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

Deadline for manuscript submissions: 29 December 2023 | Viewed by 11265

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Special Issue Editors

Prof. Dr. Kejun Wu

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Guest Editor

College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310027, China
Interests: process intensification; microreactor; ionic liquids; reaction engineering; experimental and computational fluid dynamics; advanced materials manufacturing

Dr. An Su

E-Mail Website
Guest Editor

College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
Interests: artificial intelligence; databases; process optimization; flow chemistry

Special Issue Information

Dear Colleagues,

Industry 4.0 brings together a number of digital and physical advances that have the potential to transform chemical manufacturing. Various smart manufacturing technologies, such as process control and automation, production simulation, smart measuring devices and predictive asset management, can be used by the chemical industry to increase productivity and reduce risk.

This special issue on “Application of Chemical Smart Manufacturing in Industry 4.0” seeks high quality works focusing on the latest novel advances in smart manufacturing technology for chemicals. Topics include, but are not limited to:

Smart data collection devices and database systems for chemical manufacturing
IoT and cloud platforms for data exchange for chemical manufacturing
Big-data and AI-based predictive analytics and decision-making for chemical manufacturing
Decision implementation mechanisms for chemical process control, automation, and optimization
Digital twins of chemical industry equipment

Dr. Kejun Wu
Dr. An Su
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 control
process optimization
automation
process simulation
digital twins
data analytics
artificial intelligence
smart manufacturing

Published Papers (7 papers)

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Research

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Open AccessArticle

Semantic Hybrid Signal Temporal Logic Learning-Based Data-Driven Anomaly Detection in the Textile Process

Xu Huo

and

Kuangrong Hao

Processes 2023, 11(9), 2804; https://doi.org/10.3390/pr11092804 - 21 Sep 2023

Viewed by 263

Abstract

The development of sensor networks allows for easier time series data acquisition in industrial production. Due to the redundancy and rapidity of industrial time series data, accurate anomaly detection is a complex and important problem for the efficient production of the textile process. This paper proposed a semantic inference method for anomaly detection by constructing the formal specifications of anomaly data, which can effectively detect exceptions in process industrial operations. Furthermore, our method provides a semantic interpretation of exception data. Hybrid signal temporal logic (HSTL) was proposed to improve the insufficient expressive ability of signal temporal logic (STL) systems. The epistemic formal specifications of fault offline were determined, and a data-driven semantic anomaly detector (SeAD) was constructed, which can be used for online anomaly detection, helping people understand the causes and effects of anomalies. Our proposed method was applied to time-series data collected from a representative textile plant in Zhejiang Province, China. Comparative experimental results demonstrated the feasibility of the proposed method. Full article

(This article belongs to the Special Issue Application of Chemical Smart Manufacturing in Industry 4.0)

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Open AccessArticle

All-Factor Average Method of Reserve Parameters with Crude Oil Volume and Mass Constraints

and

Processes 2023, 11(9), 2558; https://doi.org/10.3390/pr11092558 - 26 Aug 2023

Viewed by 329

Abstract

In order to make the average value of each reserve parameter of a set of oil reserves more representative, this paper puts forward the all-factor average method of reserve parameters with crude oil volume and mass constraints. In the first step, the two constraint methods of crude oil volume and mass are adopted to calculate the average value of various parameters of the total items. The weight coefficient when the parameter is averaged is, respectively, the partial derivative of the volume or mass reserve calculation formula with respect to the parameter. Compared with the original calculation results, the average of their parameters all show a shift towards values with a significant share of reserves, especially effective porosity, oil saturation, and crude oil volume factor. The all-factor average method considers a more comprehensive set of factors than the original method. Therefore, each new average parameter should also be much more representative. Since the current reserve specification stipulates that each parameter needs to retain a certain number of decimal places, there inevitably is some carry error between the reserve results calculated by the parameters of the total items and the accumulated reserves of each unit. The second step is to select the optimal average value of each reserve parameter by using the full permutation combination selection method to reduce the carry error. A set of parameters that minimizes the sum of squared relative errors of crude oil volume and mass reserves is selected using the full permutation combination selection method, which is the optimal selection of the average value of the total set of items. Compared with the original method, the full permutation combination selection method can effectively reduce the carry error. Full article

(This article belongs to the Special Issue Application of Chemical Smart Manufacturing in Industry 4.0)

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Open AccessArticle

Selective and Efficient Catalytic Oxygenation of Alkyl Aromatics Employing H₂O₂ Catalyzed by Simple Porphyrin Iron(II) under Mild Conditions

and

Processes 2023, 11(4), 1187; https://doi.org/10.3390/pr11041187 - 12 Apr 2023

Viewed by 629

Abstract

The excessive utilization of additives in chemical reactions is a troublesome problem in industrial processes, due to their adverse effects on equipment and processes. To acquire oxidative functionalization of alkyl aromatics under additive-free and mild conditions, a large library of metalloporphyrins was applied to the oxygenation of alkyl aromatics as catalysts with H₂O₂ as an oxidant. On the basis of systematic investigation of the catalytic performance of metalloporphyrins, it was discovered that, surprisingly, only porphyrin irons(II) possessed the ability to catalyze the oxygenation of alkyl aromatics with H₂O₂ under additive-free conditions and with satisfying substrate scope. Especially with 5,10,15,20-tetrakis(2,6-dichlorophenyl) porphyrin iron(II) (T(2,6-diCl)PPFe) as the catalyst, the substrate conversion reached up to 27%, with the selectivity of 85% to the aromatic ketone in the representative oxygenation of ethylbenzene with H₂O₂ as oxidant and without any additive used. The study of apparent kinetics and mechanisms in the optimal oxygenation system was also conducted in detail. Based on thorough exploration and characterization, the source of the superior catalytic performance of T(2,6-diCl)PPFe was acquired mainly as its planar structure, the low positive charge in the metal center, and better solubility in the oxygenation mixture, which favored the approach of reactants to the catalytic center, and the interaction between the metal center and H₂O₂. The beneficial interaction between T(2,6-diCl)PPFe and H₂O₂ was verified through cyclic voltammetry measurements and UV–vis absorption spectra. In comparison to previous studies, in this work, an efficient, selective, and additive-free means was developed for the oxygenation of alkyl aromatics under mild conditions, which could act as a representative example and a valuable reference for industrial processes in oxygenation of alkyl aromatics, and a great advance in the realization of oxygenation of alkyl aromatics under additive-free and mild conditions. Full article

(This article belongs to the Special Issue Application of Chemical Smart Manufacturing in Industry 4.0)

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Open AccessFeature PaperArticle

Continuous-Flow Hydrogenation of Nitroaromatics in Microreactor with Mesoporous Pd@SBA-15

and

Processes 2023, 11(4), 1074; https://doi.org/10.3390/pr11041074 - 03 Apr 2023

Cited by 1 | Viewed by 1122

Abstract

The hydrogenation of nitroaromatics to prepare aromatic amines plays a crucial role in the chemical industry. Traditional hydrogenation has the risk of hydrogen leakage from the equipment, and its catalyst has the disadvantage of being easily deactivated and difficult to recover. In this study, we designed an efficient and stable mesoporous catalyst, Pd@SBA-15, which was constructed by impregnating the nanopores of the mesoporous material SBA-15 with palladium nanoparticles. The catalyst was then filled in a micro-packed-bed reactor (MPBR) for continuous flow hydrogenation. The designed continuous flow hydrogenation system has two distinctive features. First, we used mesoporous Pd@SBA-15 instead of the traditional bulk Pd/C as the hydrogenation catalyst, which is more suitable for exposing the active sites of metal Pd and reducing the agglomeration of nanometals. The highly ordered porous structure enhances hydrogen adsorption and thus hydrogenation efficiency. Secondly, the continuous flow system allows for precise detection and control of the reaction process. The highly efficient catalysts do not require complex post-treatment recovery, which continues to operate for 24 h with barely any reduction in activity. Due to the high catalytic activity, the designed mesoporous Pd@SBA-15 showed excellent catalytic performance as a hydrogenation catalyst in a continuous flow system with 99% conversion of nitroaromatics in 1 min. This work provides insights into the rational design of hydrogenation systems in the chemical industry. Full article

(This article belongs to the Special Issue Application of Chemical Smart Manufacturing in Industry 4.0)

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Open AccessArticle

Metalloporphyrin-Based Metal–Organic Frameworks for Photocatalytic Carbon Dioxide Reduction: The Influence of Metal Centers

and

Processes 2023, 11(4), 1042; https://doi.org/10.3390/pr11041042 - 30 Mar 2023

Cited by 1 | Viewed by 1178

Abstract

Photocatalysis is one of the most promising technologies to achieve efficient carbon dioxide reduction reaction (CO₂RR) under mild conditions. Herein, metalloporphyrin-based metal–organic frameworks (MOFs) with different metal centers, denoted as PCN-222, were utilized as visible-light photocatalysts for CO₂ reduction. Due to the combination of the conjugated planar macrocyclic structures of metalloporphyrins and the stable porous structures of MOFs, all PCN-222 materials exhibited excellent light-harvesting and CO₂-adsorbing abilities. Among the studied MOFs of varied metal centers (M = Pt, Fe, Cu, Zn, Mn), PCN-222(2H&Zn) exhibited the highest photocatalytic CO₂RR performance, with an average CO yield of 3.92 μmol g⁻¹ h⁻¹ without any organic solvent or sacrificial agent. Furthermore, this was three and seven times higher than that of PCN-222(Zn) (1.36 μmol g⁻¹ h⁻¹) and PCN-222(2H) (0.557 μmol g⁻¹ h⁻¹). The superior photocatalytic activity of PCN-222(2H&Zn) was attributed to its effective photoexcited electron–hole separation and transportation compared with other PCN-222(2H&M) materials. The obtained results indicate that Zn ions in the porphyrin’s center played an important role in the reaction of active sites for the adsorption–activation of CO₂. In addition, PCN-222(2H&Zn) showed the highest CO₂ selectivity (almost 100%) and stability. This work provides a clear guide for the design of efficient photocatalysts. Full article

(This article belongs to the Special Issue Application of Chemical Smart Manufacturing in Industry 4.0)

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Open AccessArticle

Evaluation Techniques for Shale Oil Lithology and Mineral Composition Based on Principal Component Analysis Optimized Clustering Algorithm

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Processes 2023, 11(3), 958; https://doi.org/10.3390/pr11030958 - 21 Mar 2023

Cited by 2 | Viewed by 743

Abstract

Shale oil reservoirs are characterized by complex lithology, complex mineral composition and strong heterogeneity. This causes great difficulty in lithologic evaluation. In this paper, a method of lithology identification is proposed by means of intersection plot method and machine learning method, and lithology evaluation is carried out by combining the calculation of mineral content with a multi-mineral optimization model. The logging response characteristics of five lithologies are analyzed by using the logging curves selected by principal component analysis (PCA) discriminant analysis. In lithology identification, the system clustering algorithm is selected to identify shale oil reservoir lithology through layer-by-layer subdivision of sample lithology classification. Logging data has high vertical resolution and good continuity, and mineral prediction using logging data can ensure high accuracy. In this paper, the method of calculating mineral content by using multi-mineral optimization model has achieved good results in practice. Full article

(This article belongs to the Special Issue Application of Chemical Smart Manufacturing in Industry 4.0)

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Review

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Open AccessReview

A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.0

and

Processes 2023, 11(2), 330; https://doi.org/10.3390/pr11020330 - 19 Jan 2023

Cited by 1 | Viewed by 5950

Abstract

With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention for its performance in solving particularly complex problems in industrial chemistry and chemical engineering. Therefore, this review provides an overview of the application of AI techniques, in particular machine learning, in chemical design, synthesis, and process optimization over the past years. In this review, the focus is on the application of AI for structure-function relationship analysis, synthetic route planning, and automated synthesis. Finally, we discuss the challenges and future of AI in making chemical products. Full article

(This article belongs to the Special Issue Application of Chemical Smart Manufacturing in Industry 4.0)

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