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Processes
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Artificial Intelligence (AI) and Automation-Driven Innovations in Chemical Engineering
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Artificial Intelligence (AI) and Automation-Driven Innovations in Chemical Engineering

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 4854

Special Issue Editor

Dr. Dewei Wang

E-Mail Website
Guest Editor
Pacific Northwest National Laboratory, Richland, WA 99352, USA
Interests: fuel cell; physics-informed machine learning; two-phase flow; flow visualization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in artificial intelligence (AI) and automation are rapidly transforming chemical engineering, driving innovation across research, process design, and industrial practice. These technologies offer powerful tools to enhance system efficiency, minimize environmental impact, and accelerate the transition to sustainable, low-carbon chemical processes.

This Special Issue, entitled “Artificial Intelligence (AI) and Automation-Driven Innovations in Chemical Engineering”, invites the submission of original research and review articles that demonstrate the integration of intelligent algorithms, data-driven modeling, and autonomous systems in chemical engineering applications. Topics of interest include, but are not limited to, the following:

  • Machine learning and AI-based process modeling, control, and optimization;
  • Smart and autonomous chemical manufacturing systems;
  • AI-assisted materials and catalyst discovery;
  • Applications in carbon capture, utilization, and storage (CCUS);
  • Data-driven strategies for energy-efficient and sustainable process design.

This Special Issue aims to highlight interdisciplinary research where AI and automation serve as key enablers of next-generation chemical engineering solutions, particularly in the context of decarbonization, renewable energy integration, waste management, and resource efficiency.

Dr. Dewei Wang
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 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

  • artificial intelligence (AI)
  • automation
  • process optimization
  • sustainable systems
  • carbon capture and utilization (CCU)
  • waste processing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

18 pages, 1689 KB  
Article
Biogas Prediction Enhancement for a Swine Farm Bio-Digester Using a Lag-Based Surrogate Machine Learning Model
by María Estela Montes-Carmona, Ivan Andres Burgos-Castro, Rogelio de Jesús Portillo-Vélez, Pedro Javier García-Ramírez, Luis Felipe Marín-Urías and Miguel Ángel Hernández-Pérez
Processes 2026, 14(9), 1452; https://doi.org/10.3390/pr14091452 - 30 Apr 2026
Viewed by 229
Abstract
Biogas production estimation has been one of the most important and challenging objectives for anaerobic digestion processes due to the complexity of its dynamics and the lack of high-quality open-access datasets. This study presents a hybrid modeling framework that combines a mechanistic model, [...] Read more.
Biogas production estimation has been one of the most important and challenging objectives for anaerobic digestion processes due to the complexity of its dynamics and the lack of high-quality open-access datasets. This study presents a hybrid modeling framework that combines a mechanistic model, based on ordinary differential equations (ODEs), with a machine learning model. Rather than relying exclusively on experimental data, the proposed approach leverages physics-informed synthetic data generation, complemented by a lag-based feature engineering to capture inherent temporal dependencies in the process dynamics available in operational data of a bio-digester. Two configurations were evaluated: a baseline model and an enhanced version incorporating lag features and a simplified temperature profile. This specific computational enhancement provides a robust predictive core that successfully avoids the severe predictive degradation observed in purely mechanistic approaches at high spatial discretizations. While the improved surrogate model achieved high predictive performance (R2=0.9788, RMSE=131.80 [L/d]), additional analyses reveal that this resilience is driven by temporal memory and remains sensitive to noise and feature composition. Instead of presenting the model as a final independent physical validation, this work is rigorously framed as a proof-of-concept digital twin core, acknowledging the gap that still exists between simulation-based ODE emulation and unstructured real-world reliability. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Automation-Driven Innovations in Chemical Engineering)
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16 pages, 3967 KB  
Article
Real-Time Detection of Electrohydrodynamic Atomization Modes via a YOLOv8-Based Deep Learning Model
by Xiong Ran, Heming Xu, Xiangfei Wei, Jinxin Wang and Wei-Cheng Yan
Processes 2026, 14(2), 313; https://doi.org/10.3390/pr14020313 - 15 Jan 2026
Viewed by 405
Abstract
A YOLOv8-based deep learning model was developed to address real-time detection and dynamic regulation needs of the electrohydrodynamic atomization process. An EHDA experimental system was built to obtain images of six typical atomization modes, forming a dataset with 6000 images. After annotation and [...] Read more.
A YOLOv8-based deep learning model was developed to address real-time detection and dynamic regulation needs of the electrohydrodynamic atomization process. An EHDA experimental system was built to obtain images of six typical atomization modes, forming a dataset with 6000 images. After annotation and mosaic augmentation, the dataset served as the training data for the model. The YOLOv8 adopts a “backbone-neck-head” architecture to extract and fuse features, decouple classification and detection, and optimize performance. Experimental results demonstrate that on the test set, the model attains a precision value, recall rate, and mAP50 of 0.995, alongside an mAP50-95 of 0.8. Additionally, its prediction accuracy exceeds 0.99 across all operational modes. Compared with 10 models, it has the best precision and mAP50, as well as low computational complexity, combining high accuracy and lightweight advantages, which can be effectively used for real-time detection of EHDA modes. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Automation-Driven Innovations in Chemical Engineering)
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25 pages, 3348 KB  
Article
An AI-Assisted Thermodynamic Equilibrium Simulator: A Case Study on Steam Methane Reforming in Isothermal and Adiabatic Reactors
by Julles Mitoura dos Santos Junior, Antonio Carlos Daltro de Freitas and Adriano Pinto Mariano
Processes 2025, 13(8), 2508; https://doi.org/10.3390/pr13082508 - 8 Aug 2025
Cited by 1 | Viewed by 3428
Abstract
This study presents TeS v.3, a thermodynamic equilibrium simulator integrated with an artificial intelligence agent (AI), ThermoAgent, to enhance the analysis of complex chemical systems. Developed in Python, the simulator employs Gibbs energy minimization for isothermal reactors and entropy maximization for [...] Read more.
This study presents TeS v.3, a thermodynamic equilibrium simulator integrated with an artificial intelligence agent (AI), ThermoAgent, to enhance the analysis of complex chemical systems. Developed in Python, the simulator employs Gibbs energy minimization for isothermal reactors and entropy maximization for adiabatic reactors. ThermoAgent leverages the LangChain framework to interpret natural language commands, autonomously execute simulations, and query a scientific knowledge base through a Retrieval-Augmented Generation (RAG) approach. The validation of TeS v.3 demonstrated high accuracy, with coefficients of determination (R2 > 0.95) compared to reference simulation data and strong correlation (R2 > 0.88) with experimental data from the steam methane reforming (SMR) process. The SMR analysis correctly distinguished the high conversions in isothermal reactors from the limited conversions in adiabatic reactors, due to the reaction temperature drop. ThermoAgent successfully executed simulations and provided justified analyses, combining generated data with information from reference publications. The successful integration of the simulator with the AI agent represents a significant advancement, offering a powerful tool that accurately calculates equilibrium and accelerates knowledge extraction through intuitive interaction. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) and Automation-Driven Innovations in Chemical Engineering)
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