A Machine Learning Approach for Biomass Characterization

Ahmed, Mobyen Uddin; Andersson, Peter; Andersson, Tim; Aparicio, Elena Tomas; Baaz, Hampus; Bergström, Albert; Bengtsson, Daniel; Orisio, Daniele; Škvařil, Jan; Zambrano, Jesús

doi:10.1016/j.egypro.2019.01.316

Cited by 21 publications

(11 citation statements)

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“…First, starting at the lower end of the spectra, the first peak of interest occurs at 970 nm (Figure 3), where matching correlations between glucan, xylan, acetate, stalk rind, and cob can be observed. This peak is associated with amorphous hydroxyl content (Ahmed et al, 2019) and stronger peak signals are observed for rind and cob compared to other tissues (Figure 4A). These specimens are dried to approximately 5% moisture content so the signal increased hydroxyl absorbance may be due to higher relative abundance of these groups present on polysaccharides.…”

Section: Correlation Of Spectra To Chemical and Anatomical Compositionmentioning

confidence: 90%

“…Various chemometric tools have been implemented to make predictions of chemical composition from NIR spectra with PLS being the most common technique. Neural networks (NNs) (Li X. et al, 2015;Jin et al, 2017;Ahmed et al, 2019), support vector machines (SVMs) (Balabin and Lomakina, 2011) and Gaussian process regression (GPR) have also been applied to NIR spectra to predict the moisture content of biomass. GPR is commonly used to predict biomass properties in remote sensing but lacks any significant use in NIRS predictions of biomass properties (Hultquist et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

et al. 2022

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Feedstock heterogeneity is a key challenge impacting the deconstruction and conversion of herbaceous lignocellulosic biomass to biobased fuels, chemicals, and materials. Upstream processing to homogenize biomass feedstock streams into their anatomical components via air classification allows for a more tailored approach to subsequent mechanical and chemical processing. Here, we show that differing corn stover anatomical tissues respond differently to pretreatment and enzymatic hydrolysis and therefore, a one-size-fits-all approach to chemical processing biomass is inappropriate. To inform on-line downstream processing, a robust and high-throughput analytical technique is needed to quantitatively characterize the separated biomass. Predictive correlation of near-infrared spectra to biomass chemical composition is such a technique. Here, we demonstrate the capability of models developed using an “off-the-shelf,” industrially relevant spectrometer with limited spectral range to make strong predictions of both cell wall chemical composition and the relative abundance of anatomical components of the corn stover, the latter for the first time ever. Gaussian process regression (GPR) yields stronger correlations (average R2v = 88% for chemical composition and 95% for anatomical relative abundance) than the more commonly used partial least squares (PLS) regression (average R2v = 84% for chemical composition and 92% for anatomical relative abundance). In nearly all cases, both GPR and PLS outperform models generated using neural networks. These results highlight the potential for coupling NIRS with predictive models based on GPR due to the potential to yield more robust correlations.

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Section: Correlation Of Spectra To Chemical and Anatomical Compositionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

et al. 2022

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“…48 Also, the biomass characterization, specifically the moisture content, was determined with different AI techniques by Ahmed et al 49 Results showed that machine learning approaches have a great potential to be employed in future near-infrared spectroscopy applications. 49 In another attempt, Çepelioğullar et al tried to model the activation energy of lignocellulosic forest residue and olive oil residue as biomass feedstocks by different neural network topologies. 50 Xing et al employed ANN and SVM to specify the higher heating values of lignocellulosic samples for biomass-fueled energy processes.…”

Section: Machine Learning Scenariosmentioning

confidence: 99%

Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches

Karimi

Khosravi

Fathollahi

et al. 2022

Energy Science & Engineering

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Heat capacity is among the most well‐known thermal properties of cellulosic biomass samples. This study assembles a general machine learning model to estimate the heat capacity of the cellulosic biomass samples with different origins. Combining the uncertainty and ranking analyses over 819 artificial intelligence models from seven different categories confirmed that the least‐squares support vector regression (LSSVR) with the Gaussian kernel function is the best estimator. This model is validated using 700 laboratory heat capacities of four cellulosic biomass samples in wide temperature ranges (absolute average relative deviation = 0.32%, mean square errors = 1.88 × 10−3, and R2 = 0.999991). The data validity investigation approved that only one out of 700 experimental data is an outlier. The LSSVR model considers the effect of the cellulosic samples' crystallinity, temperature, and sulfur and ash content on their heat capacity. The overall prediction accuracy of the LSSVR is more than 62% better than the achieved accuracy using the empirical correlation.

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“…In [14], Ding et al describe an approach in order to characterize wood chips (as a resource for paper production) from images using artificial neural networks and regression models based on color features, size determination by means of granulometry and near infrared (NIR) sensors for moisture detection. Since then, NIR became a commonly used technique, especially for online moisture measurements [15][16][17][18][19]. These approaches are often combined with regression modeling or artificial neural networks/deep learning approaches.…”

Section: Introductionmentioning

confidence: 99%

Image-Based Model for Assessment of Wood Chip Quality and Mixture Ratios

Plankenbühler

Kolb

Grümer³

et al. 2020

Processes

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This article focuses on fuel quality in biomass power plants and describes an online prediction method based on image analysis and regression modeling. The main goal is to determine the mixture fraction from blends of two wood chip species with different qualities and properties. Starting from images of both fuels and different mixtures, we used two different approaches to deduce feature vectors. The first one relied on integral brightness values while the latter used spatial texture information. The features were used as input data for linear and non-linear regression models in nine training classes. This permitted the subsequent prediction of mixture ratios from prior unknown similar images. We extensively discuss the influence of model and image selection, parametrization, the application of boosting algorithms and training strategies. We obtained models featuring predictive accuracies of R2 > 0.9 for the brightness-based model and R2 > 0.8 for the texture based one during the validation tests. Even when reducing the data used for model training down to two or three mixture classes—which could be necessary or beneficial for the industrial application of our approach—sampling rates of n < 5 were sufficient in order to obtain significant predictions.

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A Machine Learning Approach for Biomass Characterization

Cited by 21 publications

References 0 publications

Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

Near-Infrared Spectroscopy can Predict Anatomical Abundance in Corn Stover

Determination of the heat capacity of cellulosic biosamples employing diverse machine learning approaches

Image-Based Model for Assessment of Wood Chip Quality and Mixture Ratios

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