The development of a waste management and classification system based on deep learning and Internet of Things DOI
Zhongyong Chen, Yao Xiao, Qi Zhou

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 197(1)

Published: Dec. 26, 2024

Language: Английский

Advancements in biomass waste conversion to sustainable biofuels via gasification DOI
Kunmi Joshua Abioye, Ricky Rajamanickam,

Temidayo O. Ogunjinmi

et al.

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: 505, P. 159151 - 159151

Published: Jan. 6, 2025

Language: Английский

Citations

2
Bayesian Hyperparameter Optimization of Machine Learning Models for Predicting Biomass Gasification Gases DOI Creative Commons
Pınar Cihan

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1018 - 1018

Published: Jan. 21, 2025

Predicting biomass gasification gases is crucial for energy production and environmental monitoring but poses challenges due to complex relationships variability. Machine learning has emerged as a powerful tool optimizing managing these processes. This study uses Bayesian optimization tune parameters various machine methods, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting (LightGBM), Elastic Net, Adaptive (AdaBoost), Regressor (GBR), K-nearest Neighbors (KNN), Decision Tree (DT), aiming identify the best model predicting compositions of CO, CO2, H2, CH4 under different conditions. Performance was evaluated using correlation coefficient (R), Root Mean Squared Error (RMSE), Absolute Percentage (MAPE), Relative (RAE), execution time, with comparisons visualized Taylor diagram. Hyperparameter optimization’s significance assessed via t-test effect size Cohen’s d. XGBoost outperformed other models, achieving high R values optimal conditions (0.951 0.954 0.981 0.933 CH4) maintaining robust performance suboptimal (0.889 0.858 0.941 0.856 CH4). In contrast, (KNN) Net showed poorest stability. underscores importance hyperparameter in enhancing demonstrates XGBoost’s superior accuracy robustness, providing valuable framework applying management monitoring.

Language: Английский

Citations

1
Exploring Insights in Biomass and Waste Gasification via Ensemble Machine Learning Models and Interpretability Techniques DOI Creative Commons
Ocident Bongomin, Charles Nzila, Josphat Igadwa Mwasiagi

et al.

International Journal of Energy Research, Journal Year: 2024, Volume and Issue: 2024(1)

Published: Jan. 1, 2024

This comprehensive review delves into the intersection of ensemble machine learning models and interpretability techniques for biomass waste gasification, a field crucial sustainable energy solutions. It tackles challenges like feedstock variability temperature control, highlighting need deeper understanding to optimize gasification processes. The study focuses on advanced modeling random forests gradient boosting, alongside methods Shapley additive explanations partial dependence plots, emphasizing their importance transparency informed decision‐making. Analyzing diverse case studies, explores successful applications while acknowledging overfitting computational complexity, proposing strategies practical robust models. Notably, finds consistently achieve high prediction accuracy (often exceeding R 2 scores 0.9) gas composition, yield, heating value. These (34% reviewed papers) are most applied method, followed by artificial neural networks (26%). Heating value (12%) was studied performance metric. However, is often neglected during model development due complexity permutation Gini importance. paper calls dedicated research utilizing interpreting models, especially co‐gasification scenarios, unlock new insights process synergy. Overall, this serves as valuable resource researchers, practitioners, policymakers, offering guidance enhancing efficiency sustainability gasification.

Language: Английский

Citations

8
Experimental study of syngas production from oil palm frond gasification based on bubble cap air distributor at low temperature DOI Creative Commons
Abeth Novria Sonjaya, Prima Zuldian, Ahmad Syihan Auzani

et al.

Case Studies in Chemical and Environmental Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 101143 - 101143

Published: Feb. 1, 2025

Language: Английский

Citations

0
Process simulation and BPNNM prediction for chemical looping co-gasification of rice husk and textile wastes as cement alternative fuels DOI

Congxi Tao,

Hao Wang, Qingmei Li

et al.

Biomass Conversion and Biorefinery, Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

Language: Английский

Citations

0
DÖNGÜSEL EKONOMİ KAPSAMINDA ATIK MİKTARLARININ BULANIK MANTIK İLE HESAPLANMASI: TÜRKİYE ÖRNEĞİ DOI Open Access
Nuh Okumuş, Abdullah Kargın

Adıyaman üniversitesi sosyal bilimler enstitüsü dergisi/Adıyaman üniversitesi sosyal bilimler enstitisü dergisi, Journal Year: 2025, Volume and Issue: 49, P. 704 - 736

Published: April 28, 2025

Bu çalışmada, Türkiye’deki 81 ilin ayrı yıllık atık miktarlarını belirlemek amacıyla yapay zeka destekli bir uygulama yapıldı. için bulanık mantık temelli Fuzzy Matlab ara yüzü kullanıldı. Uygulamanın kullanılabilirliğini görmek TÜİK tarafından belirlenen 2020 yılı toplam verileri Atıkların sınıflandırılmasında Sanayi, Maden-Termik, Sağlık Kuruluşu ve Nüfus kriterleri göz önünde bulunduruldu. sınıflandırma, verilerinin bu kategoriler altında sınıflandırılmış olması nedeniyle tercih edilmiştir. ile ilden yılında toplanması gereken miktarı belirlendi. Böylece illere göre miktarını tahmin etmek yönetimi süreçlerinde verimliliği artırma amaçlandı. Elde edilen bulgular, yerel yönetimlerin ilgili paydaşların stratejilerini iyileştirmelerine yönelik somut veriler sunarak, sıfır hedeflerine ulaşma yolunda daha etkin politikalar geliştirmelerine yardımcı olabilir. Yapay zekâ sistemlerinin kullanımının artırılması, yalnızca çevresel sürdürülebilirliği artırmakla kalmayacak, aynı zamanda atıkların ekonomiye kazandırılmasına da olanak sağlayacaktır. Sonuç olarak, tür araştırmalar, Türkiye’nin yönetiminde artırırken, hedefine adım atılmasına katkı sağlayacaktır

Citations

0
Recent advancements in biomass to bioenergy management and carbon capture through artificial intelligence integrated technologies to achieve carbon neutrality DOI

Shivani Chauhan,

Preeti Solanki, Chayanika Putatunda

et al.

Sustainable Energy Technologies and Assessments, Journal Year: 2024, Volume and Issue: 73, P. 104123 - 104123

Published: Dec. 7, 2024

Language: Английский

Citations

3
Analysis of 27 supervised machine learning models for the co-gasification assessment of peanut shell and spent tea residue in an open-core downdraft gasifier DOI

S. Joseph Sekhar,

Muralikrishna Boddu,

Arun S. Gopinath

et al.

Renewable Energy, Journal Year: 2024, Volume and Issue: 235, P. 121318 - 121318

Published: Sept. 7, 2024

Language: Английский

Citations

1
Artificial intelligence in plastic recycling and conversion: A review DOI
Yi Fang,

Yuming Wen,

Leilei Dai

et al.

Resources Conservation and Recycling, Journal Year: 2024, Volume and Issue: 215, P. 108090 - 108090

Published: Dec. 18, 2024

Language: Английский

Citations

0
The development of a waste management and classification system based on deep learning and Internet of Things DOI
Zhongyong Chen, Yao Xiao, Qi Zhou

et al.

Environmental Monitoring and Assessment, Journal Year: 2024, Volume and Issue: 197(1)

Published: Dec. 26, 2024

Language: Английский

Citations

0