مستودع البحوث والمنشورات العلمية — كلية هندسة النفط والغاز

Although renewable energy sources offer enormous potential to improve environmental sustainability, maximizing economic benefits inside microgrids requires resolving their intermittency and irregularity. A viable alternative is to combine energy storage with renewable energy technologies. This article introduced a energy management system for hybrid renewable power plants that includes fuel cells, wind turbines, solar cells, battery energy storage devices, and micro-turbines. Optimization problem is formulated as Hyper Learning Binary Dragonfly Algorithm (HLBDA) for optimizing economic benefits and with objectives of minimizing operating costs and pollutant gas emissions. Suggested model is compared with existing methods like Genetic Algorithms (GA), and Crayfish Optimization Algorithm (COA). Also, stochastic framework is considered suitable solution for achieving optimal operation point in microgrids to cope with uncertain parameters. According to the simulation results, suggested method proves reductions in overall system costs and pollutant gas emissions. The proposed system achieved significant superiority across all indicators. In the area of cost reduction, the algorithms demonstrated remarkable progress. The algorithms achieved significant improvements in cost reduction compared to genetic algorithm (GA). HLBDA algorithm achieved a 12.4% cost saving compared to GA, and the COA algorithm showed a 3.24% improvement in cost reduction. In the area of carbon emission reduction, the algorithms also showed significant progress: the HLBDA algorithm recorded the highest emission reduction rate at 9.54%, and the COA algorithm showed a 2.40% improvement in emission reduction. © 2026 Alhasnawi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

The Semiconductor Equipment Communication Standard/Generic Equipment Model (SECS/GEM) protocol is an international standard for machine-to-machine (M2M) data interchange which has been widely accepted in the semiconductor and industrial automation industries for real-time equipment control and monitoring. But there is currently no security model that offers both full-spectrum protection, as well as light-weight performance and total resilience to quantum era cyber-threats. Existing improvements, e.g., SECS/GEMsec, Secured SECS/GEM, and ES-SECS/GEM either depend on traditional cryptographic building blocks susceptible to Shor-, Grover-based attacks or ensure security of the data only partially, thereby exposing the critical IIoT infrastructure to replaying, DoS, and post-quantum cryptanalysis vulnerabilities. To close this gap, we introduce Post-SECS/GEM, a post-quantum secure solution based on a combination of CRYSTALS-Kyber for key exchange and CRYSTALS-Dilithium for authentication that maintains seamless compatibility with current message formats utilized within SECS/GEM. The suggested architecture embeds a lightweight, lattice-based security construction ensuring the fulfillment of fundamental SECS/GEM security functionalities (e.g., mutual authentication, and preserving authenticity, secrecy and integrity of messages over the same channel), with respect to an adversary who could have unlimited computation power but no quantum capability in order to attack communication between devices without modifying or upgrading existing protocols or hardware. Performance evaluation on a typical IIoT testbed indicates that Post-SECS/GEM achieves better efficiency as compared to the current SECS/GEM security extensions, with decreased key establishment and authentication latency, predictable encryption/decryption performance, as well as acceptable computing and memory overheads in line with edge-class industrial devices. These findings demonstrate that Post-SECS/GEM is an efficient post-quantum secure approach for real-time SECS/GEM-based industrial communication in the present IIoT scenarios. © 2020 IEEE.

Portable generators are widely used in Iraq to supply electricity during blackouts. However, they are noisy due to the engine’s combustion chamber and moving parts, which can negatively impact the neuroendocrine, cardiovascular, pulmonary, and digestive systems. In this work, the noise reduction of the generator using an acoustic enclosure made of local materials has been studied experimentally. The enclosure was made of medium-density fiber, galvanized iron, glass wool, cork, air gap, and compressed sponge. A 2-kW generator was tested for sound level in two scenarios: with and without multi-layered enclosure containing shredded plastic. The outcomes included determining the sound level and reduction in noise caused by the generator in decibels during the day and at night for various load conditions ranging from 0 to 25%, 50%, 75%, and 100% load, and at a distance 5 m, as well as measuring the thermal performance of the generator when the enclosure was applied. The enclosure filled with shredded plastic at 5 m reached the permissible limit according to Iraqi Law No. 41 of 2015, as the limit reached with a load of 25% is 63.3 dB during the day. It approached the permissible limit during the night with a load of 50%, which is 62.9 dB. The generator head cylinder’s temperature was below the 300 degrees Celsius upper limit permitted for air-cooled generators. The daytime temperature was 177.8 °C, and the nighttime temperature was 164.5 °C. This study introduces a cost-effective multi-layered enclosure using recycled plastic and natural materials to reduce generator noise by up to 13 dB, while maintaining safe operating temperatures. © The Author(s), under exclusive licence to Metrology Society of India 2026.

Often referred to as next-generation power system, smart grid is regarded as a revolutionary and progressive progression of current power grids. More significantly, smart grid is anticipated to significantly improve distributed intelligence, demand response, and the efficiency and dependability of future power systems with renewable energy sources by integrating cutting-edge computing and communication technology. Because millions of electronic devices are connected by communication networks across vital power facilities, cyber security becomes a crucial concern in addition to the silent aspects of smart grid. This directly affects the dependability of such a vast infrastructure. This study provided a thorough analysis of smart grid cyber security concerns. Then, recent Machine Learning-based detection techniques are summarized.

In the renewable energy technology industry such as wind and solar power, artificial intelligence (AI) technology is rejuvenating by implementing accurate prediction, automatic control, and predictive maintenance of various types of energy technologies. It is crucial to improve the reliability and efficiency of solar, wind, hydropower, and geothermal plants. In this paper, a recent progress of AI applications on solar, wind, hydropower, geothermal, and biomass systems integration in renewable energy sector is surveyed. The novelty is to integrate the AI forecasting, management and hybrid modeling approaches into a unified framework, which is practically valuable for smart grid and green energy policy. Energy prediction accuracy is also improved using ML and DL methods. And, hybrid models mixing AI with physical systems can boost performance and slash operational costs. Such models are, particularly, applicable in predictive maintenance since they shorten the time equipment off line and extend the life of renewable energy devices, such as solar panels and wind turbines. AI in renewable energy has a few roadblocks: issues with data quality and demand for heavy computing (as well as interpretability in AI-guided decisions). Environmental considerations also need to be included, including automation-driven job loss and bias in AI predictions. The future of that progress also brings improved energy distribution and security, and modernized energy trading if harmonized with technologies such as IoT, and blockchain. Robust legislative parameters and the ability to build AI algorithms would be very helpful in addressing these issues and helping us move toward a sustainable low-carbon energy future. Finally, a systematic responsible AI integration framework is presented in the conclusion of this study that explains the model, optimizes data-energy together and harmonizes policies.

Due to the applied load torque variations, the electric Direct Current (DC) motors' speed has many more fluctuations. The main objective of this research is to study the speed control of Brushless DC Motors (BLDCMs) using a Proportional Integral Derivative (PID) controller that regulates the motor voltage. This controller was automatically tuned using the Transit Search Algorithm (TSA). The effects of the proposed controller on BLDCM speed control were studied, analyzed, and compared with those of other PID gain calculation methods like Particle Swarm Optimization (PSO), Adaptive Tabu Search (ATS), Genetic Algorithm (GA), Rime-inspired metaheuristic (RIME) and Whale Optimization Algorithm (WOA). The performance is evaluated by contrasting speed-response characteristics such as Settling Time (ST), Rise Time (RT), and Percentage Overshoot (P.O.%). Different objective functions, such as the Integral Absolute Error (IAE), Integral Squared Error (ISE), Integral Time Absolute Error (ITAE), and Mean Squared Error (MSE), are used with TSA to determine the best PID gain parameters for controlling the BLDCM using each objective function separately. The main novelty of this work lies in the integration of fusion-based execution criteria with the TSA's effective search ability to achieve high-quality, superior, and robust PID gains for the BLDCM speed control process. The performance of the PID-TSA for controlling the speed of the BLDCM surpasses that of other controllers by optimizing its control and confirming the superiority of the proposed system under different applied load conditions. The proposed controller uses a fusion objective function (FOF) to achieve a fast response with zero P.O.%, ST equal to 0.027 s, and a RT reach to 0.01 s using single run, in addition, average with standard deviation values of (RT = 3.96414E-05 ± 2.85417E-05 s and ST = 0.002346233 ± 0.000439398 s) for 30 runs.

Signal forecasting in the medical field has many applications, such as signal correction and anomaly detection. According to this application, robust forecasting is required to obtain a signal identical to the original signal. This study proposes a forecasting technique that obtains a robust signal that can be used in different applications. A long short-term memory neural network (LSTM-NN) was used to predict future samples from present and past samples. An Electroencephalography (EEG) dataset was used to test this technique. Four channels were used as input examples, one of which was the predicted output. All four channel samples were fed into the four networks to predict the future samples. To decrease complexity, only one hidden layer is used for this purpose. The statistical results are promising for applications that require an almost perfectly predicted signal. The number of hidden cells is first very low (five cells only), which gives a Root Mean Square Error of less than 20, whereas when the number of hidden cells is increased to 100, the Root Mean Square Error (RMSE) is approximately 7.5 for all four channels.

Signal forecasting in the medical field has many applications, such as signal correction and anomaly detection. According to this application, robust forecasting is required to obtain a signal identical to the original signal. This study proposes a forecasting technique that obtains a robust signal that can be used in different applications. A long short-term memory neural network (LSTM-NN) was used to predict future samples from present and past samples. An Electroencephalography (EEG) dataset was used to test this technique. Four channels were used as input examples, one of which was the predicted output. All four channel samples were fed into the four networks to predict the future samples. To decrease complexity, only one hidden layer is used for this purpose. The statistical results are promising for applications that require an almost perfectly predicted signal. The number of hidden cells is first very low (five cells only), which gives a Root Mean Square Error of less than 20, whereas when the number of hidden cells is increased to 100, the Root Mean Square Error (RMSE) is approximately 7.5 for all four channels. © 2026, Iran University of Science and Technology. All rights reserved.

Signal forecasting in the medical field has many applications, such as signal correction and anomaly detection. According to this application, robust forecasting is required to obtain a signal identical to the original signal. This study proposes a forecasting technique that obtains a robust signal that can be used in different applications. A long short-term memory neural network (LSTM-NN) was used to predict future samples from present and past samples. An Electroencephalography (EEG) dataset was used to test this technique. Four channels were used as input examples, one of which was the predicted output. All four channel samples were fed into the four networks to predict the future samples. To decrease complexity, only one hidden layer is used for this purpose. The statistical results are promising for applications that require an almost perfectly predicted signal. The number of hidden cells is first very low (five cells only), which gives a Root Mean Square Error of less than 20, whereas when the number of hidden cells is increased to 100, the Root Mean Square Error (RMSE) is approximately 7.5 for all four channels. © 2026, Iran University of Science and Technology. All rights reserved.

Efficient organic nonlinear optical (NLO) material is of great interest for photonic and optoelectronic applications. The present work involved the doping of Phenazopyridine extracted from expired pharmaceutical tablets into poly(methyl methacrylate) (PMMA) to synthesize composite thin films of PMMA doped with 10, 30 and 50 wt% Phenazopyridine. FTIR, UV–Vis, 1H NMR and 13C NMR spectroscopy were used to confirm the chemical structure of the extracted compound. The electronic structure and optical properties were studied with the help of the density functional theory (DFT) and time dependent density functional theory (TD-DFT) methods at B3LYP/6–311 + G(d,p) and CAM-B3LYP/6–311 + G(d,p) levels of theory. The molecular orbitals calculated showed an efficient intramolecular charge-transfer character of Phenazopyridine with its donor–π–acceptor architecture. Open and closed aperture Z-scan methods were used to measure the nonlinear optical properties using continuous wave laser excitation at 532 nm. The composite films showed significant saturable absorption and saturable absorption index in the reverse direction, as well as negative nonlinear refraction, which showed self-defocusing properties. The nonlinear optical response was found to be higher at higher dye concentration, while the 50 wt% Phenazopyridine/PMMA film showed the highest nonlinear absorption coefficient and optical limiting performance. The nonlinear behavior observed is explained by the excited-state absorption and thermally induced nonlinear effects. The results indicate that Phenazopyridine/PMMA composites have interesting nonlinear optical properties and show that the use of outdated pharmaceutical drugs as functional materials in photonic applications seems promising.

Early detection of breast cancer significantly improves patient outcomes through timely and accurate diagnosis. This study proposes a hybrid image reconstruction method combining the Maximum Likelihood Expectation Maximization (MLEM) algorithm with the Distance Driven Method (DDM) for stationary digital breast tomosynthesis, aimed at producing high-resolution three-dimensional (3D) breast images. The method was initially validated using simulated projection data of a digital breast phantom modeled with two spheres of varying radii and attenuation coefficients. Fifteen projection images were generated over a 15° angular range (from +7° to –7° with 1° increments) to replicate realistic tomosynthesis acquisition. The focus plane was set 45 mm above the detector with a pixel size of 0.14 mm. Reconstruction results demonstrated enhanced image quality, with spatial resolution quantitatively evaluated using the Line Spread Function (LSF) across the smaller sphere. Compared to the Ray Driven Method (RDM), the MLEM-DDM approach provided better contrast, sharper edges, and fewer artifacts. Subsequently, the method was applied to experimental data from a real breast phantom. Volumetric reconstructions revealed detailed tissue structures across multiple planes, with spatial resolution assessed by line profiling through two aligned calcifications in the focus plane. The MLEM-DDM method preserved the shape and sharpness of these calcifications more effectively than MLEM-RDM. Overall, the proposed MLEM-DDM framework enhances spatial resolution and visualization quality in stationary digital breast tomosynthesis, demonstrating strong potential for improved early breast cancer detection and diagnostic accuracy. © 2026, University of Kufa. All rights reserved.

Early detection of breast cancer significantly improves patient outcomes through timely and accurate diagnosis. This study proposes a hybrid image reconstruction method combining the Maximum Likelihood Expectation Maximization (MLEM) algorithm with the Distance Driven Method (DDM) for stationary digital breast tomosynthesis, aimed at producing high-resolution three-dimensional (3D) breast images. The method was initially validated using simulated projection data of a digital breast phantom modeled with two spheres of varying radii and attenuation coefficients. Fifteen projection images were generated over a 15° angular range (from +7° to –7° with 1° increments) to replicate realistic tomosynthesis acquisition. The focus plane was set 45 mm above the detector with a pixel size of 0.14 mm. Reconstruction results demonstrated enhanced image quality, with spatial resolution quantitatively evaluated using the Line Spread Function (LSF) across the smaller sphere. Compared to the Ray Driven Method (RDM), the MLEM-DDM approach provided better contrast, sharper edges, and fewer artifacts.

Hepatitis C Virus (HCV) is a disease that infects the liver with multiple stages that spread through blood, requiring blood tests and body symptoms for diagnosis. The diagnosis should decide which stage the patient reaches. This work suggests a hybrid classification method based on optimization and machine learning techniques. A Long Short-Term Memory Neural Network (LSTM) is used as a classifier to identify the stages of the disease, based on the four stages, using 28 features comprising body symptoms and blood tests. To find the optimal number of hidden cells in the hidden layers of LSTM, the Transit Search Algorithm (TSA) is used, TSA identifies the best integer value for the number of hidden cells and selects the best features combined with this number of cells that will give the highest accuracy of classification. The preprocessing step is required to enhance the quality of the dataset and resizing it. In multiclass classification, a large dataset is essential for the network to learn effectively through all classes. To achieve this, Synthetic Minority Oversampling Techniques (SMOTE) is used to generate similar data that will increase the size of the dataset. The proposed TSA-LSTM method achieves high performance, with classification accuracy exceeding 99% outperforming previous works.

The study focuses on the Mishrif carbonate reservoir in the X oilfield, southern Iraq, which presents characterization complexities of a difficult nature due to its complex geology. The field, sited in the Zubair subzone, the most prolific and the southernmost of the Mesopotamian Plain hydrocarbon production units, is a good example of the structural complexity of the unstable shelf of the region of southern Iraq, where salt tectonics, basement faulting, and deformation of the region together produce characteristic anticline structures. This study uses an integrated petrophysical method combining core analysis from Wells 1 and 5, nuclear magnetic resonance (NMR) T2 distribution measurements, and conventional well log data to characterize the heterogeneous Mishrif carbonate reservoir comprehensively. A hierarchical pore classification system was developed based on mercury injection capillary pressure (MICP) analysis and geological observations, classifying the pore network into three different types: micropores, mesopores, and macropores. Through systematic calibration with MICP data, specific T2 relaxation time cutoffs were established to partition total porosity into these pore-size classes. The analysis shows that micropores mainly contain bound fluids, with a T2 cutoff value of 50 ms effectively describing irreducible water saturation. Strong relationships between NMR-derived porosity division and MICP-based pore size distributions confirm the integrated methodology. The method successfully quantifies critical reservoir parameters, including porosity, permeability, and pore size distribution. The results demonstrate that integrating core-calibrated NMR analysis with conventional petrophysical evaluation significantly enhances characterization accuracy in complex carbonate systems. This methodology provides essential parameters for reservoir engineering applications and establishes a robust framework applicable to similar heterogeneous carbonate reservoirs. The successful application of this integrated approach in the structurally complex Mishrif Formation emphasizes its potential for improving reservoir characterization in challenging geological settings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

Researchers commonly employ seismic analyses and fluid-substitution techniques to track subsurface carbon capture storage (CCS) and map the likely distribution of CO₂ within the storage reservoir. The fluid-substitution rock-physics model leverages well-log data and injection-simulation outputs to infer gas distribution, based on a seismic velocity model. This work introduces new insights for making more precise predictions about how CO₂ injection — and subsequent CO₂–brine–rock interactions — alter the geophysical and petrophysical properties of reservoir rocks, which in turn supports earlier detection of potential leaks. Including a dedicated model for CO₂ leakage improves the fidelity of geophysical predictions by simulating how leak-induced changes affect seismic wave speeds, thereby allowing the identification of distinctive velocity anomalies associated with leak zones. By carefully choosing relevant physical parameters — such as fluid saturation, pressure, and gas composition — and tracking their influence on wave propagation, the model offers a more realistic representation of subsurface conditions and strengthens early warning of possible leakage pathways. We developed a complete workflow for rock-physics modelling that combines geological and geophysical interpretations. This includes deriving elastic parameters from measured data, generating synthetic seismic data, and applying fluid-substitution analysis informed by CO₂ injection simulation results. We then apply the Gassmann equation to construct a new post-injection velocity model, incorporating other elastic parameter changes to assess the detection threshold of seismic velocity alterations. Because the Gassmann theory reliably models P-wave velocity in sandstone reservoirs saturated with mixtures of supercritical CO₂ and brine, it enables forecasting how seismic responses vary with different levels of gas saturation. Because acoustic impedance is sensitive to gas saturation changes, observed deviations from expected impedance values may reveal abnormal fluid distributions — potentially indicating zones of CO₂ leakage.