The line-start synchronous permanent magnet synchronous motors have the advantages of high efficiency and line-starting capability. However, this type of motor has the drawback of high cogging torque, which is the main cause of vibration and noise during operation. Analyzing the factors affecting cogging torque is essential and important in the design of these motors. This paper studies the impact of magnet width on cogging torque. Theoretical analysis and finite element method simulations for the test motor show that the cogging torque can be significantly high, reaching up to 5% of the rated torque of the motor. Research results indicate that cogging torque can be reduced by decreasing the width of the permanent magnet. However, reducing the magnet width adversely affects the startup characteristics, torque, and efficiency of the motor, demonstrating the existence of an optimal width that balances cogging torque and other parameters in motor design. Additionally, the research results have been validated through experimental modeling, showing a close correlation in the shape of the cogging torque and the back eletromotive force characteristics, with a small error of less than 5%.
Foundation design using the Allowable Stress Design (ASD) method is characterized by less consistent reliability that is difficult to quantify, leading to overly conservative or optimistic design. Therefore, this study aimed to implement the Reliability-Based Design (RBD) method in foundation design to achieve consistent reliability levels and evaluate the equivalent safety factor generated. The design process was initially conducted based on API method and updated using field test data, namely the Pile Driving Analyzer (PDA) and Static Load Test (SLT). Statistical parameter updating was performed using a statistical analysis through the Bayesian updating method. The results showed that RBD method was capable of explicitly quantifying uncertainties. Furthermore, the availability of more and higher-quality data contributed to a more accurate representation of uncertainty, causing a greater reduction in the required equivalent factor of safety. This showed that the probabilistic method enabled the application of a lower safety factor compared to the conservative value of approximately 2.5 commonly adopted in ASD, without compromising the target reliability. Through the addition of data from PDA and SLT testing, this study showed that the RBD method could significantly reduce the required safety factor to a value of 1.68.
This study investigates a hybrid cooling–lubrication approach combining nanofluid-assisted minimum quantity lubrication (MQL) with cold-air cooling in the hard milling of SKD11 steel, aiming to improve both surface quality and productivity. Response surface methodology (RSM) was employed to model the relationships between machining parameters and two key performance indicators, namely surface roughness (Ra) and material removal rate (MRR). The developed models were integrated with particle swarm optimization (PSO) for multi-objective optimization. Compared with conventional MQL, the hybrid approach reduced Ra by approximately 5–10% under similar cutting conditions, indicating enhanced cooling and lubrication performance. The optimization results revealed distinct trade-offs between surface quality and productivity. When surface quality was prioritized (wRa = 0.7), a minimum Ra of 0.160 µm was achieved with a relatively low MRR of about 653 mm³/min. In contrast, both the balanced (wRa = 0.5) and productivity-oriented (wRa = 0.3) scenarios converged to the same optimal solution (Ra ≈ 0.245 µm, MRR ≈ 1790 mm³/min). This convergence suggests that MRR dominates the optimization when its weighting is comparable to or higher than that of Ra, indicating a plateau region on the trade-off surface. The proposed RSM–PSO framework provides both an effective optimization approach and new insight into balancing surface integrity and productivity in hybrid-cooled hard milling
To make the city planning sustainable, especially in rapidly growing cities of the world like Surat in India, the implementation of effective waste management is crucial. The primary factor governing this is the ability to accurately predict the quantity of municipal solid waste (MSW) likely to be generated. This study presents a comprehensive comparative analysis of various predictive models including linear regressions, kernel approaches, gradient boosting as well as the deep learning architectures. Using historical data from Surat, systematic preprocessing and feature engineering generated 419 features representing temporal, socio-economic, climatic, COVID-19, and mobility factors. The novel contribution of this study is the systematic feature engineering framework that explicitly encodes temporal structure (419 engineered features including lagged values, rolling statistics, and seasonal decomposition), enabling simple linear models to capture complex waste generation patterns. Ten distinct models, ranging from statistical approaches to machine learning and deep learning were evaluated and compared. Advanced ensemble models, including LightGBM (R² = 0.983), CatBoost (R² = 0.977), and XGBoost (R² = 0.970) demonstrated strong performance. The best-performing models (OLS and Gaussian Process Regression) achieved R² = 0.997 with Mean Absolute Percentage Error (MAPE) = 1.43%. In this study, linear models trained within a few milliseconds and achieved per-sample inference times on the order of 0.004-0.008 ms, whereas the tuned MLP and tree-based ensembles required seconds of training and millisecond-level inference, corresponding to differences of roughly two to three orders of magnitude in computational cost. Other notable performers include Lasso regression (R² = 0.979), tuned MLP (R² = 0.968), and Random Forest (R² = 0.960). The results demonstrate that feature engineering has greater influence on forecasting accuracy than model complexity, with OLS using engineered features (R² = 0.997) outperforming the MLP model (R² = 0.966) by approximately 3.1% while providing substantially faster predictions. Feature importance analysis identified lagged MSW values, rolling statistics, demographic indicators, festival effects, and COVID-19 lockdown impact as key predictors. The research finds that adoption of systematically designed feature engineering framework is a valuable tool for MSW management. The study provides comprehensive model benchmarking and practical recommendations for policymakers pursuing sustainable urban development.
Gelatine is commonly found in food, pharmaceutical, health, and cosmetic products. However, its use has generated significant debate due to concerns about the numerous sources, particularly for religious reasons. For Muslims and Jews, gelatine derived from porcine sources must be avoided, as it is strictly prohibited by religious laws. The detection in these products is important, and this led to the design of an optical biosensor based on a one-dimensional (1D) photonic crystal (PhC) with a defect layer. The aim was to detect the distinctive characteristics of gelatine with high sensitivity. Additionally, the defect layer was placed in the center of the structure, where the gelatine solution was introduced. The designed PhC structure consisted of N layers of (ZnO/SiO₂)ᴺ/defect/(ZnO/SiO₂)ᴺ. The reflectance and transmittance characteristics of the PhC were calculated by using the transfer matrix method (TMM), performed with the MATLAB software. For simplicity, the normal incidence of light on the PhC was assumed to avoid the complexities of oblique angles. Sensor performance was evaluated by calculating sensitivity (S), quality factor (Q), figure of merit (FOM), resolution (RS), and detection limit (LOD). The biosensor achieved an average S, Q, FOM, RS, and LOD of 225.7694 nm/RIU, 2249.413, 806.3185, 0.1332, and 4.13 × 10⁻⁵, respectively. These metrics showed the simple PhC design was a highly sensitive, non-invasive detection platform for monitoring and identifying gelatine-based products that did not meet Halal and Kosher requirements. The novelty of the study centered on the application of a simple 1D PhC with a defect cavity to detect gelatine concentrations in both aqueous and oily solutions, a problem that had not been addressed in detail.
With the national strategy being implemented and taking root in the infrastructure sector, the increased of water erosion in rock formations during railway construction has risen significantly, posing a considerable risk to the stability and structural integrity of engineering rock formations. Consequently, there is a critical need to investigate how water influences the mechanical properties of gabbro. Uniaxial compression experiments were performed on both arid and water-soaked gabbro pieces, simultaneously monitoring the characteristics of acoustic emission (AE) and infrared radiation (IR) response. The Particle Flow Code (PFC) was utilized to examine the microscopic processes of crack propagation, coalescence, and damage progression in the rock pieces. Observations show that water considerably impacts the mechanical characteristics of gabbro. In comparison to arid pieces, the peak strength of water-soaked pieces showed a reduction of 4.98%, while the elastic modulus was diminished by 16.5%. The failure behavior of the pieces shifted progressively from tensile splitting in the arid condition to tensile-shear failure. Both arid and water-soaked pieces displayed pre-shock-main shock patterns in their AE parameters. By creating a damage variable based on cumulative AE ring counts, the damage evolution of the pieces was categorized into three phases: incipient damage, steady damage, and expedited damage phases. Throughout loading and fracturing, the AIRT (average infrared radiation temperature) curve exhibited a downward trend, followed by a sharp rise, featuring a "V"-shaped turn prior to piece failure. The evolution of AIRT in water-soaked pieces showed strong alignment with stress variations. Numerical simulations using PFC revealed that the quantity of shear cracks in water-soaked pieces increased, constituting a larger share of the total cracks in contrast to arid pieces. After loading, water-soaked pieces generated more strong force chains, with the load borne by bonds gradually increasing crack evolution, making them more susceptible to failure.
The construction of trenches and pipelines is essential to the infrastructure sector, but because of safety and technical concerns, progress monitoring is difficult. This study assesses how well photogrammetry, a cost-effective and adaptable Industry 4.0 technology, can improve safety and sustainability in construction monitoring. The graphical user interface, computational efficiency, point cloud density, model quality, percent completion, and noise in the produced 3D models were the criteria used to evaluate the six photogrammetry tools: Autodesk Recap Pro, Agisoft Metashape Pro, COLMAP, VisualSFM, Meshroom, and Regard 3D. Performance under specified conditions was examined using a trench and pipeline dataset. The results show that Agisoft Metashape Pro and Autodesk Recap Pro performed exceptionally well, offering thorough and precise 3D reconstructions with excellent models and low noise. This research promotes the use of photogrammetry by emphasizing its advantages over conventional methods in terms of affordability and sustainability. It highlights photogrammetry's contribution to resilient and sustainable practices and provides industry experts with advice on how to choose appropriate methods for tracking building progress. The results help stakeholders feel more confident about implementing photogrammetric technologies that are suited to various building settings.
The growing demand for vehicles has spurred an increase in tire production. However, the improper disposal of these waste tires poses a significant environmental and health hazard. To address this, recent research has explored the integration of recycled steel fibers (RSF) and crumb rubber (Cr) from used tires into concrete formulations to create innovative rubberized and fibrous concrete. A notable study specifically examined the impact of adding RSF of varying lengths and a fixed volume fraction into rubberized concrete containing different proportions of Cr, where Cr partially replaced natural sand. Through the fabrication and testing of 18 reinforced concrete columns under axial compression, the findings demonstrated that RSF alone significantly enhanced the concrete’s properties, including density, compressive strength, and tensile strength, by remarkable percentages of 100.27%, 116.84%, and 107.25%, respectively. Conversely, the exclusive use of Cr resulted in a decline in these properties as its content increased. Notably, the "Co5" columns, which incorporated RSF into a concrete mix containing Cr, exhibited superior performance, showing improved displacement and ductility by a degree of approximately 44.67% and 15.65%, respectively, alongside a significant reduction in crack widths by about 29.45% compared to standard rubberized concrete (Co1&Co2). The properties and attributes of columns display promising performance as well as displacement and ductility when RSF is incorporated into concrete mix that includes Cr compared to rubberized concrete.
Sustainable commercial buildings require cost-effective and environmentally responsible design solutions. Traditional Value Engineering (VE) methods, while effective in cost reduction, often lack integration with digital tools, limiting their ability to optimize sustainability and performance. This study develops a Digital Value Engineering Model (DVEM) that aligns with Industry 4.0 principles, incorporating Building Information Modeling (BIM), Life Cycle Cost (LCC) analysis, and a weighted evaluation matrix to enhance decision-making transparency, cost efficiency, and environmental impact assessment. The model was implemented in Autodesk Revit and applied to a real-life commercial building project in Malaysia, systematically following six VE phases. The results demonstrate a 28% cost reduction while optimizing material selection, energy efficiency, and lifecycle performance. Unlike conventional VE, DVEM enables automated cost analysis, real-time sustainability assessment, and function-based decision modeling. By bridging traditional VE with modern digital workflows, this study provides a replicable, data-driven approach to optimizing commercial building design. The findings contribute to the construction industry by introducing a structured, scalable framework that enhances decision-making efficiency, resource utilization, and sustainability compliance in commercial building projects.
Ti2C is a 2D nanomaterial with an ultrathin layered structure. This material has remarkable properties due to the -O, -F, and -OH functional groups in surface termination, which makes it hydrophilic and suitable for membrane applications. Herein, we reported the chloride salt-intercalated Ti2C membrane for methylene blue (MB) removal. The modification was done by a simple mixing method between chloride salt and Ti2C. First, Ti2C was synthesized from its parent phase of Ti2AlC using in-situ HF etchant. Then, Ti2C was modified using chloride salt (NaCl, KCl, MgCl2, and CaCl2), and mixed cellulose ester (MCE) was used as a membrane support to produce a MXene-based membrane (MXM). The results show that chloride salt ions enhance the interlayer spacing of Ti2C due to the ability of salt cation to be inserted and replace the Li+ as intercalant. This result is also evidenced by the difference in d-spacing in XRD analysis. In methylene blue removal, the flux of the membrane was excellent, around 2000-3000 L m-2 h-1, with a dye removal value above 97%. The high dye removal is correlated with the electrostatic interaction between the negative surface of Ti2C and the positive charge of MB. Then, the remarkable performance was reached by MXM-KCl with flux and rejection of 3303.31 L m-2 h-1 and 99.01%, respectively. The excellent flux of MXM-KCl correlated with the Gibbs free energy hydration of K+ is lower than the other salt cations (Na+, Mg2+, and Ca2+). In addition, all membranes exhibit great fouling resistance, with an FRR value of about 90%.
As industries strive to enhance their applications to meet growing market demand, accurate speed control of single-phase induction motors (SPIMs) remains a crucial concern. This paper presents the modelling and simulation of SPIM speed control based on a hybrid Model Reference Adaptive System (MRAS) integrated with a Fuzzy-PID regulator. Superior performance was achieved by integrating MRAS with a fuzzy-PID regulator using an adaptive self-tuning mechanism. The purpose of this integration was to leverage the adaptive nature of MRAS and the robustness of the Fuzzy-PID regulator to enhance performance and reliability in SPIM drives without the need for physical sensors. The SPIM speed was modelled using differential equations representing both electrical and mechanical dynamics. MRAS was implemented using motor voltage equations, with an adaptive model estimating the rotor speed. The Fuzzy-PID regulator optimized control performance by processing the error and its rate of change through a fuzzy controller, with the output fed into a PID controller to ensure error stabilization. A review of relevant literature on SPIM and associated control theories was conducted, and several journal papers were analyzed. Simulation of the proposed MRAS-Fuzzy-PID approach in MATLAB demonstrated that sensorless speed regulation considerably reduced rise time, enabling the motor to reach the desired speed quickly while eliminating steady-state error compared to systems without controllers. The results indicate that the rise time was reduced by 65.5%, the overshoot decreased by 58.9%, the steady-state error decreased by 71.8%, and the Integral of Absolute Error (IAE) was minimized. These improvements ensured stable operation under varying load conditions, with minimal fluctuations in speed. Integrating MRAS with a Fuzzy-PID controller further enhances the speed stability, robustness, and adaptability of SPIM drives.
With the current rapid advancement of science and technology, there is an increasing focus on comprehensive research and the development of practical solutions for self-driving electric cars to address challenges, including environmental pollution, renewable energy utilization, emission control, and battery recycling. In this study, automatic direction control is achieved for electric vehicles by implementing line-tracing autonomous vehicles equipped with computer vision-based cameras, utilizing Particle Swarm Optimization (PSO), the Takagi-Sugeno Fuzzy model, and the PID control system. Line-tracing autonomous vehicles are devices capable of recognizing and tracking black or painted lines on the road. The lines are designed to be easily recognizable with a clear contrast, such as a white line on a black background. The autonomous vehicle follows a distinct, marked line to guide its journey. In this study, we integrate computer vision techniques with Particle Swarm Optimization (PSO) and a Takagi-Sugeno fuzzy control system for automatic direction control. Additionally, the speed and turning direction of the electric vehicle are regulated by a controller that combines proportional, integral, and derivative (PID) stages. According to real-world experiments with road-following autonomous vehicles using camera image processing, the highest success rate of 99.8% is achieved when the car employs intelligent algorithms to navigate turns of 10, 20, 30, and 40 degrees. Likewise, tests have demonstrated that the electric vehicle can achieve a perfect success rate of 100% when driving on a straight road.
Polyoxometalates (POMs) have emerged as exceptionally versatile catalysts for green chemical reactions, demonstrating significant potential in the sustainable valorization of biomass. Their tunable Br & oslash;nsted/Lewis acidity and redox properties enable a broad range of chemical transformations, offering remarkable flexibility in process design. This mini review provides a summary of recent advances in the thermocatalytic conversion of biomass using POMs, addressing their utilization as both homogeneous and heterogeneous catalysts. Key reaction pathways, including solvolysis, oxidation, esterification, and condensation, are highlighted as fundamental processes in biomass valorization. A central focus is placed on the crucial challenge of catalyst regenerability and stability, examining strategies to ensure the long-term viability and economic feasibility of these systems while facing the apparent low-temperature stability challenge of POMs. Finally, this review synthesizes current regeneration methods and presents a forwardlooking perspective on the future challenges and opportunities in the field of biomass conversion catalyzed by polyoxometalates.
This paper presents a study to improve the performance of Fe-Mn-C cast steels containing 1.7% and 2.7% Cr by weight, using two treatment methods (thermal and mechanical) applied separately to two different steel grades. This approach enables an extended service life for components such as crusher liners, mill hammers, and level crossings, without requiring complete recasting. The experimental techniques used for characterization included spark optical emission spectroscopy, optical microscopy, scanning electron microscopy, as well as micro-and macro-hardness testing. Steel 1, with a composition of 15.51% Mn, 2.68% Cr, and 1.29% C, was heat-treated at 1070 degrees C and quenched in water, using different holding times and thicknesses. It was found that increasing the holding time from 30 to 50 minutes and reducing the thickness from 150 to 100 millimeters led to a complete and homogeneous dissolution of carbides. As a result, Steel 1 exhibited increased ductility. Steel 2 contains 13.45% Mn, 1.72% Cr, and 1.21% C. It underwent manual mechanical treatment, which resulted in surface hardening due to the transformation of austenite into martensite.
The global waste crisis, with 2.01 billion tons of municipal solid waste generated annually, necessitates innovative solutions for sustainable energy conversion. This study addresses the critical need to optimize evaporator design in Organic Rankine Cycle (ORC) systems for waste-to-energy applications, where evaporators represent 35-40% of total system costs. We developed an integrated optimization framework combining Function Analysis System Technique (FAST) diagrams with Value Engineering (VE) principles to systematically analyze component functionality and cost structures. Applied to an 887 kW shell-and-tube evaporator, our methodology revealed that tubes, baffles, and shells account for 90% of production costs. The optimization approach achieved a 22.6% cost reduction (from Rp. 310,261,000 to Rp. 243,739,146) while simultaneously improving thermal efficiency by 15.3% (heat transfer coefficient increased from 850 to 980 W/m2 & centerdot;K, thermal effectiveness from 0.72 to 0.83). This challenges the conventional trade-off between cost and performance by demonstrating simultaneous optimization of both parameters. The methodology provides a replicable frameworkfor ORC system optimization with an 8-month payback period and 12% reduction in annual operating costs, contributing to the economic viability of waste-to-energy technologies.
Soft clay soils represent a significant challenge for embankment construction due to high compressibility, low shear strength, low bearing capacity, and excessive settlement potential. This study presents an enhanced finite-element model to evaluate the performance of Mortar Column Inclusion (Inklusi Kolom Mortar, or IKM) as a rigid inclusion supporting embankments over slightly overconsolidated soft clays, as implemented in the Serang-Panimbang Toll Road Project (STA 75+600 to STA 75+800). The propose approach integrates depth-dependent multilinear lateral resistance with structural "dummy" plate elements to capture soil arching within the Load Transfer Platform (LTP) and lateral column-soil interaction-an approach not previously applied in rigid inclusions modeling for soft clays. Studies on numerical modeling of IKM systems in slightly overconsolidated soft clays remain limited. Finite element analyses are conducted using PLAXIS 2D and 3D with axisymmetric, unit-cell, and plane strain approaches. The results show that the "dummy" plate simulates soil arching in the LTP and improves the representation of negative skin friction, neutral-plane transition, and axial load distribution. Depth-dependent lateral resistance enhances predictions of column bending moments and horizontal deformation within varying soil layers. Field validation indicates good agreement, with inclinometer displacement predicted at 40.79 mm (difference < 10%). Predicted vertical settlements of 20.71 cm (centerline) and 19.38 (edge) are also consistent with settlement plate readings of 15.80 and 11.00 cm, respectively. These findings confirm that the enhanced model provides a comprehensive evaluation of stress distribution, pile deformation, and global stability for ground improvement design in soft clays.
Efficient water management is essential for ensuring sustainability and reducing operational costs, especially in small to medium-scale buildings such as schools, health clinics, and office facilities. This paper presents the design and implementation of a costeffective automated water flow monitoring system, integrating a Mitsubishi FX3U-14MT PLC with an Arduino module to facilitate real-time flow measurement and precise control of solenoid valves. The PLC is programmed using ladder logic, while the Arduino is responsible for processing sensor data, thereby enhancing measurement accuracy and contributing to overall system flexibility. In contrast to conventional industrial automation solutions, this system is specifically designed for small-scale applications, offering an effective balance of affordability, simplicity, and reliability. Experimental testing demonstrates that the system achieves high measurement accuracy, operational stability over extended use, and optimized energy efficiency, ensuring long-term reliability in water flow management. Additionally, the system's modular design enables straightforward adaptation to various facility sizes and plumbing configurations. These findings validate the proposed system as an accessible yet effective automation solution, particularly suitable for environments where implementing large-scale industrial control systems may be impractical. Future research could focus on incorporating adaptive control algorithms and enhancing sensor integration to further improve system performance and flexibility.
In this study, we explore the activity and selectivity of the CO2 reduction reaction (CO2RR) to CO and HCOOH on pure and transition metal-doped NiCoPO(100) surfaces using density functional theory (DFT) calculations. The novelty of this work lies in demonstrating that substitutional doping with Mn, Fe, and Cu significantly alters the thermodynamic landscape of CO2RR, particularly in enhancing selectivity toward HCOOH. While CO remains the dominant product on most surfaces, Mn-doped NiCoPO(100) uniquely reverses this trend by reducing the limiting potential for HCOOH formation to a value lower than that for CO production. Furthermore, Mn doping suppresses the competitive hydrogen evolution reaction (HER), steering the reaction pathway more selectively toward formic acid. These findings introduce Mn-doped NiCoPO as a promising and tunable catalyst platform for selective CO2 to HCOOH conversion, providing valuable insights for designing efficient catalysts for sustainable carbon utilization.
The widespread use of conventional plastic packaging poses significant environmental challenges. As a sustainable alternative, bioplastics derived from cellulose sourced from agricultural waste are gaining interest. This study explores the development of biodegradable bioplastic films derived from durian rind cellulose, with glycerol used as a plasticizer. Cellulose was isolated from durian rind using chemical extraction methods, resulting in a 29% yield with 70.2% purity. Bioplastic films were synthesized by incorporating varying amounts of glycerol into the cellulose matrix. The successful integration of cellulose and glycerol were confirmed by Fourier Transform Infrared spectroscopy. Morphology analysis revealed that increasing glycerol disrupted the dense fiber structure, leading to more flexible and visually transparent films. This was consistent with colorimetric analysis, which showed increased transparency with higher glycerol concentrations. Glycerol addition also resulted in greater water vapor permeability and water absorption, attributed to the plasticizer's hydrophilic nature. Biodegradability tests indicated that all bioplastic samples fully degraded within 10 days in soil, with faster degradation occurring at higher glycerol levels. In food packaging trials using sponge cake as a model, the bioplastic films effectively prevented mold growth over 10 days. However, moisture loss led to a reduction in water activity and an increase in product hardness. Conversely, samples wrapped in commercial polyethylene (PE) plastic retained moisture and texture but showed significant mold growth. These findings demonstrate the potential of durian rind cellulose as a sustainable raw material for biodegradable packaging, and highlight the critical role of glycerol concentration in tailoring film properties for food applications.
The current global energy demand relies more on Combined Cycle Power Plants (CCPPs) for their high efficiency and reduced environmental footprint. However, the performance of these plants is very sensitive to several environment parameters including temperature, pressure, humidity, and exhaust vacuum. This paper is intended to use machine learning (ML) approach to model and optimize CCPP energy production based on these factors. The proposed method uses a dataset with hourly environmental measurements, to provide detailed analysis using ML techniques including Random Forests and Neural Networks to identify any potential nonlinear relationships and predict energy output. The results showed that ambient temperature has the most significant influence on energy production, followed by vacuum, pressure, and humidity. In addition, this paper also highlighted optimal environmental conditions that maximize energy output, which can help and support power plant operators in optimizing their operation factors. In summary, the recommendations and outcomes of this paper provide necessary steps for integrating advanced ML techniques into CCPP operations, enhancing both efficiency and sustainability.
