Life Cycle Assessment of Peach Nectar: a comparative analysis between conventional and bioenergy-from-waste integrated food chains

Modern food systems are characterized by a high energy intensity as well as by the production of large amounts of waste, residuals and food losses. This inefficiency presents major consequences, in terms of GHG emissions, waste disposal, and natural resource depletion. The research hypothesis is that residual biomass material could contribute to the energetic needs of food systems, if recovered as an integrated renewable energy source (RES), leading to a sensitive reduction of the impacts of food systems, primarily in terms of fossil fuel consumption and GHG emissions. In order to assess these effects, a comparative life cycle assessment (LCA) has been conducted to compare two different food systems: a fossil fuel-based system and an integrated system with the use of residual as RES for self-consumption. The food product under analysis has been the peach nectar, from cultivation to end-of-life. The aim of this LCA is twofold. On one hand, it allows an evaluation of the energy inefficiencies related to agro-food waste. On the other hand, it illustrates how the integration of bioenergy into food systems could effectively contribute to reduce this inefficiency. Data about inputs and waste generated has been collected mainly through literature review and databases. Energy balance, GHG emissions (Global Warming Potential) and waste generation have been analyzed in order to identify the relative requirements and contribution of the different segments. An evaluation of the energy “loss” through the different categories of waste allowed to provide details about the consequences associated with its management and/or disposal. Results should provide an insight of the impacts associated with inefficiencies within food systems. The comparison provides a measure of the potential reuse of wasted biomass and the amount of energy recoverable, that could represent a first step for the formulation of specific policies on the integration of bioenergies for self-consumption.

Lo spreco alimentare domestico in Italia: stime, cause ed impatti

Non esiste una definizione standard di spreco alimentare, così come non esistono metodologie uniformi per calcolarlo. Gli studi finora realizzati sullo spreco sono carenti, i dati raccolti spesso insufficienti. Il cibo viene sprecato ad ogni stadio della filiera alimentare, dal campo alla tavola. Nei Paesi Membri dell’Unione Europea, le famiglie – secondo dati elaborati da Eurostat- sono le principali responsabili dello spreco. Secondo la FAO, ogni europeo spreca ogni anno 179 chili di alimenti. Last Minute Market, spin off accademico che si occupa di ridurre e recuperare lo spreco, ha stimato che a livello domestico in Italia si sprecano mediamente il 17% dei prodotti ortofrutticoli acquistati, il 15% di pesce, il 28% di pasta e pane, il 29% di uova, il 30% di carne e il 32% di latticini. Da un punto di vista economico, lo sperpero alimentare significa una perdita di 1.693 euro l’anno per famiglia. Per inquadrare lo spreco alimentare domestico in Italia e gettare luce su dati contrastanti emersi da diversi studi finora realizzati, la tesi – dopo aver presentato stime a livello globale, europeo e italiano – si concentra sull’analisi dei dati emersi da un questionario sullo spreco domestico, compilato da 3.087 italiani tra il mese di novembre e quello di dicembre 2012. L’indagine socio-economica è stata realizzata in collaborazione con la Commissione Europea (DG JCR, Istituto per la Tutela della Salute dei Consumatori) e il Karlsruhe Institut für Technologie. Il questionario è stato posto sulla piattaforma online surveymonkey. La tesi ha avuto come obiettivi l’identificazione di dati quantitativi circa “quanto si spreca” , “cosa si spreca”, l’individuazione delle cause sociali, valoriali, comportamentali e di stile di vita, dello spreco alimentare delle famiglie italiane, l’impatto economico dello spreco sul budget domestico e l’elaborazione di profili di consumatori attraverso la cluster analysis.

Analysis and modelling of the performance of technologies for flue gas treatment in Waste-to-Energy processes

Waste management represents an important issue in our society and Waste-to-Energy incineration plants have been playing a significant role in the last decades, showing an increased importance in Europe. One of the main issues posed by waste combustion is the generation of air contaminants. Particular concern is present about acid gases, mainly hydrogen chloride and sulfur oxides, due to their potential impact on the environment and on human health. Therefore, in the present study the main available technological options for flue gas treatment were analyzed, focusing on dry treatment systems, which are increasingly applied in Municipal Solid Wastes (MSW) incinerators. An operational model was proposed to describe and optimize acid gas removal process. It was applied to an existing MSW incineration plant, where acid gases are neutralized in a two-stage dry treatment system. This process is based on the injection of powdered calcium hydroxide and sodium bicarbonate in reactors followed by fabric filters. HCl and SO2 conversions were expressed as a function of reactants flow rates, calculating model parameters from literature and plant data. The implementation in a software for process simulation allowed the identification of optimal operating conditions, taking into account the reactant feed rates, the amount of solid products and the recycle of the sorbent. Alternative configurations of the reference plant were also assessed. The applicability of the operational model was extended developing also a fundamental approach to the issue. A predictive model was developed, describing mass transfer and kinetic phenomena governing the acid gas neutralization with solid sorbents. The rate controlling steps were identified through the reproduction of literature data, allowing the description of acid gas removal in the case study analyzed. A laboratory device was also designed and started up to assess the required model parameters.