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Upgrading Sustainability in Clean Energy: Optimization for Proton Exchange Membrane Fuel Cells Using Heterogeneous Comprehensive Learning Bald Eagle Search Algorithm
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Clean energy applications widely recognize Proton Exchange Membrane Fuel Cells (PEMFCs) for their high efficiency and environmental compatibility. Accurate parameter identification of PEMFC models is essential for enhancing system performance and reliability, particularly under dynamic operating conditions. This paper presents a novel optimization-based approach called Heterogeneous Comprehensive Learning-Bald Eagle Search (HCLBES) with enhanced exploration and exploitation capabilities for the effective modeling of PEMFC. The algorithm combines the exploration strength of the Bald Eagle Search with comprehensive learning and heterogeneity mechanisms to achieve a balanced global and local search space. In this algorithm, the number of agents is divided into two subagents. Each subagent is assigned to focus solely on either exploration or exploitation. The comprehensive learning strategy generates exemplars for both subgroups. In the exploration sub-agent, exemplars are generated using the personal best experiences of agents within that same exploration space. The exploitation subagent generates the exemplars using the personal best experiences of all agents. This separation preserves exploration diversity even if exploitation converges prematurely. The algorithm is applied to optimize parameters of the 250 W and 500 W PEMFC models under varying conditions. Simulation results demonstrate the outperformance of the HCLBES algorithm in terms of convergence speed, estimation accuracy, and robustness compared to recent optimization algorithms. The effectiveness of HCLBES was also verified through statistical metrics and different commercial PEMFC models, including BCS 500 W stacks, Horizon 500, and NedStack PS6. Experimental validation confirms that the proposed algorithm effectively captures the nonlinear behaviours of PEMFCs under dynamic operating conditions. This research aligns with the Sustainable Development Goals (SDGs) by promoting clean and affordable energy (SDG 7) through the enhanced efficiency and reliability of PEMFCs, thereby supporting sustainable industrialization and innovation (SDG 9).

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Publication Date
Fri Nov 01 2013
Journal Name
Iraq Journal Of Agricultural Research
FUEL CONSUMPTION AND ECONOMY COSTS FOR MF 7140 TRACTOR AND MOLD BOARD AND DISC PLOWS IN SILT CLAY LOAM SOIL
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The experiment was conducted in Baghdad for study effect using mold board and disc plows as main factor , and second factor was three speeds 1.85 , 3.75 and 5.62 km / hr , and sub-second factor was three levels of soil moisture 21,18 and 14 % to determined data fuel consumption and economy costs machine unit in silt clay loam with depth 22cm. The experiment was a split – split plot arrangement in a randomized complete block design with three replications and statistical analysis using Least Significant Design 0.05 was used to compare the means of treatments. Mold board recorded least fuel consumption and cast fixed and variable and management and total costs of tractor and plow costs and total cost. Increasing forward speeds of the t

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Publication Date
Tue Mar 01 2022
Journal Name
International Journal Of Nonlinear Analysis And Applications
Semi-parametric regression function estimation for environmental pollution with measurement error using artificial flower pollination algorithm
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Artificial Intelligence Algorithms have been used in recent years in many scientific fields. We suggest employing flower pollination algorithm in the environmental field to find the best estimate of the semi-parametric regression function with measurement errors in the explanatory variables and the dependent variable, where measurement errors appear frequently in fields such as chemistry, biological sciences, medicine, and epidemiological studies, rather than an exact measurement. We estimate the regression function of the semi-parametric model by estimating the parametric model and estimating the non-parametric model, the parametric model is estimated by using an instrumental variables method (Wald method, Bartlett’s method, and Durbin

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Publication Date
Tue Jun 01 2021
Journal Name
Iop Conference Series: Earth And Environmental Science
Implementation of hierarchically porous zeolite-polymer membrane for Chromium ions removal
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Abstract<p>This work deals with the preparation of a zeolite/polymer flat sheet membrane with hierarchical porosity and ion-exchange properties. The performance of the prepared membrane was examined by the removal of chromium ions from simulated wastewater. A NaY zeolite (crystal size of 745.8 nm) was prepared by conventional hydrothermal treatment and fabricated with polyethersulfone (15% PES) in dimethylformamide (DMF) to obtain an ion-exchange ultrafiltration membrane. The permeate flux was enhanced by increasing the zeolite content within the membrane texture indicating increasing the hydrophilicity of the prepared membranes and constructing a hierarchically porous system. A membrane contain</p> ... Show More
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Publication Date
Wed Sep 18 2024
Journal Name
Water Science & Technology
Mathematical modeling of osmotic membrane bioreactor process for oily wastewater treatment
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ABSTRACT<p>To evaluate the disposal effluent from the Al-Daura refinery in Iraq, which comprises oily wastewater, a mathematical model has been developed for both forward osmosis (FO) and osmotic membrane bioreactor (OsMBR). The procedure is explained mathematically, accounting for both the concentration and polarization aspects. As a result of mathematical modeling, the water flux was determined by the osmotic pressure, the concentration, and the polarization of the feed and draw solutions. Based on traditional methods of predicting water flux using external and internal concentration polarizations, it is determined that water flux will occur in the first model (Model-1). To increase the accura</p> ... Show More
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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Sport Sciences
The effect of using the McCarthy model according to cognitive style (rigid- flexibility) in learning some skills in artistic gymnastics for women
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The aim of the research is to identify the cognitive method (rigidity flexibility) of third-stage students in the collage of Physical Education and Sports Sciences at The University of Baghdad, as well as to recognize the impact of using the McCarthy model in learning some of skills in gymnastics, as well as to identify the best groups in learning skills, the experimental curriculum was used to design equal groups with pre test and post test and the research community was identified by third-stage students in academic year (2020-2021), the subject was randomly selected two divisions after which the measure of cognitive method was distributed to the sample, so the subject (32) students were distributed in four groups, and which the pre te

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Publication Date
Fri Dec 01 2023
Journal Name
City And Environment Interactions
Towards local sustainability: A case study to evaluate outdoor urban spaces in Baghdad using physiological equivalent temperature index
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Publication Date
Mon Mar 02 2026
Journal Name
International Journal Of Inventions In Engineering &amp; Science Technology
A Review: Campus Violence Detection Using Deep Learning Models
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This paper offers a systemic review of the deep learning methods to detect violence on campus, which is a critical issue in intelligent surveillance to improve the student safety and prompt cut off of violent accidents. The review reviews studies published 2018-2025, concentrating on model structure to detect fights, bullying, vandalism, and aggressive behavior on problematic campuses due to occlusion and light variations and complicated human interactions. The research design includes a comparative study of different deep learning networks, such as CNNs, RNNs, 3D CNNs, attention-based networks, transformers, graph neural networks, neuro-fuzzy, and multimodal systems and federated learning methods. The paper also assesses benchmark

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Publication Date
Mon Dec 10 2018
Journal Name
Day 1 Mon, December 10, 2018
Wellbore Trajectory Optimization Using Rate of Penetration and Wellbore Stability Analysis
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Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.

In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation

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Publication Date
Mon Dec 10 2018
Journal Name
Day 1 Mon, December 10, 2018
Wellbore Trajectory Optimization Using Rate of Penetration and Wellbore Stability Analysis
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Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.

In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation

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Publication Date
Wed Mar 10 2021
Journal Name
Baghdad Science Journal
Detecting Textual Propaganda Using Machine Learning Techniques
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Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation.  Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annota

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