detecting Anomalies in Time Series
by Xu Feng, Hongzhou Chai, Jinkai Feng and Yunlong Wu
Algorithms 2025, 18(10), 658; https://doi.org/10.3390/a18100658 - 17 Oct 2025
Viewed by 567
Abstract
In complex process systems, accurate real-time anomaly detection is essential to ensure operational safety and reliability. This study proposes a novel detection method that combines information granulation with kernel principal component analysis (KPCA). Here, information granulation is introduced as a general framework, with [...] Read more.
In complex process systems, accurate real-time anomaly detection is essential to ensure operational safety and reliability. This study proposes a novel detection method that combines information granulation with kernel principal component analysis (KPCA). Here, information granulation is introduced as a general framework, with the principle of justifiable granularity (PJG) adopted as the specific implementation. Time series data are first granulated using PJG to extract compact features that preserve local dynamics. The KPCA model, equipped with a radial basis function kernel, is then applied to capture nonlinear correlations and construct monitoring statistics including T2 and SPE. Thresholds are derived from training data and used for online anomaly detection. The method is evaluated on the Tennessee Eastman process and Continuous Stirred Tank Reactor datasets, covering various types of faults. Experimental results demonstrate that the proposed method achieves a near-zero false alarm rate below 1% and maintains a missed detection rate under 6%, highlighting its effectiveness and robustness across different fault scenarios and industrial datasets. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 3rd Edition)
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