Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.7
2.3
Journals
Algorithms
Special Issues
Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes
share announcement

Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2125

Special Issue Editors

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
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. 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 Issues, Collections and Topics in MDPI journals
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

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 of 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 modelling and optimization algorithms for industrial processes. The 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:

  1. Data-Driven Modeling:
    Intelligent data representation;
    Integration/hybrid modeling.
  2. Machine Learning and Optimization:
    Advanced machine learning algorithms;
    Hybrid models with optimization algorithms;
    Adaptive learning algorithms.
  3. Intelligent Process Monitoring:
    Real-time data monitoring and analysis;
    Soft sensing technologies;
    Operating mode perception and recognition.
  4. Decision Support Systems:
    Intelligent decision support systems;
    The integration of optimization algorithms;
    Human–machine collaboration for enhanced decision making.

Prof. Dr. Sheng Du
Dr. Zixin Huang
Prof. Dr. Li Jin
Prof. Dr. Xiongbo Wan
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 1600 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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 799 KiB  
Article
A Data-Driven Approach to Discovering Process Choreography
by Jaciel David Hernandez-Resendiz, Edgar Tello-Leal and Marcos Sepúlveda
Algorithms 2024, 17(5), 188; https://doi.org/10.3390/a17050188 - 29 Apr 2024
Viewed by 278
Abstract
Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes [...] Read more.
Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes a complete and modular data-driven approach that implements natural language processing techniques, text similarity, and process mining techniques (discovery and conformance checking) through a set of methods and formal rules that enable analysis of the data contained in the event logs and the intra-organizational process models of the participants in the collaboration, to identify patterns that allow the discovery of the process choreography. The approach enables merging the event logs of the inter-organizational collaboration participants from the identified message interactions, enabling the automatic construction of an IOBP model. The proposed approach was evaluated using four real-life and two artificial event logs. In discovering the choreography process, average values of 0.86, 0.89, and 0.86 were obtained for relationship precision, relation recall, and relationship F-score metrics. In evaluating the quality of the built IOBP models, values of 0.95 and 1.00 were achieved for the precision and recall metrics, respectively. The performance obtained in the different scenarios is encouraging, demonstrating the ability of the approach to discover the process choreography and the construction of business process models in inter-organizational environments. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
►▼ Show Figures

Figure 1

17 pages, 2232 KiB  
Article
Advancements in Data Analysis for the Work-Sampling Method
by Borut Buchmeister and Natasa Vujica Herzog
Algorithms 2024, 17(5), 183; https://doi.org/10.3390/a17050183 - 29 Apr 2024
Viewed by 246
Abstract
The work-sampling method makes it possible to gain valuable insights into what is happening in production systems. Work sampling is a process used to estimate the proportion of shift time that workers (or machines) spend on different activities (within productive work or losses). [...] Read more.
The work-sampling method makes it possible to gain valuable insights into what is happening in production systems. Work sampling is a process used to estimate the proportion of shift time that workers (or machines) spend on different activities (within productive work or losses). It is estimated based on enough random observations of activities over a selected period. When workplace operations do not have short cycle times or high repetition rates, the use of such a statistical technique is necessary because the labor sampling data can provide information that can be used to set standards. The work-sampling procedure is well standardized, but additional contributions are possible when evaluating the observations. In this paper, we present our contribution to improving the decision-making process based on work-sampling data. We introduce a correlation comparison of the measured hourly shares of all activities in pairs to check whether there are mutual connections or to uncover hidden connections between activities. The results allow for easier decision-making (conclusions) regarding the influence of the selected activities on the triggering of the others. With the additional calculation method, we can uncover behavioral patterns that would have been overlooked with the basic method. This leads to improved efficiency and productivity of the production system. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
►▼ Show Figures

Figure 1

19 pages, 689 KiB  
Article
Root Cause Tracing Using Equipment Process Accuracy Evaluation for Looper in Hot Rolling
by Fengwei Jing, Fenghe Li, Yong Song, Jie Li, Zhanbiao Feng and Jin Guo 
Algorithms 2024, 17(3), 102; https://doi.org/10.3390/a17030102 - 26 Feb 2024
Viewed by 950
Abstract
The concept of production stability in hot strip rolling encapsulates the ability of a production line to consistently maintain its output levels and uphold the quality of its products, thus embodying the steady and uninterrupted nature of the production yield. This scholarly paper [...] Read more.
The concept of production stability in hot strip rolling encapsulates the ability of a production line to consistently maintain its output levels and uphold the quality of its products, thus embodying the steady and uninterrupted nature of the production yield. This scholarly paper focuses on the paramount looper equipment in the finishing rolling area, utilizing it as a case study to investigate approaches for identifying the origins of instabilities, specifically when faced with inadequate looper performance. Initially, the paper establishes the equipment process accuracy evaluation (EPAE) model for the looper, grounded in the precision of the looper’s operational process, to accurately depict the looper’s functioning state. Subsequently, it delves into the interplay between the EPAE metrics and overall production stability, advocating for the use of EPAE scores as direct indicators of production stability. The study further introduces a novel algorithm designed to trace the root causes of issues, categorizing them into material, equipment, and control factors, thereby facilitating on-site fault rectification. Finally, the practicality and effectiveness of this methodology are substantiated through its application on the 2250 hot rolling equipment production line. This paper provides a new approach for fault tracing in the hot rolling process. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop