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Processes
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Artificial Intelligence-Based Analytics for Data-Driven Decision-Making in Industrial Process Engineering
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Artificial Intelligence-Based Analytics for Data-Driven Decision-Making in Industrial Process Engineering

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "AI-Enabled Process Engineering".

Deadline for manuscript submissions: 20 June 2026 | Viewed by 10533

Special Issue Editors

Dr. Xiaojun Jin

E-Mail Website
Guest Editor
National Engineering Research Center of Biomaterials, Nanjing Forestry University, Nanjing 210037, China
Interests: artificial intelligence; machine vision; robotics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jian Zhang

E-Mail Website
Guest Editor
School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, China
Interests: pattern recognition and intelligent systems; artificial intelligence; data mining and big data analytics
Prof. Dr. Dong-Qing Yuan

E-Mail Website
Guest Editor
School of Science, Jiangsu Ocean University, Lianyungang 222005, China
Interests: spectral detection; intelligent manufacturing; intelligent decision-making systems

Special Issue Information

Dear Colleagues,

The rise in artificial intelligence (AI) has revolutionized data analytics. This Special Issue aims to present cutting-edge research and methodologies that leverage AI for data-driven decision-making.

We welcome original research, comprehensive reviews, and innovative methodologies that demonstrate how AI can address complex domain-specific challenges and facilitate practical data-driven solutions. Submissions may encompass both theoretical contributions and empirical studies, emphasizing robust model architectures, efficient training strategies, and interpretable results for informed decision-making.

We welcome contributions that cover a wide range of topics, including, but not limited to, the following:

  • AI architectures for object detection and classification;
  • AI techniques for anomaly recognition;
  • Intelligent monitoring systems;
  • Resource optimization for responsible AI development.

We look forward to receiving contributions that push the boundaries of AI-based analytics and bridge the gap between state-of-the-art research and practical decision-making.

Dr. Xiaojun Jin
Prof. Dr. Jian Zhang
Prof. Dr. Dong-Qing Yuan
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 250 words) can be sent to the Editorial Office for assessment.

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

  • data analytics
  • decision-making
  • anomaly recognition
  • Intelligent monitoring systems
  • artificial intelligence

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Published Papers (6 papers)

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Research

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31 pages, 9303 KB  
Article
Automatic Quadrotor Dispatch Missions Based on Air-Writing Gesture Recognition
by Pu-Sheng Tsai, Ter-Feng Wu and Yen-Chun Wang
Processes 2025, 13(12), 3984; https://doi.org/10.3390/pr13123984 - 9 Dec 2025
Viewed by 150
Abstract
This study develops an automatic dispatch system for quadrotor UAVs that integrates air-writing gesture recognition with a graphical user interface (GUI). The DJI RoboMaster quadrotor UAV (DJI, Shenzhen, China) was employed as the experimental platform, combined with an ESP32 microcontroller (Espressif Systems, Shanghai, [...] Read more.
This study develops an automatic dispatch system for quadrotor UAVs that integrates air-writing gesture recognition with a graphical user interface (GUI). The DJI RoboMaster quadrotor UAV (DJI, Shenzhen, China) was employed as the experimental platform, combined with an ESP32 microcontroller (Espressif Systems, Shanghai, China) and the RoboMaster SDK (version 3.0). On the Python (version 3.12.7) platform, a GUI was implemented using Tkinter (version 8.6), allowing users to input addresses or landmarks, which were then automatically converted into geographic coordinates and imported into Google Maps for route planning. The generated flight commands were transmitted to the UAV via a UDP socket, enabling remote autonomous flight. For gesture recognition, a Raspberry Pi integrated with the MediaPipe Hands module was used to capture 16 types of air-written flight commands in real time through a camera. The training samples were categorized into one-dimensional coordinates and two-dimensional images. In the one-dimensional case, X/Y axis coordinates were concatenated after data augmentation, interpolation, and normalization. In the two-dimensional case, three types of images were generated, namely font trajectory plots (T-plots), coordinate-axis plots (XY-plots), and composite plots combining the two (XYT-plots). To evaluate classification performance, several machine learning and deep learning architectures were employed, including a multi-layer perceptron (MLP), support vector machine (SVM), one-dimensional convolutional neural network (1D-CNN), and two-dimensional convolutional neural network (2D-CNN). The results demonstrated effective recognition accuracy across different models and sample formats, verifying the feasibility of the proposed air-writing trajectory framework for non-contact gesture-based UAV control. Furthermore, by combining gesture recognition with a GUI-based map planning interface, the system enhances the intuitiveness and convenience of UAV operation. Future extensions, such as incorporating aerial image object recognition, could extend the framework’s applications to scenarios including forest disaster management, vehicle license plate recognition, and air pollution monitoring. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Analytics for Data-Driven Decision-Making in Industrial Process Engineering)
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18 pages, 776 KB  
Article
A Comprehensive Approach to Identifying the Parameters of a Counterflow Heat Exchanger Model Based on Sensitivity Analysis and Regularization Methods
by Salimzhan Tassanbayev, Gulzhan Uskenbayeva, Aliya Shukirova, Korlan Kulniyazova and Igor Slastenov
Processes 2025, 13(10), 3289; https://doi.org/10.3390/pr13103289 - 14 Oct 2025
Viewed by 430
Abstract
The study presents a robust methodology for simultaneous state and parameter estimation in nonlinear thermal systems, demonstrated on a counter-current heat exchanger model operating with nitrogen under industrial conditions. To address challenges of ill-conditioning and parameter correlation, local sensitivity analysis is combined with [...] Read more.
The study presents a robust methodology for simultaneous state and parameter estimation in nonlinear thermal systems, demonstrated on a counter-current heat exchanger model operating with nitrogen under industrial conditions. To address challenges of ill-conditioning and parameter correlation, local sensitivity analysis is combined with regularization through optimal parameter subset selection using orthogonalization and D-optimal experimental design. The Unscented Kalman Filter (UKF) is employed to jointly estimate the augmented state vector in real time, leveraging high-fidelity dynamic simulations generated in Unisim Design with the Peng–Robinson equation of state. The proposed framework achieves high estimation accuracy and numerical stability, even under limited sensor availability and measurement noise. Monte Carlo simulations confirm robustness to ±2.5% uncertainty in initial conditions, while residual autocorrelation analysis validates estimator optimality. The approach provides a practical solution for real-time monitoring and model-based control in industrial heat exchangers and offers a generalizable strategy for building identifiable, noise-resilient models of complex nonlinear systems. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Analytics for Data-Driven Decision-Making in Industrial Process Engineering)
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14 pages, 1619 KB  
Article
Process-Oriented Dual-Layer Knowledge GraphRAG for Reservoir Engineering Decision Support
by Bin Jiang, Zhaonian Liu, Ning Wang, Zhuoyang Li, Yinliang Shi and Botao Lin
Processes 2025, 13(10), 3230; https://doi.org/10.3390/pr13103230 - 10 Oct 2025
Viewed by 919
Abstract
This study presents a dual-layer GraphRAG framework for petroleum engineering question answering, in which instance-level facts and domain-level concepts are explicitly separated and integrated into retrieval-augmented generation. To evaluate the framework, a benchmark of 40 expert-constructed Q&A pairs was developed, covering factual, definitional, [...] Read more.
This study presents a dual-layer GraphRAG framework for petroleum engineering question answering, in which instance-level facts and domain-level concepts are explicitly separated and integrated into retrieval-augmented generation. To evaluate the framework, a benchmark of 40 expert-constructed Q&A pairs was developed, covering factual, definitional, and explanatory queries derived from a real offshore oilfield dataset. Results show that the dual-layer graph consistently outperforms a single-layer baseline. Answer accuracy improves from 0.65 to 0.70, faithfulness from 0.54 to 0.61, and context relevance from 0.69 to 0.72, confirming that the system retrieves factual parameters more reliably and provides conceptually grounded explanations. Gains in evidence recall and coverage are more modest, highlighting areas for further optimization. A case study illustrates the framework’s ability to expand petroleum terminology (e.g., “sandstone → clastic rock”), producing responses that are not only quantitatively more reliable but also qualitatively more informative. The dual-layer design effectively addresses the semantic consistency gap in petroleum QA, offering practical value for reservoir evaluation, lithology interpretation, and technical decision support. These findings demonstrate the potential of GraphRAG to enhance knowledge management and intelligent services in petroleum engineering. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Analytics for Data-Driven Decision-Making in Industrial Process Engineering)
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Review

Jump to: Research

25 pages, 1397 KB  
Review
Multi-Source Data Integration and Model Coupling for Watershed Eco-Assessment Systems: Progress, Challenges, and Prospects
by Li Ma, Zihe Xu, Lina Fan, Hongxia Jia, Hao Hu and Lixin Li
Processes 2025, 13(9), 2998; https://doi.org/10.3390/pr13092998 - 19 Sep 2025
Viewed by 836
Abstract
The integrated assessment of watershed ecosystems is increasingly critical for sustainable water resource management amid global environmental change. Multi-source data integration—encompassing in situ monitoring, remote sensing, and model-based observations—has significantly expanded the spatial and temporal scales at which watershed processes can be analyzed. [...] Read more.
The integrated assessment of watershed ecosystems is increasingly critical for sustainable water resource management amid global environmental change. Multi-source data integration—encompassing in situ monitoring, remote sensing, and model-based observations—has significantly expanded the spatial and temporal scales at which watershed processes can be analyzed. Concurrently, advances in model coupling strategies, ranging from loose to embedded architectures, have enabled more dynamic and holistic representations of interactions among hydrology, water quality, and ecological systems. However, a unifying operational framework that links multi-source data, cross-scale coupling, and rigorous uncertainty propagation to actionable, real-time decision support is still missing, largely due to gaps in interoperability and stakeholder engagement. Addressing these limitations demands the development of intelligent, adaptive modeling frameworks that leverage hybrid physics-informed machine learning, cross-scale process integration, and continuous real-time data assimilation. Open science practices and transparent model governance are essential for ensuring reproducibility, stakeholder trust, and policy relevance. The recent literature indicates that loose coupling predominates, physics-informed ML tends to generalize better in data-sparse settings, and uncertainty communication remains uneven. Building on these insights, this review synthesizes methods for data harmonization and cross-scale integration, compares coupling architectures and data assimilation schemes, evaluates uncertainty and interoperability practices, and introduces the Smart Integrated Watershed Eco-Assessment Framework (SIWEAF) to support adaptive, real-time, stakeholder-centered decision-making. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Analytics for Data-Driven Decision-Making in Industrial Process Engineering)
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19 pages, 441 KB  
Review
Recent Advances and Applications of Nondestructive Testing in Agricultural Products: A Review
by Mian Li, Honglian Yin, Fei Gu, Yanjun Duan, Wenxu Zhuang, Kang Han and Xiaojun Jin
Processes 2025, 13(9), 2674; https://doi.org/10.3390/pr13092674 - 22 Aug 2025
Cited by 2 | Viewed by 1432
Abstract
With the rapid development of agricultural intelligence, nondestructive testing (NDT) has shown considerable promise for agricultural product inspection. Compared with traditional methods—which often suffer from subjectivity, low efficiency, and sample damage—NDT offers rapid, accurate, and non-invasive solutions that enable precise inspection without harming [...] Read more.
With the rapid development of agricultural intelligence, nondestructive testing (NDT) has shown considerable promise for agricultural product inspection. Compared with traditional methods—which often suffer from subjectivity, low efficiency, and sample damage—NDT offers rapid, accurate, and non-invasive solutions that enable precise inspection without harming the products. These inherent advantages have promoted the increasing adoption of NDT technologies in agriculture. Meanwhile, rising quality standards for agricultural products have intensified the demand for more efficient and reliable detection methods, accelerating the replacement of conventional techniques by advanced NDT approaches. Nevertheless, selecting the most appropriate NDT method for a given agricultural inspection task remains challenging, due to the wide diversity in product structures, compositions, and inspection requirements. To address this challenge, this paper presents a review of recent advancements and applications of several widely adopted NDT techniques, including computer vision, near-infrared spectroscopy, hyperspectral imaging, computed tomography, and electronic noses, focusing specifically on their application in agricultural product evaluation. Furthermore, the strengths and limitations of each technology are discussed comprehensively, quantitative performance indicators and adoption trends are summarized, and practical recommendations are provided for selecting suitable NDT techniques according to various agricultural inspection tasks. By highlighting both technical progress and persisting challenges, this review provides actionable theoretical and technical guidance, aiming to support researchers and practitioners in advancing the effective and sustainable application of cutting-edge NDT methods in agriculture. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Analytics for Data-Driven Decision-Making in Industrial Process Engineering)
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31 pages, 3016 KB  
Review
Image Recognition Technology in Smart Agriculture: A Review of Current Applications Challenges and Future Prospects
by Chunxia Jiang, Kangshu Miao, Zhichao Hu, Fengwei Gu and Kechuan Yi
Processes 2025, 13(5), 1402; https://doi.org/10.3390/pr13051402 - 4 May 2025
Cited by 4 | Viewed by 6211
Abstract
The implementation of image recognition technology can significantly enhance the levels of automation and intelligence in smart agriculture. However, most researchers focused on its applications in medical imaging, industry, and transportation, while fewer focused on smart agriculture. Based on this, this study aims [...] Read more.
The implementation of image recognition technology can significantly enhance the levels of automation and intelligence in smart agriculture. However, most researchers focused on its applications in medical imaging, industry, and transportation, while fewer focused on smart agriculture. Based on this, this study aims to contribute to the comprehensive understanding of the application of image recognition technology in smart agriculture by investigating the scientific literature related to this technology in the last few years. We discussed and analyzed the applications of plant disease and pest detection, crop species identification, crop yield prediction, and quality assessment. Then, we made a brief introduction to its applications in soil testing and nutrient management, as well as in agricultural machinery operation quality assessment and agricultural product grading. At last, the challenges and the emerging trends of image recognition technology were summarized. The results indicated that the models used in image recognition technology face challenges such as limited generalization, real-time processing, and insufficient dataset diversity. Transfer learning and green Artificial Intelligence (AI) offer promising solutions to these issues by reducing the reliance on large datasets and minimizing computational resource consumption. Advanced technologies like transformers further enhance the adaptability and accuracy of image recognition in smart agriculture. This comprehensive review provides valuable information on the current state of image recognition technology in smart agriculture and prospective future opportunities. Full article
(This article belongs to the Special Issue Artificial Intelligence-Based Analytics for Data-Driven Decision-Making in Industrial Process Engineering)
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