International Journal of Current Engineering and Technology
The International Journal of Current Engineering and Technology (IJCET) is a peer-reviewed, open access scholarly journal established in 2010 with the objective of promoting excellence in engineering research and technological innovation. The journal publishes original research articles, review papers and technical communications across all major disciplines of engineering and applied sciences, providing an international platform for academicians, researchers and industry professionals to share high-quality contributions. E-ISSN: 2277-4106 (Online) P-ISSN: 2347-5161 (Print) Frequency: Bimonthly: Jan/Feb, Mar/Apr, May/Jun, Jul/Aug, Sep/Oct, Nov/Dec Editorial Office: editor.ijcet@evegenis.org, editorial.ijcet@gmail.com All submissions undergo a rigorous double-blind peer review process to ensure originality, technical soundness and academic relevance. The journal encourages interdisciplinary research and aims to support the dissemination of innovative solutions addressing contemporary engineering challenges. Aim and Scope IJCET aims to promote cutting-edge research and industrial innovation in the field of engineering and technology. The journal covers, but is not limited to, the following areas: ■ Mechanical, Civil, Electrical and Electronics Engineering ■ Computer Science, Information Technology and Artificial Intelligence ■ Communication Systems, Signal Processing and Embedded Systems ■ Materials Science, Manufacturing and Industrial Engineering ■ Renewable Energy, Sustainable Technologies and Environmental Engineering ■ Robotics, Automation, Internet of Things (IoT) and Smart Systems Key Commitments ■ Global Reach: The journal maintains an international perspective, welcoming submissions and catering to an audience from all corners of the world. ■ Current Relevance: A strong emphasis is placed on research that addresses contemporary global issues, emerging trends, and recent developments across various disciplines. ■ Quality Assurance: All submitted manuscripts undergo a rigorous double-blind peer-review process to ensure the highest standards of academic validity, originality, and excellence.
An ML-Enabled Framework for Resilient Data Pipelines with Intelligent Anomaly Detection and Automated Recovery Mechanisms
The contemporary data-intensive computing systems demand the establishment of strong data pipelines that can be utilized in order to sustain the active continuous running of the system in case of the occurrence of runtime anomalies and system errors. In this paper, introduce an ML-based framework of resilient data pipelines that combines smart anomaly detection with automated recovery controls to allow self-healing data pipeline processes. The pipeline will be run on the Google Cluster Traces dataset, which is systematically preprocessed, including data cleaning, anomaly labeling, and min-max normalization to generate high-quality inputs for training the model. The suggested model utilizes a hybrid CNN-LSTM model, where former part of workload trace patterns is extracted by the CNN, and the latter part captures the temporal relationships between successive pipeline actions by the LSTM, allowing for accurate anomaly classification. As an experimental analysis of the Google Cluster Traces dataset shows, the proposed CNN-LSTM model achieves an accuracy (acc) of 95.45% and precision (prec), recall (rec), and F1-score (F1) of 93.65%, 96.34%, and 94.51%, respectively, which is superior to all the base models addressed in this work. The framework is intended to be deployed in cloud computing systems and has been shown to apply to distributed, data-intensive systems that require high availability and consistent service reliability. It is also extended to IoT and cyber-physical systems as a direction for future work.
Copyright (c) 2023 International Journal of Current Engineering and Technology
Crowd Monitoring by Video Surveillance System Through Real-Time Object Detection and Tracking Using AI Techniques
Real-time video surveillance is an essential component of the modern security system, particularly in the context of monitoring public areas, particularly in densely populated areas such as airports, train stations, and urban junctions. Occlusion, quick movement, and high item density are some of the issues that traditional surveillance systems face when accurately detecting and effectively monitoring objects. YOLO, which stands for "You Only Look Once," is a framework that includes DeepSORT, which stands for "Simple Online and Realtime Tracking with a Deep Association Metric," for effective object tracking. This framework is proposed in this study as a robust multi-object identification and tracking system. By utilizing appearance traits and Kalman filtering, YOLO guarantees that objects are localized and classified quickly and accurately, whereas DeepSORT ensures that identification is maintained consistently between frames. Under various lighting and crowd density situations, the combined model was implemented and tested on video feeds captured from the real world. Given that the system can handle occlusions and re-identify lost targets with high accuracy, low latency, and resilience, as demonstrated by the results of the experiments, it is a feasible option for applications that include intelligent surveillance. When applied in dynamic and complex public contexts, this technique dramatically improves situational awareness and assists proactive security management.
Multi Object, Detection, Model, YOLO, Video
Copyright (c) 2026 International Journal of Current Engineering and Technology
Characterisation of Ginger Drying Practices In Humid Tropical Regions and Proposal for Suitable Equipment
The main objective of this study was to propose suitable equipment for drying in humid tropical regions. Following the design and distribution of two online survey forms via Google Forms one for producers and the other for consumers a total of 547 completed forms were collected. The main findings showed that, in terms of growing areas, all ten regions of Cameroon are suitable for growing this plant, with the Littoral Region leading the way (15.70%), followed by the Central Region (14.40%), the West Region (13.10%), the North-West Region (11.50%) and the Adamaoua Region (10.20%). It also emerged that three main varieties are cultivated: white ginger (58.15%), black ginger (28.43%) and turmeric (11.82%). With regard to preservation methods, indirect solar drying was found to be the most widely used technique (67.40%) and the most recommended (96.50%), ahead of traditional drying. Furthermore, we have designed a PV/T solar dryer for fruit and vegetables. The design was carried out using the functional analysis method, which enabled us to identify the problem and define the functions to be fulfilled. The technological solutions were derived from the functional analysis of requirements and resulted in a hybrid PV/T solar dryer. The proposed dryer consists of two units: a drying chamber with a volume of 0.35 m³, in which air circulation is provided by two (02) continuous fans operating at 1.2 m/s, and a PV/T collector comprising a 150 Wp solar PV panel, coupled with climate data from Ngaoundéré.
Drying, Ginger, Characterisation, Humid Tropical Zone, Adamaoua, PV/T dryer
RE-FusionNet: A Resource-Efficient Multi-Task Network for Joint Traffic Element Detection and State Recognition
Autonomous driving is dependent upon reliable perception of traffic elements including traffic lights and traffic signs in order to ensure both safety and efficiency in making decisions. However, latest computer vision-based approaches treat object detection and semantic state recognition as either separate tasks, or they utilize expensive, hardware-dependent sensor technology to continuously feed in video data that can greatly increase the processing cost and limit deployments in resource-limited environments. This problem has been addressed through the development of Re-FusionNet; a lightweight, attention-enhanced convolutional architecture designed to perform both joint traffic light and traffic sign detection along with semantic state recognition utilizing only RGB image data. The Re-FusionNet utilizes a dual-path fusion encoder (DPFE), differential attention module (DAM), along with a compact convolutional backbone to enable contextual feature representation to be enhanced and to focus on relevant traffic-related objects. In addition to its ability to process images sequentially by only sampling a few frames from a video stream rather than continuously feeding it into the network, it enables efficient capture of contextual information. Finally, all three tasks are performed simultaneously via a single multi-task prediction head that predicts both object location and state, allowing for joint training. Results demonstrate that the Re-FusionNet achieves an average precision (mAP) of 97.3% @ 0.5 IoU on the BDD100K dataset while achieving a state recognition accuracy of 90.0%, and an mAP of 94.2% @ 0.5 IoU on the LISA dataset. Moreover, due to the low computational requirements, it can achieve frame rates above 150 fps. Overall, results clearly show that the Re-FusionNet represents an efficient and deployable method for real-time traffic perception applications within autonomous vehicles and intelligent transportation systems.
Attention-Based Perception, Autonomous Driving, Traffic Light Detection, Traffic Sign Detection, Multi-Task Learning
Smart Parking Lot Helper: A Technological Advancement in Parking Management
With the increasing number of vehicles on the road, parking management has become a significant challenge in urban areas. The Smart Parking Lot Helper aims to optimize parking space utilization using IoT and AI technologies. This paper explores sensor-based detection, mobile application integration, automated fee deduction systems, and the overall benefits of a smart parking system. We analyse existing research in smart parking solutions, present a feasible implementation plan, and discuss the potential impacts on smart cities.
AI, IoT, Parking Management, Sensor-Based Detection, Smart Parking System
Review of Query Optimization Techniques in Database Management Systems
In database management systems (DBMSs), query optimization is a frequent practice. Its primary goal is to maximize query execution efficiency among many possibilities. In recent years, the world of data-driven applications has grown rapidly, and effective query processing is an essential element of high-performance systems and efficient resource use in cloud computing environments. In this paper, the authors review key query optimization strategies used in state-of-the-art DBMSs. These include heuristic-based optimization, cost-based optimization, semantic-based optimization for both join and indexing strategies and sub-query rewriting methods. The study contextually explores new AI/ML-based optimization techniques that contribute towards enhancing adaptive query execution and automated tuning in the dynamic environment. Apart from this, the paper also discusses the role of query execution plans, cost models and optimization frameworks and how they can improve the performance of databases in enterprise systems, databases in a distributed environment of the cloud and big data analytics platforms. Using a systematic literature review of recent relevant studies, researchers identify current progress and challenges in query optimization alongside possible future research directions. The paper shows that in the context of many complex (possibly correlated) workloads, modern database systems need some degree of intelligent/elastic behaviour to operate with bounded latency and resource consumption.
Database Management Systems (DBMS), Query Optimization, Query Processing, Distributed Databases, Big Data Analytics, Cloud Databases
Review of Artificial Intelligence Driven Digital Transformation Strategies in Large Enterprises
Digital transformation and Artificial Intelligence (AI) have become important sources of innovation and competitive edge in the contemporary business. In a more dynamic and technology-intensive corporate world, companies are implementing the use of more sophisticated digital solutions to increase efficiency, better decision-making, and provide superior customer experiences. This paper discusses how AI-based digital transformation can be applied in large enterprises with respect to its conceptual framework, enabling technologies, and strategic approaches. It brings out major attributes of digital transformation, such as its technology-based, data-driven, customer-oriented, and agile nature, and new practices as digital strategy, organizational change management, and data governance. The paper also discusses the fundamental AI technologies, such as Machine Learning, Natural Language Processing, Robotic Process Automation, and Computer Vision, which facilitate intelligent automation and data-driven innovation. Also, it addresses strategic growth strategies that are embraced by large firms, including entering the new market, product development, market expansion, partnership, and mergers and acquisition. The research also identifies key procedures towards successful implementation such as market research, objective setting, performance checking and flexibility. In general, the article reinforces the idea that the successful AI-attracted transformation involves the balanced combination of technology, strategy, and organizational preparedness to attain sustainable growth and long-term business success.
Digital Transformation, Large Enterprises, Artificial Intelligence, Strategic Approaches, Market Penetration, Transformation Frameworks
Microservices Architecture: Principles, Design Patterns, and Implementation Challenges
Microservices architecture has become a prevailing pattern for creating scalable, robust, and cloud-native software applications to overcome the constraints of traditional monolithic applications. Microservices enable independent development and implementation, and scaling by separating applications into small, self-managed, business-ready services, thereby improving agility, fault isolation, and service maintainability. This paper has given a detailed description of the microservices architecture, its fundamental concepts, and the primary design archetypes. It discusses the fundamental concepts of service decomposition, loose coupling and high cohesion and outlines their significance in minimizing the complexity of a system and enabling continuous delivery. Most popular architectural patterns, including API Gateway, Service Discovery, and hybrid approaches, and their pros and cons, as well as their application in the real-life distributed environment, are also discussed in the paper. Moreover, the paper explains the challenges of microservices for cloud-native development, using containers and orchestration platforms. The paper also considers emerging trends in optimizing resources through AI-based autoscaling and intelligent resource management. This review is helpful for researchers and practitioners aiming to design, deploy, and manage efficient microservices-based systems and to identify future research trends.
Microservices architecture, cloud-native systems, service decomposition, API gateway, service discovery, AI-driven resource management
A Comprehensive Survey of Database Administration and Governance in Salesforce Platforms
Salesforce, as a leading cloud-based Customer Relationship Management (CRM) platform, plays a critical role in enabling digital transformation, scalable data management, and intelligent business operations. This paper presents a comprehensive analysis of Salesforce platform architecture, database administration, and data governance mechanisms. It explores the Salesforce ecosystem, including its multi-tenant architecture, data storage strategies, and metadata-driven framework that support secure, flexible, and efficient data environments. The study further examines key database administration aspects such as data modeling, lifecycle management, and performance optimization to enhance scalability and operational efficiency. Additionally, it highlights Salesforce security and governance features, including identity management, access control, encryption, and monitoring, which ensure data protection and regulatory compliance. A detailed literature review emphasizes recent advancements in Salesforce-based systems, particularly in multi-cloud integration, interoperability, and AI-driven governance. The findings indicate that Salesforce significantly improves data security, system performance, and decision-making capabilities. However, challenges such as integration complexity and lack of standardized governance frameworks still require further research and development.
Salesforce, Cloud CRM, Data Governance, Multi-Cloud Integration, Database Administration, Data Security, Scalability
Comparative Study on Air Pollution Prediction using Machine Learning Techniques
The Air Quality Index (AQI) measures how air pollution affects human health. As pollution levels rise in Indian cities, we need reliable prediction models for better environmental management. This paper analyses different machine learning techniques for predicting AQI. We use Support Vector Regression (SVR), Random Forest Regression (RFR), and CatBoost Regression (CR) on data from New Delhi, Bangalore, Kolkata, and Hyderabad. We evaluate model performance using Root Mean Square Error (RMSE) and accuracy. Experimental results show that RFR performs best in most cities, while CR is most effective in New Delhi. To tackle dataset imbalance, we use the Synthetic Minority Oversampling Technique (SMOTE), which improves prediction accuracy for all models. Additionally, we assess other models, including SARIMA, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) for Ahmedabad. Among these, SVM with a radial basis function (RBF) kernel shows the best results. The findings emphasize how combining data balancing methods with machine learning models can improve AQI prediction. This approach can help with pollution control strategies and better decision-making.
Air Pollution Prediction, Machine Learning, Deep Learning, NF-VAE, IoT, Time-Series Forecasting, Multivariate Data, Smart Cities
Safety-by-Design Approach for Preventing Falls and Formwork Collapse in Construction
Construction sites remain highly prone to accidents, with falls from heights and formwork collapses being main causes of fatalities worldwide. Traditional safety practices emphasize worker training and hazard mitigation; however, the Safety-by-Design (SbD) approach shifts the focus toward eliminating risks at the design stage itself. This study explores the role of SbD in addressing fall hazards and formwork failures through a comprehensive review of existing literature. Evidence indicates that a significant proportion of construction accidents are linked to design-related factors, highlighting the critical responsibility of designers in improving safety outcomes. SbD promotes the integration of safety considerations into planning, material selection, and structural design, including provisions such as guardrails, scaffolding systems, and stable formwork structures. The approach not only reduces accidents and associated costs but also enhances productivity and fosters a proactive safety culture. Despite its potential, challenges remain in implementation, particularly in developing countries, necessitating further research and strategic adoption frameworks.
Safety-by-Design, Construction Safety, Falls from Heights, Formwork Collapse, Design Risk Management
Implementing AI-Driven Test Automation Frameworks in Enterprise Digital Transformation Projects
The growing Complexity of the enterprise applications and the speed at which the digital transformation is taking place has rendered the traditional testing methods ineffectual, and this has necessitated intelligent automation solutions. The significance of this study is that currently organizations rely on AI-based tools to guarantee the quality of software, lessen the number of manual data flows, and facilitate constant delivery. This paper discusses the application of AI-based test automation in efficiency and scalability of enterprise systems. The use of artificial intelligence (AI) and machine learning (ML) is demonstrated to reduce the maintenance issue and enhance the detectability of defects and script stability. The main methods of test optimization, automatic test generation, and intelligent prioritization are discussed to show how they can simplify the work of testing and enable faster releases. The combination of AI and CI/CD pipelines also allows the processes of constant validation, fast deployment, and automatic failure analysis. The paper also identifies such challenges as prioritization, issues of collaboration, cost of maintenance, and fragmentation of devices. All in all, the results of the research point to the idea that AI-based test automation is a strategic enabler of enterprise agility, operational resilience, and sustainable digital development.
Enterprises, Digital Transformation, Test Automation, Artificial Intelligence (AI), Machine Learning (ML).
Clustering and Classification Techniques in Machine Learning-based Intrusion Detection Systems: A Review
The rapid proliferation of networked digital systems has substantially expanded the threat landscape for cyber intrusions, necessitating robust and adaptive security mechanisms. Intrusion Detection Systems (IDS) constitute a critical component of network security infrastructure by identifying unauthorized access attempts and malicious behavior in real time. Traditional signature-based detection techniques, while effective against known threats, exhibit notable limitations in identifying novel and polymorphic attack patterns. This limitation has driven extensive research into machine learning (ML)-based IDS frameworks that leverage statistical and structural properties of network traffic for automated threat detection. Among ML paradigms, clustering and classification methods have emerged as particularly effective strategies. Classification algorithms employ supervised learning to discriminate between normal and attack traffic based on labeled training data, while clustering techniques utilize unsupervised learning to discover anomalous groupings in unlabeled network data. This paper presents a systematic and comprehensive review of clustering and classification techniques applied in ML-based IDS, examining foundational algorithms including Logistic Regression, K-Nearest Neighbor, Decision Tree, Random Forest, Support Vector Machine, K-Means, and Hierarchical Clustering. The study further analyzes widely adopted benchmark datasets, evaluates performance metrics, and discusses current research challenges. Identified gaps include the handling of high-dimensional feature spaces, computational constraints in IoT environments, and the development of lightweight hybrid detection models. Directions for future research are outlined to guide the design of more intelligent, scalable, and efficient intrusion detection frameworks.
A Review of Environmental Sustainability in Piping System Design and Material Selection
Piping systems are vital infrastructure in the industry, and the classic design methods are oriented on the issues of safety and reliability and pay insignificant attention to the lifecycle environmental impact. This paper is a critical analysis of the concept of environmental sustainability in piping systems. It will analyze the engineering principles of sustainability, material selection, and design solutions. The paper addresses the failure of traditional piping regulations, such as ASME B31, to address the issue of energy usage, greenhouse gas production, and ecological disruption. The concept of life cycle assessment (LCA) is brought up as one of the methods to estimate the environmental effectiveness of the piping materials and the installation practices over the course of their lifecycle. Recycled metals, bio-based materials and smart materials are discussed as new materials that could reduce environmental footprints and enhance longevity of the systems. Moreover, the paper also addresses digital technologies such as BIM, digital twins and monitoring through IoT as a solution towards the improvement of energy efficiency and sustainable decisions. It highlights the importance of combined, data-driven solutions in order to build resilient and environmentally friendly piping solutions.
Environmental sustainability, Piping systems, Sustainable material selection, Life cycle assessment (LCA), Energy-efficient design, Industrial infrastructure
Healthcare Predictive Analytics based on Machine Learning Techniques for Identifying Cardiovascular Risks Screening
Cardiovascular diseases (CVDs) can be considered a severe concern to the universal health that affects the mortality rates. Clinical decision-making and early diagnosis can be challenged with the help of intelligent systems, which are based on data-driven models. The suggested solution would utilize the DenseNet-based deep learning model in making accurate predictions of heart disease using UCI Cleveland dataset. The data undergoes pre-processing with systematic data treatment of missing values, removal of duplicates, feature selection via correlation and standardization. In terms of predicting cardiac disease, the proposed model performs exceptionally well, with an accuracy of 99.65, precision of 98.38, recall of 98.77, and F1-score of 97.96. To provide comparative analysis, conventional machine learning classifiers such as KNN, MLP and Logistic Regression. The effectiveness model as demonstrated by the comprehensive performance gains indicate a high level of capturing of complex clinical patterns, which is effective in screening early cardiovascular risks in healthcare predictive analytics and is able to maintain model interpretability, hence making it a promising solution in early and automated heart disease detection in clinical environment.
Cardiovascular prediction, Heart disease UCI Cleveland dataset, Machine learning, DenseNet, Deep learning artificial intelligence.
Fake News Generation and Detection: Adversarial use of Generative AI in Text Synthesis
The extensive growth of online media platforms, as well as the extensive use of generative artificial intelligence, has made the production and distribution of fake news particularly more active. Models of languages and vision have now been advanced to create a high level of reality in text, images and videos that become even harder to detect any misinformation by means of the old standard verification channels. The paper is a detailed analysis of fake news and its main premises, the core issues, and the role of generative AI in the development and detection of fake news that keeps changing. The paper examines uni-modal deepfake detection models in text, image and video modalities, and cross-modal consistency analysis, which utilizes semantic consistency between heterogeneous data. In addition, the study contrasts traditional machine learning approaches with more modern ones, detailing the benefits and downsides of each, as well as their suitability for large-scale and multilingual tasks. A number of popular benchmark datasets are presented to give an idea of the experimental evaluation practice. Additionally, stability metrics such as recall (REC), accuracy (ACC), precision (PRE), F1-score (F1), and ROC-AUC are assessed to ascertain the efficacy of detection. Find that deep learning models are more generalizable and perform better overall, but they are still vulnerable to adversarial manipulation and new generative methods.
Fake News Detection, Generative Artificial Intelligence, Deep Learning, Natural Language Processing, Deepfake Detection
Artificial Intelligence-Driven Prediction of Hotel Booking Demand for Revenue Optimization
The rising competitiveness in the hospitality industry requires proper and smart forecasting models to maximize the hotel booking demand and revenue management plans. The proposed study is a hybrid CNN-Bi-LSTM model to predict the canceling of hotel bookings based on the Kaggle hotel booking demand dataset. An organized preprocessing system adopted, such as the missing value imputation, the treatment of ADR outliers, the encoding of labels, feature engineering, correlation-based feature importance analysis and, to enhance the model's dependability and data quality, the class balancing performed using SMOTE. The architecture proposed combines Convolutional Neural Networks (CNN) to obtain meaningful feature representations and Bidirectional Long Short-Term Memory (Bi-LSTM) networks to learn temporal patterns of booking. The applied approach has proven to be effective based on the results of performance evaluation with the help of confusion matrix-based measures 98.95% accuracy (acc), 98.82% precision (prec), 99.91% recall (rec), and 98.90% F1-score (F1). A comparative analysis indicates that the hybrid CNN-Bi-LSTM is much better than the classical models, which include Decision Tree, Random Forest, and standalone XGBoost. The results affirm the soundness, generalization and applicability of the suggested framework regarding the intelligent hotel booking demand forecasting and decision-making
Hotel Industry, CNN-Bi-LSTM, Revenue Management, Demand Predictions, Hospitality Sector
Typology of Housing and Household Energy Behaviors in Cameroon: Diagnosis for better Energy Efficiency
This research analyzes the determinants of domestic energy efficiency in Cameroon through a quantitative study of 333 households in Douala, Yaoundé, and Ngaoundéré. Using Chi-square χ^2independence tests, the study explores correlations between sociodemographic factors, housing characteristics, and installed energy capacity. Key Results include: Material Inertia: A strong geographical dependence exists between cities and construction materials p < 0.001. Adobe bricks with cement mortar, dominant in Ngaoundéré, offer under-exploited thermal efficiency potential compared to the cement-dominated metropolitan areas of Douala and Yaoundé. Generational Transition: Refrigeration equipment follows a life cycle, with a critical acquisition phase between ages 25 and 35, identifying this as the pivot segment for introducing high-performance technologies. The Performance Paradox: Analysis demonstrates that physical size and equipment type are not reliable indicators of installed power. Only the Energy Class (A+, A++) allows for a real and significant reduction in power demand < 50 kW. Information Deficit: A major obstacle was identified: 69% of users are unaware of their appliance's energy class, a technical ignorance that increases with the surface area of the dwelling.
Habitats, Behaviors, Households; Diagnosis, Energy Efficiency
Typologie of housing and household Energy behaviors in Cameroon : diagnosis for better Energy efficiency.
This research analyzes the determinants of domestic energy efficiency in Cameroon through a quantitative study of 333 households in Douala, Yaoundé, and Ngaoundéré. Using Chi-square independence tests, the study explores correlations between sociodemographic factors, housing characteristics, and installed energy capacity. Key Results include:Material Inertia: A strong geographical dependence exists between cities and construction materials p < 0.001. Adobe bricks with cement mortar, dominant in Ngaoundéré, offer under-exploited thermal efficiency potential compared to the cement-dominated metropolitan areas of Douala and Yaoundé.Generational Transition: Refrigeration equipment follows a life cycle, with a critical acquisition phase between ages 25 and 35, identifying this as the pivot segment for introducing high-performance technologies.The Performance Paradox: Analysis demonstrates that physical size and equipment type are not reliable indicators of installed power. Only the Energy Class (A+, A++) allows for a real and significant reduction in power demand < 50 kW.Information Deficit: A major obstacle was identified: 69% of users are unaware of their appliance's energy class, a technical ignorance that increases with the surface area of the dwelling.<!-- [if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f"> <v:stroke joinstyle="miter"/> <v:formulas> <v:f eqn="if lineDrawn pixelLineWidth 0"/> <v:f eqn="sum @0 1 0"/> <v:f eqn="sum 0 0 @1"/> <v:f eqn="prod @2 1 2"/> <v:f eqn="prod @3 21600 pixelWidth"/> <v:f eqn="prod @3 21600 pixelHeight"/> <v:f eqn="sum @0 0 1"/> <v:f eqn="prod @6 1 2"/> <v:f eqn="prod @7 21600 pixelWidth"/> <v:f eqn="sum @8 21600 0"/> <v:f eqn="prod @7 21600 pixelHeight"/> <v:f eqn="sum @10 21600 0"/> </v:formulas> <v:path o:extrusionok="f" gradientshapeok="t" o:connecttype="rect"/> <o:lock v:ext="edit" aspectratio="t"/> </v:shapetype><v:shape id="_x0000_i1025" type="#_x0000_t75" style='width:15.75pt; height:18pt' o:ole=""> <v:imagedata src="file:///C:\Users\SIMOK~1\AppData\Local\Temp\msohtmlclip1\01\clip_image001.wmz" o:title=""/> </v:shape><![endif]--> <!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1025" DrawAspect="Content" ObjectID="_1834224128"> </o:OLEObject> </xml><![endif]-->
Habitats; Behaviors; Households; Diagnosis; Energy Efficiency
Design and Modification of Thermoelectric Tube using Different Materials
By using thermoelectric generators it is possible to develop independent electric energy source in burning and heating systems – in households and industrial heating. With this energy source it is possible to provide with electricity systems appliances, lightening and other consumers. Potential placements for generators are on furnace walls, on flue way walls, on flue pipe walls after furnace. Generators can improve efficiency of combustion, because there is amount of waste heat that leaves through flue pipe in burning process. There are no boundaries for combining elements in system as just the space for their placement. As more elements are linked in chain, as much electric energy is produced. This is one of methods to develop a cogeneration process in domestic boiler house or industrial heating, or burning systems. In research thermoelectric element current, voltage, power and efficiency are defined in different temperature differences.
Water as a Refrigerant for Heat Pump Technology: A Review
Even though water (R718) is one of the oldest refrigerants, it is very difficult to use it in compression refrigeration cycle due to its low molecular mass, high boiling point, low specific cooling capacity and a relatively higher value of kinematic viscosity. This paper gives us a glimpse about one of the technique which can be used to use water in compression cycle and also compares its theoretical analysis with the conventional refrigerants already being used. This paper can study the effect on various parameters under similar operation conditions for the comparative study of water and other refrigerants.
Cold Testing of IC Engines using Solar Energy
Cold testing of IC engines is becoming essential with the growing demand for environment friendly testing processes. This paper explains the processes of hot and cold testing and gives an example of sample calculations needed to energise a test bed using solar energy.
Mathematical Analysis for Small-Scale Osmotic Power Generation
According to the ‘Guardian’ the rise in the carcinogens and other harmful compounds are on rise since 1971. The increase have been noted to be 2% per year. United States contribute to 10% increase between 1999 and 2011. Harnessing energy from various renewable sources is the need of the hour and this paper puts forward a method which is in research stage at Statkraft*. The paper gives a mathematical investigation for it’s use for small scale applications and the methodology is further generalized. The results obtained can be used for respective applications as they are largely pertinent.
The paper aims in improvisation of the grinding process for elastic materials like rubber, plastic, composites, metals, waxes etc. Nowadays, we find a lot of wastage of these materials. Some of them like plastic, artificial rubber composites are very much harmful for our environment. This research will basically help in cautious use of the above mentioned pollutants. For example, Thermoplastics are difficult to grind small sized particle at ambient temperature because they are soften adhere in lumpy masses and clog screens. In cryogenic grinding when thermoplastic is chilled by dry ice, liquid carbon dioxide or liquid nitrogen they can be finally grounded to powder suitable for electrostatic spraying and other powder processes. Advantages of these processes are to increase productivity through optimized particle size, elimination of caking product within the mill, increases protection from the fire and product oxidation due to inert milling atmosphere..
Cryogenic system for High Temperature Superconducting Power Cable-A Review
Materials which shows superconductivity above liquid nitrogen temperature are known as high temperature superconductors. J.G. Bednorz and K.A. Mueller were awarded noble prize for discovering high temperature superconductors in 1987. Since last 3 decades there has been significant augmentation in the field of superconductivity. The primary advantage of HTS power cable over conventional cable is the ability to carry more power per circuit with lower energy cost per megawatt delivered. Power transmission is loosely defined as the transfer of electrical energy from source to a load over conductors that carry relatively large currents, while being maintained at large voltage. In order to achieve liquid nitrogen temperature, efficient cryogenic system is required. Hence, it is integrated with HTS power cables. Several HTS cables have been designed, developed and operated in various parts around the world. Most of them are used for experimentation purpose. Very few of them are used in electrical grids. Use of high temperature superconductivity in electrical grids will have a major positive impact on power system operation and control. HTS power cable is placed into heat-insulated pipe which is filled with cryogen. Circulation of cryogen removes heat seeping through the insulation. HTS power cables with different design considerations can be used for transmission of AC and DC.
Review on Effective Utilization of Electric Energy for Water Heating by Water Changing Property
Electricity is major parameter in our life which having wide application. By survey we use maximum electric energy for comfort life. In during various application of domestic we use hot water. To heat a water use various equipment which based on electric energy. In this experiment pH of water will be changes by using natural resources by observe what will be effect on heating purpose. Energy important and major source use in industries as well as Domestic purposes. This project will give one idea about how will improve efficiency and to save energy of heating coil which are used in industry and domestic for to heat water. We know that now days we concentrated on to saving energy because one day all natural energy sources will be end. So now we concentrate on saving energy in any field and to utilize energy so it span time will increase. In America they are used shell gases which are new technique but one day it will be vanish. If we survey in world there are so many energy required to produce electricity and every person used electric energy so we need highest energy efficiency equipment. In boiler there is convention and non -convention method also some time we used coal but with considering time is money so they change input from coal to electric energy. This experiment we get best utilization of nature resource for to save energy. There will be getting best method to reduce electric energy so whenever coil or electric furnace boiler used there will be water treated process will conduct. There is hypothesis is if change in pH of water due to that electricity required to heat water is also vary.
Review on Heat Transfer Enhancement of a Plate Fin Type Heat Exchanger by Different Shapes of Fins
Heat transfer enhancement techniques as passive, active or a combination of passive and active methods are commonly used in areas such as process industries, heating and cooling in evaporators, thermal power plants, air- conditioning equipment, refrigerators, radiators for space vehicles, automobiles (in tracked vehicle it plays very important roll), etc. Passive techniques, where inserts, extended surfaces like fins of various shapes and sizes are used in the flow passage to enhance the heat transfer rate, are benefited compared with active techniques, because the insert manufacturing process is easier and it can be easily applied in an existing heat exchanger. In design of compact heat exchangers, passive techniques can play an important role if a proper passive configuration/combinations can be selected according to the heat exchanger working condition (both flow and heat transfer conditions). Ribs, fins, dimples, Vortices etc., are the most commonly passive heat transfer enhancement devices. In this paper, major emphasis is given to work with different shapes of fins, and their arrangement because, these are found effective heat transfer enhancement tools.
Review on Optimization of Tilt Angle for Solar CSP for effective steam generation
Ample of Solar power is available in universe . It can be used for various applications . One of them is steam generation . Parabolic shaped dishes Concentrating Solar Power (CSP) technology can be found one of the most important option for generating renewable electricity at the required scale .The paper presented here , we have tried to do the optimization of tilt angle of Solar concentrators by using Taguchi Method . This is a statistical approach to optimize the variable process parameters and improve the quality of concerned components that are manufactured. The objective of this study is to illustrate the procedure adopted in using Taguchi Method to find best tilt angle for CSP . First, a Taguchi experiment was used to perform an efficient experimental design and analyzed the robustness of the tilt angles for fixed south-facing PV modules. The objective is to maximize the steam generated from the modules .
Solar driers applied for drying of agricultural and foodproductswitha retrospective study
Solar drying has several advantages over the traditional drying method and it has been now modernized and developed for drying various agricultural and food products. Solar energy drying which is proven to be an environment friendly and economically viable in the developing countries. The working condition with respective to design of various solar driers with thermal drying factors has been reviewed.
Performance investigation of centrifugal pump by varying blade angles of the Impeller–A Review
Centrifugal pumps are probably among the most often used machinery in industrial facilities as well as in common life. Centrifugal pump is widely used as a universal machine, so improving hydrodynamic performances of the centrifugal pump is important for energy saving. With the development of computational fluid dynamics (CFD), numerical simulation has become the main method for performance prediction and structure design of the pump. The impeller is the most important part in a centrifugal pump since it is the place where the mechanical energy is converted into hydraulic energy. Hence the parameters related to the impeller are directly affecting the performance of the pump. One of them is the blade exit angle; if it is not correctly designed, it can negatively affect the head and the hydraulic efficiency of the pump. The previous research suggests that a larger blade angle is suitable. Also, exit blades angle variation contributes more in the performance.