Estimation of biomass higher heating value (HHV) based on the proximate analysis: Smart modeling and correlation

Dashti, Amir; Noushabadi, Abolfazl Sajadi; Raji, Mojtaba; Razmi, Amir Reza; Ceylan, Selim; Mohammadi, Amir H.

doi:10.1016/j.fuel.2019.115931

Cited by 79 publications

(41 citation statements)

References 32 publications

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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%

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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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Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

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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“…Dengan mengevaluasi 382 data proksimat dari berbagai jenis biomassa. Model empiris MPR memiliki akurasi tertinggi dibandingkan model yang sudah ada sebelumnya, yang di dalamnya melibatkan 3 variabel terpenting yaitu FC,VM, dan Ash [9]. Nilai HHV juga dapat diprediksi menggunakan nilai dari komposisi unsurnya atau data dari analisa ultimat bahan bakar tersebut, dengan kesalahan relatif rata-rata mendekati 1,2% [10].…”

Section: Gambar 1 Komposisi Analisis Proksimat Dari Basis Yang Berbedaunclassified

Prediksi Komposisi Unsur Dan HHV Sampel Bahan Bakar Sampah Incinerator PLTSa UNSIKA Menggunakan Analisa Proksimat

Firdaus

2021

JTM

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Pemanfaatan energi alternatif pada era modern ini sangat cepat perkembangannya, dimulai dari untuk kebutuhan sehari-hari hingga pemanfaatan di skala industri. Akan tetapi dalam proses perancangannya dibutuhkan data yang sangat banyak agar memperoleh hasil yang akurat. Salah satu di antaranya ialah nilai HHV dan komposisi unsur pada biomassa, yang dalam proses pengujian memerlukan waktu dan biaya yang cukup besar. Belum lama ini banyak penelitian tentang hubungan dari analisa proksimat dengan HHV maupun komposisi unsur tersebut, yang sangat bermanfaat dalam perancangan tungku atau pengoperasian yang menggunakan bahan bakar biomassa. Pada incinerator PLTSa di Universitas Singaperbangsa Karawang dibutuhkan komposisi unsur untuk menganalisis gas buang pembakarannya dan HHV untuk nilai kalor pembakarannya. Untuk sampel yang di ambil ada 3 buah sampel, yang terdiri dari campuran sampah kertas, daun mangga, daun pisang, dan daun ketapang. Untuk unsur-unsurnya terdiri dari unsur C (karbon), H (hidrogen), dan O (oksigen), untuk nilainya masing-masing berkisar antara 32,87-33,43%, 4,22-4,26%, 26,05-26,47%. Untuk nilai HHV-nya berkisar antara 15689,83-15690,53 kJ/kg.

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“…All of these convincing reasons make the biofuels, such as biodiesel and bioethanol, suitable and major alternatives for fossil fuels [2,5,6]. Biodiesel has high adaptability to the environment and on the other hand is reproducible fuels [7,8]. For these convincing reasons, this fuel is a suitable replacement for petroleum diesel [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Developing a Robust Model Based on the Gaussian Process Regression Approach to Predict Biodiesel Properties

Pustokhina

Seraj

Hafsan³

et al. 2021

International Journal of Chemical Engineering

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Biodiesel is assumed a renewable and environmentally friendly fuel that possesses the potential to substitute petroleum diesel. The basic purpose of the present study is to design a precise algorithm based on Gaussian Process Regression (GPR) model with several kernel functions, i.e., Rational Quadratic, Squared Exponential, Matern, and Exponential, to estimate biodiesel properties. These properties include kinematic viscosity (KV), pour point (PP), iodine value (IV), and cloud point (CP) as a function of fatty acid composition. In order to develop this model, some variables are assumed, such as molecular weight, carbon number, double bond numbers, monounsaturated fatty acids, polyunsaturated fatty acid, weight percent of saturated acid, and temperature. The performance and efficiency of the GPR model are measured through several statistical criteria and the results are summarized in root mean square error (RMSE) and coefficients of determination ( R 2 ). R 2 and RMSE are sorted as 0.992 & 0.15697, 0.998 & 0.96580, 0.966 & 1.38659, and 0.968 & 1.56068 for four properties such as KV, IV, CP, and PP, respectively. It is worth to mention this point that the kernel function Squared Exponential shows a great performance for IV and PP and kernel functions Exponential and Matern indicate appropriate efficiency for CP and KV properties, respectively. On the other hand, the results of the offered GPR models are compared with those of the previous models, LSSVM-PSO and ANFIS. The outcomes proved the superiority of this model over two former models in point of estimating the biodiesel properties.

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Estimation of biomass higher heating value (HHV) based on the proximate analysis: Smart modeling and correlation

Cited by 79 publications

References 32 publications

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

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

Prediksi Komposisi Unsur Dan HHV Sampel Bahan Bakar Sampah Incinerator PLTSa UNSIKA Menggunakan Analisa Proksimat

Developing a Robust Model Based on the Gaussian Process Regression Approach to Predict Biodiesel Properties

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