Results 201 to 210 of about 1,208,874 (255)

pyDMS: A Python package for the determination of physics‐informed dual‐mode sorption (DMS) parameters

AIChE Journal, EarlyView.
Abstract Sorption in glassy polymer membranes is commonly modeled with the dual‐mode sorption (DMS) model. Fitting the DMS model to sorption isotherms presents challenges, as multiple parameter sets may prove satisfactory. This work presents pyDMS, an open‐source Python package for the computation of DMS parameters obtained via a physics‐informed ...
Brandon C. Tapia, Pablo A. Dean, Jing Ying Yeo, Albert X. Wu, Zachary P. Smith   +4 more
wiley   +1 more source

Modular design and supervisory control for enhanced economic resilience in continuous pharmaceutical manufacturing

AIChE Journal, EarlyView.
Abstract Conventional continuous plants use fixed‐capacity equipment, resulting in high capital risk and suboptimal performance under fluctuating demands. To address these, this study proposes an integrated framework for modular process design and supervisory control capable of adapting to demand uncertainty for long‐term economic viability.
T. Asrav, M. Alvarado‐Morales, G. Sin
wiley   +1 more source

Model predictive control with inline parameter adaptation for direct crystal growth rate regulation

AIChE Journal, EarlyView.
Abstract In batch cooling crystallization, many interactive factors, including supersaturation, reactor dimensions, and operating conditions, govern crystal growth and significantly influence product properties. Variables such as temperature or refractive index are used as surrogate control variables but have limitations in capturing growth rate ...
Huitian Yu, Jiewen Zhao, Heiko Briesen
wiley   +1 more source

Monte Carlo simulation and experimental validation of SPECT systems via energy spectrum decomposition and image profile analysis. [PDF]

EJNMMI Phys
Peng Z, Wasaye MA, Siddique I, Wali F, Saeed M.   +4 more
europepmc   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Range of dose enhancement factor for gold nanoparticles in radiotherapy using Monte Carlo simulations. [PDF]

Radiat Environ Biophys
Li WB, Bernal MA, Francis Z.
europepmc   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Estimating the Economic Impact of Neonatal Calf Diarrhea Agents With Decision Tree Analysis and the Monte Carlo Simulation Model. [PDF]

Vet Med Sci
Küçükoflaz M, Özbek V, Sariözkan S, Aydin E, Danaci UK, Zaman Cİ, Ekinci G, Aydin AK.   +7 more
europepmc   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source