A comparative analysis of biomass torrefaction severity index prediction from machine learning

Chen, Wei‐Hsin; Aniza, Ria; Arpia, Arjay A.; Lo, Hsiu-Ju; Hoang, Anh Tuan; Goodarzi, Vahabodin; Gao, Jianbing

doi:10.1016/j.apenergy.2022.119689

Cited by 66 publications

(14 citation statements)

References 52 publications

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“…16,17 The interconnections between the production processes of these energy resources, which are frequently based on biomass feedstock, give rise to the potential for integrated and optimized biomass-processing systems. 18,19 This twopronged method not only reduces reliance on fossil fuels but also conforms to the principles of the circular economy by converting organic waste into useful resources. [20][21][22] Aside from environmental concerns, the use of holistic bioenergy has socioeconomic benefits in terms of community development and the diversification of economies.…”

Section: Introductionmentioning

confidence: 92%

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Nguyen,

Sharma,

Ağbulut

et al. 2024

Biofuels Bioprod Bioref

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Biochar is emerging as a potential solution for biomass conversion to meet the ever increasing demand for sustainable energy. Efficient management systems are needed in order to exploit fully the potential of biochar. Modern machine learning (ML) techniques, and in particular ensemble approaches and explainable AI methods, are valuable for forecasting the properties and efficiency of biochar properly. Machine‐learning‐based forecasts, optimization, and feature selection are critical for improving biomass management techniques. In this research, we explore the influences of these techniques on the accurate forecasting of biochar yield and properties for a range of biomass sources. We emphasize the importance of the interpretability of a model, as this improves human comprehension and trust in ML predictions. Sensitivity analysis is shown to be an effective technique for finding crucial biomass characteristics that influence the synthesis of biochar. Precision prognostics have far‐reaching ramifications, influencing industries such as biomass logistics, conversion technologies, and the successful use of biomass as renewable energy. These advances can make a substantial contribution to a greener future and can encourage the development of a circular biobased economy. This work emphasizes the importance of using sophisticated data‐driven methodologies such as ML in biochar synthesis, to usher in ecologically friendly energy solutions. These breakthroughs hold the key to a more sustainable and environmentally friendly future.

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Section: Introductionmentioning

confidence: 92%

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Nguyen,

Sharma,

Ağbulut

et al. 2024

Biofuels Bioprod Bioref

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“…The former has lower heat requirements and lower total costs. 107,108 However, when compared to other processes, oxidative dry torrefaction yielded a smaller quantity of solid biochar. 104,109 In torrefaction, biomass is converted into a hydrophobic state by reducing the number of hydroxyl groups existing in the biomass cell wall; 103 thus, high carbon, energy-dense products were produced in a shorter period than was required by other processes.…”

Section: Torrefactionmentioning

confidence: 99%

“…Dry torrefaction is a strong technique that can be performed with or without oxidation. The former has lower heat requirements and lower total costs 107,108 . However, when compared to other processes, oxidative dry torrefaction yielded a smaller quantity of solid biochar 104,109 .…”

Section: Biochar Production Approaches From Waste Feedstocksmentioning

confidence: 99%

Biochar‐based catalysts derived from biomass waste: production, characterization, and application for liquid biofuel synthesis

Nguyen,

Sharma,

Rowinski

et al. 2024

Biofuels Bioprod Bioref

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Among the feasible options for valorizing lignocellulosic biomass into value‐added products, biochar is considered to be extremely appealing and it could be applied easily in several established thermochemical processes. Biochar possesses a large surface area due to its porous nature, which permits easy use in various applications, including catalytic biofuel production. This work summarizes research on biochar generation processes, its critical physicochemical properties, and factors that influence them. Important studies adopting biochar catalysts for liquid biofuel production are critically reviewed, and the reaction mechanisms and biofuel yield are analyzed. Finally, challenges in the use of biochar‐based catalysts for biofuel production are presented, together with possible solutions. In general, this current work aims to provide the critical characteristics of biochar as a potential catalyst to biofuel synthesis toward sustainable bioenergy production.

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“…A significant step towards an environmentally sustainable circular economy is represented by applying machine learning (ML) and the internet-of-things (IoT) to produce algal bioplastics and algae cultivation. The quality of torrefied biomass under various conditions can be effectively predicted by ML, specifically multivariate adaptive regression splines (MARS) and artificial neural networks (ANNs), as has been demonstrated by emerging studies, contributing to the optimization of biofuel production [48]. ML and IoT can enhance the cultivation of microalgae and seaweed, creating algae-based bioplastics [49][50][51][52][53].…”

Section: Introductionmentioning

confidence: 99%

Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

Bin Abu Sofian,

Lim,

Manickam

et al. 2023

ChemBioEng Reviews

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The growing potential of sustainable materials such as polyhydroxyalkanoates (PHAs), polylactic acid (PLA), alginate, carrageenan, and ulvan for bioplastics production presents an opportunity to promote a sustainable circular economy. This review investigates their properties, applications, and challenges. Bioplastics derived from algae offer an environmentally friendly alternative to petroleum‐based plastics, a shift of paramount importance to society due to the escalating environmental concerns associated with traditional plastics. The role of the internet‐of‐things (IoT) and machine learning in refining these bioplastics' production and development processes is emphasized. IoT monitors cultivation conditions, data collection, and process control for more sustainable production. Machine learning can enhance algae cultivation, increasing the supply of raw materials for algal bioplastics and improving their efficiency and output. The study results indicate the promise of algae‐based bioplastics, IoT, and machine learning in fostering a more environmentally sustainable future. By harnessing these advanced technologies, optimization of bioplastic production is possible, potentially revolutionizing the materials industry and addressing existing challenges toward achieving a sustainable circular economy.

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A comparative analysis of biomass torrefaction severity index prediction from machine learning

Cited by 66 publications

References 52 publications

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Machine learning for the management of biochar yield and properties of biomass sources for sustainable energy

Biochar‐based catalysts derived from biomass waste: production, characterization, and application for liquid biofuel synthesis

Towards a Sustainable Circular Economy: Algae‐Based Bioplastics and the Role of Internet‐of‐Things and Machine Learning

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