Engineering articles list

Alzheimer Disease is a chronic neurological brain disease. Early diagnosis of Alzheimer illness may the prevent the occurrence of memory cellular injury. Neuropsychological tests are commonly used to diagnose Alzheimer’s disease. The above technique, has a limited specificity and sensitivity. This article suggests solutions to this issue an early diagnosis model of Alzheimer’s disease based on a hybrid meta-heuristic with a multi-feed-forward neural network. The proposed Alzheimer’s disease detection model includes four major phases: pre-processing, feature extraction, feature selection and classification (disease detection). Initially, the collected raw data is pre-processed using the SPMN12 package of MATLAB. Then, from the pre-processed data, the statistical features (mean, median and standard deviation) and DWT are extracted. Then, from the extracted features, the optimal features are selected using the new Hybrid Sine cosine firefly (HSCAFA). This HSCAFA is a conceptual improvement of standard since cosine optimization and firefly optimization algorithm, respectively. Finally, the disease detection is accomplished via the new regression- based multi-faith neighbors’ network (MFNN). The final detected outcome is acquired from regression-based MFNN. The proposed methodology is performed on the PYTHON platform and the performances are evaluated by the matrices such as precision, recall, and accuracy.

Dr. Rajasekhar Butta

ABSTRACT “Numbers are the highest degree of knowledge. It is knowledge itself.”- Plato “The world is built on the power of numbers.” Pythagoras “Everything around you is numbers.” Shakuntala Devi. The date, we were born on, was not an accident. Each number in our date of birth is connected with certain energies and vibrations. Similarly, each and every letter in our name (first as well as full name) also has particular frequency, energies and vibrations. The combined and correlated energies of numbers and letters of an individual reveal about the past, present and future. The careful study of these universal divine energies can reveal, who you are, why you are here, where are you going and how will you get there. In other words what is your personality, nature, thinking, and what is your future in terms of family career relationships and finances etc. In this study, an attempt has been made to analyze -How certain patterns of name numbers and date of birth related core numbers can emerge as a directional guide for an individual to choose any specific career/profession (by studying the patterns of over 1100+ date of births and names of the Engineers cum Arbitrators).

Jyotsnaa G Bansal

Objective: Improving quality work by infusing a “sense of belongingness” in lowest-level workers. The present work focuses on the management of human resources in an enterprise where contract workers are involved in carrying out quality work related to the parent organisation. In today’s world, where the number of supervisors has reduced significantly, it is a challenge to maintain the quality of work at a satisfactory level. The paper proposes to enhance the quality of work by infusing a “sense of belongingness” into lowest-level workers. Methodologies adopted: Case studies. The first case study was conducted between 2007-2011 during a project related to Rural Electrification Work in the Bokaro district under the scheme RGGVY. The author was deputed to supervise the rural electrification work of 300 villages with thousands of kilometres of 11kv and 415V distribution lines, along with over 400 distribution transformers in four blocks of the Bokaro district in Jharkhand. Contractors engaged local workers for erection of all infrastructure. The author explained the process of erection to local communities and brought about a sense of belongingness in them towards the infrastructure being developed. The second case review was conducted between 2013-16 at Chandrapura, Bokaro, during the operation and maintenance work of the 220KV switchyard. Six workers were involved in the maintenance work of the switchyard. They rectified faults during emergencies and took care of housekeeping. However, they only followed orders and never worked proactively. The author divided the workplace into eighteen parts, each maintaining three parts. During monthly walk-in inspections, one worker was awarded as the best contractor’s employee of the month of that section, which introduced a sense of competition among them. Analysis: During the first case study, after pointing out the benefits of the infrastructure being developed and how the quality of work will help in its sustainability for an extended period, villagers realised its importance in their well-being. As a result, they kept vigil over the contractor’s work during the erection process. In second case study, the repetitive external motivation (awards and appreciation) infused a “sense of belongingness” in them. Thus, all employees started functioning proactively. As a result, the occurrence of electrical faults was reduced drastically, and housekeeping improved. Findings: These two case studies lead the author to coin the term “sense of belongingness”, which can lead to improve the quality of work by the lowest-level workers in a company. There are five ways to develop a “Sense of Belongingness” (SOB) among workers- external motivation, which can lead to internal motivation; mutual respect; a sense of duty (every person has their own responsibility); brainstorming sessions (to make them feel as an integral part of the department); encouraging them to do more than expected. Conclusion: All these steps help to develop a “sense of belongingness” among the lowest-level workers in an organisation. Without these principles, it will be a challenge to achieve quality work. The paper addresses all the processes in detail to improve the work culture in a department and, ultimately, an organisation.

RAJIV RANJAN SINHA

Background: Exhaust noise is known to be a major pollutant in the environment and workplaces due to the development of industry and transportation. Exhaust noise can be reduced to normal levels by mufflers or silencers. A reactive muffler efficiently dampens noise at low frequencies by reflecting sound waves. Therefore, muffler design is of great importance in exhaust noise reduction. Transmission loss (TL) is an essential characteristic of mufflers, demonstrating their acoustical properties. Any acoustical appliance is selected based on its damping performance and reliability. Predicting TL through experimentation is different from theoretical calculations. Methods: In the present study, a double-expansion chamber muffler was designed as a reflective muffler on a laboratory scale by equations. Next, TL was evaluated by an impedance tube applying a 4-microphone technique to determine the acoustical performance of the designed muffler. Results: Findings revealed that the TL of the muffler at 312 Hz frequency obtained 27.5 dB agreement with the required TL of the muffler of 25 dB. In addition, the TL of the muffler against frequency attenuates noise in broadband frequencies. Conclusions: These results indicated that the built muffler provides desired TL for exhaust chambers. Therefore, equations can be used as a precise method for muffler design. Furthermore, multi-expansion chamber mufflers are useful for reducing noise at a wide range of frequencies.

Niloofar

Purpose Noise pollution is a common health hazard worldwide which is emitted along with chemical air pollutants, simultaneously from many sources. Some studies have been conducted to control these pollutants, simultaneously with promising results being achieved. Dielectric beads have been used in air pollution control technologies, successfully and probable effectiveness of them in noise reduction can be promising in dual use of them in the exhausts emitting noise and air pollution, simultaneously. Methods In order to investigate the effectiveness of dielectric beads in noise reduction, two types of them; namely glass and ceramic beads, were placed separately inside the connecting tube of a double-expansion chamber muffler. Then the transmission loss (TL) of muffler was examined using impedance tube. A factorial design was used to evaluate and optimize the effect of noise related parameters on TL of such a system. Results Results show that the presence of dielectric beads has significant effect on TL of muffler. The maximum TL was obtained as 74.76 dB for muffler with ceramic beads, under the optimal condition of 5250 Hz and 120 dB. Measurement of TL and sound absorption coefficient (SAC) of glass and ceramic beads showed that the noise reduction in muffler with ceramic and glass beads is probably due to SAC in ceramic beads and noise reflections in glass beads, respectively. Conclusion These results promise the dual use of dielectric beads in the exhausts emitting noise and air pollution simultaneously.

Niloofar

Sulfur dioxide, a noxious air pollutant, can cause health and environmental effects, and its emissions should be controlled. Nonthermal plasma is one of the most effective technologies in this area. This study evaluated the efficiency of a packed-bed plasma reactor (PBR) and in-plasma catalysis (IPC) in SO2 removal process which were finally optimized and modeled by the use of the central composite design (CCD) approach. In this study, SO2 was diluted in zero air, and the NiCeMgAl catalyst was selected as the catalyst part of the IPC. The effect of three main factors and their interaction were studied. ANOVA results revealed that the best models for SO2 removal efficiency and energy yielding were the reduced cubic models. According to the results, both PBR and IPC reactors were significantly energy efficient compared with the nonpacked plasma reactor and had high SO2 removal efficiency which was at least twice larger than that of the nonpacked one. Based on the results, the efficiency of IPC was better than in PBR, but its performance decreased over time. However, the PBR had relatively high SO2 removal efficiency and energy efficiency compared to the nonpacked reactor, and its performance remained constant over the studied time. In optimization, the maximum SO2 removal efficiency and energy efficiency were 80.69% and 1.04 gr/kWh, respectively (at 1250 ppm, 2.5 L/min, and 18 kV as the optimum condition) obtained by the IPC system which were 1.5 and 1.24 times greater than PBR, respectively. Finally, the model’s predictions showed good agreement with the experiments.

Niloofar

Air pollution is a major health problem in developing countries and has adverse effects on human health and the environment. Non-thermal plasma is an effective air pollution treatment technology. In this research, the performance of a dielectric barrier discharge (DBD) plasma reactor packed with glass and ceramic pellets was evaluated in the removal of SO2 as a major air pollutant from air in ambient temperature. The response surface methodology was used to evaluate the effect of three key parameters (concentration of gas, gas flow rate, and voltage) as well as their simultaneous effects and interactions on the SO2 removal process. Reduced cubic models were derived to predict the SO2 removal efficiency (RE) and energy yield (EY). Analysis of variance results showed that the packed-bed reactors (PBRs) studied were more energy efficient and had a high SO2 RE which was at least four times more than that of the non-packed reactor. Moreover, the results showed that the performance of ceramic pellets was better than that of glass pellets in PBRs. This may be due to the porous surface of ceramic pellets which allows the formation of microdischarges in the fine cavities of a porous surface when placed in a plasma discharge zone. The maximum SO2 RE and EY were obtained at 94% and 0.81 g kWh−1, respectively under the optimal conditions of a concentration of gas of 750 ppm, a gas flow rate of 2 l min−1, and a voltage of 18 kV, which were achieved by the DBD plasma packed with ceramic pellets. Finally, the results of the model's predictions and the experiments showed good agreement.

Niloofar

In this intelligent transportation systems era, traffic congestion analysis in terms of vehicle detection followed by tracking their speed is gaining tremendous attention due to its complicated intrinsic ingredients. Specifically, in the existing literature, vehicle detection on highway roads are studied extensively while, to the best of our knowledge the identification and tracking of heavy-construction vehicles such as rollers are not yet fully explored. More specifically, heavy- construction vehicles such as road rollers, trenchers and bulldozers significantly aggravate the congestion in urban roads during peak hours because of their deadly slow movement rates accompanied by their occupation of majority of road portions. Due to these reasons, promising frameworks are very much important, which can identify the heavy-construction vehicles moving in urban traffic-prone roads so that appropriate congestion evaluation strategies can be adopted to monitor traffic situations. To solve these issues, this article proposes a new deep-learning based detection framework, which employs Single Shot Detector (SSD)-based object detection system consisting of CNNs. The experimental evaluations extensively carried out on three different datasets including the benchmark ones MIO-TCD localization dataset, clearly demonstrate the enhanced performance of the proposed detection framework in terms of confidence scores and time efficiency when compared to the existing techniques.

Sreelatha R

The need for renewable energy sources in addressing global energy demands is growing, especially in Nigeria where electricity demand often exceeds supply. Solar photovoltaic (PV) systems have become a viable solution, with federal universities in Nigeria, as major electricity consumers, recognizing their potential. However, determining the right size of PV systems for individual faculties within these universities is a complex task. This study attempted to simplify this process by introducing an innovative approach to size PV systems in these faculties. The research method used the Extended Kalman Artificial Neural Network (EKF-ANN) and the Crow Search Algorithm (CSA) to enhance the accuracy of PV system sizing. Data was collected on the study site, load demand, weather conditions, system components, and operational control and systems models to establish sizing criteria. The study focused on the optimal size of a solar PV system at the Faculty of Law building, University of Port-Harcourt, and how to improve its accuracy. The results showed that using global solar insolation parameters, EKF-ANN predicted values for global temperature, flock size, and maximal iteration. This optimized system could generate surplus power for effective grid supply. The study found that the optimal size of the series-connected panels for the Faculty of Law building was 96, 83, 73, and 65 units, with corresponding insolation values ranging from 3.737 to 4.368 kW/m2. It was concluded that the combination of CSA and EKF-ANN in solar PV sizing is suitable for achieving optimal outcomes for energy storage and grid supply. Nonetheless, the study recommended additional investigation into real-time and grid-connected solutions to enhance the proposed approach's effectiveness.

FXintegrity Publishing

Micro Gas Turbines (MGTs) are compact power generation systems that offer several advantages such as highpower density, low emissions, and fuel flexibility. They are commonly used in remote areas where grid connectivity is limited or unreliable. However, MGTs suffer from inherent instability issues due to their small size and high rotational speeds. These instabilities can lead to irregular speed responses, affecting the overall performance and reliability of the system. To address these concerns, the researchers utilized a genetic algorithm (GA)-based approach and conducted sensitivity studies to analyze the iteration parameter of the GA and its impact on the speed response of the MGTs. To evaluate the performance of the developed solution, they employed the Mean Step of Absolute Speed Error (MSASE) evaluation metric and compared the outcomes of the proposed strategy with a baseline Proportional Integral (PI)-only solution. The results demonstrated that the proposed solution surpassed the baseline approach by delivering a superior error response. Similarly, the findings suggested that the optimal iteration parameter setting for the GA was a maximum of 30 compared to 20 and 10 consequently lessening the settling time from 140s to 60s. Accordingly, the researchers concluded that optimizing the GA's iteration parameter could lead to enhanced stability in the speed response of the MGT units. Subsequently, this can bolster the power generation capacities of the units, highlighting the potential for enhanced efficiency and stability in MGT operations. As a final recommendation, the study advised practitioners working with MGTs to adopt the proposed GA-based speed control strategy to optimize the overall performance and reliability of these units.

FXintegrity Publishing

This study was conducted to proposea hierarchical temporal memory (HTM) approach for fault detection in the Onitsha-Alaoji transmission line in Nigeria. Using a mixed research method, the study employed the Hawkins HTM model with two objectives and their corresponding research questions. The study gathered primary and secondary data to detect and evaluate faults in the Onitsha-Alaoji transmission line in Nigeria using HTM and compares its efficacy to current fault detection methods. With the use of simulation and descriptive methods of data analysis, results showed that partial discharge (PD) is the fault type that is being detected and it is commonly found as a fault leading to transmission line errors. More so, fault detection simulations were conducted at 40 km using typical power spectral density analysis. The first fundamental shifted from about 10 kHz to roughly 13 kHz during a fault. The HTM model outperformed sequence learning methods, resulting in a 90% mean test classification accuracy (CA) over extreme learning machine(ELM) and online sequential learning–extreme learning machine (OS-ELM), with OS-ELM performing poorly.The study concluded and recommended that the proposed HTM model be used to identify various PD fault types that plague the Onitsha-Alaoji transmission line in Nigeria. With the increased efficacy and reliability of the proposed model compared to existing methods, it is recommended for future implementation in this transmission line and potentially other fault-prone power transmission lines in Nigeria.

FXintegrity Publishing

This study was conducted to optimize the integration of solar-photovoltaic-distributed energy resources (SPVDERs) within the Nigerian power system networks using an AI-based Particle Swarm Optimization (PSO) Algorithm. By employing a mixed research method, primary and secondary data were gathered to calculate flow analysis, NR method's equations, PSO's position update model, particle swarm optimizer algorithm, and application modeling including Solar-PV DER modeling. The AI-based PSO algorithm design was developed for optimizing SPV-DER integration in Nigerian power system networks, and key parameters and variables that needed consideration were identified. The study also established how the performance of the AI-based PSO algorithm could be evaluated and compared with other optimization techniques for SPV-DER integration within Nigerian power system networks. The study's results showed that voltage limits were within acceptable ranges, and solar power contributions were estimated at 880.10MW with 46,718 panels needed. The study concluded and recommended that investing in AI-powered tools for efficient power distribution; monitoring and resource optimization for sustainable energy sources would optimize performance and unleash Nigeria's sustainable energy potential.

FXintegrity Publishing

This study was conducted to develop and evaluate the Optimal Poly-1-Order (OP-1) model for approximating solar photovoltaic (PV) power generation. Using a mixed research method, the study employed Ibrahim’s simulation and prediction of grid-connected PV system theory with two objectives and their corresponding research questions. The study gathered primary and secondary data to approximate the implementation of a solar-PV system with an OP-1 model for generating electricity: optimizing energy production, load demands, and financial viability in the medical hostel facility of the University of Port Harcourt, Rivers State, Nigeria. With the use of simulation and descriptive methods of data analysis, results showed that the lighting system had 400 lights, each with 12W power. It operated for a total of 18 hours. Daily power consumption was 36,400 Wh. More so, it showed that 60 fans with 100W power were used during the same hours, resulting in a daily power usage of 108,000 Wh. Based on a comprehensive economic evaluation, the OP-1 solar-PV system was found to be economically viable for powering the medical hostel. The system met electricity demand, resulting in a remarkable 407% ROI and substantial savings for the grid, despite a lower optimized size of 193kW compared to the base peak generation of 383.90k. The study concluded and recommended that the proposed OP-1 Solar-PV power plant can meet the facility's electricity needs with a peak generation of 383.90kW and detailed energy analysis. Deploying this efficient solar-PV setup guarantees reliable and green electricity for the Medical Hostel, slashing the campus's carbon footprint and grid reliance.

FXintegrity Publishing

As the demand for renewable energy continues to rise, it becomes crucial to discover effective ways to enhance grid-connected photovoltaic (PV) battery energy storage systems. The Institute of Petroleum Studies (IPS) complex at the University of Port Harcourt in Rivers State, Nigeria, embarked on a quest to determine the optimal approach for optimizing their PV battery energy storage system. This research aimed to fulfill this need by employing a diverse research methodology, incorporating the innovative MOALO theory. To begin with, the research gathered primary and secondary data to construct models for the power grid, solarPV, and battery. Furthermore, it meticulously analyzed the load profile of the IPS complex, at the University of Port Harcourt. Leveraging the power of the MOALO theory.The researchers accurately sized the system and evaluated the potential outcomes of simultaneously interconnecting all loads. To gauge the system's performance, there was a calculation of various parameters such as economics, random walk, boundary conditioning, entrapping ants, and ant trap development. Remarkably, the outcome showed that the fitness responses between the two trial runs, facilitated by the integration of MOALO, were strikingly similar, revealing a typical concaveconnected shape, which is characteristic of a multi-objective solver. The optimal multi-objective cost implication of the system was estimated to be around 4,300 USD, with a power mismatch performance of approximately -1.7819e+09. Based on these compelling findings, the study concluded that MOALO serves as an impressive optimization tool capable of minimizing power mismatches and optimizing costs. Moreover, it recommended the generation of excess power as a means to achieve sustainability.

FXintegrity Publishing

The inclusion of hydroelectric power is crucial to Nigeria's overall energy mix, playing a significant role in electricity generation. However, the Shiroro hydro plant, one of the main facilities located on the Kaduna River, is currently facing operational obstacles due to deteriorating infrastructure and inadequate maintenance practices. To overcome these challenges and improve efficiency within Nigeria's hydroelectric power sector, a hybrid-optimization approach has been proposed. This study sought to enhance the efficiency of the Shiroro hydro plant by implementing this innovative method. To achieve our objectives and address pertinent research questions, a mixed research method combining primary and secondary data was employed. The analysis included hydropower modeling and hydro-turbine input-output modeling. Three optimizer models, namely the particle swarm optimizer (PSO), Ant colony optimizer (ACO), and Artificial bee colony optimizer (ABCO), were utilized to formulate objective functions and task representations. The study involved comparing the daily output and fitness response of the Shiroro hydro plant through swarm optimizer iterations. The findings revealed a clear correlation between the turbine's power output and the water flow rate and water column height, suggesting that altering these factors could significantly improve the plant's performance. The comparison of the PSO, ACO, and ABCO models demonstrated that PSO and ABCO generated optimal or near-optimal solutions, while ACO produced suboptimal results. Consequently, the study concluded that enhancing the Shiroro hydro plant's output was feasible by increasing the water flow rate and column height. Additionally, the utilization of PSO and ABCO models proved to be an effective means of accurately predicting the turbine's output. As a result, the study recommended the integration of hybrid optimization techniques to monitor and identify any deviations in the Shiroro hydro plant's daily power output. This approach would enable prompt maintenance to be carried out, preventing significant damage to the plant. Ultimately, this research contributes valuable insights into improving the efficiency and performance of Nigeria's Shiroro hydro plant.

FXintegrity Publishing

In these researched analyses of different forging product for controlling the forging waste in forging process. In forging process forging waste are carry with forging operation which identify by parting line of products. Parting which defines the product upper and lower die meshing area. Those extra material are used in forging operation which goes to out side of die that material is called as waste of forging. The foraging waste are not use for further any product manufacturing process for their hardness. There for such waste are to controlled for increasing the product utilization any minimising the production cost. In that paper we concentrate to minimizing waste of forging by amylases of different products.

Vijay jadhav

Freeze desalination (FD) is a method in which saline water is cooled below its freezing point and freshwater is separated from the brine in the form of ice crystals. FD is relatively insensitive to the salinity of the feed solution, making it suitable for desalination of high concentration brines such as the brine rejected from the seawater desalination plants. The design of the FD system and the thermochemical behavior of the brine upon freezing are critical factors in the energy performance of this method. To date, thermochemical properties of the concentrated seawater during cooling, such as the threshold of formation of ice and salt-hydrates and their corresponding cooling load of formation, are not well known. Likewise, the optimal configuration of the FD system to achieve the maximum energy efficiency has not been investigated. This work provides comprehensive data about the cooling load of freezing of concentrated brine rejected from seawater desalination plants along with the threshold of formation of ice and salt-hydrates backed-up by validation. Furthermore, the optimal configuration of the FD system is identified and the effects of the compressor isentropic efficiency and effectiveness of the system’s heat exchangers on the work consumption of the FD system were investigated.

Aly Elhefny