Electrophoretic deposition of graphene oxide on carbon brush as bioanode for microbial fuel cell operated with real wastewater

Microbial fuel cell (MFC) is a promising technology for simultaneous wastewater treatment and energy harvesting. The properties of the anode material play a critical role in the performance of the MFC. In this study, graphene oxide was prepared by a modified hummer's method. A thin layer of graphene oxide was incorporated on the carbon brush using an electrophoretic technique. The deoxygenated graphene oxide formed on the surface of the carbon brush (RGO-CB) was investigated as a bio-anode in MFC operated with real wastewater. The performance of the MFC using the RGO-CB was compared with that using plain carbon brush anode (PCB). Results showed that electrophoretic deposition of graphene oxide on the surface of carbon brush significantly enhanced the performance of the MFC, where the power density increased more than 10 times (from 33 mWm^?2 to 381 mWm^?2). Although the COD removal was nearly similar for the two MFCs, i.e., with PCB and RGO-CB; the columbic efficiency significantly increased in the case of RGO-CB anode. The improved performance in the case of the modified electrode was related to the role of the graphene in improving the electron transfer from the microorganism to the anode surface, as confirmed from the electrochemical impedance spectroscopy measurements.     

Integrated standalone hybrid solar PV,fuel cell and diesel generator power system for battery or supercapacitor storage systems in Khorfakkan,United Arab Emirates

Renewable energy resources play a very important rule these days to assist the conventional energy systems for doing its function in the UAE due to high greenhouse gas (GHG) emissions and energy demand. In this paper, the analysis and performance of integrated standalone hybrid solar PV, fuel cell and diesel generator power system with battery energy storage system (BESS) or supercapacitor energy storage system (SCESS) in Khorfakkan city, Sharjah were presented. HOMER Pro software was used to model and simulate the hybrid energy system (HES) based on the daily energy consumption for Khorfakkan city. The simulation results show that using SCESS as an energy storage system will help the performance of HES based on the Levelized cost of energy (LCOE) and greenhouse gas (GHG) emissions. The HES with SCESS has renewable fraction (68.1%) and 0.346 $/kWh LCOE. The HES meets the annual AC primary load of the city (13.6 GWh) with negligible electricity excess and with an unmet electrical load of 1.38%. The reduction in GHG emissions for HES with SCESS was 83.2%, equivalent to saving 814,428 gallons of diesel.     

A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell

Cell temperature and water content of the membrane have a significant effect on the performance of fuel cells. The current-power curve of the fuel cell has a maximum power point (MPP) that is needed to be tracked. This study presents a novel strategy based on a salp swarm algorithm (SSA) for extracting the maximum power of proton-exchange membrane fuel cell (PEMFC). At first, a new formula is derived to estimate the optimal voltage of PEMFC corresponding to MPP. Then the error between the estimated voltage at MPP and the actual terminal voltage of the fuel cell is fed to a proportional-integral-derivative controller (PID). The output of the PID controller tunes the duty cycle of a boost converter to maximize the harvested power from the PEMFC. SSA determines the optimal gains of PID. Sensitivity analysis is performed with the operating fuel cell at different cell temperature and water content of the membrane. The obtained results through the proposed strategy are compared with other programmed approaches of incremental resistance method, Fuzzy-Logic, grey antlion optimizer, wolf optimizer, and mine-blast algorithm. The obtained results demonstrated high reliability and efficiency of the proposed strategy in extracting the maximum power of the PEMFC.     

Effect of Fe2+ and Fe0 Applied Photo‐Fenton Processes on Sludge Disintegration

The disintegration of waste active sludge was investigated by photo‐Fenton processes. A batch study was conducted to evaluate parameters, such as Fe²⁺ and Fe⁰ ions and H₂O₂, governing the activated sludge integration by the photo‐Fenton process. Under optimum conditions, the concentration of soluble chemical oxygen demand (SCOD) with the classical Fenton process (CFP) increased very rapidly in the first five minutes due to the sufficient presence of reaction components in the medium, and then the rate of increase declined. In the modified Fenton process (FTP), the SCOD concentration increased more slowly as metallic iron powder must first be dissolved. The photo‐Fenton process proved to be a feasible and efficient process for the disintegration of waste sludge.     

New design of CHP for hypermarket of higher energy efficiency,less-fugitive refrigerant emissions and a remarkable reduction in initial and operating cost

High initial cost of power plant, limited power capacity of the existing plant and the frequent black out of many districts due to the continuous increase in electrical demands, in addition to the adverse effect of the blackout on hypermarkets, especially on foodstuff such as meat, chicken, etc. strongly encourage the adoption of a regenerating process and the principle of combined heat and power. This work investigates the upgrading of the combined cooling heat and power system and improving the regenerating process adopted in the first design of an existing project. Linking between refrigeration and air-conditioning systems is proposed in scheme-II. A single-effect absorption chiller operating in the range of refrigeration scale (i.e. aqueous-ammonia refrigeration, not air-conditioning scale, LiBr–H₂O employed in scheme-I) will be adopted using pressurised hot water recovered from the generators. The chilled water system will be serving three categories in the following order, namely, refrigeration stores, cold stores and air-conditioning units, respectively. The chilled water output of the first category will be the input of the second one. This will enhance the overall system efficiency, reduce the generator load and lessen the fugitive refrigerant emissions, which mainly participate in ozone-depleting potential and global-warming potential. This is in addition to the dramatic reduction in initial and operating cost, which can be estimated to be 50% and 79.6%, respectively.     

An iterative code to investigate heat pump performance improvement by exhaust gases heat recovery

The race between the development of technologies and energy demand has drawn the guidelines of energy strategies for the next two decades. Indeed, the governmental organizations as well as the private sectors are spending huge effort to come up with new adequate strategies that allow to decrease energy consumption. Having said that, heat pump becomes an essential system in our daily life not only in residential building but also in hospital, industrial and touristic building. Nonetheless, (HP)s have very high energy consumption rate. Thus, and to be in line with the new trends in energy strategies, it is convenient to find new methods to enhance the performance of heat pump in order to reduce energy consumption. In this frame, the present paper suggests an approach to enhance the performance the heat pumps using the heat recovery from generators. For this purpose, an in-house code is developed allowing to simulate two new proposed systems (condenser upstream exhaust gases heat recovery system (CU-EGHRS) and condenser downstream exhaust gases heat recovery system (CD-EGHRS). It has been shown that the increase in the performance of the heat pump depends on the capacity of the generator. Also, the CD-EGHRS is shown to be the best. For instance, in the case of a 15 kVA generator, the enhancement could reach 42% for the CD-EGHRS. This enhancement increases to 5640% in the case of a 180 kVA generator.     

Permeability prediction and construction of 3D geological model: application of neural networks and stochastic approaches in an Iranian gas reservoir

Determination of petrophysical parameters by using available data has a specific importance in exploration and production studies for oil and gas industries. Modeling of corrected permeability as a petrophysical parameter can help in decision making processes. The objective of this study is to construct a comprehensive and quantitative characterization of a carbonate gas reservoir in marine gas field. Artificial neural network is applied for prediction of permeability in accordance with other petrophysical parameters at well location. Correlation coefficient for this method is 84 %. In the study, the geological reservoir model is developed in two steps: First, the structure skeleton of the field is constructed, and then, reservoir property is distributed within it by applying new stochastic methods. Permeability is modeled by three techniques: kriging, sequential Gaussian simulation (SGS) and collocated co-simulation using modeled effective porosity as 3D secondary variable. This paper enhances the characterization of the reservoir by improving the modeling of permeability through a new algorithm called collocated co-simulation. Kriging is very simple in modeling the reservoir permeability, and also, original distribution of the data changes considerably in this model. In addition, the SGS model is noisy and heterogeneous, but it retains the original distribution of the data. However, the addition of a 3D secondary variable in third method resulted in a much more reliable model of permeability.     

Fuzzy modeling and particle swarm optimization for determining the optimal operating parameters to enhance the bio-methanol production from sugar cane bagasse

This work aims to maximize the production of bio-methanol from sugar cane bagasse through pyrolysis. The maximum value of the bio-methanol yield can be obtained as soon as the optimal operating parameters in a pyrolysis batch reactor are well defined. Using the experimental data, the fuzzy logic technique is used to build a robust model that describes the yield of bio-methanol production. Then, Particle Swarm Optimization (PSO) algorithm is utilized to estimate the optimal values of the operating parameters that maximize the bio-methanol yield. Three different operating parameters influence the yield of bio-methanol from sugar cane bagasse through pyrolysis. The controlling parameters are considered as the reaction temperature (°C), reaction time (min), and nitrogen flow (L/min). Accordingly, during the optimization process, these parameters are used as the decision variables set for the PSO optimizer in order to maximize the yield of bio-methanol, which is considered as a cost function. The results demonstrated a well-fitting between the fuzzy model and the experimental data compared with previous predictions obtained by an artificial neural network (ANN) model. The mean square errors of the model predictions are 0.11858 and 0.0259, respectively, for the ANN and fuzzy-based models, indicating that fuzzy modeling increased the prediction accuracy to 78.16% compared with ANN. Based on the built model, the PSO optimizer accomplished a substantial improvement in the yield of bio-methanol by 20% compared to that obtained experimentally, without changing system design or the materials used.     

Modelling and simulation of Proton Exchange Membrane fuel cell with serpentine bipolar plate using MATLAB

This report presents experimental results derived from a Proton Exchange Membrane fuel cell with a serpentine flow plate design. The investigation seeks to explore the effects of some parameters like cell operational temperature, humidification and atmospheric pressure on the general performance and efficiency of PEM fuel cell using MATLAB. A number of codes were written to generate the polarization curve for a single stack and five (5) cell stack fuel cell at various operating conditions. Detailed information of hydrogen and oxygen consumption and the effect they have on the fuel cell performance were critically analysed. The investigation concluded that the open circuit voltage generated was less than the theoretical voltage predicted in the literature. It was also noticed that an increase in current or current density reduced the voltage derived from the fuel cell stack. The experiment also clearly confirmed that when more current is being drawn from the fuel cell, more water will also be generated at the cathode section of the cell hence the need for an effective water management to improve the performance of the fuel cell. Other parameters like the stack efficiency and power density were also analysed using the experimental results obtained.