In the sustainability context, the performance of energy-producing technologies, using different energy sources, needs to be scored and compared. The selective criterion of a higher level of useful energy to feed an ever-increasing demand of energy to satisfy a wide range of endo- and exosomatic human needs seems adequate. In fact, surplus energy is able to cover energy services only after compensating for the energy expenses incurred to build and to run the technology itself. This paper proposes an energy sustainability analysis (ESA) methodology based on the internal and external energy use of a given technology, considering the entire energy trajectory from energy sources to useful energy. ESA analysis is conducted at two levels: (i) short-term, by the use of the energy sustainability index (ESI), which is the first step to establish whether the energy produced is able to cover the direct energy expenses needed to run the technology and (ii) long-term, by which all the indirect energy-quotas are considered, i.e., all the additional energy requirements of the technology, including the energy amortization quota necessary for the replacement of the technology at the end of its operative life. The long-term level of analysis is conducted by the evaluation of two indicators: the energy return per unit of energy invested (EROI) over the operative life and the energy payback-time (EPT), as the minimum lapse at which all energy expenditures for the production of materials and their construction can be repaid to society. The ESA methodology has been applied to the case study of H2 production at small-scale (10–15 kWH2) comparing three different technologies: (i) steam-methane reforming (SMR), (ii) solar-powered water electrolysis (SPWE), and (iii) two-stage anaerobic digestion (TSAD) in order to score the technologies from an energy sustainability perspective.
Dark fermentation (DF), a key biohydrogen-producing process, is generally operated as a black-box, by monitoring different operative macroscopic process parameters without evaluating or tracking the physiology of the biotic phase. The biotic phase in DF is constituted by a large variety of microorganisms, mainly fermentative bacteria. The present study uses two (electro)optical techniques, flow cytometry (FC) and frequency-dependent polarizability anisotropy (FDPA) measurements, to gain insights into the physiology of open mixed consortia throughout the DF process. The mixed consortia for DF were obtained from a methanogenic sludge, selecting spore-forming bacteria by means of an acid treatment. Then, DF systems with and without pH control were studied, using as substrate a mixture of maize and grass silage (9:1 w/w). Over the course of fermentation, the butyric pathway was dominant in both systems, and relevant titers of acetate, formate, and ethanol were detected; while hydrogen yields amounted to 20.80 ± 0.05 and 17.08 ± 0.05 NmL/gVS under pH-regulated and non-regulated conditions, respectively. The cytometric pattern analysis of the culture together with microscopic observations made it possible, over the course of fermentation, to identify and track the predominant morphologies in play (i.e., free spore, rod-shaped, and endospore, which are typical of Clostridium spp.). Furthermore, the use of the fluorescent dye DiBAC 4 (3) in FC and FDPA measurements provided similar information regarding the physiological state (PS) of the mixed consortia during the different phases of the culture.
Extending the shelf-life and ensuring microbiological safety of food products while preserving the nutritional properties are key aspects that must be addressed. Heat processing of food matrices has been the golden standard during the last decades, while certain non-thermal processing options have recently gained ground. In the present study, experimental pulsed light (PL) surface inactivation treatments of Salmonella enteritidis on almonds kernels are performed. The PL system is set to test different operative conditions, namely power (1000, 1250, and 1500 W) and frequency (1.8, 3.0, and 100.0 Hz) at different treatment times (from 5 to 250 s), which result in applied fluence doses in the 0–100 J·cm−2 range. Additionally, temperature measurements are collected at each operative condition on the almond surface (using infrared (IR) thermography) and at the superficial layer of the almond (1-mm depth using a thermocouple). The observed PL inactivation kinetics are then modelled using four different models. The best goodness-of-fit is found for the two-parameter Weibull model (R2 > 0.98 and RMSE < 0.33 for all cases). The maximum achieved log-CFU reductions are 6.02 for the 1.8-Hz system, 4.69 for the 3.0-Hz system, and 3.66 for 100.0-Hz system. The offset between the collected temperature readings by the two sensors is contrasted against the inactivation rate (following the two-parameter Weibull model). It was found that the highest inactivation rate corresponds approximately to the point where the infrared camera detects a slowdown in the surface heating.
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