Al-Khwarizmi Engineering Journal

The petroleum sector generates significant amounts of oily wastewater requiring proper treatment before being released into the environment. The high stability, hydrophilicity, and large surface areas of membranes provide great opportunities for treating oily wastewater. For this work, electrospun nanofiber (ESNF) membranes of polyacrylonitrile (PAN) and polysulfone (PSU) were manufactured and evaluated for their separation capabilities. Multiple concentrations of the PAN and PSU polymers were used to optimize the copolymer nanofiber (NF) membranes. The water flux, oil rejection, and porosity evaluation of the membranes were compared. In addition, the surface and profile of the ESNF membranes were evaluated in terms of morphology and roughness, wettability, and tensile strength, and the results of scanning electron microscopy, atomic force microscopy, contact angle, and tensile tests were incorporated. The ESNF membranes provided positive results in the separation of oily wastewater. In particular, the best results were observed for the membranes composed of 75% PAN and 25% PSU, which reached 99.7% oil rejection, a maximum water flux of 400 L/m2.h, and 96% porosity, demonstrating great potential for wastewater treatment on an industrial scale. However, this study focused primarily on short-term performance evaluation. The long-term mechanical and chemical stability of ESNF membranes under continuous operational conditions remains to be examined.

This study investigates the potential of spent tea leaf-derived activated carbon (STLAC) as an adsorbent for phenol removal from simulated wastewater using a packed-bed adsorber. STLAC was prepared via chemical activation through impregnation with potassium hydroxide, followed by carbonization under controlled conditions. The physicochemical characterization of the prepared activated carbon has been confirmed. Response surface methodology (RSM) with central composite design (CCD) was utilized to optimize four operating parameters: flow rate, initial phenol concentration, bed height, and pH. A quadratic mathematical model was developed to describe the relationship between these parameters and removal efficiency. Results demonstrate the high phenol removal capacity of STLAC, achieving a maximum efficiency of 91.92% under optimal operating conditions: flow rate (11.46 mL/min), initial phenol concentration (19 mg/L), bed height (17.8 cm), and pH (6.8). The first breakthrough and saturation points were observed at 31 and 70 min, respectively. Regeneration studies have confirmed the activity of STLAC for reuse in the adsorption after five cycles, with an efficiency exceeding 75%. STLAC exhibited high efficiency in phenol removal, offering advantages in terms of sustainability and cost-effectiveness. To our knowledge, this study is the first to systematically optimize phenol removal using STLAC in a continuous packed-bed column using RSM-CCD methodology and utilize the optimized parameters for determining breakthrough characteristics. This study underscores the potential of converting agricultural waste into value-added products for environmental remediation. These results demonstrate that STLAC is a promising, low-cost, and sustainable adsorbent for removing phenol from industrial wastewater.

In this study, cefixime (CFX) was removed from aqueous solutions by using activated carbon (AC) made from broad bean peels (BBPs). To evaluate the adsorbent performance, a conventional experimental approach (one factor at a time) was applied to clarify the impact of the most influential variables (i.e., pH, contact time, and adsorbent dosage) and identify the maximum adsorption capacity. The optimization approach of response surface methodology with a Box–Behnken design (BBD) was employed to investigate the synergistic influences of the three independent variables and determine the optimum experimental conditions. Removal efficiency (%) was evaluated at different pH (2–10), contact time (10–150 min), and adsorbent dosage (0.1–2.5 g L-1). Results revealed that the measurements adequately fitted the Langmuir isotherm, with a maximum monolayer adsorption capacity (qm) of 17.5 mg/g. Moreover, based on the optimization approach, 95% removal efficiency was reached at optimum values of 6, 100 min, and 2.16 for pH, contact time, and adsorbent dosage, respectively. The BET surface area was determined to be 375 m²/g, and the total pore volume was 0.204 m³/g. For multilayer sorption, the isotherm and kinetic models were suggested, followed by exothermic an physical adsorption mechanism. The findings of this study indicate that AC prepared from BBPs can be used as an adsorbent to remove CFX from aqueous solutions.

Efforts to combat the recent spread of infectious diseases require innovative solutions for characterizing their transmission. This study introduces a system for helping people maintain safe distances from one another, thereby decreasing the transmission of infectious diseases in crowded public spaces. Real-time video footage from surveillance cameras was processed by the cutting-edge “You Only Look Once” ver. 8 (YOLOv8) computer vision model, which can detect and segment individuals in images. YOLOv8 was combined with an embedded system that includes an Atmel 8-bit AVR microcontroller and an Arduino Uno board, a buzzer, and an LCD. This proposed system can generate alerts when people breach the set social distancing limit of 0.5 m. Its new network design increases human detection to an accuracy range of 97%–98%. This proposed system was trained on 70% of the dataset, and validation and testing were performed on 15% and 15% of the dataset, respectively. Combining deep learning with embedded systems creates an intelligent vision-based monitoring system for crowded spaces, addressing key issues pertaining to disease transmission reduction and public health protection. The proposed system can be implemented at a low cost, such as by simply using a resource-constrained embedded device or a microcontroller. This approach overcomes the functional challenge of utilizing artificial intelligence-based surveillance in a scalable, decentralized, and economical manner. In general, the proposed system has a high frame per second rate, which is satisfactory for real-time operation on edge hardware. The novelty of this method relies on the use of the YOLOv8 model to achieve precise performance while balancing accuracy and speed on edge/embedded devices for practical, real-world epidemic control.

This work introduces a novel design of a compact broadband metamaterial (MM) absorber with polarization-insensitive, wide-angle reception for ambient electromagnetic (EM) power harvesting applications. The proposed MM unit cell consists of two layers of low-cost FR-4 substrate. The top layer contains a resonator structure and four lumped resistors. A copper sheet, serving as a ground plane, is placed on the backside of the bottom layer to reduce transmission losses. The top and bottom layers are separated by a thin air layer to enhance absorption at operational frequencies. The overall size of the MM unit cell is 12×12×4.57 mm³, with measurements of 0.16λ₀×0.16λ₀×0.06λ₀ at the lowest frequency in the absorption spectrum. The unit cell’s input impedance is carefully engineered to align with that of free space, an approach facilitating efficient EM power absorption and suitable redirection to resistive loads. The absorber’s performance is assessed for different polarization and incident angles for transverse electric (TE) and transverse magnetic (TM) modes. The simulation results indicate that the introduced absorber attains broadband absorption, surpassing 90% in the frequency range of 4 GHz to 7 GHz. Moreover, the MM unit cell achieves harvesting efficiency above 80% under normal incidence and continues to exhibit outstanding performance over different incidence angles for the TE and TM modes. This study substantially improves MM-based energy harvesting through integrating wide-angle reception, high-absorption broadband, compact size, and polarization insensitivity, which make it an ideal option for supplying energy to wireless sensor networks.

A Smart Energy Management System SEMS, which uses the Internet of Things (IOT), has been developed in response to the increasing demand for energy-efficient solutions. A cost-effective and efficient IoT-based SEMS that optimizes energy usage, reduces costs, and improves sustainability is presented in this work. The suggested solution uses smart meters, cloud-based analytics, and inexpensive sensors (total cost is less than US$20) to track and control energy use in real time. Through the use of Machine Learning (ML) algorithms and data-driven decision-making, the system can forecast future consumption trends and provide consumers with relevant data. Energy conservation is achieved through the system’s affordability, which makes it appropriate for residential, commercial, and industrial applications without requiring significant infrastructure investments. The system’s effectiveness in reducing energy waste while maintaining user convenience is demonstrated by experimental results. The considerable potential of IoT-based technologies in creating an economical and sustainable framework for energy management is demonstrated by this study. Furthermore, energy consumption prediction systems based on ML are presented. In addition, a scalable approach for more intelligent and environment-friendly energy management systems in future smart cities is developed using OPNET simulation for about 1000 nodes (smart meters). The findings demonstrated that boosting family classifiers achieved the best accuracy with a 98% prediction rate. When 1000 nodes are simulated, the latency for the proposed system may reach as low as 0.21 ms with approximately 20 bytes/s of network traffic, according to the network simulation results that used OPNET.
requiring significant infrastructure investments. The system’s effectiveness in reducing energy waste while maintaining user convenience is demonstrated by experimental results. The considerable potential of IoT-based technologies in creating an economical and sustainable framework for energy management is demonstrated by this study. Furthermore, energy consumption prediction systems based on ML are presented. In addition, a scalable approach for more intelligent and environment-friendly energy management systems in future smart cities is developed using OPNET simulation for about 1000 nodes (smart meters). The findings demonstrated that boosting family classifiers achieved the best accuracy with a 98% prediction rate. When 1000 nodes are simulated, the latency for the proposed system may reach as low as 0.21 ms with approximately 20 bytes/s of network traffic, according to the network simulation results that used OPNET.

Artificial intelligence (AI)-driven controllers have increased the accuracy, flexibility, and efficiency of flow rate control for gas and liquid flows. Comparing to traditional proportional–integral–derivative (PID) controllers , they are not usually practical for nonlinear dynamics and time-varying disturbances, causing limited stability and control accuracy. The design and performance of individual AI-based controllers for individual machines without systematically have been investigated by many studies which compared these devices for various industrial and energy applications. This research fills this gap by examining 34 peer-reviewed studies from 2021 to 2024 obtained from Scopus and Web of Science databases and classifying them on the basis of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses structure. Quantitative results show that AI controllers have better overall performance than traditional PID controllers, and their response is faster by 12%–85%. AI controllers exhibit a 15%–67% reduction in steady-state errors and an 18%–40% decrease in overshoot relative to PID controllers, indicating high accuracy and system stability. The data indicate that fuzzy and hybrid controllers have high flexibility for managing nonlinear and dynamic flow phenomena, and model predictive and optimization-based controllers have high accuracy for multivariable processes. Furthermore, AI control technology for energy, hydrogen, and process fluid applications improves operability, reduces energy overhead, and enables real-time flexible management under ever-changing loads. This review lays excellent groundwork for next-generation intelligent control frameworks and opens a new paradigm of advanced, data-driven strategies toward subsequent flow regulation and energy system applications.

Given the competitive nature of the automotive industry, supplier performance must be analyzed and monitored to ensure continuous production while minimizing costs and maximizing quality and service. This research seeks to analyze the practices of vendor performance monitoring by non-Malaysian companies in localization strategies that align with national automotive policies. A case study was performed in ABC Company, a well-established Japanese vehicle manufacturer in Malaysia, through three main research approaches, namely, semistructured interview, site visit, and documentation review. The interview centered on their top management to understand how they monitor supplier performance and determine the current issues they face, while the site visit and documentation review focused on actual practices. A vendor known as WHN Company was chosen for additional analysis in the case study. Result reveals the poor performance of vendors with several root causes such as material shortages, manpower limitations, machine capacity issues, and financial constraints. A cause and effect analysis was performed to deeply understand the root causes. ABC terminates the existing vendors that have been classed in “C” category for 3 months and treated as underperformers. ABC then identifies and develops new qualified vendors to replace them. This case illustrates the importance of vendor monitoring, thorough assessment, and development for optimal performance. It also evaluates the importance of competitiveness and localization of the automotive sector’s strategies on supply chain resilience for sustaining adaptive monitoring mechanisms

Clinical settings have an accurate neck–shaft angle (NSA) because it directly affects the diagnosis and treatment of hip-related conditions such as hip dysplasia and osteoarthritis. Variations in NSA measurement can lead to a difference in the patient’s condition during imaging and emphasize the importance of standardized protocols to ensure reliability. Reducing measurement anomalies enables health professionals to improve surgical planning and patient results significantly. A functioning method and its effect on clinical applications to evaluate different imaging techniques and software platforms based on correlation coefficients in the class can be used to determine stability and reliability. The primary goal of this research is to identify a valid, reliable NSA measurement using different software and methods. The final results indicate that the 3D method for measuring NSA, primarily using artificial-intelligence-driven automatic measurement radiographic analysis, is the most effective way to determine the NSA because it has low radiation doses, low costs, high accuracy, and a simple interface. Technology using available radiographic images provide much less radiation risk than computed tomography (CT) with a high accuracy rate of more than 98%. In addition, it streamlines measurement by reducing human error and variability found in manual methods and campaigns’ clinical availability. Therefore, this computer imaging system advances technology in surgical planning and improves the patient’s results of total hip arthroplasty (THA) with accurate NSA measurements. However, using 2D technology, the EOS 2D/3D imaging system is best suited for measuring the NSA due to its low radiation exposure, high accuracy, and user-friendly Interface. This system produces much less radiation than traditional imaging techniques such as CT and generates detailed 2D and 3D images required for accurate surgical schemes. This system’s capacity to provide accurate measurement under increasing clinical efficiency makes it an excellent option for achieving the best results in THA.

The selection of natural fibres for toe caps in safety footwear requires careful evaluation of multiple performance criteria. This process makes the task a multicriteria decision-making problem, as numerous alternatives and requirements must be considered simultaneously. This study applied the CRiteria Importance Through Intercriteria Correlation (CRITIC) method to compute objective criterion weights, which were then integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to rank and identify the optimum fibre amongst 12 alternatives. A sensitivity analysis was conducted to evaluate the impact of weight variations on the selection results. The combined CRITIC-TOPSIS method effectively determined the optimal natural fibre for safety toe cap applications. Sensitivity analysis confirmed the robustness of the results whilst highlighting the influence of criterion weight variations on final rankings. The proposed integration offers a systematic and reliable approach for material selection in safety footwear. The findings provide useful guidance for the development of natural fibre–reinforced polymer composites and support lightweight and sustainable alternatives for toe caps in safety footwear.