Development of glass-ceramics from combination of industrial wastes together with boron mining waste

The utilization of borate mineral wastes with glass-ceramic technology was first time studied and primarily not investigated combinations of wastes were incorporated into the research. These wastes consist of; soda lime silica glass, meat bone and meal ash and fly ash. In order to investigate possible and relevant application areas in ceramics, kaolin clay, an essential raw material for ceramic industry was also employed in some studied compositions. As a result, three different glass-ceramic articles obtained by using powder sintering method via individual sintering processes. Light weight micro porous glass-ceramic from borate mining waste, meat bone and meal ash and kaolin clay was developed. In some compositions in related study, soda lime silica glass waste was used as an additive providing lightweight structure with a density below 0.45 g/cm3 and a crushing strength of 1.8±0.1 MPa. In another study within the research, compositions respecting the B2O3–P2O5–SiO2 glass-ceramic ternary system were prepared from; borate wastes, meat bone and meal ash and soda lime silica glass waste and sintered up to 950ºC. Low porous, highly crystallized glass-ceramic structures with density ranging between 1.8 ± 0,7 to 2.0 ± 0,3 g/cm3 and tensile strength ranging between 8,0 ± 2 to 15,0 ± 0,5 MPa were achieved. Lastly, diopside - wollastonite (SiO2-Al2O3-CaO )glass-ceramics from borate wastes, fly ash and soda lime silica glass waste were successfully obtained with controlled rapid sintering between 950 and 1050ºC. The wollastonite and diopside crystal sizes were improved by adopting varied combinations of formulations and heating rates. The properties of the obtained materials show; the articles with a uniform pore structure could be useful for thermal and acoustic insulations and can be embedded in lightweight concrete where low porous glass-ceramics can be employed as building blocks or additive in cement and ceramic industries.

Messa a punto di processi biotecnologici per la produzione di biocombustibili da matrici organiche di rifiuto

The bioproduction of materials and energy from renewable sources (industrial biotechnology) is getting more and more interest in order to improve environmental sustainability of chemical industrial processes and to decrease their dependence from oil. Anaerobic digestion of organic waste matrices (agricultural and industrial wastes, organic fraction of municipal wastes, sewage sludges etc.) may play an important role in the implementation of industrial biotechnology being a well developed strategy in the valorization of complex matrices, as it can mineralize them while producing bioenergy in the form of a biogas rich in methane. In this research the potential of anaerobic digestion in the treatment of polluted sewage sludge was studied by developing three set of anaerobic microcosms with sludges differently contaminated by xenobiotic compounds. The effect of different incubating temperatures and of exogenous carbon and vitamine sources was investigated along with the role of the occurring microbial populations in the pollutant degradation activity. So, while confirming the potential of anaerobic digestion for the biomethanization of sewage sludges, this work proved the effectiveness of this technology in the removal of pollutants too. Moreover, since the degradation of lignocellulose appears to be a limiting step in the anaerobic treatment of a wide range of biomass, the possibility of optimizing anaerobic digestion of lignocellulosic substrates was also studied. To this aim a research was carried out at the BOKUUniversity of Natural Resources and Applied Life Sciences, Department for Agrobiotechnology, IFA - Tulln, where mixed cellulolytic cultures were isolated from biogas plants while assessing the metabolic pathway leading to cellulose degradation and verifying their capability to grow on lignocellulose too, proving that on the long term such bacterial cultures could be used as inoculum in order to improve the hydrolysis of lignocellulose in anaerobic digestion plants.

Integration of by-products and waste from agri-food supply chains into the «water-energy-food» nexus (RECOW-ER): the role of fungi

The agricultural sector is undoubtedly one of the sectors that has the greatest impact on the use of water and energy to produce food. The circular economy allows to reduce waste, obtaining maximum value from products and materials, through the extraction of all possible by-products from resources. Circular economy principles for agriculture include recycling, processing, and reusing agricultural waste in order to produce bioenergy, nutrients, and biofertilizers. Since agro-industrial wastes are principally composed of lignin, cellulose, and hemicellulose they can represent a suitable substrate for mushroom growth and cultivation. Mushrooms are also considered healthy foods with several medicinal properties. The thesis is structured in seven chapters. In the first chapter an introduction on the water, energy, food nexus, on agro-industrial wastes and on how they can be used for mushroom cultivation is given. Chapter 2 details the aims of this dissertation thesis. In chapters three and four, corn digestate and hazelnut shells were successfully used for mushroom cultivation and their lignocellulosic degradation capacity were assessed by using ATR-FTIR spectroscopy. In chapter five, through the use of the Surface-enhanced Raman Scattering (SERS) spectroscopy was possible to set-up a new method for studying mushroom composition and for identifying different mushroom species based on their spectrum. In chapter six, the isolation of different strains of fungi from plastic residues collected in the fields and the ability of these strains to growth and colonizing the Low-density Polyethylene (LDPE) were explored. The structural modifications of the LDPE, by the most efficient fungal strain, Cladosporium cladosporioides Clc/1 strain were monitored by using the Scanning Electron Microscope (SEM) and ATR-FTIR spectroscopy. Finally, chapter seven outlines the conclusions and some hints for future works and applications are provided.

Integrating Machine Learning Paradigms for Predictive Maintenance in the Fourth Industrial Revolution era

In the last decade, manufacturing companies have been facing two significant challenges. First, digitalization imposes adopting Industry 4.0 technologies and allows creating smart, connected, self-aware, and self-predictive factories. Second, the attention on sustainability imposes to evaluate and reduce the impact of the implemented solutions from economic and social points of view. In manufacturing companies, the maintenance of physical assets assumes a critical role. Increasing the reliability and the availability of production systems leads to the minimization of systems’ downtimes; In addition, the proper system functioning avoids production wastes and potentially catastrophic accidents. Digitalization and new ICT technologies have assumed a relevant role in maintenance strategies. They allow assessing the health condition of machinery at any point in time. Moreover, they allow predicting the future behavior of machinery so that maintenance interventions can be planned, and the useful life of components can be exploited until the time instant before their fault. This dissertation provides insights on Predictive Maintenance goals and tools in Industry 4.0 and proposes a novel data acquisition, processing, sharing, and storage framework that addresses typical issues machine producers and users encounter. The research elaborates on two research questions that narrow down the potential approaches to data acquisition, processing, and analysis for fault diagnostics in evolving environments. The research activity is developed according to a research framework, where the research questions are addressed by research levers that are explored according to research topics. Each topic requires a specific set of methods and approaches; however, the overarching methodological approach presented in this dissertation includes three fundamental aspects: the maximization of the quality level of input data, the use of Machine Learning methods for data analysis, and the use of case studies deriving from both controlled environments (laboratory) and real-world instances.