Abstract
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.
Highlights
Solid biomass combustion plants—from kilowatt-sized furnaces for domestic heating to combined heat and power plants in the megawatt range—suffer from a decisive drawback in comparison to many other alternatives due to the low quality and the challenging properties of the utilized feedstock
Since the approach ought to be applicable for biomass furnaces in the megawatt range, we evaluate several simplified training approaches based on few input parameters which can be applied during the ongoing operation of biomass power plants
The corresponding root mean squared error (RMSE) values lie below 10%
Summary
Solid biomass combustion plants—from kilowatt-sized furnaces for domestic heating to combined heat and power plants in the megawatt range—suffer from a decisive drawback in comparison to many other (mainly fossil) alternatives due to the low quality and the challenging properties of the utilized feedstock. Austrian ÖNORM M7133 used among European plant operators) which defines distinct fuel quality classes based on several parameters, such as particle size distributions, moisture and ash content [7]. These norms are often used for definitions, agreements and contract management along the wood chip supply chain. In the biomass plant operator’s daily practice, even the defined limits (e.g., regarding fine particle content or ash content) are regularly breached and/or not controlled
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