Evaluation of high calorific value in biomass

Abstract

In order to know the Gross calorific value (GCV) of Biomass is necessary to analyse samples of weight lower than a gram, Because of the small density of this material which make that they are very voluminous samples, so that we can introduce them into combustion camera of the calorímeter in order to realise the tests. Because of that is necessary to study how this factor impacts in the evaluation of GCV of the studied sample. Moreover, it is necessary to know the contents of nitrogen (N) and sulphur (S) of the studied materials because during the tests the nitrogen reacts giving rise to HNO3 and the SO2 is oxidised and if rise to H2SO4. These quimic reactions absorb of detach heat which can mask the obtained values as GCV of the studied sample. As at the beginning of the test we don’t know the contents in N and S of the samples, we are going to study are so that we don’t have realise these determinations in each test, we realise work to see how they can impact this omission in the obtained facts. The study lie in realise tests in which it is supposed that the contents in N or in S are the maximum that these materials can contain and then the same test is repeated with the minimum contain of N or S. After that the differences between the obtained values are studied to the GCV of the original sample in both proves. So we can see how these factors impact in the obtained results. The test were realised in the laboratory using a calorimeter PARR 1261 with isoperibolie determination and evaluating the GCV of hops which had been mashed in order to make an homogeneous sample. In the test we evaluated the effects of the principal factors P, S and N, so that their interactions PxS, PxN, SxN and PxSxN. In order to confirm the obtained results in the tests we realised a statistic calculus of mena middle and the typical deviation and their variations through an analysis of variance so that a proof of the function F of Fisher in order to see the deviations produced in the variance of each one of the studied factors.