Modelling of thermochemical processes of waste recycling: A review

被引:2
作者
Han, Bing [1 ]
Kumar, Dileep [1 ]
Pei, Yang [1 ]
Norton, Michael [1 ]
Adams, Scott D. [1 ]
Khoo, Sui Yang [1 ]
Kouzani, Abbas Z. [1 ]
机构
[1] Deakin Univ, Sch Engn, Geelong, Vic 3216, Australia
关键词
Modelling; Software; Pyrolysis; Circular economy; Waste recycling; BIOMASS FAST PYROLYSIS; LIGNOCELLULOSIC BIOMASS; TECHNOECONOMIC ANALYSIS; NEURAL-NETWORK; SEWAGE-SLUDGE; HEAT-TRANSFER; GASIFICATION; SIMULATION; REACTOR; KINETICS;
D O I
10.1016/j.jaap.2024.106687
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Thermochemical processes such as pyrolysis and gasification are excellent platforms for recycling and valorisation of agriculture, municipal and mining waste. The advancement of these processes involves successfully navigating and resolving intricate challenges, and saving significant effort and time. Considerable efforts have been made to formulate methodologies for computer-assisted modelling of thermochemical conversion processes. These efforts aim to pinpoint optimal operational parameters that enhance performance, reduce costs, and promote environmental sustainability. This study provides a comprehensive review of the contemporary advancements in modelling the thermochemical conversion of waste materials. Special attention is directed towards the recent progressions in chemical process modelling, Computational Fluid Dynamics (CFD), and Machine Learning (ML) models. The merits of each modelling technique are individually underscored, while certain limitations and bottlenecks within existing models are pinpointed as areas warranting further investigation. Through the integration of advanced modelling approaches, the future outlook of waste recycling targets achieving high efficiency and sustainability, addressing the escalating challenges posed by burgeoning global waste streams.
引用
收藏
页数:10
相关论文
共 50 条
[41]   An Overview of the Non-Energetic Valorization Possibilities of Plastic Waste via Thermochemical Processes [J].
Moussa, Kazem ;
Awad, Sary ;
Krawczak, Patricia ;
Al Takash, Ahmad ;
Faraj, Jalal ;
Khaled, Mahmoud .
MATERIALS, 2024, 17 (07)
[42]   Recycling of seashell waste in concrete: A review [J].
Mo, Kim Hung ;
Alengaram, U. Johnson ;
Jumaat, Mohd Zamin ;
Lee, Siew Cheng ;
Goh, Wan Inn ;
Yuen, Choon Wah .
CONSTRUCTION AND BUILDING MATERIALS, 2018, 162 :751-764
[43]   A feasibility assessment of an integrated plastic waste system adopting mechanical and thermochemical conversion processes [J].
Mastellone M.L. .
Resources, Conservation and Recycling: X, 2019, 4
[44]   Recent advances of thermochemical conversion processes for biorefinery [J].
Seo, Myung Won ;
Lee, See Hoon ;
Nam, Hyungseok ;
Lee, Doyeon ;
Tokmurzin, Diyar ;
Wang, Shuang ;
Park, Young-Kwon .
BIORESOURCE TECHNOLOGY, 2022, 343
[45]   Recent advances and future prospects of thermochemical biofuel conversion processes with machine learning [J].
Jeon, Pil Rip ;
Moon, Jong-Ho ;
Ogunsola, Nafiu Olanrewaju ;
Lee, See Hoon ;
Ling, Jester Lih Jie ;
You, Siming ;
Park, Young-Kwon .
CHEMICAL ENGINEERING JOURNAL, 2023, 471
[46]   Comprehensive Review of Textile Waste Recycling: From Origins to Innovations [J].
Kim, Eun Hyup ;
Lee, Hoik .
FIBERS AND POLYMERS, 2025, 26 (04) :1449-1464
[47]   Thermochemical Conversion of Animal-Derived Waste: A Mini-Review with a Focus on Chicken Bone Waste [J].
Macavei, Mircea Gabriel ;
Gheorghe, Virginia-Cora ;
Ionescu, Gabriela ;
Volceanov, Adrian ;
Patrascu, Roxana ;
Marculescu, Cosmin ;
Magdziarz, Aneta .
PROCESSES, 2024, 12 (02)
[48]   Machine learning for municipal sludge recycling by thermochemical conversion towards sustainability [J].
Sun, Lianpeng ;
Li, Mingxuan ;
Liu, Bingyou ;
Li, Ruohong ;
Deng, Huanzhong ;
Zhu, Xiefei ;
Zhu, Xinzhe ;
Tsang, Daniel C. W. .
BIORESOURCE TECHNOLOGY, 2024, 394
[49]   Review and recommendations for sustainable pathways of recycling commodity plastic waste across different economic regions [J].
Darko, Charles ;
Yung, Plisylia Wong Shi ;
Chen, Anlong ;
Acquaye, Adolf .
RESOURCES ENVIRONMENT AND SUSTAINABILITY, 2023, 14
[50]   Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review [J].
Guo, Hao-nan ;
Wu, Shu-biao ;
Tian, Ying-jie ;
Zhang, Jun ;
Liu, Hong-tao .
BIORESOURCE TECHNOLOGY, 2021, 319