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Journals
Processes
Special Issues
Application AI in Chemical Engineering
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Application AI in Chemical Engineering

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: closed (15 June 2022) | Viewed by 12690

Special Issue Editors

Prof. Dr. Xu Ji

E-Mail Website
Guest Editor
School of Chemical Engineering, Sichuan University, Chengdu 610065, China
Interests: process system engineering theory and application research; material processing digital technology and its industrialization; process industry technology economy and resource output rate; industrial intelligence theory and technology; big data technology and artificial intelligence in the process industry field; process system reliability analysis
Dr. Yiyang Dai

E-Mail Website
Guest Editor
School of Chemical Engineering, Sichuan University, Chengdu 610065, China
Interests: chemical process safety monitoring and management technology (chemical process fault diagnosis; alarm management; chemical safety information platform; chemical process safety analysis); computer-aided process design and optimization
Dr. Li Zhou

E-Mail Website
Guest Editor
School of Chemical Engineering, Sichuan University, Chengdu 610065, China
Interests: chemical resource network integration optimization; industrial process ontology construction; intelligent platform development

Special Issue Information

Dear Colleagues,

With the rapid development of industrial informatization, data-driven modeling and analysis techniques such as big data and artificial intelligence have been paid much attention and applied in research and practices of chemical processes, such as process modeling and process safety analysis. There are still many challenges that need to be studied and resolved to facilitate further applications of AI in industrial productions.

This Special Issue on “Application AI in Chemical Engineering” aims to gather outstanding research and provide comprehensive coverage of all aspects related to the applications of AI in chemical engineering, and it will bring together high-quality research articles on the different aspects of AI technology in chemical engineering, including current status and remaining challenges. Topics include, but not are limited to, the following:

  • Theoretical investigations and numerical experiments for data-driven process modeling;
  • AI-based fault detection and diagnosis methods and industrial applications;
  • Intelligent manufacturing for chemical processes.

Prof. Dr. Xu Ji
Dr. Yiyang Dai
Dr. Li Zhou
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 data association or causality analysis
  • Data-driven process modeling
  • Process safety maintenance
  • Fault detection and diagnosis
  • Intelligent manufacturing

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

18 pages, 3023 KiB  
Article
HAZOP Ontology Semantic Similarity Algorithm Based on ACO-GRNN
by Yujie Bai, Dong Gao and Lanfei Peng
Processes 2021, 9(12), 2115; https://doi.org/10.3390/pr9122115 - 24 Nov 2021
Cited by 2 | Viewed by 1730
Abstract
Hazard and operability (HAZOP) is an important safety analysis method, which is widely used in the safety evaluation of petrochemical industry. The HAZOP analysis report contains a large amount of expert knowledge and experience. In order to realize the effective expression and reuse [...] Read more.
Hazard and operability (HAZOP) is an important safety analysis method, which is widely used in the safety evaluation of petrochemical industry. The HAZOP analysis report contains a large amount of expert knowledge and experience. In order to realize the effective expression and reuse of knowledge, the knowledge ontology is constructed to store the risk propagation path and realize the standardization of knowledge expression. On this basis, a comprehensive algorithm of ontology semantic similarity based on the ant clony optimization generalized neural network (ACO-GRNN) model is proposed to improve the accuracy of semantic comparison. This method combines the concept name, semantic distance, and improved attribute coincidence calculation method, and ACO-GRNN is used to train the weights of each part, avoiding the influence of manual weighting. The results show that the Pearson coefficient of this method reaches 0.9819, which is 45.83% higher than the traditional method. It could solve the problems of semantic comparison and matching, and lays a good foundation for subsequent knowledge retrieval and reuse. Full article
(This article belongs to the Special Issue Application AI in Chemical Engineering)
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15 pages, 3903 KiB  
Article
A Hybrid Modeling Framework for Membrane Separation Processes: Application to Lithium-Ion Recovery from Batteries
by Maria João Regufe, Vinicius V. Santana, Alexandre F. P. Ferreira, Ana M. Ribeiro, José M. Loureiro and Idelfonso B. R. Nogueira
Processes 2021, 9(11), 1939; https://doi.org/10.3390/pr9111939 - 29 Oct 2021
Cited by 6 | Viewed by 2867
Abstract
This study proposed a hybrid modeling framework for membrane separation processes where lithium from batteries is recovered. This is a pertinent problem nowadays as lithium batteries are popularized in hybrid and electric vehicles. The hybrid model is based on an artificial intelligence (AI) [...] Read more.
This study proposed a hybrid modeling framework for membrane separation processes where lithium from batteries is recovered. This is a pertinent problem nowadays as lithium batteries are popularized in hybrid and electric vehicles. The hybrid model is based on an artificial intelligence (AI) structure to model the mass transfer resistance of several experimental separations found in the literature. It is also based on a phenomenological model to represent the transient system regime. An optimization framework was designed to perform the AI model training and simultaneously solve the Ordinary Differential Equation (ODE) system representing the phenomenological model. The results demonstrate that the hybrid model can better represent the experimental validation sets than the phenomenological model alone. This strategy opens doors for further investigations of this system. Full article
(This article belongs to the Special Issue Application AI in Chemical Engineering)
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11 pages, 720 KiB  
Article
Development of an Adaptive Model for the Rate of Steel Corrosion in a Recirculating Water System
by Xiaochuan Huang, Yan Gao, Ling Zhu and Ge He
Processes 2021, 9(9), 1639; https://doi.org/10.3390/pr9091639 - 11 Sep 2021
Cited by 3 | Viewed by 1595
Abstract
The stable quality of circulating water ensures the long-term stable operation of various processes in petrochemical production and achieves energy savings and emission reduction while reducing environmental pollution and yielding economic benefits to petrochemical enterprises. However, traditional circulating water quality evaluation and modeling [...] Read more.
The stable quality of circulating water ensures the long-term stable operation of various processes in petrochemical production and achieves energy savings and emission reduction while reducing environmental pollution and yielding economic benefits to petrochemical enterprises. However, traditional circulating water quality evaluation and modeling for corrosion rate prediction suffer from adaptability and accuracy problems. To address these problems, the water quality analysis data of the circulating water in the field were subjected to data preprocessing and water quality index calculation to perform feature engineering, followed by modeling using a machine learning method that integrates the adaptive immune genetic algorithm and random forest (RF) algorithm and can intelligently select the water quality parameters to be used as the input variables for the RF modeling. Finally, the method was validated using an industrial example, and the results indicate that the method is capable of removing interference variables and is suitable for carbon steel corrosion rate prediction based on water quality models. The proposed method provides a basis for water quality management and real-time decision-making by circulating water field personnel. Full article
(This article belongs to the Special Issue Application AI in Chemical Engineering)
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20 pages, 5165 KiB  
Article
A Hybrid Intelligent Fault Diagnosis Strategy for Chemical Processes Based on Penalty Iterative Optimization
by Yuman Yao, Jiaxin Zhang, Wenjia Luo and Yiyang Dai
Processes 2021, 9(8), 1266; https://doi.org/10.3390/pr9081266 - 22 Jul 2021
Cited by 2 | Viewed by 1965
Abstract
Process fault is one of the main reasons that a system may appear unreliable, and it affects the safety of a system. The existence of different degrees of noise in the industry also makes it difficult to extract the effective features of the [...] Read more.
Process fault is one of the main reasons that a system may appear unreliable, and it affects the safety of a system. The existence of different degrees of noise in the industry also makes it difficult to extract the effective features of the data for the fault diagnosis method based on deep learning. In order to solve the above problems, this paper improves the deep belief network (DBN) and iterates the optimal penalty term by introducing a penalty factor, avoiding the local optimal situation of a DBN and improving the accuracy of fault diagnosis in order to minimize the impact of noise while improving fault diagnosis and process safety. Using the adaptive noise reduction capability of an adaptive lifting wavelet (ALW), a practical chemical process fault diagnosis model (ALW-DBN) is finally proposed. Then, according to the Tennessee–Eastman (TE) benchmark test process, the ALW-DBN model is compared with other methods, showing that the fault diagnosis performance of the enhanced DBN combined with adaptive wavelet denoising has been significantly improved. In addition, the ALW-DBN shows better performance under the influence of different noise levels in the acid gas absorption process, which proves its high adaptability to different noise levels. Full article
(This article belongs to the Special Issue Application AI in Chemical Engineering)
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15 pages, 1864 KiB  
Article
Text Mining of Hazard and Operability Analysis Reports Based on Active Learning
by Zhenhua Wang, Beike Zhang and Dong Gao
Processes 2021, 9(7), 1178; https://doi.org/10.3390/pr9071178 - 7 Jul 2021
Cited by 14 | Viewed by 2675
Abstract
In the field of chemical safety, a named entity recognition (NER) model based on deep learning can mine valuable information from hazard and operability analysis (HAZOP) text, which can guide experts to carry out a new round of HAZOP analysis, help practitioners optimize [...] Read more.
In the field of chemical safety, a named entity recognition (NER) model based on deep learning can mine valuable information from hazard and operability analysis (HAZOP) text, which can guide experts to carry out a new round of HAZOP analysis, help practitioners optimize the hidden dangers in the system, and be of great significance to improve the safety of the whole chemical system. However, due to the standardization and professionalism of chemical safety analysis text, it is difficult to improve the performance of traditional models. To solve this problem, in this study, an improved method based on active learning is proposed, and three novel sampling algorithms are designed, Variation of Token Entropy (VTE), HAZOP Confusion Entropy (HCE) and Amplification of Least Confidence (ALC), which improve the ability of the model to understand HAZOP text. In this method, a part of data is used to establish the initial model. The sampling algorithm is then used to select high-quality samples from the data set. Finally, these high-quality samples are used to retrain the whole model to obtain the final model. The experimental results show that the performance of the VTE, HCE, and ALC algorithms are better than that of random sampling algorithms. In addition, compared with other methods, the performance of the traditional model is improved effectively by the method proposed in this paper, which proves that the method is reliable and advanced. Full article
(This article belongs to the Special Issue Application AI in Chemical Engineering)
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