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Processes
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Industrial IoT-Enabled Modeling and Optimization for the Process Industry
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Industrial IoT-Enabled Modeling and Optimization for the Process Industry

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 3809

Special Issue Editors

Dr. Gongzhuang Peng

E-Mail Website
Guest Editor
Engineering Research Institute, University of Science and Technology Beijing, Beijing 100083, China
Interests: industrial big data; machine learning; scheduling
Dr. Shenglong Jiang

E-Mail Website
Guest Editor
School of Materials Science and Engineering, Chongqing University, Chongqing 400044, China
Interests: modeling, simulation, and optimization of manufacturing and energy systems

Special Issue Information

Dear Colleagues,

The process industry is the pillar of national economies and includes the chemical, iron and steel, and non-ferrous industries. Given severe resource and market pressure, there is an urgent need to improve the efficiency and decarbonization of process industries through smart manufacturing strategies. Industrial IoT creates the core of smart manufacturing by integrating advanced sensing, communication, and data mining technologies. It facilitates complicated decision making in all aspects of the process industry, including supply chains, product quality, energy scheduling, and equipment diagnosis, through the acquisition and utilization of whole-process data. Industrial IoT has greatly facilitated the modeling and optimization of manufacturing processes, but it also brings a number of challenges, e.g., how to integrate mechanism knowledge with industrial big data in the modeling of industrial process and how to deal with multiple and coupled objectives in the optimization of the production process.

This Special Issue aims to summarize new theories and their applications in Industrial IoT-based modeling and optimization for complex industrial processes, especially in industry applications. Topics include, but not are limited to, the following:

  • Industrial IoT-enabled process modeling;
  • Process monitoring and fault diagnosis;
  • Industrial process optimization;
  • Production and logistics optimization;
  • Smart manufacturing;
  • Machine learning applications in the process industry.

Dr. Gongzhuang Peng
Dr. Shenglong Jiang
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. Processes 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 2400 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

  • process modeling
  • industrial big data
  • production planning and scheduling
  • process optimization

Published Papers (5 papers)

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Research

17 pages, 4576 KiB  
Article
Rotating Machinery Fault Diagnosis under Time–Varying Speed Conditions Based on Adaptive Identification of Order Structure
by Xinnan Yu, Xiaowang Chen, Minggang Du, Yang Yang and Zhipeng Feng
Processes 2024, 12(4), 752; https://doi.org/10.3390/pr12040752 - 08 Apr 2024
Viewed by 456
Abstract
Rotating machinery fault diagnosis is of key significance for ensuring safe and efficient operation of various industrial equipment. However, under nonstationary operating conditions, the fault–induced characteristic frequencies are often time–varying. Conventional Fourier spectrum analysis is not suitable for revealing time–varying details, and nonstationary [...] Read more.
Rotating machinery fault diagnosis is of key significance for ensuring safe and efficient operation of various industrial equipment. However, under nonstationary operating conditions, the fault–induced characteristic frequencies are often time–varying. Conventional Fourier spectrum analysis is not suitable for revealing time–varying details, and nonstationary fault feature extraction methods are still in desperate need. Order spectrum can reveal the rotational–speed–related time–varying frequency components as spectral peaks in order domain, thus facilitating fault feature extraction under time–varying speed conditions. However, the speed–unrelated frequency components are still nonstationary after angular–domain resampling, thus causing wide–band features and interferences in the order spectrum. To overcome such a drawback, this work proposes a rotating machinery fault diagnosis method based on adaptive separation of time–varying components and order feature extraction. Firstly, the rotational speed is estimated by the multi–order probabilistic approach (MOPA), thus eliminating the inconvenience of installing measurement equipment. Secondly, adaptive separation of the time–varying frequency component is achieved through time–varying filtering and surrogate test. It effectively eliminates interference from irrelevant components and noise. Finally, a high–resolution order spectrum is constructed based on the average amplitude envelope of each mono–component. It does not involve Fourier transform or angular–domain resampling, thus avoiding spectral leakage and resampling errors. By identifying the fault–related spectral peaks in the constructed order spectrum, accurate fault diagnosis can be achieved. The Rényi entropy values of the proposed order spectrum are significantly lower than those of the traditional order spectrum. This result verifies the effective energy concentration and high resolution of the proposed order spectrum. The results of both numerical simulation and lab experiments confirm the effectiveness of the proposed method in accurately presenting the time–varying frequency components for rotating machinery diagnosing faults. Full article
(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)
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18 pages, 13951 KiB  
Article
Modeling and Switched Control of Modular Reconfigurable Flight Array for Faulty Redundancy
by Bin Ren, Chunxi Yang, Xiufeng Zhang and Wenyuan Mao
Processes 2024, 12(4), 646; https://doi.org/10.3390/pr12040646 - 24 Mar 2024
Viewed by 593
Abstract
The modular reconfigurable flight array (MRFA) is composed of multiple identical flight unit modules, which has several advantages such as structural variability, strong versatility, and low cost. Due to the redundant properties of MRFA, it keeps stable by adopting a suitable control law [...] Read more.
The modular reconfigurable flight array (MRFA) is composed of multiple identical flight unit modules, which has several advantages such as structural variability, strong versatility, and low cost. Due to the redundant properties of MRFA, it keeps stable by adopting a suitable control law when it suffers actuator fault or actively stops some actuators. To address the attitude stability issue of the modular flight array when actuators actively stop or encounter failures during the flight process, a modeling method based on a switched system is proposed at first, and an arbitrary switched controller design method based on the segmented Lyapunov functions and the average dwell time is also given. By introducing the actuator efficiency matrix, the dynamic switched model of the modular flight array is described. Then, a group of arbitrary switched linear feedback gains is designed to ensure the exponential stability of the flight array if the switched process satisfies the constraint of the average dwell time. Simulation and experiment results indicate that when there is an accident in the actuator states, the switched controllers can achieve precise tracking of the desired trajectory, thus confirming the effectiveness of the proposed modeling method and controller. Full article
(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)
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17 pages, 5584 KiB  
Article
Measurement Method of Bar Unmanned Warehouse Area Based on Binocular Vision
by Shuzong Yan, Dong Xu, He Yan, Ziqiang Wang, Hainan He, Xiaochen Wang and Quan Yang
Processes 2024, 12(3), 466; https://doi.org/10.3390/pr12030466 - 25 Feb 2024
Viewed by 715
Abstract
With the development of Industry 4.0 and the implementation of the 14th Five-Year Plan, intelligent manufacturing has become a significant trend in the steel industry, which can propel the steel industry toward a more intelligent, efficient, and sustainable direction. At present, the operation [...] Read more.
With the development of Industry 4.0 and the implementation of the 14th Five-Year Plan, intelligent manufacturing has become a significant trend in the steel industry, which can propel the steel industry toward a more intelligent, efficient, and sustainable direction. At present, the operation mode of unmanned warehouse area for slabs and coils has become relatively mature, while the positioning accuracy requirement of bars is getting more stringent because they are stacked in the warehouse area according to the stacking position and transferred by disk crane. Meanwhile, the traditional laser ranging and line scanning method cannot meet the demand for precise positioning of the whole bundle of bars. To deal with the problems above, this paper applies machine vision technology to the unmanned warehouse area of bars, proposing a binocular vision-based measurement method. On the one hand, a 3D reconstruction model with sub-pixel interpolation is established to improve the accuracy of 3D reconstruction in the warehouse area. On the other hand, a feature point matching algorithm based on motion trend constraint is established by means of multi-sensor data fusion, thus improving the accuracy of feature point matching. Finally, a high-precision unmanned 3D reconstruction of the bar stock area is completed. Full article
(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)
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15 pages, 3195 KiB  
Article
Online Partition-Cooling System of Hot-Rolled Electrical Steel for Thermal Roll Profile and Its Industrial Application
by Qiuna Wang, Jiquan Sun, Jiaxuan Yang, Haishen Wang, Lijie Dong, Yanlong Jiao, Jieming Li, Zhenyang Zhi and Lipo Yang
Processes 2024, 12(2), 410; https://doi.org/10.3390/pr12020410 - 18 Feb 2024
Viewed by 607
Abstract
The shape and convexity are crucial quality assessment indicators for hot-rolled electrical steel strips. Besides bending rolls, shifting rolls, and the original roll profile, the thermal roll profile also plays a significant role in controlling the shape and convexity during the hot-rolling process. [...] Read more.
The shape and convexity are crucial quality assessment indicators for hot-rolled electrical steel strips. Besides bending rolls, shifting rolls, and the original roll profile, the thermal roll profile also plays a significant role in controlling the shape and convexity during the hot-rolling process. However, it is always overlooked due to its dynamic uncertainty. To solve this problem, it is necessary to achieve online cooling-status control for the local thermal expansion of rolls. Based on the existing structure of a mill, a pair of special partition-cooling beams with an intelligent cooling system was designed. For high efficiency and practicality, a new online predictive model was established for the dynamic temperature field of the hot-rolling process. An equivalent treatment was applied to the boundary condition corresponding to the practical cooling water flow. In addition, by establishing the corresponding target distribution curve for the partitioned water flow cooling, online water-flow-partitioning control of the thermal roll profile was achieved. In the practical application process, a large number of onsite results exhibited that the predicted error was within 5% compared to the experimental results. The temperature difference between the upper and lower rolls was within 5 °C, and the temperature difference on both sides of the rolls was controlled within 0.7 °C. The hit rate of convexity (C40) increased by 33%. It was demonstrated that the partition-cooling processes of hot rolling are effective for the local shape and special convexity. They are able to serve as a better control method in the hot-rolling process. Full article
(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)
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23 pages, 945 KiB  
Article
Novel Multi-Criteria Group Decision Making Method for Production Scheduling Based on Group AHP and Cloud Model Enhanced TOPSIS
by Xuejun Zhang, Zhimin Lv, Yang Liu, Xiong Xiao and Dong Xu
Processes 2024, 12(2), 305; https://doi.org/10.3390/pr12020305 - 01 Feb 2024
Viewed by 627
Abstract
Optimized production scheduling can greatly improve efficiency and reduce waste in the steel manufacturing industry. With the increasing demands on the economy, the environment, and society, more and more factors need to be considered in the production scheduling process. Currently, only a few [...] Read more.
Optimized production scheduling can greatly improve efficiency and reduce waste in the steel manufacturing industry. With the increasing demands on the economy, the environment, and society, more and more factors need to be considered in the production scheduling process. Currently, only a few methods are developed for the comprehensive evaluation and prioritization of scheduling schemes. This paper proposes a novel MCGDM (multi-criteria group decision making) method for the ranking and selection of production scheduling schemes. First, a novel indicator system involving both qualitative and quantitative indicators is put forward. Diverse statistical methods and evaluation functions are proposed for the evaluation of quantitative indicators. The evaluation method of qualitative indicators is proposed based on heterogeneous data, cloud model theory, and group decision-making techniques. Then, a novel Group AHP model is proposed to determine the weights of all evaluation indicators. Finally, a novel cloud-model-enhanced TOPSIS (technique for order of preference by similarity to ideal solution) method is proposed to rank alternative production scheduling schemes. A practical example is presented to show the implementation details and demonstrate the feasibility of our proposed method. The results and comparative analysis indicate that our hybrid MCGDM method is more reasonable, flexible, practical, and effective in evaluating and ranking production scheduling schemes in an uncertain environment. Full article
(This article belongs to the Special Issue Industrial IoT-Enabled Modeling and Optimization for the Process Industry)
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