Chemical analysis and reactivity of biomass pyrolysis products. Application to the development of carbon-neutral biofuels and chemicals

In this dissertation the pyrolytic conversion of biomass into chemicals and fuels was investigated from the analytical point of view. The study was focused on the liquid (bio-oil) and solid (char) fractions obtainable from biomass pyrolysis. The drawbacks of Py-GC-MS described so far were partially solved by coupling different analytical configurations (Py-GC-MS, Py-GC-MIP-AED and off-line Py-SPE and Py-SPME-GC-MS with derivatization procedures). The application of different techniques allowed a satisfactory comparative analysis of pyrolysis products of different biomass and a high throughput screening on effect of 33 catalysts on biomass pyrolysis. As the results of the screening showed, the most interesting catalysts were those containing copper (able to reduce the high molecular weight fraction of bio-oil without large yield decrease) and H-ZSM-5 (able to entirely convert the bio-oil into “gasoline like” aromatic products). In order to establish the noxious compounds content of the liquid product, a clean-up step was included in the Py-SPE procedure. This allowed to investigate pollutants (PAHs) generation from pyrolysis and catalytic pyrolysis of biomass. In fact, bio-oil from non-catalytic pyrolysis of biomass showed a moderate PAHs content, while the use of H-ZSM-5 catalyst for bio-oil up-grading determined an astonishing high production of PAHs (if compared to what observed in alkanes cracking), indicating an important concern in the substitution fossil fuel with bio-oil derived from biomass. Moreover, the analytical procedures developed in this thesis were directly applied for the detailed study of the most useful process scheme and up-grading route to chemical intermediates (anhydrosugars), transportation fuels or commodity chemicals (aromatic hydrocarbons). In the applied study, poplar and microalgae biomass were investigated and overall GHGs balance of pyrolysis of agricultural residues in Ravenna province was performed. A special attention was put on the comparison of the effect of bio-char different use (fuel or as soil conditioner) on the soil health and GHGs emissions.

A Bayesian changepoint analysis on spatio-temporal point processes

Changepoint analysis is a well established area of statistical research, but in the context of spatio-temporal point processes it is as yet relatively unexplored. Some substantial differences with regard to standard changepoint analysis have to be taken into account: firstly, at every time point the datum is an irregular pattern of points; secondly, in real situations issues of spatial dependence between points and temporal dependence within time segments raise. Our motivating example consists of data concerning the monitoring and recovery of radioactive particles from Sandside beach, North of Scotland; there have been two major changes in the equipment used to detect the particles, representing known potential changepoints in the number of retrieved particles. In addition, offshore particle retrieval campaigns are believed may reduce the particle intensity onshore with an unknown temporal lag; in this latter case, the problem concerns multiple unknown changepoints. We therefore propose a Bayesian approach for detecting multiple changepoints in the intensity function of a spatio-temporal point process, allowing for spatial and temporal dependence within segments. We use Log-Gaussian Cox Processes, a very flexible class of models suitable for environmental applications that can be implemented using integrated nested Laplace approximation (INLA), a computationally efficient alternative to Monte Carlo Markov Chain methods for approximating the posterior distribution of the parameters. Once the posterior curve is obtained, we propose a few methods for detecting significant change points. We present a simulation study, which consists in generating spatio-temporal point pattern series under several scenarios; the performance of the methods is assessed in terms of type I and II errors, detected changepoint locations and accuracy of the segment intensity estimates. We finally apply the above methods to the motivating dataset and find good and sensible results about the presence and quality of changes in the process.