School of Engineering & Technology

Permanent URI for this collection

http://10.10.25.38:4000/handle/123456789/659

Browse

Now showing 1 - 20 of 99

FSL-TM: Review onthe Integration of Federated Split Learning with TinyML in the Internet of Vehicles
(2026) Goyal, Nitin
The Internet of Vehicles, or IoV, is expected to lessen pollution, ease traffic, and increase road safety. IoVentities’ interconnectedness, however, raises the possibility of cyberattacks, which can have detrimental effects. IoV systems typically send massive volumes of raw data to central servers, which may raise privacy issues. Additionally, model training on IoV devices with limited resources normally leads to slower training times and reduced service quality. We discuss a privacy-preserving Federated Split Learning with Tiny Machine Learning (TinyML) approach, which operates on IoV edge devices without sharing sensitive raw data. Specifically, we focus on integrating split learning (SL) with federated learning (FL) and TinyML models. FL is a decentralised machine learning (ML) technique that enables numerous edge devices to train a standard model while retaining data locally collectively. The article intends to thoroughly discuss the architecture and challenges associated with the increasing prevalence of SL in the IoV domain, coupled with FL and TinyML. Theapproachstarts with the IoV learning framework, which includes edge computing,FL,SL,andTinyML,andthenproceedstodiscusshowthesetechnologiesmightbeintegrated.Weelucidate thecomprehensiveoperationalprinciplesofFederatedandsplitlearningbyexaminingandaddressingmanychallenges. We subsequently examine the integration of SL with FL and various applications of TinyML. Finally, exploring the potential integration of FL and SL with TinyML in the IoV domain is referred to as FSL-TM. It is a superior method for preserving privacy as it conducts model training on individual devices or edge nodes, thereby obviating the necessity for centralised data aggregation, which presents considerable privacy threats. The insights provided aim to help both researchers and practitioners understand the complicated terrain of FL and SL, hence facilitating advancement in this swiftly progressing domain.
Deep Learning for Monitoring and Managing Oil Spill in Underwater Sensor Networks: Enabling Technologies
(2026) Goyal, Nitin
Environmental problems can significantly impact people’s lives and must be addressed. The oil spill is one of the biggest applications of underwater wireless sensor networks (UWSNs) that threaten aquatic life, so it is essential to focus on it. Oil spill count has risen in recent years because of the rise in shipping and marine transportation industries. Timely and accurate detection of oil spills can improve the response process and get the necessary resources to the affected areas more efficiently. This work was motivated by the fact that oil spill detection is critical to maritime protection. Since oil spill detection is a complex process, it faces various challenges. One of the biggest challenges is similar visual appearance which makes it difficult to identify an oil spill and a similar oil slick in synthetic aperture radar (SAR) imagery. Various radar images are frequently utilized for this purpose. Despite being widely used for earth observation, SAR images have noisy and illegible image quality, which makes classification challenging. Previously, detecting and classifying SAR images required manual involvement, which made the process time consuming. Therefore, researchers focused on automating such tasks by incorporating deep learning (DL) techniques. Numerous papers proposed the applications of DL in various radar images for oil spill monitoring but faced multiple problems. This study aims to thoroughly investigate oil spills and their detection techniques, utilizing DL techniques applied to various radar images. These studies have originated in diverse nations with distinct environments. However, compared to other types of radar images, synthetic aperture radar (SAR) images are more effective in pinpointing the location of oil spills. However, they are also rather complex.
Current trends in microplastic removal using biodegradation approaches and advancement A review
(2026) Kumar, Dhushyant
Due to improper disposal and mismanagement of plastic waste, plastic converted into microplastic when exposed to the environment causes tremendous burdens to nature. It becomes the most persistent pollutant in air, water, and land environments due to the inert property of plastic. It enters the environment from various sources, such as industrial, household, and agricultural waste, affecting the lives of humans, other living beings, and the entire ecosystem. Much research and experimentation have been conducted to eliminate microplastics from the envi ronment with the help of conventional methods, such as physical, chemical, and biological, which remove microplastics but are inefficient in eliminating them from the environment. Some processes are good for nature and the environment, as they can cause secondary pollution. Therefore, there is a need for some scientifically proven new methods to achieve the required level of results and be environmentally friendly. Bioremediation is a sound technique used to degrade plastic with the help of bacteria, fungi, algae, and insects. However, some studies reveal that the pretreatment (UV, thermal, chemical, and physical) can change the inert property of plastic, making it more available to the microbes and increasing the biodegradation process without affecting the microbes’ life. Such a treatment gives hope for removing plastic from the environment in an efficient way.
Unveiling the exceptional photocatalytic activity of SmO/g-C3N4 nanocomposites for dye industries wastewater treatment
(2025) Sharma, Anshu
On a global scale, wastewater dye pollution has become a major environmental issue. Photocatalysis is a well- known and promising technology for removing organic pollutants, such as dyes, from wastewater. In this work, SmO/g-C3N4 nanocomposites were fabricated and used as a photocatalyst for the degradation of anionic dyes, cationic dyes, and mix dyes due to their high specific surface area and outstanding photocatalytic capa bilities. Characterization of the synthesized nanocomposites included Fourier-transform infrared (FTIR), Scan ning electron microscopy (SEM), Ultraviolet-visible diffuse reflectance spectroscopy (UV-DRS), X-ray Diffraction (XRD), Transmission electron microscopy (TEM) and X-ray photoelectron spectroscopy (XPS). The photocatalytic activity of SmO/g-C3N4 nanocomposites was studied against anionic dyes (Rose bengal (RB) and Xylenol Orange (XO)), cationic dyes (Auramine O (AO) and Crystal Violet (CV)) and their mixture (Mix Dyes). The results indicated that SmO/g-C3N4 nanocomposite (SmG-1.5) exhibited superior photocatalytic activity compared to its precursors, degrading dyes at rates of Rose bengal-84.7 %, Xylenol Orange-75.3 %, Auramine O-82.2 %, Crystal Violet-72.3 %, and Mix dyes-80 %. Thus, SmO/g-C3N4 nanocomposites demonstrate significant potential as an efficient photocatalyst for degrading dyes in wastewater.
Synergistic photocatalysis of VO2-A/g-C3N4 composites for efficient degradation of anionic and cationic dyes: Towards a sustainable environmental solution
(2025) Sharma, Anshu
Due to the outflow of dye effluent from numerous industrial operations, dye pollution is a severe environmental issue. Because of its efficiency and environmental friendliness, photocatalytic degradation is a technology promising for treating dye pollutants. This study used a simple hydrothermal process to synthesise VO2-A/g-C3N4 nanocomposite and its photocatalytic activity for the breakdown of anionic dyes (Xylenol Orange (XO, Rose Bengal (RB)) and cationic dyes (Crystal Violet (CV), Auramine O (AO)) and Mix dyes (1:1 mixture of anionic and cationic dyes) were assessed. XRD, XPS, SEM, FTIR, Photoluminescence, TEM, UV-DRS and BET analysis were used to characterize the nanocomposite.SEM and TEM analyses revealed a distinct morphology of the VAG-4 nanocomposite, with small, irregular VO2-A nanoparticles dispersed and wrapped around the g-C3N4 surface. UV-DRS analysis, using the Tauc relation, indicated that VO2-A incorporation shifted the absorption edge to longer wavelengths, with VAG-4 showing a peak at 487 nm (1.91 eV). BET analysis of VAG-4 shows a specific surface area of 47.1 m2/g, a pore volume of 0.2125 cm3/g, and an average pore size of 27.9 nm, supporting its potential for effective photocatalytic applications. We assessed the photocatalytic activity ofVO2-A/g-C3N4 nanocomposite for the degradation of the anionic dyes (RB, XO), cationic dyes (AO, CV) and Mix dyes. The results demonstrated that the VAG-4 nanocomposite with degradation percentages of RB (83.5 %), XO (75.5 %), AO (73.4 %), and CV (78.2 %) exhibited more enhanced photocatalytic activity than the individual precursors. The nanocomposite’s outstanding photocatalytic activity suggests that it has the potential for practical appli cations in environmental cleanup.
Smart crop disease monitoring system in IoT using optimization enabled deep residual network
(2025) Kumar Tiwari, Anoop
Abstract The Internet of Things (IoT) has recently attracted substantial interest because of its diverse applications. In the agriculture sector, automated methods for detecting plant diseases offer numerous advantages over traditional methods. In the current study, a new model is developed to categorize plant diseases within an IoT network. The IoT network is simulated for monitoring crop diseases. Routing is performed with Henry Gas Chicken Swarm Optimization (HGCSO), which is designed by integrating Henry Gas Solubility Optimization (HGSO) and Chicken Swarm Optimization (CSO). The f itness parameters of the model include delay, energy, distance, and link lifetime (LLT). At the Base Station (BS), plant disease categorization is performed by collecting plant leaf images. Preprocessing is done on the input images using median filtering. Various features, such as Histogram of Oriented Gradient (HoG), statistical features, Spider Local Image Features (SLIF), and Local Ternary Patterns (LTP) are extracted. Plant disease categorization is carried out using a Deep Residual Network (DRN), which is trained using the developed Caviar Henry Gas Chicken Swarm Optimization (CHGCSO) that combines the CAViaR model with HGCSO. Comparative results show an accuracy of 94.3%, a maximum sensitivity of 93.3%, a maximum specificity of 92%, and an F1-score of 93%, indicating that the CHGCSO-based DRN outperforms existing methods. Graphic Abstract
Scalable energy optimization of resources for mobile cloud computing using sensor enabled cluster based system
(2025) Kumar, Rakesh
Abstract The rising craze of sensor enabled mobile devices promotes its usage in Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC) and other distributed computing environments derived from the cloud computing environment. As the computing paradigm shifts from centralized to distributed computing and mobile devices are getting smarter and resource rich, it facilitate the user to do computation to its proximity. Hence, it is quite useful to incorporate the Wireless Sensor Networks (WSN) with distributed computing environment to better cater to the user needs. The proposed work enhances the MCC and MEC by incorporating sensor enabled computing along with the application of energy optimization techniques such as coyote optimization, Fuzzy Logic (FL), data redundancy and data compression. A new framework called Sensor Enabled-Scalable Key Parameter Yield of Resources (SE-SKYR) framework is proposed in this research work by integrating SKYR framework with cluster-based sensing mechanism. The proposed work uses SKYR framework which is a cloudlet based MCC framework and works well for MEC as well. Cloudlet is used as the main computing component available at the local level which suits both MEC and MCC. The existing system uses the concept of relay node to transmit data pack ets in transmission path from sensor nodes to server via edge cloud and hence causes delay in transmission of data. In the proposed work, we have introduced a Scalable Energy Optimization of Resource (SEOR) algorithm to optimize the energy consumption by various resources. SE-SKYR framework along with SEOR algorithm addresses the problems faced by the existing system. The complexity of the proposed SEOR algorithm is less as compared to its existing counterparts and is also comprehended from the results.
Privacy-Preserving and Collaborative Federated Learning Model for the Detection of Ocular Diseases
(2025) Goyal, Nitin
Abstract Ocular diseases significantly impact the health of the public globally. According to the World Health Organization (WHO) reports, at least 1 billion people suffer from near or distance vision impairment that could have been prevented or has yet to be addressed. These conditions cause difficulty in living a healthy lifestyle and impair individual quality of life. The article explores the application of federated learning in detecting two vision-threatening ocular diseases- diabetic retinopathy and diabetic macular edema. A federated learning framework enhances the technological capabilities of artificial intelligence that leverages decentralised data sources without creating data banks to maintain privacy. The methodology implements a federated learning environment with 2, 3, and 4 clients, using MobileNetV2 as the backbone deep learning model. The model is trained on a composite of 2 datasets procured from the Kaggle repository, comprising coloured fundus images labelled for diabetic retinopathy, diabetic macular edema, and normal cases. The federated learning process involves training at the client end to build client models called local models. The clients in a federated learning system only share updates regarding their local models. The original data is never shared with a central server. The server integrates these local models into the central global models using aggregation strategies such as FedAvg, FedProx, etc. Performance metrics, including prediction accuracy, class-wise accuracy, precision, recall, and F1 score, are calculated across 30 communication rounds. The results demonstrate that the federated learning model achieves an average prediction accuracy of 96%, and a class-wise accuracy of 100% in detecting diabetic macular edema and diabetic retinopathy. The high performance of the federated learning system highlights the significance of federated learning as a viable solution for ocular disease detection while ensuring data privacy.
Prediction of international rice production using long short term memory and machine learning models
(2025) Arya, Suraj
Rice, a staple food source globally, is in high demand and production across the world. Its consumption varies in different countries, with each nation having its unique way of incorporating rice into its diet. Recognizing the global nature of rice, its production is a crucial aspect of ensuring its availability, agriculture forecasting, economic stability, and food security. By predicting its production, we can develop a global plan for its production and stock, thereby preventing issues like famine. This paper proposes machine learning (ML) and deep learning (DL) models like linear regression, ridge regression, random forest (RF), adaptive boosting (AdaBoost), categorical boosting (CatBoost), extreme gradient boosting (XGBoost), gradient boosting, decision tree, and long short-term memory (LSTM) to predict international rice production. A total of nine ML and one DL models are trained and tested on the international dataset, which contains the rice production details of 192 countries over the last 62 years. Notably, linear regression and the LSTM algorithm predict rice production with the highest percentage of R-squared (R2), 98.40% and 98.19%, respectively. These predictions and the developed models can play a vital role in resolving crop-related international problems, uniting the global agricultural community in a common cause.
Optimized double transformer residual super-resolution network-based X-ray images for classification of pneumonia identification
(2025) Saini, Sumit
Pneumonia is an infectious disease characterized by inflammation of the lungs’ air sacs, which results in the accumulation of fluid or pus. Medical images is important for the timely identification and precise diagnosis of illnesses; chest X-rays are a commonly utilized modality for respiratory disorders including pneumonia. In this research, optimized double transformer residual super-resolution network-related chest x-ray imageries for the classification of pneumonia identification (DTRSN-XRI-CPI). The procedure involves pre-processing the input image using region-aware neural graph collaborative filtering (RNGCF) to reduce noise, enhance contrast, and eliminate high and low frequencies from the collected dataset. Next, the Synchro-squeezed fractional wavelet transform (SFWT) is utilized for the feature extraction to extract color features such as color, shape, spatial, texture, and relation from the image. Hence, the weight parameters for DTRSN are optimized using the Hunter Prey Optimization Algorithms (HPOA). Then the DTRSN-XRI-CPI is implemented in Python and the performance metrics like precision, accuracy, recall, specificity, F1-score, and ROC are analysed. The performance of the DTRSN-XRI-CPI approach attains 20.7 %, 22.6 % and 30.5 % higher accuracy; 21.8 %, 29.3 % and 30.5 %higher precision and 21.8 %, 29.5 % and 32.6 % higher recall when analysed through existing an intelligent compu tational framework based on deep learning for the identification and classification of pneumonia illness (ICPD- DL-ICF), an adaptive and altruistic deep feature selection approach based on PSO for pneumonia detection from chest X-rays (APSO-DFSM-PDCX) and a deep learning system that uses explainable AI (DLAIB-PI-EAI) techniques respectively.
Optimal trained ensemble of classification model for satellite image classification
(2025) Kumar, Sushil
Satellite image classification is the most significant remote sensing method for computerized analysis and pattern detection of satellite data. This method relies on the image’s diversity struc tures and necessitates a thorough validation of the training data using the chosen classification algorithm. The classifying of remotely sensed images refers to the process of classifying pixels into a fixed set of distinct classes based on their data values. The pixel is categorized into that class if it complies with a specific set of requirements. A new SIC scheme is suggested in this work, where, Contrast limited adaptive histogram equalization (CLAHE) is deployed for pre processing the image. Further, statistical features, Vegetation Indices (VI) and Improved Hybrid Vegetation Index (IHVI) are extracted from the preprocessed image. Then, we deploy 2 phase classification process for classifying the image. In the first phase, Bi-Directional Gated Recurrent Unit (BI-GRU), Convolutional Neural Network (CNN) and Deep Max Out (DMO) are used. In 2nd phase, optimized RNN is used for determining the final classification result. Also, the train- ing of the model is done by the new Chaotic Opposition Behavior Improvised Coot Algorithm (COBI-CA) optimization via tuning the optimal weights. The COOT method has been enhanced and is now the suggested algorithm. Lastly, we conduct an analysis to compare the suggested Satellite Image Classification (SIC) model’s performance to the traditional approaches in terms of statistical analysis, precision, accuracy, and error measure. In comparison to other conven tional methods, the accuracy of the suggested model is 97%.
Mathematical model for understanding the relationship between diabetes and novel coronavirus
(2025) Kumari, Preety
A new model is proposed to explore interactions between diabetes and novel coronavirus. The model accounted for both the omicron variant and variants varying from omicron. The model investigated compartments such as hospitalization, diabetes, co-infection, omicron variant, and quarantine. Additionally, the impact of different vaccination doses is assessed. Sensitivity analysis is carried out to determine disease prevalence and control options, emphasizing the significance of knowing epidemics and their characteristics. The model is validated using actual data from Japan. The parameters are fitted with the help of ”Least Square Curve Fitting” method to describe the dynamic behavior of the proposed model. Simulation results and theoretical findings demonstrate the dynamic behavior of novel coronavirus and diabetes mellitus (DM). Biological illustrations that illustrate impact of model parameters are evaluated. Furthermore, effect of vaccine efficacy and vaccination rates for the vaccine’s first, second, and booster doses is conducted. The impact of various preventive measures, such as hospitalization rate, quarantine or self-isolation rate, vaccine dose-1, dose-2, and booster dose, is considered for diabetic individuals in contact with symptomatic or asymptomatic COVID-19 infectious people in the proposed model. The findings demonstrate the significance of vaccine doses on people with diabetes and individuals in fectious with omicron variant. The proposed work helps with subsequent prevention efforts and the design of a vaccination policy to mitigate the effect of the novel coronavirus.
Joint trust‑based detection and signature‑based authentication technique for secure localization in underwater wireless sensor network
(2025) Goyal, Nitin
Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interference. This research addresses this issue by proposing a Joint Trust-based Detection and Signature-based Authentication (JTDSA) technique, aimed at improving both the security and accuracy of node localization in UWSNs. The technique utilizes Intrusion Detection System (IDS) methods to calculate the trust values of secondary-anchor nodes (SNs), ensuring only the most trusted SNs are selected. Furthermore, a digital signature algorithm secures the Received Signal Strength (RSS) data from tampering. The method incorporates a hybrid approach combining Angle of Arrival (AoA) and RSS ranging techniques to achieve robust localization. Simulation results demonstrate that JTDSA improves localization accuracy by 4% compared to exist ing methods, achieving an overall accuracy of 99.2%. The technique also reduces packet drop by 35% and enhances attack detection accuracy by 3%. These results indicate that the proposed JTDSA technique offers an effective solution for secure and reliable localization in UWSNs
Electrokinetic remediation of chromium contaminated soil: Impact of particle size on treatment efficiency and bioavailability
(2025) Das, Bhasker
The electrokinetic remediation (EKR) is a potential method employed for removal and recovery of heavy metals from soil and various waste materials. However, it demonstrated promising efficacy in laboratory settings, diminished in practical implementations as a result of insufficient comprehension of in-situ conditions. In this study, experimental investigations were conducted to determine the effect of soil particle size on the performance of EKR. Four distinct soil particle sizes were utilized i.e., retained on 1.18 mm (EKR-A), 300 μm (EKR-B), 150 μm (EKR-C), and passing through 150 μm (EKR-D). The alteration in bioavailability as well as physiochemical properties of Chromium (Cr) was investigated through sequential extraction process (SEP) along with soil characterization techniques such as FE-SEM-EDX, XRD, FT-IR. Studies of soil particle size, composition and morphology indicate that as particle size decreases, pollutant concentration increases. Consensus was reached through the research that the treatment efficiency is substantially impacted by the particle size of the soil; in other words, smaller particle sizes led to diminished efficacy. The cumulative Cr removal percentages for EKR-A, EKR-B, EKR-C, and EKR-D were achieved as 27 %, 19 %, 10 %, and 7 %, respectively. The SEP study revealed that the initial soil Cr-concentration was predominated with oxidizable fraction (63–81 %) and the EKR facilitates the extraction of pollutants from the soil matrix by increasing their leachability from 1 % to 30 %, thus providing both removal and recovery of Cr as a feasible option.
Efficient removal of Lambda-cyhalothrin from simulated water using Co-composted-biochar: A modern day substituent of conventional bioadsorbent
(2025) Kumar, Dushyant
Lambda-cyhalothrin, a type II pyrethroid, is widely employed as an outdoor and indoor insect repellent due to its target specificity and moderate toxicity. However, its extensive use elevated its residual concentration to sig nificant detectable levels in waterbodies, making it an immediate problem. Furthermore, as a lipophile, its accumulation in multicellular organisms has resulted in irreversible acute and chronic effects on numerous physiochemical and biological activities. Thus, to address its removal as a pollutant from waterbodies, bio- adsorbents are considered a cost-effective and environmentally sound cleanup method. Modern day bio- adsorbents like co-composted-biochar, because of their potential adsorption capability rather than the conven tional ones, may become a commonly used remediation tool. Therefore, this study aims at producing co- composted-biochar (COMBI) by combining kitchen-derived putrescible waste and biochar to effectively remove Lambda-cyhalothrin (LC) from spiked up water. It has been further analysed for its physico-chemical characteristics using instrumentation such as BET, SEM-EDS and FTIR. The results showed that biochar and COMBI had a much greater surface area (2.174 m 2 /g-1.718 m 2 /g) and pore diameters (3.648 nm–3.628 nm) than the compost sample. Simultaneously, the response surface methodology (RSM) was employed to optimise the removal efficiency of COMBI against LC. A Box-Behnken Design (BBD) was developed with an experimental layout to perform tests for determining the effects of four basic parameters i.e. pH, contact time (minute), adsorbate concentration (g/l) and adsorbent dose (g) on (%) removal of LC. It was noted that variation among removal efficiencies for the experimental runs ranges between 94.8% and 99.9%, within the majority attaining an efficiency of greater than 95%. During the experiments, the ideal conditions were found to be as pH 2 in a contact time of 140 min and a dose of adsorbent 0.01 g. Thus, COMBI could be a promising and potential candidate for the elimination of LC.
Developmentofprediction models for strength behavior of MSWIA mixedwithfiberandcement
(2025) Singh, NeelaM
Engineersareincreasinglyinterestedinusingmunicipalsolidwasteincineratorash(MSWIA)toconstructdifferentgeotechni cal structures. This practice not only helps manage waste but also positively impacts the environment. Stabilizing such wastes by adding admixture and reinforcing material significantly improves their load bearing capacity. This study investigates the strength behavior of the MSWIA mixed with cement and fiber, providing valuable insights for the practical application of these materials. Unconfined compressive strength (UCS) and split tensile strength (STS) tests were conducted to understand the behavior. The aspect ratio of the fiber, cement content, fiber content, and curing period varied during testing. The impact of individual variables was evaluated based on the results of the tests through sensitivity analysis. Further, a mathematical model based on the Artificial Neural network (ANN) and Multivariable linear regression model (MLRM) was developed, and results were compared. The coefficient of determination ‘R2’ for UCS in ANN was 0.996 and in STS was 0.995 while for MLRM,‘R2’was0.94 for UCSand 0.93 for STS. From the results, it was found that the ANN can predict the strength better than MLRM. Also, a correlation between the UCS and STS for the MSWIA mixed with fiber and cement was established.
Deep learning approaches to address cold start and long tail challenges in recommendation systems: a systematic review
(2025) Jangid, Manisha
Recommendation systems (RS) have become prevalent across different domains includ ing music, e-commerce, e-learning, entertainment, and social media to address the issue of information overload. While traditional RS approaches have achieved significant success in delivering recommendations, they still face issues including sparse data, diversity, cold start, and long tail problem. The emergence of deep learning as a prominent and exten sively studied topic has shown significant potential in addressing these challenges in RS. Deep learning captures intricate patterns of interaction and precisely reflects user prefer ences, allowing for encoding complex abstractions in data representation and enhancing information processing capabilities. This paper provides an extensive survey of the existing literature on recommendation systems. We will begin by providing a foundational under standing of the core concepts and terminology of recommendation systems and signifi cance of deep learning. Secondly, we talk about the original studies being conducted on deep learning methods and solutions to address “Cold start and long tail” challenges in recommendation. Thirdly, we examine the potential future directions of research pertain ing to deep learning-based recommender systems (DLRS). Our review provides valuable insights for both researchers and practitioners in using deep learning to address challenges in recommendation and in developing effective and efficient recommendation systems.
Solidification and Stabilization of Spent Pine cone Biochar using Chemically Bonded Phosphate Cement
(2023) Tyagi, S; Annachhatre, A
Spent biochar is produced after adsorption of heavy metal which is hazardous by nature. A suitable disposal technique is required to prevent the leaching of heavy metals from spent biochar into the environment. This study highlights the solidification and stabilization (S/S) of copper loaded spent pine-cone biochar by chemically bonded phosphate cement (CBPC). The response surface methodology (RSM) was used to conduct S/S experiments in order to evaluate the compressive strength of CBPC products. The CBPC samples were prepared by varying biochar content (5-50 wt. %); W:S (0.15-0.3) and curing time(3-28d). Results illustrated that CBPC products containing biochar had higher compressive strength upto 12.8 MPa in comparison to CBPC without biochar i.e., upto 10.8 MPa. XRD and SEM analysis confirmed the presence of K-struvite (MgKPO4.6H2O), copper containing phases (Ca Cu-Si), copper phosphate precipitates (Cu3(PO4)2) and filling of pore spaces by spent biochar. Highest compressive strength of 12.8 MPa was obtained at an optimized biochar content of 25%, W:S of 0.18 and curing time of 28 d. The evaluation of leaching potential by TCLP illustrated that stabilization of Cu (II) upto 99.9% was achieved in CBPC product. The risk assessment study revealed that there is no significant danger due to leaching of heavy metals from final CBPC product indicating that it can be readily disposed in the hazardous landfill sites.
Simultaneous Placement of Multiple Rooftop Solar PV Integrated Electric Vehicle Charging Stations for Reliability Benefits
(2023) Reddy, G; Rani, D; Gope, S; Narayana, B
Electric Vehicles (EVs) are known to be future mode of transportation because of their environment-friendly nature. The increase in electric vehicle (EV) penetration needs to set up the new charging stations to meet the demand. The EV charging shows the negative impact on distribution system and system failures lead to the unavailability of power to charge EVs. The EVs not charging due to system failure is to be considered but ignored in the previous studies. Incorporating the Vehicle-to-Grid (V2G) technologies into charging station (CS) improves the system reliability. In this paper, solar rooftop PV units are integrated with CSs to overcome the negative impacts of EV charging and further enhance the reliability of the system. To extracts the maximum benefits from the solar PV integrated charging stations (PVCS), optimal placement is done with objective of reliability improvement. EV reliability is evaluated by using a novel index called as expected energy not charged (EENC). The reliability of both distribution system and EVs are considered as objective functions simultaneously, hence, placement problem becomes multi-objective. The optimal placement is done by considering different EV penetration levels. A multi-objective Grasshopper optimization algorithm (MOGOA) is applied to solve the optimal placement problem of PVCS. The EENS value is improved by 6.18% and 13.9% as compared to base case for case 1 and case 2 respectively. The EENC is improved by 11.51% in case 2 as compared to case 1.
Simulation of thermal comfort and energy demand in buildings of sub-Himalayan eastern India - Impact of climate change at mid (2050) and distant (2080) future
(2023-02) Thapa, S; Rijal, H; Pasut, W; Singh, R
The global warming associated with climate change predicted by the Intergovernmental Panel on Climate Change (IPCC) is expected to deteriorate the indoor climate of free-running buildings. Features like proper orientation and wall thickness are important for the design of a building that is resilient to the impact of climate change. However, the implementation of these features is sometimes difficult, especially in a rugged hilly location. A whole building simulation was per formed using DesignBuilder for an existing 3-storey free running multi-family concrete building located in the sub-Himalayan region of eastern India, for thermal comfort and energy demand during the present and the climate change scenarios of 2050 and 2080. The results show an increasing trend in the indoor operative temperature during the future climatic scenario, with the condition inside the top roof-exposed floor deteriorating the most. A decrease of 59.8% and 81.2% in the annual heating energy and an increase of 221.9% and 467.0% in the annual cooling energy were predicted for the future climate of 2050 and 2080 compared to the present. Para metric analysis performed considering orientation, wall U-value, infiltration rate and window-to wall ratios revealed that the east/south-east facing orientation would perform the best with re gard to overheating due to climate change. Further, the use of autoclaved aerated concrete (AAC) brick is recommended along with the decrease in air infiltration rate and window-to-wall ratio to improve the thermal performance of the indoor environment. In addition, we have also proposed a method to assess the under-cooling of an indoor environment.

Browse

Recent Submissions