A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches

Xing, Jiangkuan; Luo, Kun; Wang, Haiou; Gao, Zhiyun

doi:10.1016/j.energy.2019.116077

Cited by 137 publications

(53 citation statements)

References 39 publications

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“…Other authors have observed energy density of 13 GJ/m³ in 7.5-year-old Eucalyptus trees (Pereira et al, 2013) and ranging from 1.82 and 3.58 GJ/m³ in several combinations of Eucalyptus and coffee production residues (parchment, chaff and husk) (Tan et al, 2020); the first one presented higher and the second one lower in comparison to the results in the present study, due to the variability of the wood species. Recent studies have shown that the main determining factor of the higher heating value of biomass is the ash content (Xing et al, 2019), which was not observed in our study, where the highest ash content values were not necessarily associated with the highest calorific values of the species.…”

Section: Resultscontrasting

confidence: 93%

Energy potential of wood waste from a tropical urban forest

Klingenberg

Nolasco

Júnior

et al. 2020

RSD

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Urban tropical forest species generate large amounts of wood waste by pruning and removing urban trees, which can be an accessible source of biomass that could be used to generate energy instead of being disposed of irregularly. The objective of this study was to evaluate the potential for energy production of the wood residue of seven species most used in urban forestry in the State of São Paulo, Brazil, by determining the physical, chemical and energetic characteristics. Wood waste of 7 common urban forests species in the State of São Paulo were collected in the city of Piracicaba, characterized (humidity, basic density and bulk density), chemically (extract content, volatile materials, fixed carbon and ash content) and energetically (higher, lower, useful calorific power, density energy and thermogravimetric analysis). The highest value of basic density was found in the species Cenostigma pluviosum (653.76 kg/m³), all species had higher calorific values greater than 19 MJ/kg and the energy density of the species varied between 4.45 to 10,80 GJ/m³. The use of these wood residues for direct combustion is a viable alternative and can be considered as a solution to replace the incorrect disposal, which is still a common practice in many cities in developing countries.

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Section: Resultscontrasting

confidence: 93%

Energy potential of wood waste from a tropical urban forest

Klingenberg

Nolasco

Júnior

et al. 2020

RSD

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“…A possible explanation is that the ultimate analysis is generally more expensive and time‐consuming than proximate analysis (Cordero et al, 2001). Specifically, 14 papers predicted the HHV of biomass using the composition data from proximate analysis covering a variety of biomass types (e.g., woody biomass, herbaceous and agricultural and animal biomass) (Akkaya, 2016; Ceylan et al, 2017; Dashti et al, 2019; Estiati et al, 2016; Ghugare, Tiwary, Elangovan, et al, 2014; Hosseinpour et al, 2017, 2018; Keybondorian et al, 2017a, 2017b; Ozveren, 2017; Samadi et al, 2019; Suleymani & Bemani, 2018; Uzun et al, 2017; Xing, Luo, Wang, Gao, et al, 2019). The sizes of datasets have large variations, ranging from 50 samples to 830 samples, while 40% of the reviewed studies in Category 1 used the dataset with 300–400 samples and the rest are either below 300 or above 400.…”

Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

“…Across all of those studies, nine of them compared the performance of AI‐based models with traditional empirical correlation, and they showed higher R 2 of the AI models than that of traditional approaches (Akkaya, 2016; Ceylan et al, 2017; Dashti et al, 2019; Estiati et al, 2016; Ghugare, Tiwary, Elangovan, et al, 2014; Ghugare, Tiwary, Tambe, 2014; Huang et al, 2016; Xing, Luo, Wang, & Fan, 2019; Xing, Luo, Wang, Gao, et al, 2019). One interesting AI application is predicting ultimate analysis data based on the proximate analysis data, and the trained model has demonstrated superior performance compared with traditional linear regression (Ghugare, Tiwary, Tambe, 2014).…”

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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“…1 (2020) 1-7 ISSN 2548-9011 merupakan parameter yang mengacu pada total energi yang dilepaskan oleh satu kg bahan bakar ketika dibakar sepenuhnya. HHV suatu biomassa dapat ditingkatkan memalui beberapa proses salah satunya proses termokimia, sehingga didapatkan efisiensi pembakaran yang lebih baik [10]. HHV secara eksperimen dapat diukur dengan menggunakan bomb calorimeter [11] akan tetapi metode ini memerlukan waktu dan biaya yang tinggi.…”

Section: Pendahuluanunclassified

Evaluasi Prediksi Higher Heating Value (HHV) Biomassa Berdasarkan Analisis Proksimat

Dirgantara

Ariyanti

2020

risal.fis.

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Abstrak – Biomassa merupakan salah satu energi terbarukan yang sangat mudah ditemui, ramah lingkungan dan cukup ekonomis. Keberadaan biomassa dapat dimaanfaatkan sebagai pengganti bahan bakar fosil, baik itu minyak bumi, gas alam maupun batu bara. Analisi diperlukan sebagai dasar biomassa sebagai energi seperti proksimat dan kalor. Analisis terpenting untuk menilai biomassa sebagai bahan bakar adalah nilai kalori atau higher heating value (HHV). HHV secara eksperimen diukur menggunakan bomb calorimeter, namun pengukuran ini kurang efektif, karena memerlukan waktu serta biaya yang tinggi. Penelitian mengenai prediksi HHV berdasarkan analisis proksimat telah dilakukan sehingga dapat mempermudah dan menghemat biaya yang diperlukan peneliti. Dalam makalah ini dibahas evaluasi persamaan untuk memprediksi HHV berdasarkan analisis proksimat pada biomassa berdasarkan data dari penelitian sebelumnya. Prediksi nilai HHV menggunakan lima persamaan yang dievaluasi dengan 25 data proksimat biomassa dari penelitian sebelumnya, kemudian dibandingkan berdasarkan nilai error untuk mendapatkan prediksi terbaik. Hasil analisis menunjukan, persamaan A terbaik di 7 biomassa, B di 6 biomassa, C di 6 biomassa, D di 5 biomassa dan E di 1 biomassa.Kata kunci: bahan bakar, biomassa, higher heating value, nilai error, proksimat Abstract – Biomass is a renewable energy that is very easy to find, environmentally friendly, and quite economical. The existence of biomass can be used as a substitute for fossil fuels, both oil, natural gas, and coal. Analyzes are needed as a basis for biomass as energy such as proximate and heat. The most critical analysis to assess biomass as fuel is the calorific value or higher heating value (HHV). HHV is experimentally measured using a bomb calorimeter, but this measurement is less effective because it requires time and high costs. Research on the prediction of HHV based on proximate analysis has been carried out so that it can simplify and save costs needed by researchers. In this paper, the evaluation of equations is discussed to predict HHV based on proximate analysis on biomass-based on data from previous studies. HHV prediction values using five equations were evaluated with 25 proximate biomass data from previous studies, then compared based on error value to get the best predictions. The analysis shows that Equation A predicts best in 7 biomass, B in 6 biomass, C in 6 biomass, D in 5 biomass, and E in 1 biomass. Key words: fuel, biomass, higher heating value, error value, proximate

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A comprehensive study on estimating higher heating value of biomass from proximate and ultimate analysis with machine learning approaches

Cited by 137 publications

References 39 publications

Energy potential of wood waste from a tropical urban forest

Energy potential of wood waste from a tropical urban forest

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

Evaluasi Prediksi Higher Heating Value (HHV) Biomassa Berdasarkan Analisis Proksimat

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