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Abstract Economically harvesting energy from a microbial fuel cell (MFC), increasing its electrical power production, and developing its role as a practical energy supply, needs a low-cost and high-performance design of the MFC compartments. According to this strategy, a novel monolithic membrane electrode assembly (MEA) was fabricated and evaluated as an air–cathode in a single-chamber MFC (SCMFC). The MEA was made of bacterial cellulose (BC), conductive multi-walled carbon nanotubes (CNT), and nano-zycosil (NZ). BC, as a nano-celluloses with oxygen barrier property, can maintain anaerobic conditions for the anode compartment. Binder-less CNT coating on BC avoids costly binders such as poly-tetra fluoro ethylene (PTFE) and Nafion and decreases the MEA charge transfer resistance. NZ, as a very cheap modifier, not only prevents the anolyte leakage but also provides more MEA’s active sites for the oxygen reduction reaction (ORR). The electrochemical performance of the MEA was compared to a PTFE- based gas diffusion electrode (GDE) in the SCMFC. The MEA cell provided a pulse power density of 1790 mW/m2, roughly twice as high as the pulse power density of GDE (920 mW/m2). SCMFC’s internal resistance decreased from 1.84 KΩ (with GDE) to 0.8 KΩ (with MEA). Also, the cell’s columbic efficiency increased from 4.2% (with GDE) to11.7% (with MEA). Additionally, the capacitance of the MEA (65 mF) was much higher than the value for GDE (0.73 mF). Thus, the MEA compared to the GDE showed higher performance in the SCMFC for electricity generation and wastewater treatment at a lower cost.

Cette modélisation présente la partie de contrôle d'une pile à combustible à membrane d'échange de protons (PEMFC). L'objectif du modèle de représenter la chaîne de contrôle de la tension de la PEMFC. Ce modèle est basé sur une représentation macroscopique énergétique (REM) de la pile à combustible, puis conduit à une structure dite de contrôle maximal (SCM). La SCM est une inversion étape par étape de la REM (structure de contrôle basée sur un modèle d'inversion). Le processus de conception de la commande est basé sur une définition explicite du problème. Par exemple, les entrées de réglage, les objectifs du système ou les contraintes sont mises en évidence pour organiser la commande. De plus, le SMC montre les endroits où les capteurs sont nécessaires et où les contrôleurs sont requis. Malheureusement, le SCM n'est qu'une structure de contrôle théorique. Par conséquent, une structure réaliste nécessite certaines simplifications, ce qui conduit à une structure de contrôle dite pratique. This modeling presents the control part of a proton exchange membrane fuel cell (PEMFC). The objective of the model is to represent the voltage control chain of the PEMFC. This model is based on an energetic macroscopic representation (EMR) of the fuel cell and then leads to a so-called maximum control structure (MCS). The SCM is a step-by-step inversion of the REM (control structure based on an inversion model). The control design process is based on an explicit problem definition. For example, control inputs, system objectives, or constraints are highlighted to organize the control. In addition, the MSC shows where sensors are needed and where controllers are required. Unfortunately, the SCM is only a theoretical control structure. Therefore, a realistic structure requires some simplifications, which leads to a so-called practical control structure. AdditionnalInformation: Modèle créé lors d'une thèse en génie électrique, par Loïc Boulon (ses recherches portent sur la modélisation et le contrôle des véhicules électriques hybrides, des sources d'énergie et d'énergie et de puissance, et des systèmes de piles à combustible). Il a travaillé en vue de l'obtention du diplôme de doctorat à l'Université de Franche-Comté, Belfort, France, où sa thèse de doctorat a été réalisée en collaboration avec le FEMTO-ST, Belfort, et les Laboratoires L2EP, Lille. Co-encadrement par Daniel Hissel (PU au laboratoire FEMTO-ST), Alain Bouscayrol (PU au laboratoire L2EP) et Marie-Célie Péra (PU au laboratoire FEMTO-ST) AdditionnalInformation: Model created during a thesis in electrical engineering, by Loïc Boulon (his research focuses on the modeling and control of hybrid electric vehicles, energy and power sources, and fuel cell systems). He worked towards his Ph.D. degree at the University of Franche-Comté, Belfort, France, where his dissertation was complet in collaboration with the FEMTO-ST, Belfort, and the L2EP Laboratories, Lille. Co-supervision by Daniel Hissel (PU at FEMTO-ST laboratory), Alain Bouscayrol (PU at L2EP laboratory) and Marie-Célie Péra (PU at FEMTO-ST laboratory) Données computationnelles ou de simulation: - Langage de programmation : Langage Matlab - Plate-forme/OS : Microsoft Windows, Mac OS, Linux,... - Version : MATLAB R2018a - Statut de développement : Actif Simulation or computational data: - Programming language: Matlab language - Platform/OS: Microsoft Windows, Mac OS, Linux,... - Version: MATLAB R2018a - Development status: Active Project: Thèse de doctorat en génie électrique - Loïc Boulon, Contrat doctoral (Region Bourgogne Franche-Comté) Label: PHE (Plateforme Hydrogène-Energie) Audience: Research

AbstractThe integration of power plants and desalination systems has attracted increasing attention over the past few years as an effective solution to tackle sustainable development and climate change issues. In this light, this paper introduces a novel modelling and optimization approach for a combined-cycle power plant (CCPP) integrated with reverse osmosis (RO) and multi-effect distillation (MED) desalination systems. The integrated CCPP and RO–MED desalination system is thermodynamically modelled utilizing MATLAB and EES software environments, and the results are validated via Thermoflex software simulations. Comprehensive energy, exergic, exergoeconomic, and exergoenvironmental (4E) analyses are performed to assess the performance of the integrated system. Furthermore, a new multi-objective water cycle algorithm (MOWCA) is implemented to optimize the main performance parameters of the integrated system. Finally, a real-world case study is performed based on Iran's Shahid Salimi Neka power plant. The results reveal that the system exergy efficiency is increased from 8.4 to 51.1% through the proposed MOWCA approach, and the energy and freshwater costs are reduced by 8.4% and 29.4%, respectively. The latter results correspond to an environmental impact reduction of 14.2% and 33.5%. Hence, the objective functions are improved from all exergic, exergoeconomic, and exergoenvironmental perspectives, proving the approach to be a valuable tool towards implementing more sustainable combined power plants and desalination systems.

This article proposes an effective evolutionary hybrid optimization method for identifying unknown parameters in photovoltaic (PV) models based on the northern goshawk optimization algorithm (NGO) and pattern search (PS). The chaotic sequence is used to improve the exploration capability of the NGO algorithm technique while evading premature convergence. The suggested hybrid algorithm, chaotic northern goshawk, and pattern search (CNGPS), takes advantage of the chaotic NGO algorithm’s effective global search capability as well as the pattern search method’s powerful local search capability. The effectiveness of the recommended CNGPS algorithm is verified through the use of mathematical test functions, and its results are contrasted with those of a conventional NGO and other effective optimization methods. The CNGPS is then used to extract the PV parameters, and the parameter identification is defined as an objective function to be minimized based on the difference between the estimated and experimental data. The usefulness of the CNGPS for extraction parameters is evaluated using three distinct PV models: SDM, DDM, and TDM. The numerical investigates illustrate that the new algorithm may produce better optimum solutions and outperform previous approaches in the literature. The simulation results display that the novel optimization method achieves the lowest root mean square error and obtains better optima than existing methods in various solar cells.

This paper presents an experimental study of indoor thermal environment near a full-scale glass facade with different types of shading devices under varying climatic conditions in winter. Interior glazing and shading temperature, operative temperature and radiant temperature asymmetry were measured for facade sections with roller shades and venetian blinds at different positions. Interior glass surface temperatures can be high during sunny days with low outdoor temperature. Shading systems significantly improved operative temperature and radiant temperature asymmetry during cold sunny days, depending on their properties and tilt angle. During cloudy days the impact was smaller, however the shading layers could still decrease the amount of heat loss through the facade. A transient building thermal model, which also calculates indoor environmental indices under the presence of solar radiation, was developed and compared with the experimental measurements. Part II of this paper uses this validated model with a transient, two-node thermal comfort model (including transmitted solar radiation) for assessment of indoor environmental conditions with different building envelope and shading properties, facade location and orientation.

A vehicle model is used to evaluate a novel powertrain that is comprised of a dual energy storage system (Dual ESS). The system includes two battery packs with different chemistries and the necessary electronic controls to facilitate their coordination and optimization. Here, a lithium-ion battery pack is used as the primary pack and a Zinc-air battery as the secondary or range-extending pack. Zinc-air batteries are usually considered unsuitable for use in vehicles due to their poor cycle life, but the model demonstrates the feasibility of this technology with an appropriate control strategy, with limited cycling of the range extender pack. The battery pack sizes and the battery control strategy are configured to optimize range, cost and longevity. In simulation the vehicle performance compares favourably to a similar vehicle with a single energy storage system (Single ESS) powertrain, travelling up to 75 km further under test conditions. The simulation demonstrates that the Zinc-air battery pack need only cycle 100 times to enjoy a ten-year lifespan. The Zinc-air battery model is based on leading Zinc-air battery research from literature, with some assumptions regarding achievable improvements. Having such a model clarifies the performance requirements of Zinc-air cells and improves the research community's ability to set performance targets for Zinc-air cells.

This paper presents the direct power control (DPC) of asymmetrical six-phase permanent magnet synchronous generator (A6PMSG) which supplied the isolated load via two PWM rectifiers. The study concerns the method of control by the converter (DPC). The proposed method is around two hysteresis controllers that enable the adjustment of active and reactive power. The research in this paper is verified by MATLAB SIMULINK software. It has been demonstrated through the simulation results that the proposed strategy can be an attractive and practical solution to multi-phase machines applications.

Biodiesel can be produced from vegetable oils, animal fats, and waste cooking oils by transesterification with ethanol (also called ethanolysis) in order to substitute fossil fuels. In this work, the batch ethanolysis of high oleic sunflower oil was transferred into a continuous microstructured device, which induces a better control of heat and mass transfers. Various parameters were studied, notably the initial ethanol to oil molar ratio. An innovative method using NIR spectroscopy was also developed to on-line monitor the transesterification reaction of high oleic sunflower oil with ethanol in microreactors (circular PFA tube 1/1600 OD, 0.0200 ID). The reactions were monitored directly in the microreactors through sequential scans of the reaction medium by the means of an adequate probe. The asset of the method is that no sample collection or preparation is necessary. Partial Least Squares regression was used to develop calibration and prediction models between NIR spectral data and analytical data obtained by a reference method (gas chromatography with flame ionization detection, GC–FID). This method is fast, safe, reliable, nondestructive and inexpensive contrary to conventional procedures, such as gas chromatography and high performance liquid chromatography generally used to determine the composition of crude transesterification medium.