AI in chemical engineering: From promise to practice
Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
Solubility of Glibenclamide in supercritical solvent versus pressure and temperature via development of machine learning and rain optimization algorithm. [PDF]
Alotaibi HF +9 more
europepmc +1 more source
ABSTRACT Hybrid modeling combines first‐principles equations with a data‐driven subcomponent. Training for the data‐driven part is sensitive to measurement noise when training targets are constructed using pointwise time derivatives. Beyond differentiation errors, hybrid models involve solving an inverse problem to estimate the data‐driven term, which ...
Hangjun Cho +4 more
wiley +1 more source
Utilizing deep learning algorithms and artificial neural networks to forecast the viscosity, thermal conductivity, and electrical conductivity of Fe<sub>3</sub>O<sub>4</sub>/TiO<sub>2</sub> magnetic hybrid nanofluid. [PDF]
Jebur SK +6 more
europepmc +1 more source
Abstract The demand for LiOH is driven by the growth of the electric vehicle industry. Evaporative crystallization of LiOH·H2O is energy intensive, whereas ethanol‐based antisolvent crystallization has emerged as a more sustainable alternative. From a process design perspective, the crystallization yield depends on the ethanol dosage, and thermodynamic
Xiaoqi Xu +3 more
wiley +1 more source
A Post-Quantum Authentication and Key Agreement Protocol Based on Lattice-Based KEM for Secure Network Environments. [PDF]
Chen X, Wu W, Liang G, Tan H, Yu Y.
europepmc +1 more source
CFD modeling and sensitivity‐guided design of silicon filament CVD reactors
Abstract Filament‐based chemical vapor deposition (CVD) for silicon (Si) coatings is often treated as an adaptation of planar deposition. But this overlooks fundamental shifts in transport phenomena and reaction kinetics. In filament CVD, the filament acts as a substrate, heat source, and flow disruptor simultaneously. In this work, we ask: What really
G. P. Gakis +8 more
wiley +1 more source
Rectification of Material Model for Fibrous Materials in Compressive Mode. [PDF]
Petronienė JJ +3 more
europepmc +1 more source
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley +1 more source
Quadratic Motion Polynomials with Irregular Factorizations. [PDF]
Thimm DA +3 more
europepmc +1 more source

