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

Deadline for manuscript submissions: 15 June 2025 | Viewed by 30826

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Dr. Dimitrios Meimaroglou

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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
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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

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Published Papers (12 papers)

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Research

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16 pages, 9604 KiB

Open AccessArticle

Optimization Design of Protective Helmet Structure Guided by Machine Learning

by Yongxing Chen, Junlong Wang, Peng Long, Bin Liu, Yi Wang, Tian Ma, Xiancong Huang, Weiping Li, Yue Kang and Haining Ji

Processes 2025, 13(3), 877; https://doi.org/10.3390/pr13030877 - 16 Mar 2025

Viewed by 276

Abstract

With increasing digitization worldwide, machine learning has become a crucial tool in industrial design. This study proposes a novel machine learning-guided optimization approach for enhancing the structural design of protective helmets. The optimal model was developed using machine learning algorithms, including random forest (RF), support vector machine (SVM), eXtreme gradient boosting (XGB), and multilayer perceptron (MLP). The hyperparameters of these models were determined by ten-fold cross-validation and grid search. The experimental results showed that the RF model had the best predictive performance, providing a reliable framework for guiding structural optimization. The results of the SHapley Additive exPlanations (SHAP) method on the contribution of input features show that three structures—the transverse curvature at the foremost point of the forehead, the helmet forehead bottom edge elevation angle, and the maximum curvature along the longitudinal centerline of the forehead—have the highest contribution in both optimization goals. This research achievement provides an objective approach for the structural optimization of protective helmets, further promoting the development of machine learning in industrial design. 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, 7753 KiB

Open AccessArticle

SAM-Enhanced Cross-Domain Framework for Semantic Segmentation: Addressing Edge Detection and Minor Class Recognition

by Qian Wan, Hongbo Su, Xiyu Liu, Yu Yu and Zhongzhen Lin

Processes 2025, 13(3), 736; https://doi.org/10.3390/pr13030736 - 3 Mar 2025

Viewed by 657

Abstract

Unsupervised domain adaptation (UDA) enables training a model on labeled source data to perform well in a target domain without supervision, which is especially valuable in vision-based semantic segmentation. However, existing UDA methods often struggle with accurate semantic labeling at object boundaries and recognizing minor categories in the target domain. This paper introduces a novel UDA framework—SamDA—that incorporates the Segment Anything Model (SAM), a large-scale foundational vision model, as the mask generator to enhance edge segmentation performance. The framework comprises three core modules: a cross-domain image mixing module, a self-training module with a teacher–student network, and exponential moving average (EMA). It also includes a finetuning module that leverages SAM-generated masks for pseudo-label matching. Evaluations on the GTA5 and Cityscapes datasets demonstrate that SamDA achieves a mean IoU (mIoU) of 75.2, surpassing state-of-the-art methods such as MIC-DAFormer by 1.0 mIoU and outperforming all ResNet-based approaches by at least 15 mIoU. Moreover, SamDA significantly enhances the segmentation of small objects like bicycles, riders, and fences, with, respective, IoU improvements of 4.5, 5.2, and 3.8 compared to baseline models. 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, 4176 KiB

Open AccessArticle

A Sustainability-Oriented Digital Twin of the Diamond Pilot Plant

by Donald Ntamo, Iason Papadopoulos, Chalak Omar, Payam Soulatiantork and Mohammad Zandi

Processes 2025, 13(1), 211; https://doi.org/10.3390/pr13010211 - 13 Jan 2025

Viewed by 873

Abstract

The pharmaceutical industry is undergoing a significant transition from batch to continuous manufacturing, driven by increasing regulatory requirements and sustainability pressures. Digital twins (DTs) play a pivotal role in facilitating this transition by enabling real-time data visualisation, process optimisation, and predictive analytics. While substantial progress has been made in the development and application of DTs, particularly in industries such as energy and automotive, there remains a critical need for further research and development focused on creating sustainability-oriented digital twins tailored to pharmaceutical processes. In the pharmaceutical sector, DTs are being progressively utilised not only for real-time monitoring and analysis but also as dynamic training platforms for engineers and operators, enhancing both operational efficiency and workforce competency. This paper examines the University of Sheffield’s Diamond Pilot Plant (DiPP), a facility showcasing the future of pharmaceutical manufacturing through the integration of Industry 4.0 technologies and advanced sensors. This paper focuses on developing a data-driven model to predict energy consumption in a twin-screw granulator (TSG) within the DiPP. The model, based on second-degree polynomial regression, demonstrates strong predictive accuracy with R-squared values exceeding 0.8. By optimising energy performance indicators, this work aims to improve the sustainability of pharmaceutical manufacturing processes. This research contributes to the field of pharmaceutical manufacturing by providing a foundation for creating energy models and advancing the development of comprehensive DT. 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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15 pages, 14563 KiB

Open AccessArticle

Coal Structure Recognition Method Based on LSTM Neural Network

by Yang Chen, Cen Chen, Jiarui Zhang, Fengying Hu, Taohua He, Xinyue Wang, Qun Cheng, Jiayi He, Ya Zhao and Qianghao Zeng

Processes 2024, 12(12), 2717; https://doi.org/10.3390/pr12122717 - 2 Dec 2024

Viewed by 856

Abstract

Coal structures exhibit considerable differences in rock properties and adsorption capacities. The physical properties of coal rocks are fundamental to understanding oil and gas reservoirs, while adsorption capacity directly impacts the gas content in coal seams. The accurate recognition of coal structures is essential for evaluating productivity and guiding coalbed methane well development. This study examines coal rocks of Benxi Formation in Ordos Basin. Using core photographs and logging curves, we classified the coal structures into undeformed coal, cataclastic coal, and granulated-mylonitized coal. AC, DEN, CAL, GR, and CN15 logging curves were selected to build a coal structure recognition model utilizing a long short-term memory (LSTM) neural network. This approach addresses the gradient vanishing and exploding issues often encountered in traditional neural networks, enhancing the model’s capacity to handle nonlinear relationships. After numerous iterations of learning and parameter adjustments, the model achieved a recognition accuracy of over 85%, with 32 hidden units, a minimum batch size of 28, and up to 150 iterations. Validation with independent well data not involved in the model building process confirmed the model’s effectiveness, meeting the practical needs of the study area. The results suggest that the study area is predominantly characterized by undeformed coal, with cataclastic coal and granulated-mylonitized coal more developed along fault trends. 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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11 pages, 1981 KiB

Open AccessArticle

Image Dehazing Technique Based on DenseNet and the Denoising Self-Encoder

by Kunxiang Liu, Yue Yang, Yan Tian and Haixia Mao

Processes 2024, 12(11), 2568; https://doi.org/10.3390/pr12112568 - 16 Nov 2024

Viewed by 1222

Abstract

The application value of low-quality photos taken in foggy conditions is significantly lower than that of clear images. As a result, restoring the original image information and enhancing the quality of damaged images on cloudy days are crucial. Commonly used deep learning techniques like DehazeNet, AOD-Net, and Li have shown encouraging progress in the study of image dehazing applications. However, these methods suffer from a shallow network structure leading to limited network estimation capability, reliance on atmospheric scattering models to generate the final results that are prone to error accumulation, as well as unstable training and slow convergence. Aiming at these problems, this paper proposes an improved end-to-end convolutional neural network method based on the denoising self-encoder-DenseNet (DAE-DenseNet), where the denoising self-encoder is used as the main body of the network structure, the encoder extracts the features of haze images, the decoder performs the feature reconstruction to recover the image, and the boosting module further performs the feature fusion locally and globally, and finally outputs the dehazed image. Testing the defogging effect in the public dataset, the PSNR index of DAE-DenseNet is 22.60, which is much higher than other methods. Experiments have proved that the dehazing method designed in this paper is better than other algorithms to a certain extent, and there is no color oversaturation or an excessive dehazing phenomenon in the image after dehazing. The dehazing results are the closest to the real image and the viewing experience feels natural and comfortable, with the image dehazing effect being very competitive. 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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19 pages, 6397 KiB

Open AccessArticle

Optimization of Twist Winglets–Cross-Section Twist Tape in Heat Exchangers Using Machine Learning and Non-Dominated Sorting Genetic Algorithm II Technique

by Qiqi Cao, Zuoqin Qian and Qiang Wang

Processes 2024, 12(7), 1458; https://doi.org/10.3390/pr12071458 - 12 Jul 2024

Cited by 2 | Viewed by 1376

Abstract

This research delves into the impact of Twist Winglets–Cross-Section Twist Tape (TWs-CSTT) structures within heat exchangers on thermal performance. Utilizing Computational Fluid Dynamics (CFD) and machine learning methodologies, optimal geometrical parameters for the TWs-CSTT configuration were examined. The outcomes demonstrate that fluid undergoing a rotational motion within tubes featuring this structure leads to more effective secondary flows, intensified mixing, and improved thermal boundary layer disturbance. Moreover, by integrating machine learning with multi-objective optimization techniques, the performance of heat exchangers can be accurately predicted and optimized, facilitating enhanced heat exchanger design. Through the application of the multi-objective optimization algorithm Non-dominated Sorting Genetic Algorithm II (NSGA-II), the ideal configurations for TWs-CSTT were ascertained: L1 is the cross-sectional length of the Twisted Wings, L2 is the radius of the Central Straight Twisted, and P is the pitch. P = 50.699 mm, L1 = 4.3282 mm, L2 = 4.9736 mm for the Gaussian Process Regression (GPR) model; P = 50.864 mm, L1 = 4.4961 mm, L2 = 4.9992 mm for the LR model; and P = 50.699 mm, L1 = 4.3282 mm, L2 = 4.9736 mm for the Support Vector Regression (SVR) model, aiming to maximize heat exchange efficiency while minimizing friction losses. This study proposes a novel methodological approach to heat exchanger design, leveraging CFD and machine learning technologies to enhance energy efficiency and performance. 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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37 pages, 991 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Predicting the Liquid Steel End-Point Temperature during the Vacuum Tank Degassing Process Using Machine Learning Modeling

by Roberto Vita, Leo Stefan Carlsson and Peter B. Samuelsson

Processes 2024, 12(7), 1414; https://doi.org/10.3390/pr12071414 - 6 Jul 2024

Cited by 1 | Viewed by 1613

Abstract

The present work focuses on predicting the steel melt temperature following the vacuum treatment step in a vacuum tank degasser (VTD). The primary objective is to establish a comprehensive methodology for developing and validating machine learning (ML) models within this context. Another objective is to evaluate the model by analyzing the alignment of the SHAP values with metallurgical domain expectations, thereby validating the model’s predictions from a metallurgical perspective. The proposed methodology employs a Random Forest model, incorporating a grid search with domain-informed variables grouped into batches, and a robust model-selection criterion that ensures optimal predictive performance, while keeping the model as simple and stable as possible. Furthermore, the Shapley Additive Explanations (SHAP) algorithm is employed to interpret the model’s predictions. The selected model achieved a mean adjusted

R^{2}

of 0.631 and a hit ratio of 75.3% for a prediction error within ±5 °C. Despite the moderate predictive performance, SHAP highlighted several aspects consistent with metallurgical domain expertise, emphasizing the importance of domain knowledge in interpreting ML models. Improving data quality and refining the model framework could enhance predictive performance. 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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24 pages, 11536 KiB

Open AccessArticle

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 3873

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1138

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 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

Open AccessArticle

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 24 | Viewed by 3743

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 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

Open AccessArticle

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 13 | Viewed by 2068

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 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

Open AccessFeature PaperReview

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 35 | Viewed by 11275

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 (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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