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Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition)

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

Deadline for manuscript submissions: closed (31 January 2026) | Viewed by 24946

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Special Issue Editors

Prof. Dr. Sheng Du

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Guest Editor

School of Artificial Intelligence and 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: robot control; multi-agent cooperative control; high-precision control of electromechanical systems; active disturbance rejection control; advanced robust control; control theory and application
Special Issues, Collections and Topics in MDPI journals

Dr. Hao Liu

E-Mail Website
Guest Editor

School of Automation, Hubei University of Science and Technology, Xianning 437100, China
Interests: theory of active defense for information-physical systems; privacy-preserving system state estimation and control; robot intelligent control; telematics security and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is the second volume of “Application of Artificial Intelligence in Industrial Process Modelling and Optimization” (https://www.mdpi.com/journal/processes/special_issues/0OX7N1I66C).

The integration of artificial intelligence (AI) into industrial process modelling and optimization has proven to be revolutionary. AI can automatically learn the characteristics of industrial processes, improve modelling accuracy, and avoid relying on a large amount of prior knowledge. It can additionally optimize the control strategy of industrial processes and improve their stability and performance and automatically adapt to complex and ever-changing environments. Most importantly, by intelligently analysing industrial process data, AI can enable intelligent monitoring and diagnosis, rapidly detecting and solving problems and thereby improving production efficiency and safety. This Special Issue aims to explore the application of AI approaches in industrial process modelling and optimization. Its focus is on advancing research that harnesses the power of AI to enhance efficiency, safety, and sustainability across various industrial processes.

Scope and Objectives:

This Special Issue primarily aims to foster more research and progress in the application of AI for industrial process modelling and optimization. Its scope encompasses a wide range of industries, including, for example, 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;
The modelling of complex industrial processes;
The 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
Dr. Hao Liu
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 semimonthly 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 (14 papers)

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Research

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38 pages, 9166 KB

Open AccessArticle

AI-Based Wind Tracking and Yaw Control System for Optimizing Wind Turbine Efficiency

by Shoab Mahmud, Mir Foysal Tarif, Ashraf Ali Khan, Hafiz Furqan Ahmed and Usman Ali Khan

Processes 2026, 14(7), 1084; https://doi.org/10.3390/pr14071084 - 27 Mar 2026

Viewed by 1077

Abstract

Accurate yaw alignment is critical for maximizing power capture in horizontal-axis wind turbines, as even moderate yaw misalignment leads to significant aerodynamic losses, increased actuator usage, and accelerated mechanical wear. This research paper proposes a hybrid smart yaw control system for small-scale wind turbines that combines real-time measurements with short-term wind direction prediction to improve alignment accuracy, operational reliability, and energy efficiency under realistic operating conditions. The system integrates four wind direction information sources, such as physical wind vane sensing, live online weather data, forecast data, and a data-driven prediction module within a structured priority framework (VANE → LIVE → FORECAST → AI), to ensure continuous yaw control during sensor or communication unavailability. The prediction module is based on a long short-term memory (LSTM) neural network trained in MATLAB using live data from an online platform, with sine–cosine encoding employed to address the circular nature of directional data. The yaw controller incorporates a ±15° deadband, dwell-time logic, shortest-path rotation, and cable-safe constraints to reduce unnecessary actuation while maintaining effective alignment. The proposed system is validated through MATLAB/Simulink simulations and real-time microcontroller-based experiments using a stepper motor-driven nacelle. Compared with conventional vane-based yaw control, the hybrid AI-assisted approach reduces the average yaw error by approximately 35–45%, maintains a yaw error within ±15° for more than 90% of the operating time, increases average electrical power output by 3–5%, and reduces yaw motor energy consumption by 10–15%, while decreasing corrective yaw actuation events by 30–40%. These results demonstrate that integrating an LSTM-based wind direction predictor with multi-source wind data provides a robust, low-cost, and practically deployable yaw control solution that enhances energy capture and mechanical durability in small-scale wind turbines. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))

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16 pages, 1553 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Machine-Learning Algorithm and Decline-Curve Analysis Comparison in Forecasting Gas Production

by Dan-Romulus Jacota, Cristina Roxana Popa, Maria Tănase and Cristina Veres

Processes 2026, 14(5), 826; https://doi.org/10.3390/pr14050826 - 3 Mar 2026

Viewed by 588

Abstract

This study utilizes machine-learning algorithms to reinterpret existing datasets originally plotted using Decline-Curve Analysis (DCA), aiming to enhance predictive accuracy without requiring new field-data acquisition. Historical production records were compiled: monthly oil/gas rates, bottom-hole pressures, and cumulative productions, which were fitted to Arps equations via least-squares optimization, and key decline parameters, such as initial rate, nominal decline rate, and hyperbolic exponent, served as input data. Four machine-learning models were trained and validated: Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Linear Regression (LR), using 80/20 train–test splits and 5-fold cross-validation. Models were evaluated using Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²). The ANN emerged as the best-performing method, achieving near-unity predictive accuracy (R² ≈ 1) on the independent test set, with low error values (MSE = 0.0012 Ncm²/month², RMSE = 0.035 Ncm/month, MAE = 0.028 Ncm/month) for oil production rates. Similar levels of accuracy were obtained for gas rates and pressures. These results reflect the strong and highly regular relationships present in the dataset analyzed rather than an exact zero-error fit. The multi-layer architecture of the ANN effectively captured the nonlinear interactions between Arps parameters and transient flow regimes, outperforming the empirical and physics-constrained approaches. Linear regression yielded strong results (R² = 0.98, RMSE = 0.15 Ncm/month) but faltered in high-decline scenarios, failing to model exponential tails accurately. SVM exhibited the highest deviations (RMSE = 0.42 Ncm/month, R² = 0.89), attributable to kernel sensitivity in sparse, noisy decline data. RF provided intermediate performance (R² = 0.97). This ANN-driven approach redefines decline analysis by automating parameter tuning and uncertainty quantification, reducing forecasting errors by 85% versus classical Arps methods. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))

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26 pages, 2093 KB

Open AccessArticle

A Sustainability-Aware Federated Graph Attention Framework for Supply Chain Process Modeling

by Vasileios Alexiadis, Maria Drakaki and Panagiotis Tzionas

Processes 2026, 14(5), 781; https://doi.org/10.3390/pr14050781 - 27 Feb 2026

Viewed by 403

Abstract

Modern supply chains operate as highly interconnected networks characterized by decentralization, data silos, and increasing sustainability constraints. Although Graph Neural Networks (GNNs) have demonstrated strong capability in modeling relational dependencies in such systems, their deployment is often restricted by limited inter-organizational data sharing. Federated learning (FL) enables collaborative model training without exposing proprietary data; however, existing federated approaches rarely integrate graph structure and sustainability objectives within a unified framework. This study proposes a Sustainability-Aware Federated Graph Attention Network (FedGAT) for decentralized supply chain process modeling. The framework combines Graph Attention Networks with federated optimization and introduces an emission-weighted attention modulation mechanism that embeds environmental considerations directly into the message-passing process. A multi-tier synthetic supply chain benchmark is constructed to evaluate the approach under realistic governance and data-locality constraints. Experiments are conducted across multiple random seeds, graph scales (up to 500 nodes), and client partition settings. Results show that while centralized graph learning achieves the lowest prediction error, the proposed sustainability-aware federated model maintains statistically indistinguishable predictive performance compared to standard federated baselines (paired sign test p = 1.000), while systematically reducing attention allocated to high-emission transport links. A structured label sensitivity analysis confirms that performance gains are not attributable to circular label construction. Furthermore, a λ-ablation study demonstrates a smooth and controllable trade-off between predictive accuracy and sustainability alignment through a single governance parameter. These findings establish the feasibility of privacy-preserving, sustainability-modulated graph learning for decentralized supply chain analytics and provides a principled foundation for environmentally aligned AI deployment in multi-enterprise networks. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))

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21 pages, 3171 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Automated Fiber Placement Gap Width Prediction Using a Transformer-Based Deep Learning Approach

by Diogo Cardoso, António Ramos Silva and Nuno Correia

Processes 2026, 14(4), 609; https://doi.org/10.3390/pr14040609 - 10 Feb 2026

Viewed by 898

Abstract

Automated Fiber Placement (AFP) is a critical process in composite manufacturing, where precise fiber tow placement is essential for achieving high-quality and high-performance engineering components. However, deviations in process variables frequently lead to defects such as gaps and overlaps, which can compromise structural integrity. While various monitoring techniques exist, accurately predicting and understanding the formation of these defects from complex sensor data remains challenging. This work introduces a novel application of a Transformer-based deep learning architecture to enhance the estimation of gap widths in AFP. Leveraging a publicly available industrial AFP dataset, our methodology incorporates a customized positional encoding scheme to effectively integrate the critical spatial context of the tow layup process. The model’s predictive performance was evaluated, achieving a Mean Absolute Percentage Error (MAPE) of 1.04% and an R-squared (

R^{2}

) value of 0.9143, demonstrating its capability for accurate gap width estimation. Furthermore, SHapley Additive exPlanations (SHAP) analysis was employed to assess the complex interplay between sources of manufacturing process variation. This study establishes the Transformer architecture as a promising and interpretable data-driven tool for AFP process monitoring. The results serve as a proof of concept for attention-based virtual metrology, offering a pathway towards deeper process understanding and defect mitigation. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))

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12 pages, 1086 KB

Open AccessArticle

Research and Application of Intelligent Control System for Uniform Pellet Distribution

by Tingting Liao, Xiaoxin Zeng, Xudong Li, Zongping Li, Jianming Zhang, Chen Liu and Weisong Wu

Processes 2026, 14(3), 490; https://doi.org/10.3390/pr14030490 - 30 Jan 2026

Viewed by 372

Abstract

In pellet production, the uniformity of material distribution directly affects the subsequent roasting effect and the quality of finished products. Aiming at the problems of uneven distribution in traditional shuttle distribution systems, such as material stacking at both ends of the wide belt, insufficient parameter matching leading to uneven distribution, and reliance on manual adjustment which makes it difficult to adapt to dynamic working conditions, this paper proposes an intelligent control method based on Integral Simulation and Gradient Descent optimization (IS-GD). Firstly, this method combines the structure and operating parameters of the distribution equipment and accurately simulates the material distribution law on the wide belt during the reciprocating movement of the shuttle through integral technology. Based on the simulation results, longitudinal and lateral uniformity discriminant functions are constructed, and a phased gradient descent optimization strategy is adopted to dynamically adjust the shuttle belt speed, walking speed, and operating parameters of each stage with the goal of minimizing the uniformity index. Experimental results show that this method achieves a significant improvement in lateral distribution uniformity without affecting the stability of longitudinal distribution. This research provides reliable technical support for intelligent distribution control in pellet production and helps to improve the roasting quality and production efficiency of pellets. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))

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34 pages, 2207 KB

Open AccessArticle

Neuro-Symbolic Verification for Preventing LLM Hallucinations in Process Control

by Boris Galitsky and Alexander Rybalov

Processes 2026, 14(2), 322; https://doi.org/10.3390/pr14020322 - 16 Jan 2026

Cited by 1 | Viewed by 2208

Abstract

Large Language Models (LLMs) are increasingly used in industrial monitoring and decision support, yet they remain prone to process-control hallucinations—diagnoses and explanations that sound plausible but conflict with physical constraints, sensor data, or plant dynamics. This paper investigates hallucination as a failure of abductive reasoning, where missing premises, weak mechanistic support, or counter-evidence lead an LLM to propose incorrect causal narratives for faults such as pump restriction, valve stiction, fouling, or reactor runaway. We develop a neuro-symbolic framework in which Abductive Logic Programming (ALP) evaluates the coherence of model-generated explanations, counter-abduction generates rival hypotheses that test whether the explanation can be defeated, and Discourse-weighted ALP (D-ALP) incorporates nucleus–satellite structure from operator notes and alarm logs to weight competing explanations. Using our 500-scenario Process-Control Hallucination Dataset, we assess LLM reasoning across mechanistic, evidential, and contrastive dimensions. Results show that abductive and counter-abductive operators substantially reduce explanation-level hallucinations and improve alignment with physical process behavior, particularly in “easy-but-wrong’’ cases where a superficially attractive explanation contradicts historian trends or counter-evidence. These findings demonstrate that abductive reasoning provides a practical and verifiable foundation for improving LLM reliability in safety-critical process-control environments. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))

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16 pages, 5236 KB

Open AccessArticle

Intelligent Disassembly System for PCB Components Integrating Multimodal Large Language Model and Multi-Agent Framework

by Li Wang, Liu Ouyang, Huiying Weng, Xiang Chen, Anna Wang and Kexin Zhang

Processes 2026, 14(2), 227; https://doi.org/10.3390/pr14020227 - 8 Jan 2026

Viewed by 540

Abstract

The escalating volume of waste electrical and electronic equipment (WEEE) poses a significant global environmental challenge. The disassembly of printed circuit boards (PCBs), a critical step for resource recovery, remains inefficient due to limitations in the adaptability and dexterity of existing automated systems. This paper proposes an intelligent disassembly system for PCB components that integrates a multimodal large language model (MLLM) with a multi-agent framework. The MLLM serves as the system’s cognitive core, enabling high-level visual-language understanding and task planning by converting images into semantic descriptions and generating disassembly strategies. A state-of-the-art object detection algorithm (YOLOv13) is incorporated to provide fine-grained component localization. This high-level intelligence is seamlessly connected to low-level execution through a multi-agent framework that orchestrates collaborative dual robotic arms. One arm controls a heater for precise solder melting, while the other performs fine “probing-grasping” actions guided by real-time force feedback. Experiments were conducted on 30 decommissioned smart electricity meter PCBs, evaluating the system on recognition rate, capture rate, melting rate, and time consumption for seven component types. Results demonstrate that the system achieved a 100% melting rate across all components and high recognition rates (90–100%), validating its strengths in perception and thermal control. However, the capture rate varied significantly, highlighting the grasping of small, low-profile components as the primary bottleneck. This research presents a significant step towards autonomous, non-destructive e-waste recycling by effectively combining high-level cognitive intelligence with low-level robotic control, while also clearly identifying key areas for future improvement. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))

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19 pages, 1172 KB

Open AccessArticle

Research on Bo-BiLSTM-Based Synchronous Load Transfer Control Technology for Distribution Networks

by Cheng Long, Hua Zhang, Xueneng Su, Yiwen Gao and Wei Luo

Processes 2025, 13(12), 3999; https://doi.org/10.3390/pr13123999 - 11 Dec 2025

Viewed by 439

Abstract

The operational modes and fault characteristics of distribution networks incorporating distributed generation are becoming increasingly complex. This complexity increases the difficulty of predicting switch control action times and leads to scattered samples with data scarcity. Consequently, it imposes higher demands on rapid fault isolation and load transfer control following system failures. To address this issue, this paper proposes a switch action time prediction and synchronous load transfer control method based on Bayesian optimization of bidirectional long short-term memory (Bo-BiLSTM) networks. A distribution network simulation model incorporating distributed generation was constructed using MATLAB/Simulink (R2023a). Three-phase voltage and current at the Point of Common Coupling (PCC) were extracted as feature parameters to establish a switch operation timing database. Bayesian optimization was employed to tune the BiLSTM hyperparameters, constructing the Bo-BiLSTM prediction model to achieve high-precision forecasting of switch operation times under fault conditions. Subsequently, a load-synchronized transfer control strategy was proposed based on the prediction results. A dynamic delay mechanism was designed to achieve “open first and then close” sequential coordinated control. Physical experiments verified that the time difference between opening and closing was controlled within 2–12 milliseconds (ms), meeting the engineering requirement of less than 20 ms. The results demonstrate that the proposed control method enhances switch operation time prediction accuracy while effectively supporting rapid fault isolation and seamless load transfer in distribution networks, thereby improving system reliability and control precision. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))

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29 pages, 7233 KB

Open AccessFeature PaperEditor’s ChoiceArticle

Exposing Vulnerabilities: Physical Adversarial Attacks on AI-Based Fault Diagnosis Models in Industrial Air-Cooling Systems

by Stavros Bezyrgiannidis, Ioannis Polymeropoulos, Eleni Vrochidou and George A. Papakostas

Processes 2025, 13(9), 2920; https://doi.org/10.3390/pr13092920 - 12 Sep 2025

Cited by 1 | Viewed by 2135

Abstract

Although neural network-based methods have significantly advanced the field of machine fault diagnosis, they remain vulnerable to physical adversarial attacks. This work investigates such attacks in the physical context of a real production line. Attacks simulate failures or irregularities arising from the maintenance or production department during the production process, a scenario commonly encountered in industrial environments. The experiments are conducted using data from vibration signals and operational parameters of a motor installed in an industrial air-cooling system used for staple fiber production. In this context, we propose the Mean Confusion Impact Index (MCII), a novel and simple robustness metric that measures the average misclassification confidence of models under adversarial physical attacks. By performing a series of hardware-level interventions, this work aims to demonstrate that even minor physical disturbances can lead to a significant reduction in the model’s diagnostic accuracy. Additionally, a hybrid defense approach is proposed, which leverages deep feature representations extracted from the original classification model and integrates them with lightweight classifiers retrained on adversarial labeled data. Research findings underscore an important limitation in existing industrial artificial intelligence (AI)-based monitoring systems and introduce a practical, scalable framework for improving the physical resilience of machine fault diagnosis in real-world environments. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))

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19 pages, 3880 KB

Open AccessFeature PaperArticle

Optimal Scheduling of a Multi-Energy Hub with Integrated Demand Response Programs

by Rana H. A. Zubo, Patrick S. Onen, Iqbal M Mujtaba, Geev Mokryani and Raed Abd-Alhameed

Processes 2025, 13(9), 2879; https://doi.org/10.3390/pr13092879 - 9 Sep 2025

Cited by 2 | Viewed by 1549

Abstract

This paper presents an optimal scheduling framework for a multi-energy hub (EH) that integrates electricity, natural gas, wind energy, energy storage systems, and demand response (DR) programs. The EH incorporates key system components including transformers, converters, boilers, combined heat and power (CHP) units, and both thermal and electrical energy storage. A novel aspect of this work is the joint coordination of multi-carrier energy flows with DR flexibility, enabling consumers to actively shift or reduce loads in response to pricing signals while leveraging storage and renewable resources. The optimisation problem is formulated as a mixed-integer linear programming (MILP) model and solved using the CPLEX solver in GAMS. To evaluate system performance, five case studies are investigated under varying natural gas price conditions and hub configurations, including scenarios with and without DR and CHP. Results demonstrate that DR participation significantly reduces total operating costs (up to 6%), enhances renewable utilisation, and decreases peak demand (by around 6%), leading to a flatter demand curve and improved system reliability. The findings highlight the potential of integrated EHs with DR as a cost-effective and flexible solution for future low-carbon energy systems. Furthermore, the study provides insights into practical deployment challenges, including storage efficiency, communication infrastructure, and real-time scheduling requirements, paving the way for hardware-in-the-loop and pilot-scale validations. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))

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17 pages, 4994 KB

Open AccessArticle

Enhancing the Reliability and Durability of Micro-Sensors Using the Taguchi Method

by Chi-Yuan Lee, Jiann-Shing Shieh, Guan-Quan Huang, Chen-Kai Liu, Najsm Cox and Chia-Hao Chou

Processes 2025, 13(9), 2852; https://doi.org/10.3390/pr13092852 - 5 Sep 2025

Cited by 1 | Viewed by 3702

Abstract

This study presents the development and optimization of a flexible integrated three-in-one micro-sensor using Micro-Electro-Mechanical Systems (MEMS) technology. To enhance its reliability and performance, the Taguchi Method was employed to analyze and optimize key fabrication parameters, including the electrode area, electrode thickness, and protective layer thickness. An L4 orthogonal array design enabled efficient experimentation with minimal runs. Experimental results demonstrate that optimized parameter combinations significantly improve sensor linearity, sensitivity, and reproducibility. Comparative analysis with commercial sensors shows the superior reliability of the self-fabricated sensor, particularly in airflow velocity detection. The findings validate the use of the Taguchi Method for robust MEMS sensor design and highlight its potential for industrial heating, ventilation, and air conditioning (HVAC) applications. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))

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29 pages, 4727 KB

Open AccessArticle

A Low-Code Visual Framework for Deep Learning-Based Remaining Useful Life Prediction

by Yuhan Lin, Jianhua Chen, Sijuan Chen, Yunfei Nie, Ming Wang, Bing Zhang, Ming Yang and Jipu Wang

Processes 2025, 13(8), 2366; https://doi.org/10.3390/pr13082366 - 25 Jul 2025

Viewed by 1212

Abstract

In the context of intelligent manufacturing, deep learning-based remaining useful life (RUL) prediction has become a research hotspot in the field of Prognostics and Health Management (PHM). The traditional approaches often require strong programming skills and repeated model building, posing a high entry barrier. To address this, in this study, we propose and implement a visualization tool that supports multiple model selections and result visualization and eliminates the need for complex coding and mathematical derivations, helping users to efficiently conduct RUL prediction with lower technical requirements. This study introduces and summarizes various novel neural network models for DL-based RUL prediction. The models are validated using the NASA and HNEI datasets, and among the validated models, the LSTM model best met the requirements for remaining useful life (RUL) prediction. In order to achieve the low-code usage of deep learning for RUL prediction, the following tasks were performed: (1) multiple models were developed using the Python (3.9.18) language and were implemented on the PyTorch (1.12.1) framework, providing users with the freedom to choose their desired model; (2) a user-friendly and low-code RUL prediction interface was built using Streamlit, enabling users to easily make predictions; (3) the visualization of prediction results was implemented using Matplotlib (3.8.2), allowing users to better understand and analyze the results. In addition, the tool offers functionalities such as automatic hyperparameter tuning to optimize the performance of the prediction model and reduce the complexity of operations. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))

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25 pages, 2737 KB

Open AccessReview

Integration of Artificial Intelligence in Food Processing Technologies

by Ali Ayoub

Processes 2026, 14(3), 513; https://doi.org/10.3390/pr14030513 - 2 Feb 2026

Cited by 1 | Viewed by 4369

Abstract

The food processing industry is undergoing a profound transformation with the integration of Artificial Intelligence (AI), evolving from traditional automation to intelligent, adaptive systems aligned with Industry 5.0 principles. This review examines AI’s role across the food value chain, including supply chain management, quality control, process optimization in key unit operations, and emerging areas. Recent advancements in machine learning (ML), computer vision, and predictive analytics have significantly improved detection in food processing, achieving accuracy exceeding 98%. These technologies have also contributed to energy savings of 15–20% and reduced waste through real-time process optimization and predictive maintenance. The integration of blockchain and Internet of Things (IoT) technologies further strengthens traceability and sustainability across the supply chain, while generative AI accelerates the development of novel food products. Despite these benefits, several challenges persist, including substantial implementation costs, heterogeneous data sources, ethical considerations related to workforce displacement, and the opaque, “black box” nature of many AI models. Moreover, the effectiveness of AI solutions remains context-dependent; some studies report only marginal improvements in dynamic or data-poor environments. Looking ahead, the sector is expected to embrace autonomous manufacturing, edge computing, and bio-computing, with projections indicating that the AI market in food processing could approach $90 billion by 2030. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))

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46 pages, 10548 KB

Open AccessFeature PaperReview

A Review of Hybrid LSTM Models in Smart Cities

by Bum-Jun Kim and Il-Woo Nam

Processes 2025, 13(7), 2298; https://doi.org/10.3390/pr13072298 - 18 Jul 2025

Cited by 5 | Viewed by 4486

Abstract

Rapid global urbanization poses complex challenges that demand advanced data-driven forecasting solutions for smart cities. Traditional statistical and standalone Long Short-Term Memory (LSTM) models often struggle to capture non-linear dynamics and long-term dependencies in urban time-series data. This review critically examines hybrid LSTM models that integrate LSTM with complementary algorithms, including CNN, GRU, ARIMA, and SVM. These hybrid architectures aim to enhance prediction accuracy, integrate diverse data sources, and improve computational efficiency. This study systematically reviews principles, trends, and real-world applications, quantitatively evaluating hybrid LSTM models using performance metrics such as mean absolute error (MAE), root mean square error (RMSE), and the coefficient of determination (R²), while identifying key study limitations. The case studies considered include traffic management, environmental monitoring, energy forecasting, public health, infrastructure assessment, and urban waste management. For example, hybrid models have achieved substantial accuracy improvements in traffic congestion forecasting, reducing their mean absolute error by up to 29%. Despite the inherent challenges related to structural complexity, interpretability, and data requirements, ongoing research on attention mechanisms, model compression, and explainable AI has significantly mitigated these limitations. Thus, hybrid LSTM models have emerged as vital analytical tools capable of robust spatiotemporal prediction, effectively supporting sustainable urban development and data-driven decision-making in evolving smart city environments. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Industrial Process Modelling and Optimization (2nd Edition))

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