Artificial Intelligence and Automation in Chemical Engineering

Topic Information

Dear Colleagues,

The field of chemical engineering is rapidly evolving, driven by the pressing need to enhance process efficiency, safety, and sustainability. Artificial intelligence (AI) technologies and advanced automation tools are at the forefront of this evolution. By integrating novel computational techniques such as machine learning, data-driven modeling, and intelligent process control, chemical engineers can improve system performance, reduce environmental impact, and ensure safer operating conditions. We also see tremendous potential in digital twins, robotics, and autonomous plant operations—innovations that promise to reshape the design, monitoring, and management of chemical processes. This collection of papers will appeal to a broad spectrum of researchers and practitioners in areas such as process systems engineering, control systems, digital transformation, and industrial automation. Our aim is to foster dialogue and collaboration among experts from academia and industry, uniting them in efforts to develop more sustainable, efficient, and resilient chemical engineering practices. We believe that the integration of AI and automation will help address longstanding challenges—ranging from real-time monitoring to large-scale production—and promote advancements in safety, adaptability, and resource utilization. We look forward to your contributions, which will play a pivotal role in guiding the future of chemical engineering toward more intelligent and automated approaches.

Dr. Dewei Wang
Dr. Yucheng Fu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	6.5	7.3	2020	20.4 Days	CHF 1800	Submit
Applied Sciences applsci	2.9	6.1	2011	15 Days	CHF 2400	Submit
ChemEngineering ChemEngineering	3.7	6.0	2017	28.3 Days	CHF 1800	Submit
Chemistry chemistry	2.6	4.4	2019	13 Days	CHF 1800	Submit
Processes processes	3.4	5.7	2013	14.7 Days	CHF 2400	Submit

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Published Papers (4 papers)

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All AI Applied Sciences ChemEngineering Chemistry Processes

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24 pages, 5300 KB  

Open AccessArticle

Use of Machine Learning to Predict the Performance of Tile Adhesive Mortars

by Cecília Bérgamo Biancardi and André Silva de Carvalho

Appl. Sci. 2026, 16(11), 5357; https://doi.org/10.3390/app16115357 - 27 May 2026

Viewed by 378

Abstract

Tile adhesive mortars are industrialized products used for installing ceramic coverings and are classified according to the Brazilian standard ABNT NBR 14081/2012 on the basis of tensile adhesion performance under different curing conditions. Their formulation directly affects both technical performance and manufacturing competitiveness, [...] Read more.

Tile adhesive mortars are industrialized products used for installing ceramic coverings and are classified according to the Brazilian standard ABNT NBR 14081/2012 on the basis of tensile adhesion performance under different curing conditions. Their formulation directly affects both technical performance and manufacturing competitiveness, while conventional product development remains slow, costly and strongly dependent on trial-and-error laboratory testing. This study evaluates whether historical industrial formulation data can support the retrospective prediction of approval or failure of tile adhesive mortars under ambient, oven, immersed and open-time curing conditions. A dataset comprising 6031 individual pull-off observations collected between 2021 and 2023 by a European multinational company in the construction materials sector was used to train and compare Logistic Regression, Random Forest, Boosted Decision Tree and Support Vector Machine models in R and Azure. The study was designed as an industrial-data modelling investigation rather than as a prospective optimization experiment. The results show that ensemble tree-based models, particularly Boosted Decision Tree and Random Forest, achieved the strongest predictive performance, whereas Logistic Regression remained more suitable for inferential interpretation of formulation variables. Model performance was uneven across curing conditions: prediction was more reliable for oven and immersed curing, whereas ambient curing and open time were affected by strong class imbalance and low failure prevalence. The findings indicate that Machine Learning can support formulation screening and quality-oriented decision-making for tile adhesive mortars, provided that its use remains restricted to the formulation ranges represented in the historical dataset and is complemented by prospective experimental validation before deployment in new product development. Full article

(This article belongs to the Topic Artificial Intelligence and Automation in Chemical Engineering)

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17 pages, 1310 KB  

Open AccessArticle

Suppressing High-Frequency Action Noise in DRL-Based Process Control: A Dual Strategy for Thermal Regeneration Column

by Shuaoyun Si, Jincheng Pan, Hui Wan and Guofeng Guan

Processes 2026, 14(10), 1598; https://doi.org/10.3390/pr14101598 - 14 May 2026

Viewed by 385

Abstract

Stochastic policy reinforcement learning (RL) algorithms are widely used in industrial control due to their strong exploration ability and high sample efficiency. However, these algorithms often produce large action fluctuations and noise, making them unsuitable for steady-state chemical processes. To solve this problem, [...] Read more.

Stochastic policy reinforcement learning (RL) algorithms are widely used in industrial control due to their strong exploration ability and high sample efficiency. However, these algorithms often produce large action fluctuations and noise, making them unsuitable for steady-state chemical processes. To solve this problem, this study uses a thermal regeneration column (TRC) as the research object and selects the Soft Actor-Critic (SAC) algorithm as the baseline. Three strategies are introduced to improve the SAC algorithm: an action-amplitude-constrained reward function, a low-pass filter, and a Kalman filter. Experimental results show that the combination of the action-amplitude-constrained reward function and the Kalman filter achieves the best performance. Compared with the traditional SAC algorithm, the fluctuation amplitudes of steam consumption, cooling water consumption, sulfur concentration and methanol makeup rate are reduced by 85.50%, 82.81%, 90.84% and 85.49%, respectively. In addition, the fluctuation amplitude of the reward function decreases by 90.68%. This method not only optimizes operating costs but also ensures the stable operation of the TRC. Full article

(This article belongs to the Topic Artificial Intelligence and Automation in Chemical Engineering)

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41 pages, 887 KB  

Open AccessReview

Advances in Photocatalytic Degradation of Crystal Violet Using ZnO-Based Nanomaterials and Optimization Possibilities: A Review

by Vladan Nedelkovski, Milan Radovanović and Milan Antonijević

ChemEngineering 2025, 9(6), 120; https://doi.org/10.3390/chemengineering9060120 - 1 Nov 2025

Cited by 21 | Viewed by 4863

Abstract

The photocatalytic degradation of Crystal Violet (CV) using ZnO-based nanomaterials presents a promising solution for addressing water pollution caused by synthetic dyes. This review highlights the exceptional efficiency of ZnO and its modified forms—such as doped, composite, and heterostructured variants—in degrading CV under [...] Read more.

The photocatalytic degradation of Crystal Violet (CV) using ZnO-based nanomaterials presents a promising solution for addressing water pollution caused by synthetic dyes. This review highlights the exceptional efficiency of ZnO and its modified forms—such as doped, composite, and heterostructured variants—in degrading CV under both ultraviolet (UV) and solar irradiation. Key advancements include strategic bandgap engineering through doping (e.g., Cd, Mn, Co), innovative heterojunction designs (e.g., n-ZnO/p-Cu₂O, g-C₃N₄/ZnO), and composite formations with graphene oxide, which collectively enhance visible-light absorption and minimize charge recombination. The degradation mechanism, primarily driven by hydroxyl and superoxide radicals, leads to the complete mineralization of CV into non-toxic byproducts. Furthermore, this review emphasizes the emerging role of Artificial Neural Networks (ANNs) as superior tools for optimizing degradation parameters, demonstrating higher predictive accuracy and scalability compared to traditional methods like Response Surface Methodology (RSM). Potential operational challenges and future directions—including machine learning-driven optimization, real-effluent testing potential, and the development of solar-active catalysts—are further discussed. This work not only consolidates recent breakthroughs in ZnO-based photocatalysis but also provides a forward-looking perspective on sustainable wastewater treatment strategies. Full article

(This article belongs to the Topic Artificial Intelligence and Automation in Chemical Engineering)

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21 pages, 2564 KB  

Open AccessArticle

Exploring the Physicochemical and Toxicological Study of G-Series and A-Series Agents Combining Molecular Dynamics and Quantitative Structure–Activity Relationship

by Michail Chalaris, Antonios Koufou, Sotiria Anastasiou, Pantelis-Alexandros Roupas and Georgios Nikolaou

ChemEngineering 2025, 9(4), 91; https://doi.org/10.3390/chemengineering9040091 - 18 Aug 2025

Cited by 4 | Viewed by 2105

Abstract

This study explores the physicochemical and toxicological properties of six G-series and A-series chemical warfare agents (Sarin, Soman, Tabun, A230, A232, and A234) using an integrated computational approach combining molecular dynamics (MD) simulations and Quantitative Structure–Activity Relationship (QSAR) modeling. For the A-series nerve [...] Read more.

This study explores the physicochemical and toxicological properties of six G-series and A-series chemical warfare agents (Sarin, Soman, Tabun, A230, A232, and A234) using an integrated computational approach combining molecular dynamics (MD) simulations and Quantitative Structure–Activity Relationship (QSAR) modeling. For the A-series nerve agents, both Ellison–Hoenig and Mirzayanov structural proposals were examined. MD simulations (10 ns, NPT ensemble) provided key thermodynamic properties, including density, molar heat capacity, and diffusivity. Simulated densities for G-agents (e.g., Sarin: 1.09 g/cm³, Soman: 1.03 g/cm³) and A-agents (e.g., A230: 1.608 g/cm³, Ellison–Hoenig model) closely matched experimental data. Heat capacities ranged from 258 to 462 J/mol·K, and self-diffusion coefficients revealed lower mobility for A-agents, especially under the Ellison–Hoenig configurations. QSAR modeling focused on lipophilicity (LogP) and acute toxicity (LD₅₀). Predicted LD₅₀ values ranged from 0.012 to 0.017 mg/kg for G-agents and up to 1.23 mg/kg for A-agents. A-234 showed the highest lipophilicity (LogP = 2.97) and toxicity (LD₅₀ = 0.51 mg/kg) within its group. Additional descriptors, such as molecular weight and polar surface area, supported toxicity predictions. Strong correlations emerged between MD-derived properties and QSAR outputs, validating the integrated approach. The combined use of MD and QSAR techniques provided a comprehensive view of the agents’ environmental behavior and toxicological impact, supporting safer assessment strategies and reinforcing the importance of multidisciplinary modeling for chemical threat mitigation. Full article

(This article belongs to the Topic Artificial Intelligence and Automation in Chemical Engineering)

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