A review of biowaste remediation and valorization for environmental sustainability: Artificial intelligence approach
Biowaste remediation and valorization for environmental sustainability focuses on
prevention rather than cleanup of waste generation by applying the fundamental recovery …
prevention rather than cleanup of waste generation by applying the fundamental recovery …
Biochar as cement replacement to enhance concrete composite properties: A review
In recent years, concrete has been accessible and economical in the construction industry,
resulting in high demand for its components. Cement is known for its negative impact on the …
resulting in high demand for its components. Cement is known for its negative impact on the …
Interpretation and Prediction of the CO2 Sequestration of Steel Slag by Machine Learning
B He, X Zhu, Z Cang, Y Liu, Y Lei, Z Chen… - Environmental …, 2023 - ACS Publications
The utilization of steel slag for CO2 sequestration is an effective way to reduce carbon
emissions. The reactivity of steel slag in CO2 sequestration depends mainly on material and …
emissions. The reactivity of steel slag in CO2 sequestration depends mainly on material and …
Rapid identification of reactivity for the efficient recycling of coal fly ash: Hybrid machine learning modeling and interpretation
As the main solid waste produced by coal combustion, the large accumulation of coal fly ash
(CFA) causes serious environmental pollution and resource waste. Whether CFA can be …
(CFA) causes serious environmental pollution and resource waste. Whether CFA can be …
Application of Random Forest Model Integrated with Feature Reduction for Biomass Torrefaction
X Liu, H Yang, J Yang, F Liu - Sustainability, 2022 - mdpi.com
A random forest (RF) model integrated with feature reduction was implemented to predict the
properties of torrefied biomass based on feedstock and torrefaction conditions. Four features …
properties of torrefied biomass based on feedstock and torrefaction conditions. Four features …
Supervised and unsupervised machine learning for elemental changes evaluation of torrefied biochars
C Zhang, CB Felix, WH Chen, Y Zhang - Energy, 2024 - Elsevier
This study implements a comprehensive analysis of supervised and unsupervised learning
to evaluate elemental changes and investigate the merits of the machine learning method …
to evaluate elemental changes and investigate the merits of the machine learning method …
Predicting the high heating value and nitrogen content of torrefied biomass using a support vector machine optimized by a sparrow search algorithm
L Xiaorui, Y Jiamin, Y Longji - RSC advances, 2023 - pubs.rsc.org
A support vector machine (SVM) model with RBF kernel function combined with sparrow
search algorithm (SSA) optimization was developed to predict the HHV and nitrogen content …
search algorithm (SSA) optimization was developed to predict the HHV and nitrogen content …
Using Block Kriging as a Spatial Smooth Interpolator to Address Missing Values and Reduce Variability in Maize Field Yield Data
TM Koutsos, GC Menexes, IG Eleftherohorinos… - Agronomy, 2023 - mdpi.com
Block Kriging (a spatial interpolation method) and log10 transformation were compared for
their effectiveness in reducing relative variance (coefficient of variance: CV) and estimate …
their effectiveness in reducing relative variance (coefficient of variance: CV) and estimate …
Prediction of Fuel Properties of Torrefied Biomass Based on Back Propagation Neural Network Hybridized with Genetic Algorithm Optimization
X Liu, H Yang, J Yang, F Liu - Energies, 2023 - mdpi.com
Torrefaction is an effective technology to overcome the defects of biomass which are
adverse to its utilization as solid fuels. For assessing the torrefaction process, it is essential …
adverse to its utilization as solid fuels. For assessing the torrefaction process, it is essential …