International Journal of Engineering
Volume:37 Issue: 1, Jan 2024

Deep and large-scale (D&LS) open-pit mines pose various environmental, social, and economic impacts on the mining projects’ stakeholders and local, regional, national, and international communities. Identifying these impacts and having a comprehensive model to assess these impacts altogether is critical to achieving sustainable development (SD) goals. This study develops a robust sustainability assessment model for D&LS open-pit mining projects. The model comprehensively considers 99 impact factors across environmental, social, and economic dimensions. The sustainability score is calculated using the Z-FDAHP technique to reduce the bias and uncertainty of experts' judgments. Then, the scenario-based technique is used to apply the stakeholders' perspective to the model. The model is applied to Sungun Copper Mine (SCM) in northwest Iran for verification. Results show SCM's sustainability performance is highly sensitive to index weightings. The highest score was achieved with sole social prioritization (scenario 8 with a sustainability score of 6.364 out of 10), highlighting the critical role of community impacts. Environmental or economic focus alone (scenarios 2 and 5) was not very sustainable, with scores of 3.326 and 5.298 respectively. Scores of 5.543, 5.330, and 5.117 for sustainability can be achieved by optimizing all three SD aspects with a long-term, stakeholder-centered approach (scenarios 9, 4, and 6). The proposed sustainability assessment model exhibits robustness through its comprehensive set of 99 environmental, social, and economic indicators; its ability to customize indicator weights under different stakeholder-perspective scenarios; and validation of the quantitative scoring approach through an empirical case study, while continuous improvement would further reinforce its robustness over time.

In this research, the synthesis process of a novel super-active catalyst, named SAC-510 is investigated at experimental scale. The authors compared the analyzed data against those derived from a popular catalyst, Finix-112, and several other available alternatives. The data indicated that SAC-510 catalyst derived the optimal activity from its spherical particles. Titanium and other chemical elements were less uniformly distributed in SAC-510 than in Finix-112 samples, with the mean particle size being slightly larger than that found in Finix-112. The pores’ dispersion and sizes in SAC-510 were not as uniformly distributed as those in Finix-112 catalyst. Lastly, both SAC-510 and Finix-112 catalysts were equally adaptable for use in high-density polyethylene pipes (grade 100). Compared to other commercially available catalysts, the major advantages of SAC-510 are the economical use of hydrogen and monomers, and low purging of its valuable gases during the polymerization process. The results obtained are as follows: the increase in oxidative induction time efficiency with SAC catalyst compared to Finix catalyst by 5.8%, activity by 35.7%, hydrogen responsibility by 24.39%, 1-butene consumption by 8.38% and triethylaluminium consumption by 27.27%.

In the contemporary context, the imperative to strengthen security and safety measures has become increasingly evident. Given the rapid pace of technological advancement, the development of intelligent and efficient surveillance solutions has garnered significant interest, particularly within the realm of smart city (SC). Surveillance systems have been transformed with the emergence of edge technology (ET), the Internet of Things (IoT), and deep learning (DL) to become key components of SC, notably the domain of face recognition (FR). This work introduces a smart surveillance car robot based on the ESP32-CAM micro-controller, coupled with a FR model that combines DL models and traditional algorithms. The Haar-Cascade (HC) algorithm is employed for face detection, while feature extraction relies on a proposed convolutional neural network (CNN) and predifined DL models, VGG and ResNet. While the classification is made by two distinct algorithms: Naive Bayes (NB) and K-nearest neighbors (KNN). Validation experiments demonstrate the superiority of a composite model comprising HC, VGG, and KNN, achieving accuracy rates of 92.00%, 94.00%, and 96.00% on the LFW, AR, and ORL databases, respectively. Additionally, the surveillance car robot exhibits real-time responsiveness, including email alert notifications, and boasts an exceptional recognition accuracy rate of 99.00% on a custom database. This ET surveillance solution offers advantages of energy efficiency, portability, remote accessibility, and economic affordability.

Nowadays, due to the growing population, rising global warming, environmental pollution, and the reduction of fuel sources, the use of Distributed Generation Sources (DGs) has grown, and because of their random nature, the conventional performance of power systems is being changed. Reactive power has a considerable role in power systems management and control indexes such as loss, stability, reliability, and security, among which the loss index usually can be easily minimized and controlled. Thus the modeling and optimizing of reactive power must be done accurately and correctly. This paper uses a novel metaheuristic algorithm which is called Dandelion, to solve the constrained non-linear optimal reactive power dispatch problem, and the Improved Taguchi method based on orthogonal arrays has been applied in order to the uncertainty of DG units modeling. The applied optimal reactive power dispatch algorithm is tested and validated using standard IEEE 30-bus test power systems. These results show that the Computational time of the applied algorithm in comparison with other used algorithms is the least value and reduces the reactive power from 22.244 to 2.366 Mvar; also, the losses of the power system significantly will be decreased with the tested and introduced algorithm. Genetic Algorithm(GA), Particle swarm optimization algorithm (PSO), and Prairie dog optimization algorithm (PDO) have been utilized to solve the problem.

In this study, Al-based composites reinforced with Al59Cu25.5Fe12.5B3 quasicrystal (QC) were prepared by spark plasma sintering (SPS) method. Microstructural and mechanical properties were examined. It is observed that with addition of quasi-crystalline reinforcement the intensity of the quasi-crystalline peak has increased. Also, it is observed that by performing spark plasma sintering, the quasi-crystalline particles maintain their stability. Due to low temperature of the process and the short time of spark plasma sintering, the occurrence of destructive phases within the quasi-crystal has been prevented. based on field emission scanning electron microscopy (FESEM) images, the distribution of quasi-crystalline particles at the sample level has increased. In addition, the mechanical properties are improved by increasing the quasi-crystalline particles. Therefore, the sample with 15 vol.% of quasi-crystal has better results than other samples in improving microstructural and mechanical properties and it can be considered as an optimal sample with suitable practical properties.

In the present study employed disc pressure tests to assess the effects of hydrogen embrittlement and thermal ageing on the fatigue life of a thin-wall circular part within a rocket engine. The technique compared the pressure resistance of membranes tested under helium and hydrogen, offering a simple, sensitive, and reliable method. Disc tests were selected to mimic natural operating conditions, as they align with those of a thin-wall circular part within a rocket engine. The originality of these tests appears to lie in their enhanced performance in terms of sensitivity and reproducibility. To achieve this, tests were conducted across various conditions, including sample thicknesses of 0.75 mm, a broad range of strain rates from 10-6 s-1 to 100 s-1, temperatures spanning from 20°C to 900°C, and pressure rates from 10-2 to 2.104 MPa/min. Furthermore, a variety of materials were investigated, including copper, nickel alloy, and stainless steel. The results demonstrated that thermal aging leads to precipitation, particularly intergranular precipitation. These precipitates diminish the material's ductility, particularly when they are nearly continuous. Additionally, the material's sensitivity to hydrogen becomes significant when hydrogen, supersaturated due to rapid cooling, becomes trapped on precipitates formed at high temperatures. Furthermore, the results indicated that thermal and hydrogen-induced damage mutually reinforces each other, resulting in reduced fatigue life under high deformation.

The failure of shear-type beam-column joints in reinforced concrete (RC) frames during severe earthquake attacks is a critical concern. Traditional methods for determining joint shear capacity often lack accuracy due to improper consideration of governing parameters, impacting the behaviour of these joints. This study assesses the capabilities of machine learning techniques in predicting joint shear capacity and failure modes for exterior beam-column joints, considering their complex structural behaviour. An artificial neural network (ANN) model is proposed for predicting the shear strength of reinforced exterior beam-column joints. ANN, a component of artificial intelligence that learns from past experiences, is gaining popularity in civil engineering. The ANN model is developed using a dataset comprising material properties, specimen dimensions, and seismic loading conditions from previous experimental investigations. The model considers twelve input parameters to predict shear strength in exterior beam-column joints. Training and testing of the ANN model are conducted using established design codes, empirical formulas, and a specific algorithm. The results demonstrate the superiority of the proposed Shallow Feed Forward Artificial Neural Network (SFF-ANN) compared to previous approaches. The effectiveness of an Artificial Neural Network (ANN) model was quantitatively assessed in this study, with a focus on its performance in comparison to various design codes commonly used in structural engineering. The model was assessed using the coefficient of determination (R2) and achieved R-squared values of 99%, 94%, and 98% during the training, testing, and validation stages, respectively. The study highlights the significance of beam reinforcement as a key element in estimating shear capacity for exterior RC beam-column connections. Although the proposed models exhibit a high degree of precision, future research should focus on developing improved models using expanded datasets and advanced algorithms for enhanced pattern recognition and performance.

Cold Atmospheric Pressure Plasma (CAP) is very potent and impactful technology implemented for both technological and biomedical applications. This paper focuses on the implementation of artificial neural network (ANN) for a novel double ring electrode based cold atmospheric pressure plasma which is to operated only in the glow discharge region for its application in biomedical field. ANN inherently helps in visualizing the effective output parameters such as peak discharge current, power consumed, jet length (with sleeve) and jet length (without sleeve) for given set of input parameters of supply voltage and supply frequency using machine learning model. The capability of the ANN model is demonstrated by predicting the output parameters of the CAP beyond the experimental range. Finally, the optimized settings of supply voltage and supply frequency will be determined using the composite desirability function approach to simultaneously maximize the peak discharge current, jet length (with sleeve) and jet length (without sleeve), and minimize the power consumption.

The bioplastic film based on Polyvinyl Alcohol (PVA) for food packaging has been widely developed because of its biodegradable properties and safety. Nanocrystalline cellulose (NCC) is used as filler to improve mechanical strength. This study investigated how adding NCC into PVA films affects the physical, mechanical, and thermal properties. Combine acid hydrolysis 46 wt.% and ultrasonication process success to isolate commercial microcrystalline cellulose (MCC) became nanocrystalline cellulose (NCC). It has been characterized by x-ray diffraction (XRD), Fourier Transform Infrared (FTIR), Transmission Electron Microscope (TEM), Differential Scanning Calorimetry (DSC), and Thermal Gravimetric Analysis (TGA). NCC with needle shape form with an aspect ratio (L/D) of 12.4 has been high crystallinity index (76.4%). Addition of 6 wt.% NCC into PVA film improves the tensile strength and elongation by 35.30 MPa and 65.54%, respectively. The bioplastic film gives a barrier on the UV rays by 75% and still has good transparency. The thermal stability improves, indicated by the glass transition temperature (Tg) increase from 109 to 114°C and maximum temperature (Tmax) from 275 to 300 °C.

In this study, we present an investigation into the seismic vulnerability assessment of medium-rise reinforced concrete structures featuring vertical geometric irregularity (setback). We considered the effects of the percentage and location of the setback along the height of the building, as well as the impact of changing site classes. Additionally, we incorporated the effects of soil-structure interaction into the nonlinear response of the building. In the first part, we investigated the influence of the aforementioned parameters on the seismic response of a structure through nonlinear static analyses. We analyzed the capacity curves and the development of plastic hinges in the structural elements. In the second part, we analyzed the seismic fragility of building frames using a probabilistic study approach. We developed fragility curves to assess the vulnerability of the structures.In conclusion, the obtained results highlight the fundamental importance of considering  structural irregularities as well as the impact of different site classes on the seismic vulnerability of buildings.

The purpose of this article is to control the formation and pass static and dynamic obstacles for the quadrotor group, maintain the continuity and flight formation after crossing the obstacles, and track the moving target. Model Predictive Control (MPC) method has been used to control the status and position of quadrotors and formation control. Flight formation is based on the leader-follower method, in which the followers maintain a certain angle and distance from the leader using the formation controller. The improved Artificial Potential Field (APF) method has been used to pass obstacles, the main advantage of which compared to the traditional APF is to increase the range of the repulsive force of the obstacles, which solves the problem of getting stuck in the local minimum and not passing through the environments full of obstacles. The results of the design of the attitude and position controller showed that the quadrotors were stabilized and converged in less than 3 seconds. Formation control simulations in the spiral path showed that the followers, follow the leader. The results of the quadrotors passing through the obstacles were presented in four missions. In the first mission, 4 quadrotors crossed static obstacles. In the second mission, 4 quadrotors crossed dynamic obstacles. In these two missions, the quadrotors maintained a square flight formation after crossing the obstacles. In the third mission, the number of quadrotors increased to 6. The leader tracked the moving target and the quadrotors crossing the static obstacles.  In the last mission, the quadrotors passed through the dynamic obstacles and the leader tracked the static target. In these missions, the quadrotors maintain the hexagonal formation after crossing the obstacles. The results simulations showed that the quadrotors crossed the fixed and moving obstacles and after crossing, they preserved the flight formation.

In recent years, implants are used as prostheses to replace and protect bone. Titanium, as an implantable material, needs to improve corrosion and wear properties for better performance. Therefore, in the current study nitride coatings were applied with the aim of improving corrosion and wear properties. Cathodic arc evaporation physical vapor deposition (CAE-PVD) technique was used to deposit nanolayered CrN/CrAlN coatings on commercially pure titanium and Ti6Al4V substrates for biomaterial applications. X-ray diffraction (XRD) was used to characterize the crystal structure of the coating, and scanning electron microscopy (SEM) and field emission scanning electron microscopy (FESEM) were utilized to observe the surface morphology and cross-section of the coating. The coating adhesion was measured according to VDI 3198 standard using a Rockwell-C indenter. The corrosion behavior was evaluated by potentiodynamic polarization and spectroscope electrochemical impedance in Ringer's solution. The results showed that the nanolayered coating changed the corrosion potential from -0.368 V to -0.054 V for the titanium sample and from -0.405 V to -0.028 V for the Ti6Al4V specimen. Additionally, the corrosion current density was reduced to about one-eighth and a third for the titanium and Ti6Al4V coated samples, respectively. The capacitator circle diameters increased due to the deposition of CrN/CrAlN coating, demonstrating enhanced corrosion behavior of the coated samples compared to uncoated specimens, as the coating acted as a barrier against corrosive liquids accessing the substrate.

Adjusting the operating parameters to optimize the performance of the scroll expander has been a hot research topic among scholars. This paper innovatively combines the response surface method and NSGA2 algorithm for parameter optimization. This novel method can accurately predict the optimal operating parameters of the scroll expander and improve the overall efficiency of the scroll expander. Initially, a three-dimensional transient simulation model of the scroll expander was established, and the effects of three key operating parameters (suction pressure, exhaust pressure, and rotational speed) on the output power and isentropic efficiency of the scroll expander were analyzed through numerical simulation. On this basis, the response surface model between the input parameters and the objective function  was established by using the response surface methodology. Consequently, three different optimization algorithms  were compared, and it was found that NSGA-II had a better performance both in terms of convergence and solution performance,. Threfore, the NSGA-II algorithm was used for the multi-objective optimization. Under the premise of considering the maximum output power and isentropic efficiency, based on the established response surface model, the Pareto optimal solution was used to determine the optimal combination of its operating parameters: suction pressure of 1.62 MPa, exhaust pressure of 0.45 MPa, and rotational speed of 2,099.58 rpm.Finally, the numerical model is verified by the laboratory-built test bed of the Organic Rankine cycle low-temperature waste heat oil-free power generation system. The experimental results match well with the numerical simulation results and verify the model accuracy. The results from this pioneering and thorough thr study will provide a solid benchmark for the development and refinement of upcoming scroll machines.

The mechanisms behind temperature and material deformation in vibrothermography remain questionable, presenting a gap in understanding. This study investigates the deformation-induced mechanism, focusing solely on the heat generation associated with strain development. Both experimental and simulation approaches are incorporated. The experimental segment explores the temperature-strain relationship of SCM440 material, commonly used for rotating shafts. This behavior is examined through the connection between temperature change and material deformation during a uniaxial tensile test. Results indicate that temperature change and distribution can be predicted based on plastic strain development. Finite Element Method (FEM) simulation is utilized to model the excitation of a shaft with and without an elliptic surface crack. Various cracked shaft configurations are investigated, revealing distinct strain generation and distribution patterns. High strain alteration is notably observed around the crack tips, enabling the detection of shaft discontinuity. Consequently, a temperature prediction technique is developed to estimate temperature based on strain alteration during deformation. Adequate excitation power and the use of a high-sensitivity IR camera are recommended for the effective application of the temperature prediction technique. Additionally, this study provides insights into understanding the utility and limitations of vibrothermography for inspecting engineering component damage based on experimental temperature-strain relationships and computational predictions of strain distribution in cracked shafts under excitation. These findings offer guidance for engineering applications and future research endeavors.

The copper-based biocide is mostly used as primary additive of Antifouling (AF) paint in Indonesia especially on vessels. The evaluation for the efficacy of AF paint was conducted where anti corrosion (AC) paint was also as a reference. The panels with both paints were exposed to various sea depths of up to 3 meters until 12 months of exposures. The measurement of parameters of seawater consisting of water conductivity, pH, temperature, dissolved oxygen and salinity were carried out. There was no or less attached marine biofouling on AF-painted panels but not on AC-painted panels up to 12-months of field exposure in various sea depths. There was no difference between the properties of AF paints before and after exposure to various sea depths. The inhibitive performance of AF paint depends on the existence of AF layer containing Cu2O biocide where the thickness of that layer decreases in increase of time exposure.

In this research, the influences of laser surface remelting using different scanning speeds on the microstructure, roughness, and hardness of Commercial pure Titanium (Grade 2) were investigated. High power Nd: YAG pulsed laser was used. The laser scanning speeds used in this study were 4, 6, 8, and 10 mm/s and the other laser parameters (power, pulse frequency, beam diameter) were constant. The corrosion performance of the laser surface remelted and Cp titanium was then evaluated by potential dynamic measurements in a 3.5% NaCl solution. The results revealed that due to the diffusionless transformation after laser surface treatment and the formation of the martensite phase, the surface post-laser treatment was significantly different from those before the treatment. The results were indicated using an optical microscope, FE-SEM, XRD, AFM, and microhardness analysis. It was found the lowest scanning speed, 4 mm/s, had the slightest roughness and the smallest average grain size (26.06 nm) due to the high input energy and slow cooling rate, while the highest scanning speed (10 mm/s) had the greatest microhardness (291.5 Hv) due to the short interaction time between the substrate surface and laser beam and the higher cooling rate. The results also demonstrated the obvious improvement in the pitting resistance of Cp Ti in harsh environments as a result of the influence of laser remelting.

Energy consumption is a crucial aspect in the effort to optimize the utilization of resources and reduce energy wastage. Focusing on energy efficiency can result in operational cost savings, a reduction in greenhouse gas emissions, and support for environmental sustainability for future generations. Therefore, it is important to consider energy efficiency in daily life, especially in the use of electricity for electronic devices. This research aims to compare the energy efficiency of two different approaches to Air Conditioner (AC) usage: the manual method and the fuzzy logic method. The manual method involves eight tests with direct power measurements over a 30-minute period at various AC temperature settings, namely 18°C, 20°C, 23°C, 24°C, 25°C, 26°C, 27°C, and 30°C. On the other hand, the fuzzy logic method involves six tests allowing for dynamic temperature adjustments based on room conditions. The research findings indicate that the fuzzy logic method achieves lower average power consumption, except at 30°C, where the manual method is slightly more efficient (a difference of 140,745 watts). This difference is primarily attributed to the "cooling and fan" mode used at lower temperatures in the manual method, resulting in higher power consumption. Furthermore, this research reveals the potential of the fuzzy logic in optimizing AC power usage based on real-time conditions, achieving approximately a 41.96% energy savings. The primary contribution of this study is to provide practical insights into how the fuzzy logic method can significantly reduce AC energy consumption, support energy efficiency efforts, and contribute to environmental sustainability.

The aim of this study is to examine and analyze the axial and radial forces, electromagnetic forces (EMFs) acting on the low and high-voltage windings of an amorphous core transformer via the two different approaches: an analytic approach and a 3-D finite element method (FEM). Firstly, the analytic method is proposed to analyze the distribution of leakage magnetic field in the magnetic circuit and the forces acting on the transformer windings. The FEM embedded in the Ansys Maxwell tool is then proposed to compute and simulate the axial and radial forces under three different operating conditions: no-load, rated full-load, and short-circuit. The obtained results from two different methods such as the rated voltage, rated current, short-circuit current, axial and radial forces and EMFs in the low and high-voltage windings are finally compared to illustrate an agreement of methods. The validation of the methods is applied on a three-phase amorphous core transformer of 1600kVA-22/0.4kV.