Volatile matter values change according to the standard utilized?

In lignocellulosic biomass, the volatile matter can vary from 65 to 85%. Different standards are described in the literature for obtaining this parameter. However, it is observed that some studies of regression models have not considered these differences. They create a volatile matter content database, where the standards for obtaining the same parameter are different. Thus, the objective of the study was to verify whether different standards for volatile matter present statistically equal values. That is if they can be compared with each other, without danger of bias. For this, three types of biomasses of Brazil used were used (eucalyptus chips, pine chips, and sugarcane bagasse). The samples were collected, size reduction, size separation, and stored in the laboratory. Three standards of the American Society for Testing and Materials (ASTM D1762, ASTM E872, ASTM D3175) and one standard of the International Organization for Standardization (ISO 18123) were tested. The experimental design was completely randomized, consisting of four treatments and five replications. The central limit theorem was tested in some literature databases of the volatile matter. The results showed statistical differences when changing the type of standard used. For eucalyptus sawdust, the four standards resulted in methodologies with different averages. Still, the central limit theorem was not observed in some databases of different articles. This was explained by the non-standardization of a single standard when grouping data from different works. Therefore, different volatile content standards produce different results and when comparing values, it is important to take this assumption into account.


Introduction
The potential of solid biofuels can be assessed using different energy parameters. Through proximate analysis, dry biomass is divided into three energy variables: total moisture, total ash, volatile matter and fixed carbon (Hansted et al., 2016;Nakashima et al., 2017;Posom et al., 2016;Silva et al., 2015;Smith et al., 2019;Yang et al., 2017). Still, this muffle furnace methodology consists of a simple, low cost and widely used tool (Özyuğuran & Yaman, 2017).
In natural lignocellulosic waste, 65% to 85% are volatile matter. From 0.1% to 10% correspond to the total ash of the sample, with high values reflecting contamination by impurities. The fixed carbon can represent 15% to 25% of the biomass (García et al.;Nakashima et al., 2017).
The volatile matter of biomass is the largest fraction of the raw material. Hydrogen, light hydrocarbons, tar, water, carbon monoxide, carbon dioxide are released by heating the biofuel. The presence of flammable gases initiates combustion of the material, however, the high concentration of gases prevents the formation of high temperatures (García et al.;Nogueira et al., 2000;Obernberger & Thek, 2004).
Different types of standards are found in the literature for determining the volatile matter. The type of solid fuel analyzed defines which standard is most suitable. If the biofuel is charcoal, the standard ASTM D1762 (1984) is more suitable for the determination of volatile matter. The ASTM E872 (1982) standard adopts procedures for wood particles and the ISO 18123 (2014) standard for solid biofuels. There is also a specific standard for mineral coal and petroleum coke (ASTM D3175-2011), which has already been used for older research on biomass characterization (Channiwala & Parikh, 2002;Parikh et al.;. The granulometry, the entrance direction of the sample at the muffle (horizontal or vertical), the total test time, and the position of the samples in the equipment can change from standard to standard. There is a lack of consensus in the literature for the most appropriate standard, still, there are papers that change some points of the standards (Zhang et al., 2017;Thy et al.,2009). With the increased sharing of data, scientific articles have extracted data from different works and grouped them in a database. Specifically, some regression studies grouped information on volatile matter but did not consider the difference in standards for extracting and group the data (Table 1). Then, predictive models of higher heating values (HHV) are created in the function of all the variables of the proximate analysis. Table 1. Volatile matter grouped on the same basis for the construction of models of HHV.

Data base:
Standards found: References: Yin ( As an example, in the higher heating value model proposed by Uzun et al. (2017) in its database it is possible to find data from wheat straw that were made in accordance with the ASTM D3175 (2011) and another with the ASTM E872 (1982).
To arrive at the proposed model, it would not be prudent for them to establish single standard to collect data on volatile matter.
As the content of volatile materials represents the most representative fraction (makes up more than 50%) of the residual biomass, the objective of the work was to verify if there is a difference between the use of different standards for the determination of the volatile matter.

Biomass information
Three residual biomasses widely used in industrial boilers in Brazil were chosen. The biomass of eucalyptus sawdust (Eucalyptus urograndis) was collected in the municipality of Itu (SP) and represents the energy material used by the Pulp and Paper sector. The pine sawdust (Pinus elliottii) was collected in Sorocaba (SP) and represents the biomass energy used by the Wood Panel industries. And the sugarcane bagasse (Saccharum officinarum) was collected in Porto Feliz (SP) and represents the biofuel used by the Sucro-Alcooleiras plants.

Biomass collection
Five kilos of each residual sample were collected, which were dried in an oven (60 ° C ± 2 hrs) in order to decrease the high humidity. Then, the biomasses were packed in polyethylene bags at room temperature. The particle reduction of the biomass was carried out separately in a Willey knife mill (Marconi, model: MA 340). The particle separation was carried out with a set of screens coupled to an orbital screen shaker (Marconi, model: model MA 750). The size particle range obtained was adjusted according to the requirements of the standards used.

Determination of the content of volatile matter
The standards ASTM D1762, ASTM E872, ASTM D3175 and ISO 18123 were used as treatment conditions, totaling four treatments. These were obtained from the collection of the Coordination of Superior Level Staff Improvement (CAPES).
For the determination of the volatile matter, samples dried at 105 ° C (± 2 ° C) were used until a constant mass was obtained.
Research, Society and Development, v. 10, n. 12, e291101220476, 2021 (CC BY 4.0) | ISSN 2525-3409 | DOI: http://dx.doi.org/10.33448/rsd-v10i12.20476 4 A muffle (Jung brand, model 0212) was used with a horizontal entrance and also with a vertical entrance option. For each test, a gram per sample was used. They were placed in crucibles with a lid. These had previously been calcined at 700ºC for 30 minutes.
All stages of the analyzes followed the different requirements of each adopted standard. This included different patterns of granulometry, temperature, time, positioning inside the equipment and direction of sample entry (Table 2). Legend: Values in parentheses indicate the position of the crucibles in the muffle: on the muffle lid (1); inner edge of the muffle (2); inside the closed muffle (3). Horizontal entrance muffle (*). Vertical entry muffle (**). Source: Authors (2021).
To reach the final value of the content of volatile matter, all standards eliminate the moisture of the material at temperatures close to the boiling point of water. Thus, in order to simplify the different formulas used and converge in the same equation, the following formula was adopted: on what: VM = volatile matter (%); mi = initial dry mass (g); mf = mass after the end of the analysis (g).
The type of statistical methodology for experimental used was the design was completely randomized (Barbin, 2013), consisting of four treatments and five replications for each biomass.

Central limit theorem of literature data
The volatile matter information was extracted from Yin's databases (2011)

Results and Discussion
The highest volatile matter was found when the ASTM E872 (2013) standard was used. The lowest value was evident when using the ASTM D1762 (2013) standard (Table 3).  It is noteworthy that the standards suggest the use of only duplicates, and when one wants to compare results with other different biomasses this would make it impossible to use independent hypothesis tests, in addition to making the evidence of the reproducibility of the main results questionable (Vaux et al., 2012). Only ISO 18123 (2015) suggests the use of more than two samples in the analysis, specifying a tolerable range of analyzes from the difference of 1% of the average of the analyzes and not as a basis for duplicates.
The results showed that the determination of the content of volatile materials can vary from standard to standard and also from biomass to biomass. ISO 18123 (2015) or ASTM E872 (2013) are recommended for fresh lignocellulosic materials.
These are the most suitable standards for the biomass used, that is, non-pyrolyzed material. The ASTM D1762 (2013) and ASTM D3175 (2011) standards are suggested for pyrolyzed materials and fossil fuels, therefore, not suitable for raw materials.
It is important to confirm this information, as there are studies in the literature that did not follow these recommendations [Yin, 2011;Nhuchhen & Salam, 2012;Uzun et al., 2017;Hosseinpour et al., 2017).
Thus, gathering information on the content of volatile matter from different sources, without a standardization of standards can produce unreliable results. In the literature, it is not uncommon to find articles that collect and group contents of volatile materials without considering the most appropriate standard (Yin, 2011;Uzun et al., 2017;Hosseinpour et al., 2017;Sheng & Azevedo, 2005).
As an example, it can be mentioned adjustments of models to determine higher heating values that used the content of volatile materials in the model. It was observed that the data for the independent variable (volatile content) did not present a normal data distribution (Table 05). This may indicate that the procedures did not take place under the same conditions. That is, the use of different standards results in different averages. Hurting an important principle of statistics in defining the design of an experiment (Barbin, 2013;Quinn & Keough, 2010, Morettin, 2010. When the data were extracted from different sources and grouped in the same database, the volatile matter content data did not meet the central limit theorem. Such theorem attests that with the increase of samples, the average tends to have a normal distribution (Quinn, 2010), a fact that was not observed in the total database of the authors mentioned. However, when analyzing each source unitarily, that is, according to each extracted source, it is possible to observe that the data meet the central limit theorem, as the data are made according to a specific standard. Only in the data from Hosseinpour et al (2017) it was not possible to cross-check the information from the different data sources. The data did not inform the references of each sample linked to the papers extracted.
Such information reinforces that as the standards presented different averages, that is, they result in different sampling methodologies, thus, they cannot be compared as the same. Because they tend to cause an error bias between them and interfere with the theory of the central limit of the data when they are given as equals.
Also, consider a census to measure the height of a population with two different instruments. If one of the instruments is biased, any differences found between the populations may be related not to the actual differences in height of the people, but to parameters not equivalent to the ruler used (Nimon & Reio Jr., 2011) In short, this same interpretation can be used when different standards are applied to measure the same variable. Thus, the content of volatile matter showed that different standards function as different instruments, thus offering values that cannot be compared with each other. They are only comparable to each other when the values are obtained by the same standards.
Thus, as the range of information and data has increased, it is important to check the consistency of the database, especially when the extracted variables are not authored. Because occasionally, a database may offer numerical values for the same variable, but it is necessary to check at the source if the data are equivalent. As an example to compare data from a variable in centimeters and another in inches, it is necessary to perform a conversion before grouping the data in the same vector base. Without this, any model created from this variable is incoherent and questionable.

Conclusion
The volatile matter content has changed depending on the pattern used. Each standard has its particularities that can interfere with the results. Thus, to compare the content of volatile matter in a material (with data from the literature), it should be filtered for the same standard to be used.
In future works, it is important to observe whether the same error was made with the ash content. Mainly in works on estimating HHV as a function of proximate analysis. It is important to check if the author fixed the same standard, or if mixed several standards.