This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

In order to reduce the environmental pollution associated with the conventional energy sources and to achieve the increased global energy demand, alterative and renewable sustainable energy sources need to be developed. Microbial fuel cells (MFCs) represent a bio-electrochemical innovative technology for pollution control and a simultaneous sustainable energy production from biodegradable, reduced compounds. This study mainly considers the performance of continuous up flow dual-chambers MFC
fueled with actual domestic wastewater and bio-catalyzed with anaerobic aged sludge obtained from an aged septic tank. The performance of MFCs was mainly evaluated in terms of COD reductions and electrical power output. Results revealed that the C

In this work, electrodialysis (ED) has been demonstrated to be appropriate technique for reducing the electrical conductivity of real wastewater from fuel washing unit, which has been previously treated by other electrochemical technology (electrocoagulation and electrooxidation).  A five cell electrodialysis stack, with an active membrane area of 60 cm² per cell was employed. During a batch recirculation mode ED system, the effects of parameters such as electrical potential applied (6-18 V) and flow rate of streams (0.5-1.7 L/min.) on the performance of the total dissolved solids (TDS) separation and specific power consumption (SPC) were studied. The results indicate that the process of ED under potential (15 V) and flow

In this study, a one-dimensional model represented by Butler-Volmer-Monod (BVM) model was proposed to compute the anode overpotential and current density in a mediator-less MFC system. The system was fueled with various organic loadings of real field petroleum refinery oily sludge to optimize the favorable organic loading for biomass to operate the suggested system. The increase in each organic loading showed higher resistance to electrons transport to the anode represented by ohmic loss. On the contrary, both activation and mass transfer losses exhibited a noticeable decrement upon the increased organic loadings. However, current density was improved throughout all increased loads achieving a maximum current density of 5.2 A/m³

The study was carried out at field agriculture in Baghdad–Iraq in 2015. For purpose evaluated the performance the selected implements tillage, suitable tire pressure and speed tractor under silt clay loam to measured Effective field capacity, Actual Time for plowing One Donam ( hr), Appearance Tillage ( number of clods > 10 cm), Fuel consumption measure in two unit (L/Donam and L/hr) and Machinery Unit Energy Requirement ( kw.hr / Donam). Split – split plot design under randomized complete block design with three replications using Least Significant Design 5 % was used. Three factor used in this experiment included Two types of plows included Chisel and Disk plows which represented main plot, Three Tires Inflation Pressure was second fa

The aim of this work is to study the effect of diesel fuel percentage on the combustion processes in compression ignition engine using dual – fuel (diesel and LPG).

 The brake thermal efficiency increased with the increase of diesel fuel rate at low loads, and decreased when load increased. To get sufficient operation in engine fueled with dual fuel, it required sufficient flow rate of diesel fuel, if the engine fueled with insufficient diesel fuel erratic operation with miss fire cycles presented.

Dual-fuel operation at part load showed higher specific fuel consumption than straight diesl operation. At full loads, brake specific fuel consumption of duel fuel engine approaches that for diesel fuel values.

In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de