On the carbon cycle impact of combustion of harvested plant biomass vs. fossil carbon resources

Comparative study of the combustion, pyrolysis and gasification processes of Leucaena leucocephala: Kinetics and gases obtained

Leucaena leucocephala is a fast-growing leguminous biomass with great energetical and value-added chemical compounds potential (saccharides, biogas, bio-oil, etc.). Using the thermogravimetric and derivative thermogravimetric curves, the different trends followed by L. leucocephala during pyrolysis, 0.25 equivalence ratio (ER) of gasification, 0.50 equivalence ratio of gasification and combustion were analyzed, and the activation energies were obtained by Distributed Activation Energy Model (DAEM) method. Gas samples were collected through adsorption tubes during the gasification at 0.25 ER and 0.50 ER to observe the distribution of the main chemical products in this process by gas chromatography/mass spectrometry and were compared with pyrolysis products. It was found that small amounts of oxygen have changes in the kinetics of the process, leading to significant decreases in the activation energy at the beginning of the degradation of components such as cellulose (from 170 to 135 kJ mol-1 at 0.25 conversion at 0.50 ER gasification). The activation energy of lignin disintegration was also reduced (342 kJ mol-1), assimilating the beginnings of gasification processes such as the Boudouard reaction. 0.50 ER gasification is potentially an interesting process to obtain quality bio-oil, since a large amount of hexane is detected (44.96%), and value-added oxygenated intermediates such as alcohols and glycols. Gasification at 0.25 ER, on the other hand, is much more similar to pyrolysis, obtaining a wide variety of short-chain compounds resulting from the disintegration of the main lignocellulosic components, especially ketones such as 1-hydroxypropan-2-one (19.48%), and notable amount of furans and anhydrosugars like d-allose (5.50%).

Influence of biomass on coal slime combustion characteristics based on TG-FTIR, principal component analysis, and artificial neural network

The development and utilization of solid waste is an effective way to solve the severe environmental and energy crisis. In this study, Thermogravimetry - Fourier transform infrared spectrometry (TG-FTIR) was used to carry out the co-combustion experiment of coal slime and rice husk under different mixing ratios. With the increase of the mass percentage of rice husk in the sample, the initial ignition temperature and burnout of the sample decreased, and the comprehensive combustion performance improved gradually. The dominant reaction in the co-combustion of coal slime and rice husk was determined by statistical method. When the mass percentage of rice husk in the mixture is between 30 and 90 %, it can inhibit the release of NO_x and SO₂. Taking Kissinger-Akahira-Sunose method as an example, the calculated average activation energies of coal slime and rice husk combustion are 105.66 and 148.93 kJ/mol respectively. With the increase of the mixing ratio of rice husk in the blend, the combustion mechanism of the sample changed. Finally, the mean absolute error, root mean square error and determination coefficient of the artificial neural network model are 0.52697, 0.67866 and 0.99941 respectively.

Microwave assisted biodiesel production from chicken feather meal oil using Bio-Nano Calcium oxide derived from chicken egg shell

Environmental concerns have initiated the search for greener measures to mitigate pollution issues. Bio Nano CaO was synthesized by reducing CaO extracted from chicken egg shell using tea decoction. The synthesized material was characterized by physico-chemical techniques such as XRD, TGA, BET surface area analyser, TGA and SEM techniques. XRD studied confirmed the crystalline nature of material. The prepared material was found to be stable till 450 ^οC from TGA study. The SEM pictures displayed uniform and discrete particles which portrays the high probable sites that maximises the catalytic activity. The optimization of microwave assisted Biodiesel synthesis from chicken feather oil through Transesterification process using the bio-synthesized catalytic material was the main aim of the study. A 500 W microwave irradiation of Chicken feather meal oil using 8:1 Methanol:Oil input, 1% Bio Nano CaO concentration, 5 min of reaction time resulted in 95% conversion of chicken feather meal oil into chicken feather meal methyl esters. The Biodiesel was showed low viscosity (4.15 mm²/s), high heating value (50 MJ/kg), high flash point (153^οC), reasonable pour point (12 ^οC) and good cetane number (50 min). The future works will be concentrated on the engine studies related to Torque, fuel consumption, emission data by using the synthesized Biodiesel.

Consequences of different sample drying temperatures for accuracy of biomass inventories in forest ecosystems

Biomass estimation is one of the crucial tasks of forest ecology. Drying tree material is a crucial stage of preparing biomass estimation tools. However, at this step researchers use different drying temperatures, but we do not know how this influences accuracy of models. We aimed to assess differences in dry biomass between two drying temperatures (75 °C and 105 °C) in tree biomass components and to provide coefficients allowing for recalculation between the given temperatures. We used a set of 1440 samples from bark, branches, foliage and wood of eight European tree species: Abies alba Mill., Alnus glutinosa (L.) Gaertn., Betula pendula Roth., Fagus sylvatica L., Larix decidua Mill., Picea abies (L.) H. Karst., Pinus sylvestris L. and Quercus robur L. The differences between drying temperatures were 1.67%, 1.76%, 2.20% and 0.96% of sample dry masses of bark, branches, foliage and stem wood, respectively. Tree species influenced these differences. Our study provided coefficients allowing for recalculation of masses between the two temperatures, to unify results from different studies. However, the difference in dry mass between the two temperatures studied is lower than the range of uncertainty of biomass models, thus its influence on results of large-scale biomass assessments is low.