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Digital Chemical Engineering
Scopus
ISSN 2772-5081 · Elsevier · 318 articles in the index
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Journal Article
Data-driven safety analytics for dust explosion prevention
This study introduces a two-phase data-driven multi-model framework to automate the holistic understanding of dust explosions prevention through critical safety drivers. The proposed framework integrates the Natural Language Processing (NLP), Self-Organizing Maps (SOM), and Bayesian Networks (BN)…
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Journal Article
Transfer learning of data-driven crystallisation processes via constrained Neural Ordinary Differential Equations
Modelling complex crystallisation processes remains challenging due to limited experimental datasets, high measurement noise, and the need for generalisability across varying operating conditions. Neural Ordinary Differential Equations (NODEs) and transfer learning (TL) offer promising tools to…
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Journal Article
Self-learning multichannel olfactory display
Recreating odors is considerably more challenging than, for example, recreating colors. Odor recreation necessitates knowledge of chemistry, sensory perception, sensors, automation, artificial intelligence, and machine learning. In general, recreating an odor requires analyzing its chemical…
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Journal Article
Design space determination of freezing processes for human induced pluripotent stem cell-derived spheroids using hybrid modeling
This work presents the design space determination of freezing processes for human induced pluripotent stem (hiPS) cell-derived spheroids using hybrid modeling. First, a mechanistic model was developed considering spheroid characteristics and freezing phenomena. The developed model was then extended…
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Journal Article
Kolmogorov–Arnold and deep learning networks for industrial explainable product quality prediction
In the chemical process industry, the frequent measurement of quality variables is not always a viable path to take. It can be due to the measurement being time- or cost-consuming, leading to a sampling time of hours or even a day. As such, soft sensors were developed as a solution to this problem…
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Journal Article
Robust reinforcement learning for nonlinear process control with stability guarantees
Reinforcement learning (RL) offers a promising route to fast, nonlinear feedback control for complex process systems; however, its deployment is hindered by the lack of formal stability guarantees and sensitivity to model-plant mismatch under constraints. This paper proposes a stability and…
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Journal Article
All you need is noise — from feature selection to explainable industrial AI
Modern chemical plants record thousands of sensor tags, yet only a small fraction meaningfully influence yield, quality, or throughput. Identifying those key drivers is often more difficult than building the predictive model itself. In this work, we show that appending one or more Synthetic Noise…
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Journal Article
Advanced machine learning techniques for predicting solution gas-oil ratios in petroleum reservoirs: A comprehensive study and new empirical correlation
The precise assessment of crude oil properties is crucial for various tasks in reservoir evaluation, including estimating reserves, enhancing oil recovery, predicting oil reservoir performance, designing pipelines and production equipment, and simulating reservoirs. Therefore, the reservoir fluid…
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Journal Article
Simulation of TCM-based ionic liquids density behavior in a mixture with ethanol using machine learning approaches
The density behavior of TCM-based ionic liquids (ILs) mixed with ethanol forms the focus of this investigation, highlighting their distinctive physicochemical characteristics and industrial relevance. This research utilizes a suite of sophisticated machine learning methods, encompassing K-Nearest…
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Journal Article
DFT in catalysis: Complex equations for practical computing applications in chemistry
Density Functional Theory (DFT) has become the cornerstone of modern computational catalysis, providing a practical balance between accuracy and efficiency in describing molecular structure, bonding, and reactivity. This review presents a comprehensive overview of DFT methodology, from its…
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Journal Article
Online learning supported surrogate-based flowsheet model maintenance
Surrogate-based flowsheet model calibration is a critical extension of using flowsheet models in Digital Twin (DT) systems. However, maintaining accurate surrogates over time is increasingly challenging, especially when models are deployed in real-time or near-real-time environments, where…
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Journal Article
Smart control of heavy metal adsorption onto LDC wastes for Industry 4.0 applications
Due to the complexity and the need for automation in adsorption systems, this study develops a decision-making model using 20 Machine Learning Algorithms (MLAs), Response Surface Methodology (RSM), and lab tests. Inputs include pH, metal type, concentration, adsorbent mass, and time; output is…
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Journal Article
Early-stage chemical process screening through hybrid modeling: Introduction and case study of a reaction–crystallization process
Late-stage development of complex chemical processes presents significant challenges due to the high dimensionality and interactions of operating parameters. This complexity renders traditional factorial experimental designs impractical. Consequently, there is often a default reliance on suboptimal…
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Journal Article
Data driven prediction of hydrochar yields from biomass hydrothermal carbonization using extreme gradient boosting algorithm with principal component analysis
Dimensionality reduction plays a critical role in efficiently managing large and complex datasets in machine learning (ML) applications. This study presents an innovative integration of principal component analysis (PCA) and extreme gradient boosting (XGB) to model the hydrothermal carbonization…
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Journal Article
Control mode switching for guaranteed detection of false data injection attacks on process control systems
Control-enabled cyberattack detection approaches are necessary for enhancing the cybersecurity of process control systems (PCSs), as evidenced by recent successful cyberattacks against these systems. One type of cyberattack is false data injection attacks (FDIAs), which manipulate data over…
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Journal Article
A dual-route ammonia process: Combining renewable and low-carbon pathways
Ammonia is increasingly recognized as a versatile hydrogen carrier and energy vector with the potential to decarbonize industrial processes and global energy trade. Among the emerging production routes, blue ammonia—derived from natural gas with integrated carbon capture and storage (CCS)—offers a…
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Journal Article
Optimal rules base development in Type-2 fuzzy logic for stochastic chemical control system
This study proposes the optimal development of rule bases using Type-2 fuzzy logic specifically designed for stochastic chemical control systems. The research addresses complexities and uncertainties inherent in stochastic pH neutralisation processes with Type-2 fuzzy logic as inversed hybrid model…
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Journal Article
Grammar-guided symbolic regression as tool for model development of enzymatic reactions
The transition of the chemical industry toward renewable biocatalysis requires robust kinetic models to scale and optimize enzymatic processes. However, deriving these models typically relies on manual, trial-and-error fitting of predefined equations, while data-driven machine learning approaches…
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Journal Article
Human-in-the-loop validation of a decision-support interface for blast furnace operation
To achieve the efficient and stable operation of a blast furnace (BF) in the steel industry while retaining the critical skills of expert human operators, we developed a novel human-machine interface (HMI) grounded in ecological interface design (EID) principles. The BF is a complex, large-scale…
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Journal Article
Model-based optimization of enzyme cocktails for sustainable lignocellulosic biomass saccharification
Lignocellulosic biomass has a high potential as resource for the chemical industry, but its conversion to fermentable sugars requires efficient pre-treatment and a subsequent enzymatic saccharification. Here, we present a model-based workflow for the design of enzyme cocktails for lignocellulosic…