Economic, environmental and multi objective optimization of a clean tri-generation system based co-firing of natural gas and biomass: An emergy evaluation DOI

Haofeng Lin,

Ibrahim B. Mansir, Hawzhen Fateh M. Ameen

et al.

Process Safety and Environmental Protection, Journal Year: 2023, Volume and Issue: 173, P. 289 - 303

Published: March 8, 2023

Language: Английский

Applications of machine learning in thermochemical conversion of biomass-A review DOI
Muzammil Khan, Salman Raza Naqvi, Zahid Ullah

et al.

Fuel, Journal Year: 2022, Volume and Issue: 332, P. 126055 - 126055

Published: Sept. 24, 2022

Language: Английский

Citations

141
Machine-learning-aided thermochemical treatment of biomass: a review DOI Creative Commons
Hailong Li,

Jiefeng Chen,

Weijin Zhang

et al.

Biofuel Research Journal, Journal Year: 2023, Volume and Issue: 10(1), P. 1786 - 1809

Published: Feb. 28, 2023

Thermochemical treatment is a promising technique for biomass disposal and valorization. Recently, machine learning (ML) has been extensively used to predict yields, compositions, properties of biochar, bio-oil, syngas, aqueous phases produced by the thermochemical biomass. ML demonstrates great potential aid development processes. The present review aims 1) introduce schemes strategies as well descriptors input output features in processes; 2) summarize compare up-to-date research both ML-aided wet (hydrothermal carbonization/liquefaction/gasification) dry (torrefaction/pyrolysis/gasification) (i.e., predicting oil/char/gas/aqueous thermal conversion behavior or kinetics); 3) identify gaps provide guidance future studies concerning how improve predictive performance, increase generalizability, mechanistic application studies, effectively share data models community. processes envisaged be greatly accelerated near future.

Language: Английский

Citations

96
Co-pyrolysis of lignocellulosic biomass with other carbonaceous materials: A review on advance technologies, synergistic effect, and future prospectus DOI Open Access
Wei‐Hsin Chen,

C. Naveen,

Praveen Kumar Ghodke

et al.

Fuel, Journal Year: 2023, Volume and Issue: 345, P. 128177 - 128177

Published: March 27, 2023

Language: Английский

Citations

64
Generative AI and process systems engineering: The next frontier DOI
Benjamin Decardi‐Nelson, Abdulelah S. Alshehri, Akshay Ajagekar

et al.

Computers & Chemical Engineering, Journal Year: 2024, Volume and Issue: 187, P. 108723 - 108723

Published: May 9, 2024

Language: Английский

Citations

20
Hydrogen production optimization from sewage sludge supercritical gasification process using machine learning methods integrated with genetic algorithm DOI
Zeeshan Haq, Hafeez Ullah, Muhammad Nouman Aslam Khan

et al.

Process Safety and Environmental Protection, Journal Year: 2022, Volume and Issue: 184, P. 614 - 626

Published: June 15, 2022

Language: Английский

Citations

58
Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects DOI Creative Commons
Muzammil Khan, Wachiranon Chuenchart, K.C. Surendra

et al.

Bioresource Technology, Journal Year: 2022, Volume and Issue: 370, P. 128501 - 128501

Published: Dec. 17, 2022

Language: Английский

Citations

54
Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction DOI
Zeeshan Haq, Hafeez Ullah, Muhammad Nouman Aslam Khan

et al.

Bioresource Technology, Journal Year: 2022, Volume and Issue: 363, P. 128008 - 128008

Published: Sept. 22, 2022

Language: Английский

Citations

42
Recent advances and future prospects of thermochemical biofuel conversion processes with machine learning DOI
Pil Rip Jeon, Jong-Ho Moon, Nafiu Olanrewaju Ogunsola

et al.

Chemical Engineering Journal, Journal Year: 2023, Volume and Issue: 471, P. 144503 - 144503

Published: July 1, 2023

Language: Английский

Citations

42
Hydrogen production from plastic waste: A comprehensive simulation and machine learning study DOI Creative Commons

Mohammad Lahafdoozian,

Hossein Khoshkroudmansouri,

Sharif H. Zein

et al.

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 59, P. 465 - 479

Published: Feb. 8, 2024

Gasification, a highly efficient method, is under extensive investigation due to its potential convert biomass and plastic waste into eco-friendly energy sources valuable fuels. Nevertheless, there exists gap in comprehension regarding the integrated thermochemical process of polystyrene (PS) polypropylene (PP) capability produce hydrogen (H2) fuel. In this study comprehensive simulation using quasi-equilibrium approach based on minimizing Gibbs free has been introduced. To enhance H2 content, water-gas shift (WGS) reactor pressure swing adsorption (PSA) unit were for effective separation, increasing production 27.81 kg/h. investigate operating conditions effects three key variables gasification namely temperature, feedstock flow rate have explored sensitivity analysis. Furthermore, several machine learning models utilized discover optimize maximum capacity production. The analysis reveals that elevating temperature from 500 °C 1200 results higher up 23 % carbon monoxide (CO). However, generating above 900 does not lead significant upturn capacity. Conversely, an increase within shown decrease system both CO. Moreover, mass gasifying agent 250 kg/h be merely productive generation, almost 5 increase. Regarding pressure, yield decreases 22.64 17.4 with 1 10 bar. It also revealed more predominant effect Cold gas efficiency (CGE) compared Highest CGE Has by PP at °C. Among various models, Random Forest (RF) model demonstrates robust performance, achieving R2 values exceeding 0.99.

Language: Английский

Citations

14
A hybrid approach combining mechanism-guided data augmentation and machine learning for biomass pyrolysis DOI
Peng Jiang, Jing Fan, Lin Li

et al.

Chemical Engineering Science, Journal Year: 2024, Volume and Issue: 296, P. 120227 - 120227

Published: May 8, 2024

Language: Английский

Citations

10