Machine learning driven many‐objective moving horizon scheduling optimization
AIChE Journal, EarlyView.Abstract Industrial electrification can decarbonize chemical manufacturing, but it exposes operations to volatile electricity prices and carbon intensities. This work develops a machine learning‐enhanced many‐objective moving horizon scheduling framework that predicts objective correlation groupings from 48‐hour price and emission‐intensity profiles ...
Hongxuan Wang, Andrew Allman
wiley +1 more source
An improved accurate classification method for online education resources based on support vector machine (SVM): Algorithm and experiment. [PDF]
Educ Inf Technol (Dordr), 2022 Quan Z, Pu L.
europepmc +1 more source
AIChE Journal, EarlyView.Abstract Despite the growing use of ML in chemical engineering, the catalytic conversion of sulfur dioxide (SO2) to sulfur trioxide (SO3) remains underexplored from a data‐driven modeling perspective. This study evaluates an integrated workflow for literature‐derived SO2 oxidation data, combining data curation, preprocessing assessment, machine ...
Farough Agin +2 more
wiley +1 more source
Classification of Low Earth Orbit (LEO) Resident Space Objects' (RSO) Light Curves Using a Support Vector Machine (SVM) and Long Short-Term Memory (LSTM). [PDF]
Sensors (Basel), 2023 Qashoa R, Lee R.
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 +1 more
wiley +1 more source
Mapping Pu'er tea plantations from GF-1 images using Object-Oriented Image Analysis (OOIA) and Support Vector Machine (SVM). [PDF]
PLoS One, 2023 Liang L, Wang J, Deng F, Kong D.
europepmc +1 more source
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 +4 more
wiley +1 more source
Estimation and Optimization of Tool Wear in Conventional Turning of 709M40 Alloy Steel Using Support Vector Machine (SVM) with Bayesian Optimization. [PDF]
Materials (Basel), 2021 Alajmi MS, Almeshal AM.
europepmc +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Predictive Maintenance on Paving Machine Using Support Vector Machine (SVM)
Predictive maintenance is a strategy that uses machine operational data to predict potential damage before it occurs, thereby reducing downtime and maintaining the availability of production equipment. In 2024, PT XYZ experienced 342.75 hours of downtime with 82.30% availability and an output of 3,950,661 units, requiring more structured maintenance ...
Aidah Fidayanti, Tedjo Sukmono
openaire +2 more sources