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Application of Artificial Intelligence in Industrial Process Modelling and Optimization

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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: 31 May 2025 | Viewed by 10381

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Special Issue Editors

Prof. Dr. Sheng Du

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Guest Editor

School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: process control; intelligent control; intelligent optimization; computational intelligence; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Li Jin

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Guest Editor

School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: power system stability analysis and control; time-delay system; robust theory and application
Special Issues, Collections and Topics in MDPI journals

Dr. Pan Yu

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Guest Editor

School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Interests: multi-agent cooperative control; high-precision control of electromechanical systems; anti-disturbance control; modern robust control; control theory and applications; repetitive control
Special Issues, Collections and Topics in MDPI journals

Dr. Haipeng Fan

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Guest Editor Assistant

School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: intelligent monitoring; intelligent systems; process control

Special Issue Information

Dear Colleagues,

The integration of artificial intelligence (AI) into industrial process modelling and optimization has emerged as a transformative force. AI can automatically learn the characteristics of industrial processes, improve modeling accuracy, and avoid relying on a large amount of prior knowledge. Second, AI can optimize the control strategy of industrial processes, automatically adapt to complex and ever-changing environments, and improve the stability and performance of the processes. Most importantly, by intelligently analyzing industrial processes data, AI can achieve intelligent monitoring and diagnosis, detecting and solving problems promptly, thereby improving production efficiency and safety. This Special Issue aims to explore the application of AI approaches in industrial process modelling and optimization. The focus will be on advancing research that harnesses the power of AI to enhance efficiency, safety, and sustainability across diverse industrial processes.

Scope and Objectives:
The primary objective of this Special Issue is to foster research and progress in the application of AI for industrial processes modelling and optimization. The scope encompasses a wide range of industries, including but not limited to manufacturing, process engineering, automation, and robotics.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

AI-based modelling techniques;
Data-driven modelling techniques;
Modeling of complex industrial processes;
Integration of AI algorithms;
Adaptive control systems;
Human–machine collaboration systems;
Optimization strategies;
Intelligent optimization in industrial processes;
Data-driven decision support systems;
Applications of AI in cyber–physical systems;
AI -based process monitoring and fault diagnosis;
AI -driven cyber–physical systems.

Prof. Dr. Sheng Du
Prof. Dr. Li Jin
Dr. Pan Yu
Guest Editors

Dr. Haipeng Fan
Guest Editor Assistant

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

industrial process modeling
intelligent optimization
artificial intelligence
decision support systems
cyber–physical systems

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Published Papers (9 papers)

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Research

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20 pages, 1230 KiB

Open AccessArticle

Computer Science Techniques Applied to Temperature Control in Biodiesel Production: Mathematical Modeling, Optimization, and Sensorless Technique

by Mario C. Maya-Rodriguez, Ignacio Carvajal-Mariscal, Raúl López-Muñoz, Mario A. Lopez-Pacheco and René Tolentino-Eslava

Processes 2025, 13(3), 672; https://doi.org/10.3390/pr13030672 - 27 Feb 2025

Viewed by 465

Abstract

This paper demonstrates that biodiesel production processes can be optimized through implementing a controller based on fuzzy logic and neural networks. The system dynamics are identified utilizing convolutional neural networks, enabling tests of the reactor temperature response under different control law proposals. In addition, a sensorless technique using a convolutional neural network to replace the sensor/transmitter signal in case of failure is implemented. Two optimization functions are proposed utilizing a metaheuristic algorithm based on differential evolution, where the aim is to minimize the use of cooling for the control of the reactor temperature. Finally, the control system proposals are compared, and the results show that a neuro-fuzzy controller without optimization restrictions generated unviable ITAE (

1.9597 \times 10^{7}

) and TVU (22.3993) performance metrics, while the restriction proposed in this work managed to minimize these metrics, improving both the ITAE (

3.3928 \times 10^{6}

) and TVU (17.9132). These results show that combining the sensorless technique and our optimization method for the cooling stage enables energy saving in the temperature control processes required for biodiesel production. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization)

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23 pages, 1468 KiB

Open AccessArticle

Domain-Specific Manufacturing Analytics Framework: An Integrated Architecture with Retrieval-Augmented Generation and Ollama-Based Models for Manufacturing Execution Systems Environments

by Hangseo Choi and Jongpil Jeong

Processes 2025, 13(3), 670; https://doi.org/10.3390/pr13030670 - 27 Feb 2025

Viewed by 805

Abstract

To support data-driven decision-making in a Manufacturing Execution System (MES) environment, a system that can quickly and accurately analyze a wide range of production, quality, asset, and material information must be deployed. However, existing MES data management approaches rely on predefined queries or report templates that lack flexibility and limit real-time decision support. In this paper, we proposes a domain-specific Retrieval-Augmented Generation (RAG) architecture that extends LangChain’s capabilities with Manufacturing Execution System (MES)-specific components and the Ollama-based Local Large Language Model (LLM). The proposed architecture addresses unique MES requirements including real-time sensor data processing, complex manufacturing workflows, and domain-specific knowledge integration. It implements a three-layer structure: an application layer using FastAPI for high-performance asynchronous processing, an LLM layer for natural language understanding, and a data storage layer combining MariaDB, Redis, and Weaviate for efficient data management. The system effectively handles MES-specific challenges such as schema relationships, temporal data processing, and security concerns without exposing sensitive factory data. This is an industry-specific, customized approach focusing on problem-solving in manufacturing sites, going beyond simple text-based RAG. The proposed architecture considers the specificity of data sources, real-time and high-availability requirements, the reflection of domain knowledge and workflows, compliance with security and quality control regulations, and direct interoperability with MES systems. The architecture can be further enhanced through integration with various manufacturing systems, an advanced LLM, and distributed processing frameworks while maintaining its core focus on MES domain specialization. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization)

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34 pages, 7234 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Machine Learning Predictions for the Comparative Mechanical Analysis of Composite Laminates with Various Fibers

by Baha Eddine Ben Brayek, Sirine Sayed, Viorel Mînzu and Mostapha Tarfaoui

Processes 2025, 13(3), 602; https://doi.org/10.3390/pr13030602 - 20 Feb 2025

Cited by 1 | Viewed by 649

Abstract

This article addresses the complex behavior of composite laminates under varied layer orientations during tensile tests, focusing on carbon fiber and epoxy matrix composites. Data characterizing the mechanical load behavior are obtained using twelve composite laminates with different layer orientations and the DIGIMAT-VA software (version 2023.3). First, these data were used to elaborate a complex comparative analysis of composite laminates from the perspective of materials science. Composite laminates belong to three classes: unidirectional, off-axis oriented, and symmetrically balanced laminates, each having a specific behavior. From the perspective of designing a new material, a prediction model that is faster than the finite element analysis is needed to apply this comparative analysis’s conclusions. As a novelty, this paper introduces several machine learning prediction models for composite laminates with 16 layers arranged in different orientations. The Regression Neural Network model performs best, effectively replacing expensive tensile test simulations and ensuring good statistics (RMSE = 34.385, R² = 1, MAE = 19.829). The simulation time decreases from 34.5 s (in the case of finite element) to 0.6 s. The prediction model returns the stress–strain characteristic of the elastic zone given the new layer orientations. These models were implemented in the MATLAB system 2024, and their running proved good models’ generalization power and accuracy. Even specimens with randomly oriented layers were successfully tested. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization)

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19 pages, 1113 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Predicting and Understanding Emergency Shutdown Durations Level of Pipeline Incidents Using Machine Learning Models and Explainable AI

by Lemlem Asaye, Chau Le, Ying Huang, Trung Q. Le, Om Prakash Yadav and Tuyen Le

Processes 2025, 13(2), 445; https://doi.org/10.3390/pr13020445 - 7 Feb 2025

Viewed by 777

Abstract

Pipeline incidents pose significant concerns due to their potential environmental, economic, and safety risks, emphasizing the critical need to understand and manage this vital infrastructure. While existing studies predominantly focus on the causes of pipeline incidents and failures, few have investigated the consequences, such as shutdown duration, and most lack comprehensive models capable of accurately predicting and providing actionable insights into the risk factors. This study bridges this gap by employing machine learning (ML) techniques, including Random Forest and Light Gradient Boosting Machine (LightGBM), for classifying pipeline incidents’ emergency shutdown duration levels. These techniques are specifically designed to capture complex, nonlinear patterns and interdependencies within the data, addressing the limitations of traditional linear approaches. The proposed model has further enhanced with Explainable AI (XAI) techniques, such as Shapley Additive exPlanations (SHAP) values, to improve interpretability and provide insights into the factors influencing shutdown durations. Historical incident data, collected from the Pipeline and Hazardous Materials Safety Administration (PHMSA) from 2010 to 2022, were utilized to examine the risk factors. K-Fold Cross-Validation with 5 folds was employed to ensure the model’s robustness. The results demonstrate that the LightGBM model achieved the highest accuracy of 75.0%, closely followed by Random Forest at 74.8%. The integration of XAI techniques provides actionable insights into key factors such as pipeline material, age, installation layout, and commodity type, which significantly influence shutdown durations. These findings underscore the practical implications of the proposed approach, enabling pipeline operators, emergency responders, and regulatory authorities to make informed decisions that optimize resource allocation and mitigate risks effectively. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization)

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17 pages, 2744 KiB

Open AccessArticle

Priority/Demand-Based Resource Management with Intelligent O-RAN for Energy-Aware Industrial Internet of Things

by Seyha Ros, Seungwoo Kang, Inseok Song, Geonho Cha, Prohim Tam and Seokhoon Kim

Processes 2024, 12(12), 2674; https://doi.org/10.3390/pr12122674 - 27 Nov 2024

Viewed by 926

Abstract

The last decade has witnessed the explosive growth of the internet of things (IoT), demonstrating the utilization of ubiquitous sensing and computation services. Hence, the industrial IoT (IIoT) is integrated into IoT devices. IIoT is concerned with the limitation of computation and battery life. Therefore, mobile edge computing (MEC) is a paradigm that enables the proliferation of resource computing and reduces network communication latency to realize the IIoT perspective. Furthermore, an open radio access network (O-RAN) is a new architecture that adopts a MEC server to offer a provisioning framework to address energy efficiency and reduce the congestion window of IIoT. However, dynamic resource computation and continuity of task generation by IIoT lead to challenges in management and orchestration (MANO) and energy efficiency. In this article, we aim to investigate the dynamic and priority of resource management on demand. Additionally, to minimize the long-term average delay and computation resource-intensive tasks, the Markov decision problem (MDP) is conducted to solve this problem. Hence, deep reinforcement learning (DRL) is conducted to address the optimal handling policy for MEC-enabled O-RAN architectures. In this study, MDP-assisted deep q-network-based priority/demanding resource management, namely DQG-PD, has been investigated in optimizing resource management. The DQG-PD algorithm aims to solve resource management and energy efficiency in IIoT devices, which demonstrates that exploiting the deep Q-network (DQN) jointly optimizes computation and resource utilization of energy for each service request. Hence, DQN is divided into online and target networks to better adapt to a dynamic IIoT environment. Finally, our experiment shows that our work can outperform reference schemes in terms of resources, cost, energy, reliability, and average service completion ratio. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization)

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21 pages, 1989 KiB

Open AccessArticle

Decision Support System (DSS) for Improving Production Ergonomics in the Construction Sector

by Laura Sardinha, Joana Valente Baleiras, Sofia Sousa, Tânia M. Lima and Pedro D. Gaspar

Processes 2024, 12(11), 2503; https://doi.org/10.3390/pr12112503 - 11 Nov 2024

Cited by 2 | Viewed by 1145

Abstract

Ergonomics is essential to improving workplace safety and efficiency by reducing the risks associated with physical tasks. This study presents a decision support system (DSS) aimed at enhancing production ergonomics in the construction sector through an analysis of high-risk postures. Using the Ovako Work Posture Analysis System (OWAS), the Revised NIOSH Lifting Equation (NIOSH equation) and Rapid Entire Body Assessment (REBA), the DSS identifies ergonomic risks by assessing body postures across common construction tasks. Three specific postures—X, Y and Z—were selected to represent typical construction activities, including lifting, squatting and repetitive tool use. Posture X, involving a forward-leaning stance with arms above the shoulders and a 25 kg load, was identified as critical, yielding the highest OWAS and NIOSH values, thus indicating an immediate need for corrective action to mitigate risks of musculoskeletal injuries. The DSS provides recommendations for workplace adjustments and posture improvements, demonstrating a robust framework that can be adapted to other postures and industries. Future developments may include application to other postures and sectors, as well as the use of artificial intelligence to support ongoing ergonomic assessments, offering a promising solution to enhance Occupational Safety and Health policies. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization)

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Review

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26 pages, 949 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Biosensors for Detecting Food Contaminants—An Overview

by António Inês and Fernanda Cosme

Processes 2025, 13(2), 380; https://doi.org/10.3390/pr13020380 - 30 Jan 2025

Cited by 1 | Viewed by 1753

Abstract

Food safety is a pressing global concern due to the risks posed by contaminants such as pesticide residues, heavy metals, allergens, mycotoxins, and pathogenic microorganisms. While accurate, traditional detection methods like ELISA, HPLC, and mass spectrometry are often time-consuming and resource-intensive, highlighting the need for innovative alternatives. Biosensors based on biological recognition elements such as enzymes, antibodies, and aptamers, offer fast, sensitive, and cost-effective solutions. Using transduction mechanisms like electrochemical, optical, piezoelectric, and thermal systems, biosensors provide versatile tools for detecting contaminants. Advances in DNAzyme- and aptamer-based technologies enable the precise detection of heavy metals, while enzyme- and protein-based biosensors monitor metal-induced changes in biological activity. Innovations like microbial biosensors and DNA-modified electrodes enhance detection accuracy. Biosensors are also highly effective in identifying pesticide residues, allergens, mycotoxins, and pathogens through immunological, enzymatic, and nucleic acid-based techniques. The integration of nanomaterials and bioelectronics has significantly improved the sensitivity and performance of biosensors. By facilitating real-time, on-site monitoring, these devices address the limitations of conventional methods to ensure food quality and regulatory compliance. This review highlights the transformative role of biosensors and how biosensors are improved by emerging technologies in food contamination detection, emphasizing their potential to mitigate public health risks and enhance food safety throughout the supply chain. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization)

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18 pages, 273 KiB

Open AccessReview

Review of Intelligent Modeling for Sintering Process Under Variable Operating Conditions

by Jie Hu, Hongxiang Li, Junyong Liu and Sheng Du

Processes 2025, 13(1), 180; https://doi.org/10.3390/pr13010180 - 10 Jan 2025

Cited by 1 | Viewed by 797

Abstract

The steel industry serves as a cornerstone of a nation’s industrial system, with sintering playing a pivotal role in the steelmaking process. In an effort to enhance the intelligence of the sintering process and improve production efficiency, numerous scholars have carried out extensive research on data analysis and intelligent modeling techniques. These studies have made significant contributions to expanding production capacity, optimizing cost efficiency, and enhancing the quality of products, and supporting the sustainable development of the steel industry. This paper begins with an analysis of the sintering production process, explores the distinctive characteristics of the sintering process, and discusses the methods for identifying the operating conditions of sintering. It also provides an overview of the current state of research on both mechanism modeling and data-driven modeling approaches for the sintering process. Finally, the paper summarizes the existing challenges in sintering process modeling and offers perspectives on the future direction of research in this field. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization)

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18 pages, 430 KiB

Open AccessReview

A Review of Data-Driven Intelligent Monitoring for Geological Drilling Processes

by Sheng Du, Cheng Huang, Xian Ma and Haipeng Fan

Processes 2024, 12(11), 2478; https://doi.org/10.3390/pr12112478 - 8 Nov 2024

Cited by 3 | Viewed by 2070

Abstract

The exploration and development of resources and energy are fundamental to human survival and development, and geological drilling is a key method for deep resource and energy exploration. Intelligent monitoring technology can achieve anomaly detection, fault diagnosis, and fault prediction in the drilling process, which is crucial for ensuring production safety and improving drilling efficiency. The drilling process is characterized by complex geological conditions, variable working conditions, and low information value density, which pose a series of difficulties and challenges for intelligent monitoring. This paper reviews the research progress of the data-driven intelligent monitoring of geological drilling processes, focusing on the above difficulties and challenges. It mainly includes multivariate statistics, machine learning, and multi-model fusion. Multivariate statistical methods can effectively handle and analyze complex geological drilling data, while machine learning methods can efficiently extract key patterns and trends from a large amount of geological drilling data. Multi-model fusion methods, by combining the advantages of the first two methods, enhance the ability to handle complex multivariable and nonlinear problems. This review shows that existing research still faces problems such as limited data processing capabilities and insufficient model generalization capabilities. Improving the efficiency of data processing and the generalization capability of models may be the main research directions in the future. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization)

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