Biomass higher heating value prediction from ultimate analysis using multiple regression and genetic programming

Boumanchar, Imane; Charafeddine, Kenza; Chhiti, Younes; Alaoui, Fatima Ezzahrae M’hamdi; Sahibed-Dine, Abdelaziz; Bentiss, Fouad; Jama, Charafeddine; Bensitel, M.

doi:10.1007/s13399-019-00386-5

Cited by 47 publications

(15 citation statements)

References 46 publications

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“…However, this can be of great interest if, in association with energy recovery, the recovery of silica is the main objective, for example, in the production of abrasive products. In the case of kiwi pruning, the only works found in the literature refer to its characterization as a fuel after drying, without any other type of thermal processing such as torrefaction, including works developed by Dyjakon and García-Galindo (2019) or by Boumancher et al (2019) [85,86]. With reference to works carried out on the application of thermochemical conversion technologies, in this case pyrolysis, Rene et al (2020) studied the production of biochar from pruning kiwi, employed as an amendment, aiming to evaluate its remediation potential on smelter and mining contaminated soils [87]; however, the data from this study were not used for comparison because the methodological assumptions related to sample preparation were significantly different from those used in the present work.…”

Section: Discussionmentioning

confidence: 99%

Waste Recovery through Thermochemical Conversion Technologies: A Case Study with Several Portuguese Agroforestry By-Products

Nunes

Loureiro²,

Sá³

et al. 2020

Clean Technol.

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Agroforestry waste stores a considerable amount of energy that can be used. Portugal has great potential to produce bioenergy. The waste generated during agricultural production and forestry operation processes can be used for energy generation, and it can be used either in the form in which it is collected, or it can be processed using thermochemical conversion technologies, such as torrefaction. This work aimed to characterize the properties of a set of residues from agroforestry activities, namely rice husk, almond husk, kiwi pruning, vine pruning, olive pomace, and pine woodchips. To characterize the different materials, both as-collected and after being subjected to a torrefaction process at 300 °C, thermogravimetric analyses were carried out to determine the moisture content, ash content, fixed carbon content, and the content of volatile substances; elementary analyses were performed to determine the levels of carbon, nitrogen, hydrogen, and oxygen, and the high and low heating values were determined. With these assumptions, it was observed that each form of residual biomass had different characteristics, which are important to know when adapting to conversion technology, and they also had different degrees of efficiency, that is, the amount of energy generated and potentially used when analyzing all factors.

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Section: Discussionmentioning

confidence: 99%

Waste Recovery through Thermochemical Conversion Technologies: A Case Study with Several Portuguese Agroforestry By-Products

Nunes

Loureiro²,

Sá³

et al. 2020

Clean Technol.

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“…Various agricultural residues are essentially composed of holocellulose (cellulose and hemicellulose) and lignin. These residues have high heating values (HHV) [5][6][7]. The thermochemical conversion process is one of the best and favoured methods for the turning of biomass into biofuels due to their high energy values under acceptable thermal conditions [8,9].…”

Section: Introductionmentioning

confidence: 99%

Investigations on Mediterranean biomass pyrolysis ability by thermogravimetric analyses: thermal behaviour and sensitivity of kinetic parameters

Boukaous¹,

Abdelouahed²,

Chikhi³

et al. 2021

Comptes Rendus. Chimie

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“…The number of output (e.g., HHV) and input (e.g., fixed carbon, volatile matters, and ash content) are similar, and most of them achieved high accuracy (e.g., R 2 > 0.9, root mean squared error <1.5). Only five studies have predicted the HHV of biomass using ultimate analysis data that provides elemental composition such as the content of carbon, hydrogen, oxygen, nitrogen, and sulfur (Boumanchar et al, 2019; Darvishan et al, 2018; Duan et al, 2018; Ghugare, Tiwary, Elangovan, et al, 2014; González‐García, 2018). One study indicated that using ultimate analysis data to predict HHV is likely to be more accurate than using proximate analysis data (Vargas‐Moreno et al, 2012).…”

Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Ming-yang

Yao

2021

GCB Bioenergy

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Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy‐related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end‐use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end‐uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy‐related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

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Biomass higher heating value prediction from ultimate analysis using multiple regression and genetic programming

Cited by 47 publications

References 46 publications

Waste Recovery through Thermochemical Conversion Technologies: A Case Study with Several Portuguese Agroforestry By-Products

Waste Recovery through Thermochemical Conversion Technologies: A Case Study with Several Portuguese Agroforestry By-Products

Investigations on Mediterranean biomass pyrolysis ability by thermogravimetric analyses: thermal behaviour and sensitivity of kinetic parameters

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

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