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Once previous industrial activity has ceased, brownfields are found in urban and suburban environments and managed in different ways ranging from being left untouched to total reconversion. These situations apply to large surface areas often impacted by residual diffuse pollution. Though significant and preventing any sensitive use, residual contamination does not necessarily require treatment. Moreover, conventional treatments show their technical and economic limits in these situations and gentle remediation options such as phytomanagement might appear more relevant to the management of those sites. Thus, these sites face up two major issues: managing moderate contamination levels and providing an alternative use of economic interest. This work proposes to assess a management strategy associating the phytoremediation of organic pollution along with the production of biomass for energy generation production. A 16-week controlled growth experiment was conducted on a soil substrate moderately impacted by multiple pollution (trace elements, mainly Zn and Pb, and hydrocarbons), by associating rhizodegradation with Medicago sativa or biomass production with Robinia pseudoacacia or Alnus incana in monocultures. The effect of a microbial inoculum amendment on the performances of these treatments was also evaluated. Results showed total hydrocarbons (TH), and to a lesser extent polycyclic aromatic hydrocarbons (PAH), concentrations decreased over time, whatever the plant cover. Good biomass production yields were achieved for both tree species in comparison with the control sample, even though R. pseudoacacia seemed to perform better. Furthermore, the quality of the biomass produced was in conformity with the thresholds set by the legislation concerning its use as a renewable energy source.

Two 4 L tubular microbial fuel cells (MFCs) were installed in a municipal wastewater treatment facility and operated for more than 400 days on primary effluents. Both MFCs removed 65-70% chemical oxygen demand (COD) at a hydraulic retention time (HRT) of 11 h and reduced about 50% suspended solids. The COD removal rates were about 0.4 (total) or 0.2 (soluble) kg m(-3) day(-1). They could handle fluctuation, such as emptying the anode for 1-3 days or different HRTs. The preliminary analysis of energy production and consumption indicated that the two MFCs could theoretically achieve a positive energy balance and energy consumption could be reduced using larger tubing connectors. Through linkage to a denitrifying MFC, the MFC system improved the removal of total nitrogen from 27.1 to 76.2%; however, the energy production substantially decreased because of organic consumption in the denitrifying MFC. Establishing a carbon (electron) balance revealed that sulfate reduction was a major electron scavenger (37-64%) and methane production played a very minor role (1.3-3.3%) in electron distribution. These results demonstrate the technical viability of MFC technology outside the laboratory and its potential advantages in low energy consumption, low sludge production, and energy recovery from wastes.

We develop an open-source Python software integrating flexibility needs from Variable Renewable Energies (VREs) in the development of regional energy mixes. It provides a flexible and extensible tool to researchers/engineers, and for education/outreach. It aims at evaluating and optimizing energy deployment strategies with higher shares of VRE, assessing the impact of new technologies and of climate variability and conducting sensitivity studies. Specifically, to limit the algorithm’s complexity, we avoid solving a full-mix cost-minimization problem by taking the mean and variance of the renewable production–demand ratio as proxies to balance services. Second, observations of VRE technologies being typically too short or nonexistent, the hourly demand and production are estimated from climate time series and fitted to available observations. We illustrate e4clim’s potential with an optimal recommissioning-study of the 2015 Italian PV-wind mix testing different climate data sources and strategies and assessing the impact of climate variability and the robustness of the results.

This paper proposes a data-driven diagnostic approach for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems. Fault detection and isolation (FDI) is realized by analyzing individual cell voltages. A feature extraction method Fisher Discriminant Analysis (FDA) and a multi-class classification method Directed Acyclic Graph Support Vector Machine (DAGSVM) are utilized successively to extract the useful features from raw data and classify the extracted features into various classes related to health states. Experimental data of two different stacks are used to validate the proposed approach. The results show that five concerned faults can be detected and isolated with a high accuracy. Moreover, the light computational cost of the approach enhances the possibility of its online implementation.

A theoretical analysis of a compression/ejection hybrid cycle for domestic refrigeration is presented in this paper. In this cycle, an ejector is employed to reduce the loss of available energy due to the large temperature difference between the fresh food section and the freezer section. A theoretical model is established and simulated with FORTRAN. Thermodynamic properties of working fluids are obtained with the package REFPROP, version 8.0. The performance of this system using several refrigerants (R123, R124, R141b, R290, R152a, R717, R600a, and R134a) is presented. The results show that the entrainment ratio and the coefficient of performance depend mainly on the fluid nature and the operating conditions. According to the results of simulation of the hybrid cycle using several refrigerants, the ejection cycle can reach a higher coefficient of performance compared with the standard cycle. R141b gives the most excellent performances.

We analyzed the role of predictable and unpredictable variability in the identification of optimal renewable energy mixes in an electricity system. Renewable energy sources are the fastest growing energy generation technology, but the variable nature of production linked to climate variability raises structural, technological and economical issues. This work proposes the differentiation of the treatment applied to predictable and unpredictable variability in the context of Markowitz portfolio theory for optimal renewable deployment. The e4clim model was used as a tool to analyze the impact of predictable sources of generation variability on the optimal renewable energy mixes. Significant differences appeared, depending on the consideration of risk, all of them showing room for improvement with respect to the current situation. The application of the methods developed in this study is encouraged in mean-variance analyses, since its contribution favors scenarios where unpredictable variability in the climate-powered renewable energy sources are considered for their risk introduction.