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Open AccessArticle

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

1
Chair of Energy Process Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Fürther Straße 244f, D-90429 Nürnberg, Germany
2
Independent Researcher, D-90439 Nürnberg, Germany
*
Author to whom correspondence should be addressed.
Processes 2020, 8(6), 728; https://doi.org/10.3390/pr8060728
Received: 30 May 2020 / Revised: 12 June 2020 / Accepted: 15 June 2020 / Published: 23 June 2020
(This article belongs to the Special Issue Progress in Energy Conversion Systems and Emission Control)
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. View Full-Text
Keywords: biomass; fuel quality; regression modeling; machine learning; image analysis; biomass power plant biomass; fuel quality; regression modeling; machine learning; image analysis; biomass power plant
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MDPI and ACS Style

Plankenbühler, T.; Kolb, S.; Grümer, F.; Müller, D.; Karl, J. Image-Based Model for Assessment of Wood Chip Quality and Mixture Ratios. Processes 2020, 8, 728.

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