Artificial Intelligence in 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: closed (15 December 2023) | Viewed by 6107

Special Issue Editors

School of Light Industry and Engineering, South China University of Technology, Guangzhou 510006, China
Interests: modeling and simulation of process industry; integration optimization; energy conservation and emission reduction; process systems engineering; process modeling, optimization and integration; life cycle analysis
Special Issues, Collections and Topics in MDPI journals
School of Energy Science and Engineering, Central South University, Changsha 410083, China
Interests: energy system engineering; sustainability assessment with life cycle perspective; process modeling and optimization for energy conservation
Special Issues, Collections and Topics in MDPI journals
State Key Laboratory of Pulp & Paper Engineering, South China University of Technology, Guangzhou 510006, China
Interests: process systems engineering; process modeling, optimization, and integration; machine learning; industrial digital twin

Special Issue Information

Dear Colleagues,

With the rapid development of a new information technology, such as the Internet of Things and artificial intelligence, the process of industrial intelligence and informatization is accelerating. As the key technology of industrial intelligence, artificial intelligence (AI) technology has received wide attention. AI technologies have successfully been applied in process control, industrial synthesis and analysis, manufacturing (such as planning and configuration), waste minimization, intelligent CAD, intelligent instrumentation (such as monitoring and data analysis), fault diagnosis and treatment, etc. However, industrial production has been transformed from a traditional high-volume process to a small-volume process and is increasingly flexible. Process uncertainty and operation complexity are increasing exponentially. Thus, to promote the development of AI technologies in the industrial sector, many challenges and problems need to be solved.

This Special Issue on " Artificial Intelligence in the Process Industry " aims to summarize the latest progress in the methods and applications of AI in the process industry to address the uncertainty and complexity of the intelligent transformation in the future industrial sector. Topics will include, but are not limited to:

  • Process synthesis and analysis;
  • Process control and fault diagnosis;
  • Process modeling and simulation;
  • Industrial process optimization;
  • Soft sensors;
  • Digital twin;
  • Smart factory;
  • Smart manufacturing;
  • Machine learning applications in the process industry.

Dr. Yi Man
Dr. Sheng Yang
Dr. Yusha Hu
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

  • artificial intelligence
  • process industry
  • modeling and simulation
  • digital twin
  • machine learning
  • industrial meta models

Published Papers (5 papers)

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Research

21 pages, 4807 KiB  
Article
Improving Accuracy and Interpretability of CNN-Based Fault Diagnosis through an Attention Mechanism
by Yubiao Huang, Jiaqing Zhang, Rui Liu and Shuangyao Zhao
Processes 2023, 11(11), 3233; https://doi.org/10.3390/pr11113233 - 16 Nov 2023
Cited by 1 | Viewed by 831
Abstract
This study aims to enhance the accuracy and interpretability of fault diagnosis. To address this objective, we present a novel attention-based CNN method that leverages image-like data generated from multivariate time series using a sliding window processing technique. By representing time series data [...] Read more.
This study aims to enhance the accuracy and interpretability of fault diagnosis. To address this objective, we present a novel attention-based CNN method that leverages image-like data generated from multivariate time series using a sliding window processing technique. By representing time series data in an image-like format, the spatiotemporal dependencies inherent in the raw data are effectively captured, which allows CNNs to extract more comprehensive fault features, consequently enhancing the accuracy of fault diagnosis. Moreover, the proposed method incorporates a form of prior knowledge concerning category-attribute correlations into CNNs through the utilization of an attention mechanism. Under the guidance of thisprior knowledge, the proposed method enables the extraction of accurate and predictive features. Importantly, these extracted features are anticipated to retain the interpretability of the prior knowledge. The effectiveness of the proposed method is verified on the Tennessee Eastman chemical process dataset. The results show that proposed method achieved a fault diagnosis accuracy of 98.46%, which is significantly higher than similar existing methods. Furthermore, the robustness of the proposed method is analyzed by sensitivity analysis on hyperparameters, and the interpretability is revealed by visually analyzing its feature extraction process. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Process Industry)
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15 pages, 3903 KiB  
Article
Comparison and Analysis of Several Quantitative Identification Models of Pesticide Residues Based on Quick Detection Paperboard
by Yao Zhang, Qifu Zheng, Xiaobin Chen, Yingyi Guan, Jingbo Dai, Min Zhang, Yunyuan Dong and Haodong Tang
Processes 2023, 11(6), 1854; https://doi.org/10.3390/pr11061854 - 20 Jun 2023
Cited by 1 | Viewed by 952
Abstract
Pesticide residues have long been a significant aspect of food safety, which has always been a major social concern. This study presents research and analysis on the identification of pesticide residue fast detection cards based on the enzyme inhibition approach. In this study, [...] Read more.
Pesticide residues have long been a significant aspect of food safety, which has always been a major social concern. This study presents research and analysis on the identification of pesticide residue fast detection cards based on the enzyme inhibition approach. In this study, image recognition technology is used to extract the color information RGB eigenvalues from the detection results of the quick detection card, and four regression models are established to quantitatively predict the pesticide residue concentration indicated by the quick detection card using RGB eigenvalues. The four regression models are linear regression model, quadratic polynomial regression model, exponential regression model and RBF neural network model. Through study and comparison, it has been shown that the exponential regression model is superior at predicting the pesticide residue concentration indicated by the rapid detection card. The correlation value is 0.900, and the root mean square error is 0.106. There will be no negative prediction value when the expected concentration is near to 0. This gives a novel concept and data support for the development of image recognition equipment for pesticide residue fast detection cards based on the enzyme inhibition approach. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Process Industry)
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18 pages, 3641 KiB  
Article
An Enhanced Version of MDDB-GC Algorithm: Multi-Density DBSCAN Based on Grid and Contribution for Data Stream
by Shuo Hu, Yonglin Pang, Yong He, Yuan Yang, Henian Zhang, Linmeng Zhang, Beiyi Zheng, Caiyun Hu and Qing Wang
Processes 2023, 11(4), 1240; https://doi.org/10.3390/pr11041240 - 17 Apr 2023
Cited by 1 | Viewed by 894
Abstract
With the continuous enrichment of big data technology application scenarios, the clustering analysis of a data stream has become a research hotspot. However, the existing data stream clustering algorithms usually have some defects, such as inability to cluster arbitrary shapes, difficulty determining some [...] Read more.
With the continuous enrichment of big data technology application scenarios, the clustering analysis of a data stream has become a research hotspot. However, the existing data stream clustering algorithms usually have some defects, such as inability to cluster arbitrary shapes, difficulty determining some important parameters, and “static” clustering. In this study, a novel algorithm MDDSDB-GC is innovated. It selected MDDB-GC as the original algorithm that cannot deal with a data stream. In MDDSDB-GC, the calculation methods of contribution, grid density, and migration factor are effectively improved, and other parts are adjusted accordingly. The experiments show that MDDSDB-GC retains the advantage of MDDB-GC and obtains the ability to cluster an analysis for a data stream. At the same time, it effectively overcomes the above conventional defects, and its overall performance is better. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Process Industry)
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20 pages, 3885 KiB  
Article
Advanced Exergy Analysis of an Absorption Chiller/Kalina Cycle Integrated System for Low-Grade Waste Heat Recovery
by Zhiqiang Liu, Zhixiang Zeng, Chengwei Deng and Nan Xie
Processes 2022, 10(12), 2608; https://doi.org/10.3390/pr10122608 - 06 Dec 2022
Cited by 2 | Viewed by 1538
Abstract
Exergy analysis and advanced exergy analysis of an absorption chiller/Kalina cycle integrated system are conducted in this research. The exergy destruction of each component and overall exergy efficiency of the cascade process have been obtained. Advanced exergy analysis investigates the interactions among different [...] Read more.
Exergy analysis and advanced exergy analysis of an absorption chiller/Kalina cycle integrated system are conducted in this research. The exergy destruction of each component and overall exergy efficiency of the cascade process have been obtained. Advanced exergy analysis investigates the interactions among different components and the actual improvement potential. Results show that among all the equipment, the largest exergy destruction is in the generators and absorber. System exergy efficiency is obtained as 35.52%. Advanced analysis results show that the endogenous exergy destruction is dominant in each component. Interconnections among different components are not significant but very complicated. It is suggested that the improvement priority should be given to the turbine. Performance improvement of this low-grade waste heat recovery process is still necessary because around 1/4 of the total exergy destruction can be avoided. Exergy and advanced exergy analysis in this work locates the position of exergy destruction, quantifies the process irreversibility, presents the component interactions and finds out the system improvement potential. This research provides detailed and useful information about this absorption chiller/Kalina cycle integrated system. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Process Industry)
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17 pages, 2221 KiB  
Article
Cost Optimal Production-Scheduling Model Based on VNS-NSGA-II Hybrid Algorithm—Study on Tissue Paper Mill
by Huanhuan Zhang, Jigeng Li, Mengna Hong, Yi Man and Zhenglei He
Processes 2022, 10(10), 2072; https://doi.org/10.3390/pr10102072 - 13 Oct 2022
Cited by 4 | Viewed by 1223
Abstract
With the development of the customization concept, small-batch and multi-variety production will become one of the major production modes, especially for fast-moving consumer goods. However, this production mode has two issues: high production cost and the long manufacturing period. To address these issues, [...] Read more.
With the development of the customization concept, small-batch and multi-variety production will become one of the major production modes, especially for fast-moving consumer goods. However, this production mode has two issues: high production cost and the long manufacturing period. To address these issues, this study proposes a multi-objective optimization model for the flexible flow-shop to optimize the production scheduling, which would maximize the production efficiency by minimizing the production cost and makespan. The model is designed based on hybrid algorithms, which combine a fast non-dominated genetic algorithm (NSGA-II) and a variable neighborhood search algorithm (VNS). In this model, NSGA-II is the major algorithm to calculate the optimal solutions. VNS is to improve the quality of the solution obtained by NSGA-II. The model is verified by an example of a real-world typical FFS, a tissue papermaking mill. The results show that the scheduling model can reduce production costs by 4.2% and makespan by 6.8% compared with manual scheduling. The hybrid VNS-NSGA-II model also shows better performance than NSGA-II, both in production cost and makespan. Hybrid algorithms are a good solution for multi-objective optimization issues in flexible flow-shop production scheduling. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Process Industry)
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