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 desired voltage and less energy consumption through investigating and comparing under random current variations with the minimum number of fitness evaluation less than 20 iterations.
This study was done to investigate the impact of different nanoparticles on diesel fuel characteristics, Iraqi diesel fuel was supplied from al-Dura refinery and was treated to enhance performance by improving its characteristics. Two types of nanoparticles were mixed with Iraqi diesel fuel at various weight fractions of 30, 60, 90, and 120 ppm. The diesel engine was tested and run at a constant speed of 1600 rpm to examine and evaluate the engine's performance and determine emissions. In general, ZnO additives' performance analysis showed they are more efficient for diesel fuel engines than CeO. The performance of engine diesel fuel tests showed that the weight fraction of nanoparticles at 90 and 120 ppm give a similar
... Show MoreThis study was done to investigate the impact of different nanoparticles on diesel fuel characteristics, Iraqi diesel fuel was supplied from al-Dura refinery and was treated to enhance performance by improving its characteristics. Two types of nanoparticles were mixed with Iraqi diesel fuel at various weight fractions of 30, 60, 90, and 120 ppm. The diesel engine was tested and run at a constant speed of 1600 rpm to examine and evaluate the engine's performance and determine emissions. In general, ZnO additives' performance analysis showed they are more efficient for diesel fuel engines than CeO. The performance of engine diesel fuel tests showed that the weight fraction of nanoparticles at 90 and 120 ppm give a similar performance,
... Show Moremodel is derived, and the methodology is given in detail. The model is constructed depending on some measurement criteria, Akaike and Bayesian information criterion. For the new time series model, a new algorithm has been generated. The forecasting process, one and two steps ahead, is discussed in detail. Some exploratory data analysis is given in the beginning. The best model is selected based on some criteria; it is compared with some naïve models. The modified model is applied to a monthly chemical sales dataset (January 1992 to Dec 2019), where the dataset in this work has been downloaded from the United States of America census (www.census.gov). Ultimately, the forecasted sales
A multivariate multisite hydrological data forecasting model was derived and checked using a case study. The philosophy is to use simultaneously the cross-variable correlations, cross-site correlations and the time lag correlations. The case study is of two variables, three sites, the variables are the monthly rainfall and evaporation; the sites are Sulaimania, Dokan, and Darbandikhan.. The model form is similar to the first order auto regressive model, but in matrices form. A matrix for the different relative correlations mentioned above and another for their relative residuals were derived and used as the model parameters. A mathematical filter was used for both matrices to obtain the elements. The application of this model indicates i
... Show MoreChemotherapy is one of the most efficient methods for treating cancer patients. Chemotherapy aims to eliminate cancer cells as thoroughly as possible. Delivering medications to patients’ bodies through various methods, either oral or intravenous is part of the chemotherapy process. Different cell-kill hypotheses take into account the interactions of the expansion of the tumor volume, external drugs, and the rate of their eradication. For the control of drug usage and tumor volume, a model based smooth super-twisting control (MBSSTC) is proposed in this paper. Firstly, three nonlinear cell-kill mathematical models are considered in this work, including the log-kill, Norton-Simon, and hypotheses subject to parametric uncertainties and exo
... Show MoreWith the development of communication technologies, the use of wireless systems in biomedical implanted devices has become very useful. Bio-implantable devices are electronic devices which are used for treatment and monitoring brain implants, pacemakers, cochlear implants, retinal implants and so on. The inductive coupling link is used to transmit power and data between the primary and secondary sides of the biomedical implanted system, in which efficient power amplifier is very much needed to ensure the best data transmission rates and low power losses. However, the efficiency of the implanted devices depends on the circuit design, controller, load variation, changes of radio frequency coil’s mutual displacement and coupling coef
... Show MoreThe modern steer-by-wire (SBW) systems represent a revolutionary departure from traditional automotive designs, replacing mechanical linkages with electronic control mechanisms. However, the integration of such cutting-edge technologies is not without its challenges, and one critical aspect that demands thorough consideration is the presence of nonlinear dynamics and communication network time delays. Therefore, to handle the tracking error caused by the challenge of time delays and to overcome the parameter uncertainties and external perturbations, a robust fast finite-time composite controller (FFTCC) is proposed for improving the performance and safety of the SBW systems in the present article. By lumping the uncertainties, parameter var
... Show MoreThe objective of this study is to apply Artificial Neural Network for heat transfer analysis of shell-and-tube heat exchangers widely used in power plants and refineries. Practical data was obtained by using industrial heat exchanger operating in power generation department of Dura refinery. The commonly used Back Propagation (BP) algorithm was used to train and test networks by divided the data to three samples (training, validation and testing data) to give more approach data with actual case. Inputs of the neural network include inlet water temperature, inlet air temperature and mass flow rate of air. Two outputs (exit water temperature to cooling tower and exit air temperature to second stage of air compressor) were taken in ANN.
... Show MorePolymeric hollow fiber membrane is produced by a physical process called wet or dry/wet phase inversion; a technique includes many steps and depends on different factors (starting from selecting materials, end with post-treatment of hollow fiber membrane locally manufactured). This review highlights the most significant factors that affect and control the characterization and structure of ultrafiltration hollow fiber membranes used in different applications.
Three different types of polymers (polysulfone PSF, polyethersulfone PES or polyvinyl chloride PVC) were considered to study morphology change and structure of hollow fiber membranes in this review. These hollow fiber membranes were manufactured with different pro
... Show MoreThe present work aims to improve the flux of forward osmosis with the use of Thin Film Composite membrane by reducing the effect of polarization on draw solution (brine solution) side.This study was conducted in two parts. The first is under the effect of polarization in which the flux and the water permeability coefficient (A) were calculated. In the second part of the study the experiments were repeated using a circulating pump at various speeds to make turbulence and reduce the effect of polarization on the brine solution side.
A model capable of predicting water permeability coefficient has been derived, and this is given by the following equations:
Z=Z0 +C.R.T/9.8(d2/D2+1) [Exp. [-9.8(d