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Recently, there has been an increasing interest in the development of an approach to characterize the as-built heat loss coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.

Nitrogen oxide (NO) and nitrous oxide (N2O) formation in the circulating fluidized bed (CFB) combustion can be controlled by air staging and fuel staging. An extensive test campaign was carried out with a pilot-scale CFB test rig to observe the possibilities of the methods in the spruce bark and bituminous coal combustion as well as in co-combustion. Fuel staging with liquid petroleum gas (LPG) was done alternately from three locations with three intensities. Air staging was studied alone and during the fuel staging experiment. The experimental trends for NO and N2O emission formation during fuel staging and air staging are presented in this study. It was observed that air staging and fuel staging can have opposing effects on nitrogen oxide emission formation, and thus, when used together, a clear understanding of the fuel behavior and conditions, as well as NO x chemistry in the combustor, is needed. Under the tested conditions, it was observed that if air staging is effective, then fuel staging does not bring further benefits in the NO reduction. Instead, the LPG feed can increase the emission in the lack of oxygen. However, if it is not possible to carry out air staging, then fuel staging can be used in generating oxygen-lean reducing zones for NO. The N2O concentration was also further reduced with LPG in the tests with effective air staging.

The growing interest in sustainable development is reflected in both the market’s sensitivity to environmental and social issues and companies’ interest in the opportunities that sustainable development objectives provide. SMEs, which account for most of the world’s pollution, have significant resource constraints for a sustainable development. Sharing their scarce resources can help them to overcome these constraints and to gain agility and organisational resilience against uncertainties, but the distrust inherent in belonging to different companies prevents them from sharing the necessary information for coordination purposes. This paper presents a coordination mechanism proposal with information asymmetry to allow independent companies’ resources to be sustainably shared as a technological driver. The proposed distributed coordination mechanism is compared to both a decentralised–uncoordinated and a centralised situation. The interest of the proposal is evaluated by a computer simulation experiment employing mathematical programming models with independent objectives in the Generic Materials and Operations Planning formulation with a rolling horizon procedure in different demand, uncertainty and product scenarios. Competitive improvement is identified for all members for their excess capacity use and their operations planning.

This paper is to describe an open-source code for optimization of clean energy technologies. The model covers the whole chain of energy systems including mainly 6 areas: renewable energies, clean e ...

Abstract The empirical environmental Kuznets curve (EKC) literature is vast but far from conclusive. Many authors have analysed the existence of an EKC for various pollutants. Others have used the EKC framework to identify country characteristics that help to explain the income–environment relationship. In this framework environmental degradation is analysed using a second or third order polynomial in income and a limited number of control variables. Some authors question whether this standard framework is appropriate. This paper proposes an alternative to study the specific characteristics of countries that have experienced economic growth and an improving environment at the same time. We estimate a binary response model and find an EKC-like relation between the probability that a country's environment improves with economic growth and per capita GDP. Our evidence further suggests that the level of environmental damage is an important explanatory variable. We also confirm the importance of an open political system. These results indicate that the binary response model could be a valuable alternative to test which country specific characteristics are associated with a negative IER.

Abstract Grid-connected renewable energy systems are considered a viable solution for satisfying the swiftly growing demand. Nevertheless, the intermittent nature of renewable energy sources (RESs) hinders their performance and can not be efficiently utilized, rather imposes power quality and instability problem on the grid system. To alleviate this challenge, it is common practice to integrate RESs with efficient battery energy storage technologies. Lead-acid batteries were playing the leading role utilized as stationary energy storage systems. However, currently, there are other battery technologies like lithium-ion (Li-ion), which are used in stationary storage applications though there is uncertainty in its cost-effectiveness. In this paper, a state-of-the-art simulation model and techno-economic analysis of Li-ion and lead-acid batteries integrated with Photovoltaic Grid-Connected System (PVGCS) were performed with consideration of real commercial load profiles and resource data. The Hybrid Optimization Model for Electric Renewables (HOMER) was used for the study of the techno-economic analysis. Besides, the performance of these batteries is greatly affected by the rate of charge and discharge cycling effects which gradually degrades the capacity of the battery. This effect was also investigated with Matlab using a simplified equivalent circuit model by considering a typical stationary application datasheet. The techno-economic simulation output provided that the system with Li-ion battery resulted in a Levelized Cost of Energy (LCOE) of 0.32 €/kWh compared to the system with lead-acid battery with LCOE of 0.34 €/kWh. Besides, the Net Present Cost (NPC) of the system with Li-ion batteries is found to be €14399 compared to the system with the lead-acid battery resulted in an NPC of €15106. According to the result found, Li-ion batteries are techno-economically more viable than lead-acid batteries under the considered specifications and application profile.

Background: Phenolic compounds are food-derived bioactive compounds well-known for their antioxidant and anti-inflammatory properties. They are in the spotlight for the management of diabetes due to their positive effects on glucose homeostasis. Materials and methods: We have performed a literature review on the main topics related to the application of phenolic compounds as functional food ingredients. This includes extraction and purification from vegetable sources and agro-industrial by-products, encapsulation to improve their solubility and bioavailability, and preclinical and clinical evidence linking these compounds with anti-diabetic activity. Objectives: (1) provide an understanding of the role of phenolic compounds on diabetes; (2) identify green technologies for phenolic compounds extraction from agri-food by-products following a biorefinery scheme; (3) underline the relevance of encapsulation techniques using nanotechnology to improve their bioavailability; (4) discuss the therapeutic efficacy of polyphenols. Results: This review compiles recent relevant research on phenolic compounds extraction from renewable resources, their purification from agri-food by-products, and encapsulation strategies using eco-friendly processes. It also highlights the preclinical and clinical evidence on phenolic compounds’ antidiabetic activity, giving insight into their mechanisms of action. Conclusions: This review explores the latest advances in polyphenols and how their benefits in glucose homeostasis can be applied toward improving the health of patients with diabetes and related conditions.

An empirical model of global solar irradiance (EMGSI) under all sky conditions was developed by using solar radiation and meteorological parameters at Sodankylä. The calculated hourly global solar irradiance is in agreement with that observed at the ground during 2008–2011 and at the top of the atmosphere (TOA). This model is used to calculate the global solar irradiance at the ground and its attenuation in the atmosphere due to absorbing and scattering substances in 2000–2018. The sensitivity test indicates that the responses of global solar irradiance to changes in water vapor and scattering factors are nonlinear and negative, and global solar irradiance is more sensitive to changes in scattering (expressed by the scattering factor S/G, S and G are diffuse and global solar radiation, respectively) than to changes in water vapor. Using this empirical model, we calculated the albedos at the TOA and the surface, which are in agreement with the satellite-retrieved values. A good relationship between S/G and aerosol optical depth (AOD) was determined and used to estimate AOD in 2000–2018. An empirical model for estimation of tropospheric NO2 vertical column density (VCD) was also developed and used to calculate tropospheric NO2 VCD in 2000–2018. During 2000–2018, the estimated global solar irradiance decreased by 0.92%, and diffuse irradiance increased by 1.28% per year, which is ascribed to the increases of S/G (1.73%) and water vapor (0.43%). Annual surface air temperature increases by 0.07 °C per year. Annual mean loss of global solar irradiance caused by absorbing and scattering substances and total loss are 1.94, 1.17 and 3.11 MJ m−2, respectively. Annual mean losses of absorbing and scattering global solar irradiance show negative and positive trends, respectively, and the annual total loss increases by 0.24% per year. Annual mean losses due to absorption were much larger than those due to scattering. The calculated albedos at the TOA are smaller than at the surface. The calculated and satellite-retrieved annual albedos decrease at the TOA and increase at the surface. During 2000–2018, annual means of the AOD and the tropospheric NO2 VCD increased by 8.23% and 0.03% per year, respectively.