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With the advent of Industry 4.0, the amalgamation of sophisticated data analytics, machine learning, and artificial intelligence has become pivotal, unlocking new horizons in production efficiency, sustainability, and quality assurance. Contributions to this Special Issue highlight innovative research in advancements in work-sampling data analysis, data-driven process choreography discovery, intelligent ship scheduling for maritime rescue, process variability monitoring, hybrid optimization algorithms for economic emission dispatches, and intelligent controlled oscillations in smart structures. These studies collectively contribute to the body of knowledge on data-driven intelligent modeling and optimization, offering practical solutions and theoretical frameworks to address complex industrial challenges.<\/jats:p>","DOI":"10.3390\/a17120569","type":"journal-article","created":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T03:52:49Z","timestamp":1733975569000},"page":"569","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Recent Progress in Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8396-7388","authenticated-orcid":false,"given":"Sheng","family":"Du","sequence":"first","affiliation":[{"name":"School of Automation, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zixin","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1150-9721","authenticated-orcid":false,"given":"Li","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiongbo","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Du, S., Huang, C., Ma, X., and Fan, H. (2024). A review of data-driven intelligent monitoring for geological drilling processes. Processes, 12.","DOI":"10.3390\/pr12112478"},{"key":"ref_2","first-page":"1","article-title":"A survey on active deep learning: From model driven to data driven","volume":"54","author":"Liu","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104711","DOI":"10.1016\/j.autcon.2022.104711","article-title":"Intelligent technologies for construction machinery using data-driven methods","volume":"147","author":"Zheng","year":"2023","journal-title":"Autom. Constr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4799","DOI":"10.1109\/TFUZZ.2024.3404853","article-title":"Time series anomaly detection via rectangular information granulation for sintering process","volume":"32","author":"Du","year":"2024","journal-title":"IEEE Trans. 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Pract."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"108659","DOI":"10.1016\/j.engappai.2024.108659","article-title":"Mechanism-driven and data-driven fusion prediction of seismic damage evolution of concrete structures based on cooperative multi-particle swarm optimization","volume":"133","author":"Sun","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1109\/MITS.2022.3153491","article-title":"A data-driven intelligent energy efficiency management system for ships","volume":"15","author":"Zeng","year":"2022","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"117913","DOI":"10.1016\/j.apenergy.2021.117913","article-title":"Hybrid model-driven and data-driven control method based on machine learning algorithm in energy hub and application","volume":"305","author":"Cai","year":"2022","journal-title":"Appl. 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Algorithms, 17.","DOI":"10.3390\/a17070319"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ying, W., Wang, Z., Li, H., Du, S., and Zhao, M. (2024). Intelligent Ship Scheduling and Path Planning Method for Maritime Emergency Rescue. Algorithms, 17.","DOI":"10.3390\/a17050197"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Buchmeister, B., and Herzog, N. (2024). Advancements in Data Analysis for the Work-Sampling Method. Algorithms, 17.","DOI":"10.3390\/a17050183"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hernandez-Resendiz, J., Tello-Leal, E., and Sep\u00falveda, M. (2024). A Data-Driven Approach to Discovering Process Choreography. Algorithms, 17.","DOI":"10.3390\/a17050188"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Xu, W., Qian, J., Wu, Y., Yan, S., Ni, Y., and Yang, G. (2024). A VIKOR-Based Sequential Three-Way Classification Ranking Method. Algorithms, 17.","DOI":"10.3390\/a17110530"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tian, G., and Wang, F. (2024). Data-Driven Load Frequency Control for Multi-Area Power System Based on Switching Method under Cyber Attacks. Algorithms, 17.","DOI":"10.3390\/a17060233"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Moutsopoulou, A., Petousis, M., Stavroulakis, G., Pouliezos, A., and Vidakis, N. (2024). Novelty in Intelligent Controlled Oscillations in Smart Structures. Algorithms, 17.","DOI":"10.3390\/a17110505"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Castellanos-C\u00e1rdenas, D., Posada, N.L., Orozco-Duque, A., Sep\u00falveda-Cano, L.M., Castrill\u00f3n, F., Camacho, O., and V\u00e1squez, R.E. (2024). A Review on Data-Driven Model-Free Sliding Mode Control. Algorithms, 17.","DOI":"10.3390\/a17120543"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zarrouk, T., Nouari, M., Salhi, J., Abbadi, M., and Abbadi, A. (2024). Three-Dimensional Finite Element Modeling of Ultrasonic Vibration-Assisted Milling of the Nomex Honeycomb Structure. Algorithms, 17.","DOI":"10.3390\/a17050204"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Violante, J., Machado, M., Mendes, A., and Almeida, T. (2024). An Interface to Monitor Process Variability Using the Binomial ATTRIVAR SS Control Chart. 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