Modeling the impact of some independent parameters on the syngas characteristics during plasma gasification of municipal solid waste using artificial neural network and stepwise linear regression methods

Chu, Chih‐Peng; Boré, Abdoulaye; Liu, X.W.; Cui, Junkui; Wang, Peisheng; Liu, X.; Chen, G.Y.; Liu, B.; Ma, Wenchao; Liu, Ziyang; Tao, Yousheng; Bary, Andy I.

doi:10.1016/j.rser.2021.112052

Cited by 22 publications

(8 citation statements)

References 67 publications

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“…The plasma gasification process is a leading method for solid-waste treatment [87-133] and has been reviewed quite extensively over the past several years [82,[94][95][96]99,134,135]. This process, which usually involves thermal plasmas, is considered very effective and environmentally friendly [109,110,134,[136][137][138][139][140][141][142][143][144][145][146]. One important thing to note about the plasma gasification process is that it is a purely thermal process.…”

Section: Plasma Gasificationmentioning

confidence: 99%

“…It is interesting to see just how many areas of waste management plasma gasification is used in: the treatment of medical waste [141,155,156] and municipal solid waste (MSW) [142,157], as well as deriving fuel from enhanced landfill mining (ELM) [158] and from the conversion of coal [159] and biomass [160,161], as well as plenty more.…”

Section: Plasma Gasificationmentioning

confidence: 99%

“…However, since this type of plasma is inherently characterised by a high input power and a high pressure, this is not an optimal solution. One can only hope that the theoretical framework of plasma gasification is further developed to fix its setbacks [82], and that the development of numerical models [142,156,157,161,[164][165][166][167][168][169][170] will optimise the efficiency and effectiveness of this process in the future. For example, it may be advantageous for future numerical methods to simulate a situation in which a temperature limit is set for the plasma discharge, such that the target material is selectively thermally decomposed.…”

Section: Plasma Gasificationmentioning

confidence: 99%

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Applications of Plasma Technologies in Recycling Processes

Kusano,

Kusano

2024

Materials

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Plasmas are reactive ionised gases, which enable the creation of unique reaction fields. This allows plasmas to be widely used for a variety of chemical processes for materials, recycling among others. Because of the increase in urgency to find more sustainable methods of waste management, plasmas have been enthusiastically applied to recycling processes. This review presents recent developments of plasma technologies for recycling linked to economical models of circular economy and waste management hierarchies, exemplifying the thermal decomposition of organic components or substances, the recovery of inorganic materials like metals, the treatment of paper, wind turbine waste, and electronic waste. It is discovered that thermal plasmas are most applicable to thermal processes, whereas nonthermal plasmas are often applied in different contexts which utilise their chemical selectivity. Most applications of plasmas in recycling are successful, but there is room for advancements in applications. Additionally, further perspectives are discussed.

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Section: Plasma Gasificationmentioning

confidence: 99%

Section: Plasma Gasificationmentioning

confidence: 99%

Section: Plasma Gasificationmentioning

confidence: 99%

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Applications of Plasma Technologies in Recycling Processes

Kusano,

Kusano

2024

Materials

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“…In one study where primary data (with operational parameters) was used as model inputs, performance of MLR to model gasification was comparable to ML models (SVR and RT) . However, in another study that used secondary data comprising similar inputs, MLR was outperformed by ANN . Secondary data that included operational parameters and feedstock properties were also used to model the gasification processes of multiple wet feedstocks (animal waste, organic municipal solid waste, and sewage sludge) using XGBoost. , Gasification has been modeled and compared among a variety of ML methods such as ANN, , SVR, ,, RT, , RFR, , and XGBoost .…”

Section: Applications Of Data Science In Rrcc From Organic Waste Streamsmentioning

confidence: 99%

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Zaki,

Rowles,

Adjeroh

et al. 2023

ACS EST Eng.

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Municipal and agricultural organic waste can be treated to recover energy, nutrients, and carbon through resource recovery and carbon capture (RRCC) technologies such as anaerobic digestion, struvite precipitation, and pyrolysis. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively. We critically reviewed 616 peer-reviewed articles on the use of data science in RRCC published during 2002−2022. Although applications of machine learning (ML) methods have drastically increased over time for modeling RRCC technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts of RRCC. Integration of ML methods with LCA and TEA has the potential to costeffectively investigate the trade-off between efficiency and sustainability of RRCC, although the literature lacked such integration of techniques. Therefore, we propose an integrated data science framework to inform efficient and sustainable RRCC from organic waste based on the review. Overall, the findings from this review can inform practitioners about the effective utilization of various data science methods for real-world implementation of RRCC technologies.

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“…More recently, Chu et al 225 applied stepwise linear regression (SLR) and ANN methods to develop quantitative models for eight kinds of syngas characteristics and explored the simultaneous effects of input parameters during the plasma gasification by compiling 112 research cases. The ANN model demonstrates better performance than the SLR model for low heating value (LHV), dry gas ratio, and volume fraction of H 2 and CO, with R testing 2 = 0.807–0.939.…”

Section: Applications Of Reactive Plasmas On Up‐carbonizationmentioning

confidence: 99%

Plasma‐electrified up‐carbonization for low‐carbon clean energy

et al. 2022

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Low-value, renewable, carbon-rich resources, with different biomass feedstocks and their derivatives as typical examples, represent virtually inexhaustive carbon sources and carbon-related energy on Earth. Upon conversion to higher-value forms (referred to as "up-carbonization" here), these abundant feedstocks provide viable opportunities for energy-rich fuels and sustainable platform chemicals production. However, many of the current methods for such up-carbonization still lack sufficient energy, cost, and material efficiency, which affect their economics and carbon-emissions footprint. With external electricity precisely delivered, discharge plasmas enable many stubborn reactions to occur under mild conditions, by creating locally intensified and highly reactive environments. This technology emerges as a novel, versatile technology platform for integrated or stand-alone conversion of carbon-rich resources. The plasma-based processes are compatible for integration with increasingly abundant and cost-effective renewable electricity, making the whole conversion carbon-neutral and further paving the plasma-electrified upcarbonization to be performance-, environment-, and economics-viable.Despite the chief interest in this emerging area, no review article brings together the state-of-the-art results from diverse disciplines and underlies basic mechanisms and chemistry underpinned. As such, this review aims to fill this gap and provide basic guidelines for future research and transformation, by providing an overview of the application of plasma techniques for carbon-rich resource conversion, with particular focus on the perspective of discharge plasmas, the fundamentals of why plasmas are particularly suited for upcarbonization, and featured examples of plasma-enabled resource valorization. With parallels drawn and specificity highlighted, we also discuss the technique shortcomings, current challenges, and research needs for future work.

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Modeling the impact of some independent parameters on the syngas characteristics during plasma gasification of municipal solid waste using artificial neural network and stepwise linear regression methods

Cited by 22 publications

References 67 publications

Applications of Plasma Technologies in Recycling Processes

Applications of Plasma Technologies in Recycling Processes

A Critical Review of Data Science Applications in Resource Recovery and Carbon Capture from Organic Waste

Plasma‐electrified up‐carbonization for low‐carbon clean energy

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