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AI / Machine Learning Techniques as a Tool for Process...
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AI / Machine Learning Techniques as a Tool for Process Modeling and Product Design

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

Deadline for manuscript submissions: 10 June 2024 | Viewed by 14589

Special Issue Editor

Dr. Dimitrios Meimaroglou

E-Mail Website
Guest Editor
Laboratoire Réactions et Génie des Procédés, Université de Lorraine, CNRS, LRGP, F-54000 Nancy, France
Interests: mathematical modeling; polymer reaction engineering; monte carlo methods; machine learning techniques; product design approach; design of experiments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the industry advances rapidly and steadily into the era of the fourth industrial revolution, Artificial Intelligence (AI) has become a term of everyday use in the vocabulary of process engineers, R&D scientists and marketing agents. At the same time, this massive trend causes shifts in the strategy of entire research departments and substantial reforms of academic programs, in an attempt to catch-up with the new developments. Is AI the Trojan horse that will allow groundbreaking advancements in research or is it just another trend of the times?

This Special Issue on “AI / Machine Learning Techniques as a Tool for Process Modeling and Product Design” is devoted to the most recent developments in the fields of modeling of  physicochemical systems, notably on the basis of data-driven techniques, with specific focus on process modeling and product design applications. In this sense, original contributions are welcome in the following—or other relevant—topics of interest:

  • Smart sensors and plant digitalization;
  • Big data and analytics in industrial-scale applications;
  • Process modeling, control and optimization via data-driven techniques;
  • Implementation of machine learning methods for dimensionality reduction, fault detection, maintenance prevention and predictive modeling;
  • Deep-learning techniques in process/product design and/or performance modeling;
  • Digital twins in industrial applications;
  • Modeling of product properties and functionalities within a quality-by-design approach;
  • Hybrid models, combining knowledge-based and data-driven modeling techniques.
    Integrated models of different ML methods.

Dr. Dimitrios Meimaroglou
Guest Editor

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

  • artificial Intelligence
  • machine learning
  • deep learning
  • data-driven modeling
  • artificial neural networks
  • digital twins
  • industry 4.0
  • smart sensors
  • process modeling
  • product design

Published Papers (5 papers)

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Research

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24 pages, 11536 KiB  
Article
Data-Driven Method for Vacuum Prediction in the Underwater Pump of a Cutter Suction Dredger
by Hualin Chen, Zihao Yuan, Wangming Wang, Shuaiqi Chen, Pan Jiang and Wei Wei
Processes 2024, 12(4), 812; https://doi.org/10.3390/pr12040812 - 17 Apr 2024
Viewed by 368
Abstract
Vacuum is an important parameter in cutter suction dredging operations because the equipment is underwater and can easily fail. It is necessary to analyze other parameters related to the vacuum to make real-time predictions about it, which can improve the construction efficiency of [...] Read more.
Vacuum is an important parameter in cutter suction dredging operations because the equipment is underwater and can easily fail. It is necessary to analyze other parameters related to the vacuum to make real-time predictions about it, which can improve the construction efficiency of the dredger under abnormal working conditions. In this paper, a data-driven method for predicting the vacuum of the underwater pump of the cutter suction dredger (CSD) is proposed with the help of big data, machine learning, data mining, and other technologies, and based on the historical data of “Hua An Long” CSD. The method eliminates anomalous data, standardizes the data set, and then relies on theory and engineering experience to achieve feature extraction using the Spearman correlation coefficient. Then, six machine learning methods were employed in this study to train and predict the data set, namely, lasso regression (lasso), elastic network (Enet), gradient boosting decision tree (including traditional GBDT, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM)), and stacking. The comparison of the indicators obtained through multiple rounds of feature number iteration shows that the LightGBM model has high prediction accuracy, a good running time, and a generalization ability. Therefore, the methodological framework proposed in this paper can help to improve the efficiency of underwater pumps and issue timely warnings in abnormal working conditions. Full article
(This article belongs to the Special Issue AI / Machine Learning Techniques as a Tool for Process Modeling and Product Design)
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20 pages, 16914 KiB  
Article
A Fault Diagnosis Method for Ultrasonic Flow Meters Based on KPCA-CLSSA-SVM
by Ziyi Chen, Weiguo Zhao, Pingping Shen, Chengli Wang and Yanfu Jiang
Processes 2024, 12(4), 809; https://doi.org/10.3390/pr12040809 - 17 Apr 2024
Viewed by 274
Abstract
To enhance the fault diagnosis capability for ultrasonic liquid flow meters and refine the fault diagnosis accuracy of support vector machines, we employ Levy flight to augment the global search proficiency. By utilizing circle chaotic mapping to establish the starting locations of sparrows [...] Read more.
To enhance the fault diagnosis capability for ultrasonic liquid flow meters and refine the fault diagnosis accuracy of support vector machines, we employ Levy flight to augment the global search proficiency. By utilizing circle chaotic mapping to establish the starting locations of sparrows and refining the sparrow position with the highest fitness value, we propose an enhanced sparrow search algorithm termed CLSSA. Subsequently, we optimize the parameters of support vector machines using this algorithm. A support vector machine classifier based on CLSSA has been constructed. Given the intricate data collected from ultrasonic liquid flow meters for diagnostic purposes, the approach of employing KPCA to decrease data dimensionality is implemented, and a KPCA-CLSSA-SVM algorithm is proposed to achieve fault diagnosis in ultrasonic flow meters. By using UCI datasets, the findings indicate that KPCA-CLSSA-SVM achieves fault diagnosis accuracies of 94.12%, 100.00%, 97.30%, and 100% in the four flow meters, respectively. Compared with the Bayesian classifier diagnostic algorithm, this has been increased by 4.18%. And compared with support vector machine diagnostic algorithms improved by the SSA, it has increased by 2.28%. Full article
(This article belongs to the Special Issue AI / Machine Learning Techniques as a Tool for Process Modeling and Product Design)
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22 pages, 6485 KiB  
Article
Fault Diagnosis of Permanent Magnet Synchronous Motor of Coal Mine Belt Conveyor Based on Digital Twin and ISSA-RF
by Yourui Huang, Biao Yuan, Shanyong Xu and Tao Han
Processes 2022, 10(9), 1679; https://doi.org/10.3390/pr10091679 - 24 Aug 2022
Cited by 12 | Viewed by 2355
Abstract
Permanent magnet synchronous motors (PMSMs) have been gradually used as the driving equipment of coal mine belt conveyors. To ensure safety and stability, it is necessary to carry out real-time and accurate fault diagnosis of PMSM. Therefore, a fault diagnosis method for PMSM [...] Read more.
Permanent magnet synchronous motors (PMSMs) have been gradually used as the driving equipment of coal mine belt conveyors. To ensure safety and stability, it is necessary to carry out real-time and accurate fault diagnosis of PMSM. Therefore, a fault diagnosis method for PMSM based on digital twin and ISSA-RF (Improved Sparrow Search Algorithm Optimized Random Forest) is proposed. Firstly, the multi-strategy hybrid ISSA is used to solve the problems of uneven population distribution, insufficient population diversity, low convergence speed, etc. In addition, the fault diagnosis model of ISSA-RF permanent magnet synchronous motor is constructed based on the optimization of the number of Random Forest decision trees and that of features of each node by ISSA. Secondly, considering the operation mechanism and physical properties of PMSM, the relevant digital twin model is constructed and the real-time mapping of physical entity and virtual model is realized through data interactive transmission. Finally, the simulation and experimental results show that the fault diagnosis accuracy of ISSA-RF, 98.2%, is higher than those of Random Forest (RF), Sparrow Search Algorithm Optimized Random Forest (SSA-RF), BP neural network (BP) and Support Vector Machine (SVM), which verifies the feasibility and ability of the proposed method to realize fault diagnosis and 3D visual monitoring of PMSM together with the digital twin model. Full article
(This article belongs to the Special Issue AI / Machine Learning Techniques as a Tool for Process Modeling and Product Design)
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18 pages, 3964 KiB  
Article
Optimization Design for the Centrifugal Pump under Non-Uniform Elbow Inflow Based on Orthogonal Test and GA_PSO
by Ye Yuan, Rong Jin, Lingdi Tang and Yanhua Lin
Processes 2022, 10(7), 1254; https://doi.org/10.3390/pr10071254 - 23 Jun 2022
Cited by 9 | Viewed by 1343
Abstract
The non-uniform inflow caused by the elbow inlet is one of the main reasons for the low actual operation performance of a centrifugal pump. Orthogonal experiment and GA_PSO algorithm are used to improve the head and efficiency of a centrifugal pump with an [...] Read more.
The non-uniform inflow caused by the elbow inlet is one of the main reasons for the low actual operation performance of a centrifugal pump. Orthogonal experiment and GA_PSO algorithm are used to improve the head and efficiency of a centrifugal pump with an elbow inlet based on the method combining numerical simulation and prototype experiment in this paper. The effects of the design parameters, including elbow inlet radius ratio, blade inlet angle, blade number, blade wrap angle, blade outlet angle, impeller outlet diameter, blade outlet width and flow area ratio, on the pump head and efficiency are studied in the orthogonal experiment. The blade inlet angle is the major factor to match the non-uniform inflow and reduce the flow loss in the impeller inlet to contribute to enhancing the pump performance and cavitation characteristics. The particle swarm optimization (PSO) algorithm is optimized by integrating the genetic algorithm (GA), which ensures that the PSO-calculation result avoids falling into the local optimization and the global optimal solution is obtained as quickly as possible. The centrifugal pump with an elbow inlet is optimally designed by the GA_PSO algorithm. According to the performance test results, the efficiency of the optimized pump is 4.7% higher than that of the original pump. Full article
(This article belongs to the Special Issue AI / Machine Learning Techniques as a Tool for Process Modeling and Product Design)
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Review

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44 pages, 1411 KiB  
Review
Machine Learning in Chemical Product Engineering: The State of the Art and a Guide for Newcomers
by Cindy Trinh, Dimitrios Meimaroglou and Sandrine Hoppe
Processes 2021, 9(8), 1456; https://doi.org/10.3390/pr9081456 - 20 Aug 2021
Cited by 25 | Viewed by 8982
Abstract
Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the properties–structure–ingredients–process relationship of the different products and the necessity to discover and develop constantly and quickly new molecules and materials with tailor-made properties. In recent years, artificial intelligence [...] Read more.
Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the properties–structure–ingredients–process relationship of the different products and the necessity to discover and develop constantly and quickly new molecules and materials with tailor-made properties. In recent years, artificial intelligence (AI) and machine learning (ML) methods have gained increasing attention due to their performance in tackling particularly complex problems in various areas, such as computer vision and natural language processing. As such, they present a specific interest in addressing the complex challenges of CPE. This article provides an updated review of the state of the art regarding the implementation of ML techniques in different types of CPE problems with a particular focus on four specific domains, namely the design and discovery of new molecules and materials, the modeling of processes, the prediction of chemical reactions/retrosynthesis and the support for sensorial analysis. This review is further completed by general guidelines for the selection of an appropriate ML technique given the characteristics of each problem and by a critical discussion of several key issues associated with the development of ML modeling approaches. Accordingly, this paper may serve both the experienced researcher in the field as well as the newcomer. Full article
(This article belongs to the Special Issue AI / Machine Learning Techniques as a Tool for Process Modeling and Product Design)
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