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Municipal solid waste (MSW) management in Spain, particularly in the Valencian Community, heavily relies on mechanical–biological treatment (MBT) plants followed by landfill disposal. These MBT facilities utilize mechanical processes like shredding, screening, and sorting to segregate recyclables (metals, plastics, paper) from organic material and other nonrecyclables. While public funding supports these plants, private entities manage them through complex, long-term concession contracts. This structure restricts access to crucial data on the sale prices of the byproducts generated during MBT. Publicly available information on relevant company and administration websites is typically absent, hindering transparency surrounding byproduct revenue. This study addresses this gap by analyzing 2012’s available data on revenues obtained from byproduct sales following mechanical treatment at MBT plants within the Valencian Community and comparing them with Spanish national data. This research revealed a significant finding—the statistical distribution of average prices obtained from Ecoembes auctions in the Valencian Community mirrored the corresponding distribution for prices calculated from auctions conducted in other Spanish regions. This suggests a potential uniformity in byproduct pricing across the country. It has also been found that none of the analyzed price distributions exhibited a normal (Gaussian) distribution. The findings also highlight the need for alternative pricing models that move beyond simple averages and account for regional variations and outliers. As actual prices are not available after 2012, this lack of transparency poses a challenge in comprehensively evaluating the economic viability of MBT plants. Furthermore, it raises concerns regarding whether the revenue generated from byproduct sales reflects fair market value. Limited public access to this information can potentially indicate conflicts of interest or inefficiencies within the waste management system.

The utilisation of thermoelectric technology as a heat pump in heating applications necessitates comprehensive investigation. The scalable nature of thermoelectric technology enables its operation at elevated temperatures without the requirement of refrigerants. In this work, an accurate computational model that can simulate one- and two-stage thermoelectric heat pumps is developed. This model uses the electric-thermal analogy and the finite difference method, including the thermoelectric effects, temperature dependent properties, thermal contact resistances and all heat exchangers, even the intermediate heat exchanger in the case of a two-stage configuration. Moreover, the model has been experimentally validated by built and tested prototypes, being the first time that a two-stage thermoelectric heat pump model is experimentally validated. The discrepancy between the simulated and experimental results is below the ± 10 % for , ± 8 % for generated heat and temperature lift in the airflow, and less than the ± 6 % for consumed power. Additonally, the model simulates real tendencies under different operating conditions, proving the reliability of the developed thermoelectric heat pump model. Finally, the model is used to optimise a thermoelectric system combining one- and two-stage thermoelectric heat pumps, and hybridising them with electric resistances. An airflow of 16.5 m3/h is heated from 25 °C to 160 °C, achieving a maximum of 1.21. Lastly, the importance of considering the thermal resistances of the heat exchangers is computationally modelled and demonstrated. Not taking them into account would overestimate the performance of the TEHP system by more than the 7 %. The authors would like to acknowledge the support of the grant TED2021-130071A-I00 funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by 'European Union Next Generation EU/PRTR', as well as the Government of Navarre, as part of the 'Grants to SINAI agents for the realization of collaborative R&D projects' under the PC066-067-068 FlexORCStorage. Open access funding provided by Universidad Pública de Navarra.

Recycled waste materials obtained from industrial and agricultural processes are becoming promising thermal and acoustic insulating solutions in building applications; their use can play an important role in the environmental impact reduction. The aim of the present paper is the evaluation of the thermal performance of recycled waste panels consisting of cork scraps, rice husk, coffee chaff, and end-life granulated tires, glued in different weight ratios and pressed. Six panels obtained from the mixing of these waste materials were fabricated and analyzed. In particular, the scope is the selection of the best compromise solutions from the thermal and environmental points of view. To this aim, thermal resistances were measured in laboratory and a Life Cycle Assessment (LCA) analysis was carried out for each panel; a cross-comparative examination was performed in order to optimize their properties and find the best panels solutions to be assembled in the future. Life Cycle Analysis was carried out in terms of primary Embodied Energy and Greenhouse Gas Emissions, considering a ‘‘cradle-to-gate” approach. The obtained thermal conductivities varied in the 0.055 to 0.135 W/mK range, in the same order of magnitude of many traditional systems. The best thermal results were obtained for the panels made of granulated cork, rice husk, and coffee chaff in this order. The rubber granulate showed higher values of the thermal conductivity (about 0.15 W/mK); a very interesting combined solution was the panel composed of cork (60%), rice husk (20%), and coffee chaff (20%), with a thermal conductivity of 0.08 W/mK and a Global Warming Potential of only 2.6 kg CO2eq/m2. Considering the Embodied Energy (CED), the best solution is a panel composed of 56% of cork and 44% of coffee chaff (minimum CED and thermal conductivity).

AbstractEnhancing the efficiency of the electric vehicle’s powertrain becomes a crucial focus, wherein the control system for the traction motor plays a significant role. This paper presents a novel electric vehicle traction motor control system based on a robust predictive direct torque control approach, an improved version of the conventional DTC, where the traditional switching table and the hysteresis regulators are substituted with a predictive block based on an optimization algorithm. Additionally, a robust predictive speed loop regulator is employed instead of the proportional-integral regulator, which integrates a new cost function with a finite horizon, incorporating integral action into the control law based on a Taylor series expansion. This technique’s primary benefit is its independence from the necessity to measure and observe external disturbances, as well as uncertainties related to parameters. The effectiveness of the suggested system was confirmed through simulation and experimental results under the OPAL-RT platform. The findings indicate that the proposed approach outperforms the conventional method in terms of rejecting disturbances, exhibiting robustness to variations in parameters, and minimizing torque ripple.

[Abstract] This research work presents an artificial intelligence approach to predicting the hydrogen concentration in the producer gas from biomass gasification. An experimental gasification plant consisting of an air-blown downdraft fixed-bed gasifier fueled with exhausted olive pomace pellets and a producer gas conditioning unit was used to collect the whole dataset. During an extensive experimental campaign, the producer gas volumetric composition was measured and recorded with a portable syngas analyzer at a constant time step of 10 seconds. The resulting dataset comprises nearly 75 hours of plant operation in total. A hybrid intelligent model was developed with the aim of performing fault detection in measuring the hydrogen concentration in the producer gas and still provide reliable values in the event of malfunction. The best performing hybrid model comprises six local internal submodels that combine artificial neural networks and support vector machines for regression. The results are remarkably satisfactory, with a mean absolute prediction error of only 0.134% by volume. Accordingly, the developed model could be used as a virtual sensor to support or even avoid the need for a real sensor that is specific for measuring the hydrogen concentration in the producer gas. Junta de Andalucía; 1381442 Xunta de Galicia; ED431G 2019/01 Ministerio de Universidades; FPU19/00930

Exterior insulation finishing systems (EIFSs) can efficiently promote energy efficiency of buildings. In this study, an EIFS with high thermal efficiency is presented to improve the insulation behavior of building enclosure. Based on heat transfer analysis results, energy simulations of buildings with fire spread prevention structures were performed. Results revealed that heat flow through the wall increased by 10.3 % when using a metal rail to fix the insulation; in contrast, using non-combustible phenolic foam reduces heat flow by 37.4 %, satisfying the requirement for fire spread prevention structures. Additionally, the energy consumption decreased by 8.8 % when both mineral wool and phenolic foam were applied. Fire spread prevention structures are essential to improve the fire safety performance of buildings. This external insulation system efficiently promote energy saving in building; additionally, leveraging a phase change material to improve the thermal storage performance of the building can reduce energy consumption by up to 11.9 %.

The assessment of the ‘quality’ of built heritage is a complex transdisciplinary issue, which both public administrations and real estate developers need to carefully consider when making any interventions. Recent international climate regulations underline that currently around 75% of buildings in the EU are not energy efficient. In Italy, those inefficient buildings are more than 50 years old and, if subjected to retrofit interventions, risk being totally transformed and losing their historical value in favor of a more contemporary use. This work aimed to study the residential heritage of the second half of the 20th century in the real estate market and to understand if, how, and in what measure the building and architectonical qualities are recognized and monetized by buyers. The city of Turin was chosen as a study area, and residential building qualities were analyzed using two quality indicators to perform a GWR on market POIs. The results highlighted that housing historical qualities are not homogeneously recognized by the real estate market, in favor of green ones. This work can help both public and private bodies to identify which ‘invisible’ quality residential buildings are immediately exploitable for enhancement strategies, with more respectful retrofitting interventions and a modern protection policy.