A multivariate SIMCA index as discriminant in wood pellet quality assessment

Sgarbossa, Andrea; Costa, Corrado; Menesatti, Paolo; Antonucci, Francesca; Pallottino, Federico; Zanetti, Michela; Grigolato, Stefano; Cavalli, Raffaele

doi:10.1016/j.renene.2014.11.041

Cited by 20 publications

(11 citation statements)

References 24 publications

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“…Use of wood pellets as a sustainable energy alternative is an effective instrument in the fight against climate change [6,7]. It represents a positive globalization of wealth and local employment creation.…”

Section: Introductionmentioning

confidence: 99%

Wood pellets as a sustainable energy alternative in Portugal

2016

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a b s t r a c tThe pellet market in Portugal is a developing market where the main producers of wood pellets try to convince the small-scale buyer that wood pellets are a good alternative as a fuel because energy from them is cost-effective in long-term use. For this work, data about production, production capacity and consumption in Portugal were collected. Consumption levels are low since the majority of sales are exports. The different points of pellet production in Portugal have been located, and a price analysis has been made. To obtain data on the production capacity of the plants and get referrals from the market, the database Bioraise has been used in order to complement the information raised through prospecting and inquiries. Although it is noted that the pellet industry in Portugal is a sector in the initial stage of its development, the potential for growth is high, since internal consumption is low compared to the level of exports to other countries in which the pellet industry is booming. We conclude that the pellet industry is an important alternative in the Portuguese energy market due to the competitive price of this fuel compared to traditional fuels.

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Section: Introductionmentioning

confidence: 99%

Wood pellets as a sustainable energy alternative in Portugal

2016

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“…For the three SIMCA models, it shows three different values: 5.09 in the EN model, 5.17 in the ISO model and 3.67 in the Biomassplus one. This value identifies the class-limit of the model: below it the distance value of an observation is enclosed in the model (those values could not be compared among different models), whilst a sample with a higher distance value is excluded [53]. The differences between the translated square critical distances were related to the different limits set by the standards.…”

Section: Multivariate Simca Modelsmentioning

confidence: 99%

How Wood Fuels’ Quality Relates to the Standards: A Class-Modelling Approach

Zanetti

Costa²,

Greco

et al. 2017

Energies

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Abstract:The quality requirements of wood biofuels are regulated by a series of harmonized international standards. These standards define the technical parameter limits that influence the quality of solid biomass as a fuel. In 2014 the European reference standard for solid biofuel was replaced by the International ISO standard. In the case of wood chips, the main difference between the European and International standards is the definition of particle size distribution classes. In this context, this study analyses the quality of wood chips and its variation over the years according to the "former" (EN 14691-4) and "in force" (ISO 17225-4) standards. A Soft Independent Modelling of Class Analogy (SIMCA) model was built to predict the best quality of wood chips and to clarify the relationship between quality and standard parameters, time and changes in the standard regulations. The results show that, compared to the EN standards, classification with the ISO standards increases the samples belonging to the best quality classes and decreases the not classified samples. Furthermore, all the SIMCA models have a high sensitivity (>90%), reflect the differences introduced to the quality standards and are therefore suitable for monitoring the quality of wood chips and their changes.

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“…SIMCA has been used to discriminate adulterated and unadulterated Fourier transform infrared spectroscopy (FT-IR) minced meat samples [3], for screening Brazilian gasoline quality [4], for rapid and precise identification of herbal medicines [5,6], for discrimination of wood pellet quality [7] and detection of insect infested tomatoes [8]. Implementations of these multivariate tools in classifying oat cultivars are scarce in the literature.…”

Section: Discriminating Important Agronomic and Industrial Parametersmentioning

confidence: 99%

Discriminating Important Agronomic and Industrial Parameters of White Oat Cultivars Treated with Fungicide Based on SIMCA Algorithm

Uarrota¹,

Coelho²,

Sponchiado³

et al. 2017

JAST-B

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Selection of effective agronomic and industrial parameters of oat cultivars is a decisive step in oat breeding programs for development of new oat and elite cultivars. In this study, a new approach was utilized to distinguish the most informative agronomic and industrial parameters that are most affected with fungicide application in oat cultivars. Four subsequent field experiments from 2007 to 2010 were conducted in completely randomized block design (CRBD) with split plots. Total nine oat cultivars with or without fungicide application were evaluated for plant height, sieve yield, grain yield, lodging index, weight of hectoliter and de-hulling index. Soft independent modeling of class analogy (SIMCA) was conducted as one-class and multi-classes models to identify important variables that can be used to discriminate samples. Results showed that SIMCA was effective, and lodging index, de-hulling index, sieve yield, plant height and grain yield were most affected oat parameters. Therefore, SIMCA algorithm can be used to easily discriminate some agronomic and quality parameters of oats.

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A multivariate SIMCA index as discriminant in wood pellet quality assessment

Cited by 20 publications

References 24 publications

Wood pellets as a sustainable energy alternative in Portugal

Wood pellets as a sustainable energy alternative in Portugal

How Wood Fuels’ Quality Relates to the Standards: A Class-Modelling Approach

Discriminating Important Agronomic and Industrial Parameters of White Oat Cultivars Treated with Fungicide Based on SIMCA Algorithm

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