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Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes

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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 5041

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Special Issue Editors

Prof. Dr. Sheng Du

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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

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Guest Editor

School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China
Interests: underdrive system control; intelligent control
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Prof. Dr. Li Jin

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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
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Prof. Dr. Xiongbo Wan

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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:

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;
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 (7 papers)

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Research

16 pages, 4902 KiB

Open AccessArticle

Data-Driven Load Frequency Control for Multi-Area Power System Based on Switching Method under Cyber Attacks

by Guangqiang Tian and Fuzhong Wang

Algorithms 2024, 17(6), 233; https://doi.org/10.3390/a17060233 - 27 May 2024

Viewed by 229

Abstract

This paper introduces an innovative method for load frequency control (LFC) in multi-area interconnected power systems vulnerable to denial-of-service (DoS) attacks. The system is modeled as a switching system with two subsystems, and an adaptive control algorithm is developed. Initially, a dynamic linear data model is used to model each subsystem. Next, a model-free adaptive control strategy is introduced to maintain frequency stability in the multi-area interconnected power system, even during DoS attacks. A rigorous stability analysis of the power system is performed, and the effectiveness of the proposed approach is demonstrated by applying it to a three-area interconnected power system. Full article

(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)

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13 pages, 2252 KiB

Open AccessArticle

An Interface to Monitor Process Variability Using the Binomial ATTRIVAR SS Control Chart

by João Pedro Costa Violante, Marcela A. G. Machado, Amanda dos Santos Mendes and Túlio S. Almeida

Algorithms 2024, 17(5), 216; https://doi.org/10.3390/a17050216 - 16 May 2024

Viewed by 363

Abstract

Control charts are tools of paramount importance in statistical process control. They are broadly applied in monitoring processes and improving quality, as they allow the detection of special causes of variation with a significant level of accuracy. Furthermore, there are several strategies able to be employed in different contexts, all of which offer their own advantages. Therefore, this study focuses on monitoring the variability in univariate processes through variance using the Binomial version of the ATTRIVAR Same Sample S² (B-ATTRIVAR SS S²) control chart, given that it allows coupling attribute and variable inspections (ATTRIVAR means attribute + variable), i.e., taking advantage of the cost-effectiveness of the former and the wealth of information and greater performance of the latter. Its Binomial version was used for such a purpose, since inspections are made using two attributes, and the Same Sample was used due to being submitted to both the attribute and variable stages of inspection. A computational application was developed in the R language using the Shiny package so as to create an interface to facilitate its application and use in the quality control of the production processes. Its application enables users to input process parameters and generate the B-ATTRIVAR SS control chart for monitoring the process variability with variance. By comparing the data obtained from its application with a simpler code, its performance was validated, given that its results exhibited striking similarity. Full article

(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)

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14 pages, 3876 KiB

Open AccessArticle

Three-Dimensional Finite Element Modeling of Ultrasonic Vibration-Assisted Milling of the Nomex Honeycomb Structure

by Tarik Zarrouk, Mohammed Nouari, Jamal-Eddine Salhi, Mohammed Abbadi and Ahmed Abbadi

Algorithms 2024, 17(5), 204; https://doi.org/10.3390/a17050204 - 10 May 2024

Viewed by 530

Abstract

Machining of Nomex honeycomb composite (NHC) structures is of critical importance in manufacturing parts to the specifications required in the aerospace industry. However, the special characteristics of the Nomex honeycomb structure, including its composite nature and complex geometry, require a specific machining approach to avoid cutting defects and ensure optimal surface quality. To overcome this problem, this research suggests the adoption of RUM technology, which involves the application of ultrasonic vibrations following the axis of revolution of the UCK cutting tool. To achieve this objective, a three-dimensional finite element numerical model of Nomex honeycomb structure machining is developed with the Abaqus/Explicit software, 2017 version. Based on this model, this research examines the impact of vibration amplitude on the machinability of this kind of structure, including cutting force components, stress and strain distribution, and surface quality as well as the size of the chips. In conclusion, the results highlight that the use of ultrasonic vibrations results in an important reduction in the components of the cutting force by up to 42%, improves the quality of the surface, and decreases the size of the chips. Full article

(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)

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10 pages, 1658 KiB

Open AccessArticle

Intelligent Ship Scheduling and Path Planning Method for Maritime Emergency Rescue

by Wen Ying, Zhaohui Wang, Hui Li, Sheng Du and Man Zhao

Algorithms 2024, 17(5), 197; https://doi.org/10.3390/a17050197 - 8 May 2024

Viewed by 567

Abstract

Intelligent ship navigation scheduling and planning is of great significance for ensuring the safety of maritime production and life and promoting the development of the marine economy. In this paper, an intelligent ship scheduling and path planning method is proposed for a practical application scenario wherein the emergency rescue center receives rescue messages and dispatches emergency rescue ships to the incident area for rescue. Firstly, the large-scale sailing route of the task ship is pre-planned in the voyage planning stage by using the improved A* algorithm. Secondly, the full-coverage path planning algorithm is used to plan the ship’s search route in the regional search stage by updating the ship’s navigation route in real time. In order to verify the effectiveness of the proposed algorithm, comparative experiments were carried out with the conventional algorithm in the two operation stages of rushing to the incident sea area and regional search and rescue. The experimental results show that the proposed algorithm can adapt to emergency search and rescue tasks in the complex setting of the sea area and can effectively improve the efficiency of the operation, ensure the safety of the operation process, and provide a more intelligent and efficient solution for the planning of maritime emergency rescue tasks. Full article

(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes)

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22 pages, 799 KiB

Open AccessArticle

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 682

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 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)

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17 pages, 2232 KiB

Open AccessArticle

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 780

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). 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)

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19 pages, 689 KiB

Open AccessArticle

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 1060

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 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)

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