1,337 research outputs found
Biogas from anaerobic digestion of biomass (Organic Fraction of Municipal Solid Waste and sewage sludge): trace compounds characterization through an innovative technique (PTR-MS) and detrimental effects on SOFC energy generators, from single cells to short stacks
No full textThe use of biofuels instead of conventional fossil derived fuels is becoming an increasingly crucial topic for future energy systems where environmental issues are also taken into account. Biomass exploitation, including biowaste, appears as a promising means for the energy production and also it contributes to the carbon dioxide emissions reduction. Among the different techniques for biomass exploitation, interesting aspects are covered by the dry anaerobic fermentation of organic waste (OFMSW). In addition also interesting aspects can be achieved by the anaerobic digestion of sewage sludge in waste water treatment plants (WWTPs). Organic waste collection from local municipal areas or from sewage sludge exploitation with subsequent energy valorization through CHP systems allows to reduce the amount of waste disposed into landfills and the pollutant emissions into the atmosphere. Solid Oxide Fuel Cell (SOFC) systems are among the most promising energy generator respect to traditional power generating systems due to their higher electrical conversion values, even at partial loads. This is due to the direct conversion of chemical energy to electrical energy. Hence, fuel cells are very appealing from both energy and environmental point of view. In this work, the main goal was to demonstrate the real feasibility of a SOFC stack system fed by real biogas. This main goal has to be achieved considering three main sub-goals. The first one related to the biogas aspects, mainly on trace compounds investigation followed by the VOCs cleaning for SOFC requirements and then testing the main and the most dangerous VOCs on single cells and on short stacks. These information have been fundamentals for the SOFC generator directly fed by biogas. A 500 We SOFC stack by SOFCpower (Italy) was operated for more than 400 hrs in conjunction with a biogas feeding syste
Technical view of H2 vehicles - Safety aspects in road tunnels
No full textDibattito pubblico su questioni safety e di normativa sul tema idrogeno con focus al settore dei trasporti.
Dibattito avvenuto alla Fiera di Bologna - HESE, ottobre 2023
BEV, electric vehicles safety issues - thermal runaway and abuse testing in confined spaces
No full textUse of sewage sludges for CO2 removal
No full textSewage sludge from the Turin water purification system (SMAT; TO) is first anaerobically digested for biogas production and then used as filter material. Filter material for the purification of biogas produced by anaerobic digestion. The digested sludge must be appropriately treated. The case study was focused on the technical feasibility of the process. These samples were adopted for the removal of carbon dioxide from a biogas mixture. A sensitivity analysis was performed considering the main process parameters (temperature, residence time, activation agent, heating rate and activation flow rate). The sludge is characterised by its irregular shape and size, ranging from 1 mm to approximately 2 cm in equivalent diameter. Activation of the ground sludge was carried out in a tubular stainless steel reactor 57 cm long and 3.4 cm in outside diameter. A cylindrical electric furnace was adopted for the pyrogasification process, using N2, CO2 or air as the activating agent. A temperature control regulator, ranging from 200 to 600 °C, was adopted. These lower values compared to the literature are related to the coupling of SOFC discharges as possible agents for char production. Temperature and residence time are the most relevant parameters for char production with the pyrogasification process from sewage sludge. Temperature is the parameter that most influences the char production value. The biogas mixture (CH4/CO2=1.5) was considered. The untreated material shows an adsorption capacity of 4 mg/g, while the physically activated material shows a maximum value of about 102.5 mg/g. Future work will focus on the chemical activation of this sludge to investigate its gas-cleaning potential
Direct injection mass spectrometry technique for the odorant losses at ppb(v) level from nalophanTM sampling bags
No full textTrace compound stability is crucial for real time gas analysis and for high efficiency energy generators, where part per billion concentrations have a high impact. In this context, the container type chosen for the gas analysis becomes important. Several types of containers, such as stainless-steel canisters, glass bulbs, gas tight syringes and sampling bags can be adopted for sampling and storing such samples. Sampling bags are cheap and easy to manage, for these reasons other solutions are rarely used. Literature studies have showed how nalophanTM bags represent the best choice for the collection of samples due to the extremely low cost, the good sample stability and the low background interaction. No studies have considered the biogas trace constituents, especially at an ultra-low concentration level. Most of the studies relating to trace compound detection, use a GC–MS instrument and focus on few compounds of interest. In this paper, one of the most rapid and reliable direct injection mass spectrometry techniques was used. PTR-QMS was adopted to investigate the real biogas mixture stability. The concentration losses were studied continuously during 24 h from the sampling. These losses may be related to the wall adsorption and/or through the wall diffusion phenomena. The most stable compounds detected were acetone, methanol and formaldehyde with a concentration loss below 5%. Terpenes and sulphur compounds had a concentration loss of around 50%, while hydrocarbons and aromatic compounds showed concentration losses around 60%. The stability of the compounds monitored with acceptable losses (below 5%), was achieved within the first 3 h after the sampling was carried out
High temperature materials for CSP receivers: Experimental insights and material selection challenges
No full textSOFC single cells fed by biogas: Experimental tests with trace contaminants
No full textBiogas from biological treatments and from the waste degradation in landfills generally contains a wide range of trace impurities (e.g., sulphur compounds, siloxanes, halogens, tar compounds, etc.). This paper describes an experimental analysis performed with SOFC single cells fed by a synthetic gas polluted by H2S, HCl, D4 and a mixture of H2S + C2Cl4. The aim is to detect the threshold tolerance limit on different cell performance parameters. Results show how: hydrogen sulphide has a strong impact on the polarization losses due to the nickel sulphide formation on the electrode that causes a mass transfer resistance. Hydrogen chloride particularly limited the electrochemical processes. Octamethylcyclotetrasiloxane (D4) showed a high impact on SOFC performance even at ultra-low level (78–178 ppb(v)) as a consequence of the formation of silicon dioxide covering the anode porous sites. Sulphur added to C2Cl4, accelerated the deterioration of SOFC performance. In addition, current density variations and operating temperature are studied during sulphur poisoning. An opposite behaviour on SOFC performance was revealed by operating temperature and current density
A Prediction Model for Energy Production in a Solar Concentrator Using Artificial Neural Networks
No full textSolar energy is widely adopted today and produced by photovoltaic or concentrator solar power (CSP). Photovoltaic technology is the most prevalent, thanks to its well-established technology and low costs. CSP technology, on the other hand, has received less attention and interest, as it requires larger investments and a considerable surface. A relevant difficulty connected to the CSP is decoupling solar randomness and energy production. This paper proposes an artificial neural network (ANN) which foresees energy production using a solar parabolic dish installed at Politecnico di Torino (Energy Center Lab). The investigation was performed using a backpropagation ANN. Different learning algorithms were used: Levenberg-Marquardt, Bayesian regularization, resilient backpropagation, and scaled conjugate gradient. Seven atmospheric condition parameters were adopted (humidity, temperature, pressure, wind velocity and direction, solar radiation, and rain), to calculate the receiver temperature as an output. Bayesian regularization was found to be the optimal model for CSP energy production. The results of this investigation suggest that the ANNs are a strong, reliable, and useful tool for predicting temperature in a CSP receiver that can be of great value in the forecasting of energy production. The outcome of this investigation can simplify energy production forecasting using readily available meteorological data
Hybrid Models for Indoor Temperature Prediction Using Long Short Term Memory Networks - Case Study Energy Center
No full textIn the European Union States, household energy usage accounts on average for 40% of overall energy consumption and is responsible for a considerable amount of carbon dioxide emissions. The urgent need to take concrete action to identify solutions that can ensure more effective usage of energy in households, both because of environmental and political reasons, has been repeatedly stated by the European Parliament. White box, grey box and black box predictive models were demonstrated to be a feasible approach to predict the indoor temperature to implement an effective energy management strategy. This study has the purpose of illustrating the potentiality of an LSTM Artificial Neural Network in a short and long-term prediction of the indoor temperature in 15 offices distributed on three storeys of an existing building (Energy Center of Turin (Italy)). The indoor temperature was predicted two hours, five hours and one entire day ahead. The performance of these algorithms has been evaluated not only based on two main criteria (i.e., Root Mean Squared Error and Mean Absolute error) but also by considering the adaptability of the model between the three floors and in terms of different years. Moreover, the proposed work explains how parameters affect performances, aiming to properly identify the optimal model structure. Current results indicate that these models can provide accurate predictions for all the proposed time scales and could all potentially be used for predictive control purposes to optimise the energy demand. The novelty of this study is to show that these models can only be trained on data for a limited period and a specific plane, and then be reliable in predicting indoor temperature, both for different planes and for random periods, taking into account temperature and relative humidity. Furthermore, input parameters are limited to indoor HVAC variables, to ensure acceptable predictions regardless of outdoor parameters availability. The only exception is the outdoor temperature, because of its undeniable and proven importance, it was retained as the only exogenous input variable. Based on current literature and temperature perception capabilities, the results were considered acceptable if the RMSE was less than 0.15 or better yet 0.10, which is equivalent to an inaccuracy between the predicted and actual indoor temperature of 0.15 ◦C/0.10 ◦C. On average, the models trained on the Energy Center database
achieved an error of 0.1 ◦C in terms of RMSE
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