Applications of machine learning to the analysis of engine in-cylinder flow and thermal process: A review and outlook
F Zhao, DLS Hung - Applied Thermal Engineering, 2023 - Elsevier
To adequately elucidate the complex in-cylinder flow structures and its underlying effects on
the thermal processes inside an internal combustion engine (ICE) has long been a daunting …
the thermal processes inside an internal combustion engine (ICE) has long been a daunting …
Prediction of biodiesel production from microalgal oil using Bayesian optimization algorithm-based machine learning approaches
N Sultana, SMZ Hossain, M Abusaad, N Alanbar… - Fuel, 2022 - Elsevier
Biodiesel has appeared as a renewable and clean energy resource and a means of
diminishing global warming. This study provides Bayesian optimization algorithm (BOA) …
diminishing global warming. This study provides Bayesian optimization algorithm (BOA) …
Forecasting domestic waste generation during successive COVID-19 lockdowns by Bidirectional LSTM super learner neural network
Accurate prediction of domestic waste generation is a challenging task for municipalities to
implement sustainable waste management strategies. In the present study, domestic waste …
implement sustainable waste management strategies. In the present study, domestic waste …
Bayesian optimization algorithm-based statistical and machine learning approaches for forecasting short-term electricity demand
This article focuses on developing both statistical and machine learning approaches for
forecasting hourly electricity demand in Ontario. The novelties of this study include (i) …
forecasting hourly electricity demand in Ontario. The novelties of this study include (i) …
Biohydrogen from food waste: modeling and estimation by machine learning based super learner approach
This study demonstrated the application of a hybrid Bayesian algorithm (BA) and support
vector regression (SVR) as a potential super-learner tool (BA-SVR) to predict biohydrogen …
vector regression (SVR) as a potential super-learner tool (BA-SVR) to predict biohydrogen …
Modeling of microbial fuel cell power generation using machine learning-based super learner algorithms
Electricity generation from microbial fuel cells (MFCs) is a potential environment-friendly
technology. This study provides Bayesian Algorithm (BA) based Support Vector Regression …
technology. This study provides Bayesian Algorithm (BA) based Support Vector Regression …
Intelligent modeling with physics-informed machine learning for petroleum engineering problems.
The advancement in big data and artificial intelligence has enabled a novel exploration
mode for the study of petroleum engineering. Unlike theory-based solution methods, the …
mode for the study of petroleum engineering. Unlike theory-based solution methods, the …
[HTML][HTML] A hybrid BOA-SVR approach for predicting aerobic organic and nitrogen removal in a gas-liquid-solid circulating fluidized bed bioreactor
This study introduces the hybrid of the Bayesian optimization algorithm and support vector
regression (BOA-SVR) models to predict the removal of aerobic organic (total chemical …
regression (BOA-SVR) models to predict the removal of aerobic organic (total chemical …
Deep-learning-based reduced-order modeling to optimize recuperative burner operating conditions
This study analyzed a recuperative burner system that is critical for energy efficiency and
pollutant reduction in the firing processes required in the manufacturing industries. We …
pollutant reduction in the firing processes required in the manufacturing industries. We …
Numerical analysis of combustion dynamics in a full-scale rotating detonation rocket engine using large eddy simulations
Large eddy simulations (LESs) using detailed chemistry and leveraging adaptive mesh
refinement (AMR) are performed to gain insights into the combustion dynamics within a full …
refinement (AMR) are performed to gain insights into the combustion dynamics within a full …