Sri Sivasubramaniya Nadar College of Engineering

There is an upsurge enthusiasm for utilizing biochar produced from waste-biomass in different fields, to address the most important ecological issues. This review is focused on an overview of remediating harmful contaminants utilizing biochar. Production of biochar utilizing various systems has been discussed. Biochar has received the consideration of numerous analysts in building up their proficiency to remediate contaminants. Process parameters are fundamentally answerable for deciding the yield of biomass. Biochar derived from biomass is an exceptionally rich wellspring of carbon produced from biomass utilizing thermal combustion. Activating biochar is another particular region for the growing utilization of biochar for expelling specific contaminations. Closed-loop systems to produce biochar creates more opportunities. Decentralized biochar production techniques serve as an effective way of providing employment opportunities, managing wastes, increasing resource proficiency in circular bioeconomy. This paper also covers knowledge gaps and perspectives in the field of remediation of toxic pollutants using biochar.

In this article, the size of antenna Fresnel and Fraunhofer field regions are systematically derived starting from a general phase factor representation of the scalar diffraction theory. This is done by first expressing the phase of an arbitrary aperture field in a Taylor series in terms of a small parameter and then by subsequently imposing appropriate conditions for the Fresnel and Fraunhofer regions.

In recent times, metal oxide nanoparticles (NPs) have been regarded as having important commercial utility. However, the potential toxicity of these nanomaterials has also been a crucial research concern. In this regard, an important solution for ensuring lower toxicity levels and thereby facilitating an unhindered application in human consumer products is the green synthesis of these particles. Although a naïve approach, the biological synthesis of metal oxide NPs using microorganisms and plant extracts opens up immense prospects for the production of biocompatible and cost-effective particles with potential applications in the healthcare sector. An important area that calls for attention is cancer therapy and the intervention of nanotechnology to improve existing therapeutic practices. Metal oxide NPs have been identified as therapeutic agents with an extended half-life and therapeutic index and have also been reported to have lesser immunogenic properties. Currently, biosynthesized metal oxide NPs are the subject of considerable research and analysis for the early detection and treatment of tumors, but their performance in clinical experiments is yet to be determined. The present review provides a comprehensive account of recent research on the biosynthesis of metal oxide NPs, including mechanistic insights into biological production machinery, the latest reports on biogenesis, the properties of biosynthesized NPs, and directions for further improvement. In particular, scientific reports on the properties and applications of nanoparticles of the oxides of titanium, cerium, selenium, zinc, iron, and copper have been highlighted. This review discusses the significance of the green synthesis of metal oxide nanoparticles, with respect to therapeutically based pharmaceutical applications as well as energy and environmental applications, using various novel approaches including one-minute sonochemical synthesis that are capable of responding to various stimuli such as radiation, heat, and pH. This study will provide new insight into novel methods that are cost-effective and pollution free, assisted by the biodegradation of biomass.

Brain tumor classification is a very important and the most prominent step for assessing life-threatening abnormal tissues and providing an efficient treatment in patient recovery. To identify pathological conditions in the brain, there exist various medical imaging technologies. Magnetic Resonance Imaging (MRI) is extensively used in medical imaging due to its excellent image quality and independence from ionizing radiations. The significance of deep learning, a subset of artificial intelligence in the area of medical diagnosis applications, has macadamized the path in rapid developments for brain tumor detection from MRI to higher prediction rate. For brain tumor analysis and classification, the convolution neural network (CNN) is the most extensive and widely used deep learning algorithm. In this work, we present a comparative performance analysis of transfer learning-based CNN-pretrained VGG-16, ResNet-50, and Inception-v3 models for automatic prediction of tumor cells in the brain. Pretrained models are demonstrated on the MRI brain tumor images dataset consisting of 233 images. Our paper aims to locate brain tumors with the utilization of the VGG-16 pretrained CNN model. The performance of our model will be evaluated on accuracy. As an outcome, we can estimate that the pretrained model VGG-16 determines highly adequate results with an increase in the accuracy rate of training and validation.

Arsenic is a highly toxic metalloid that is extensively distributed in soils and water bodies, resulting in a variety of toxicity mechanisms and harmful effects on humans and environmental health. This paper comprehensively reviews the technological development in arsenic (As) removal from wastewater and contaminated soil, and provides insights into the challenges in effective arsenic removal from the environmental compartments. The arsenic removal efficiency of the available technologies is also discussed in terms of their principle of operation, efficiency, advantages, and shortcomings. Many of the existing technologies are not found economically feasible for the regions of interest or are not applicable at the community level. Some of the techniques are often responsible for producing toxic by-products. Overall, the adsorption technique demonstrated high efficiency of almost 100% and a maximum of 95% in removing arsenic from water and soil, respectively. Novel methods such as the application of nanotechnology and polymeric ligand exchangers have also been gaining traction but also seem to possess limitations similar to conventional and non-conventional techniques.

Dyes (colorants) are used in many industrial applications, and effluents of several industries contain toxic dyes. Dyes exhibit toxicity to humans, aquatic organisms, and the environment. Therefore, dyes containing wastewater must be properly treated before discharging to the surrounding water bodies. Among several water treatment technologies, adsorption is the most preferred technique to sequester dyes from water bodies. Many studies have reported the removal of dyes from wastewater using biochar produced from different biomass, e.g., algae and plant biomass, forest, and domestic residues, animal waste, sewage sludge, etc. The aim of this review is to provide an overview of the application of biochar as an eco-friendly and economical adsorbent to remove toxic colorants (dyes) from the aqueous environment. This review highlights the routes of biochar production, such as hydrothermal carbonization, pyrolysis, and hydrothermal liquefaction. Biochar as an adsorbent possesses numerous advantages, such as being eco-friendly, low-cost, and easy to use; various precursors are available in abundance to be converted into biochar, it also has recyclability potential and higher adsorption capacity than other conventional adsorbents. From the literature review, it is clear that biochar is a vital candidate for removal of dyes from wastewater with adsorption capacity of above 80%.

This paper presents a unique step-by-step procedure for the simulation of photovoltaic modules with Matlab/ Simulink. One-diode equivalent circuit is employed in order to investigate I-V and P-V characteristics of a typical 36 W solar module. The proposed model is designed with a user-friendly icons and a dialog box like Simulink block libraries.

The growing demand for electrical energy and the impact of global warming leads to a paradigm shift in the power sector. This has led to the increased usage of renewable energy sources. Due to the intermittent nature of the renewable sources of energy, devices capable of storing electrical energy are required to increase its reliability. The most common means of storing electrical energy is battery systems. Battery usage is increasing in the modern days, since all mobile systems such as electric vehicles, smart phones, laptops, etc., rely on the energy stored within the device to operate. The increased penetration rate of the battery system requires accurate modelling of charging profiles to optimise performance. This paper presents an extensive study of various battery models such as electrochemical models, mathematical models, circuit-oriented models and combined models for different types of batteries. It also discusses the advantages and drawbacks of these types of modelling. With AI emerging and accelerating all over the world, there is a scope for researchers to explore its application in multiple fields. Hence, this work discusses the application of several machine learning and meta heuristic algorithms for battery management systems. This work details the charging and discharging characteristics using the black box and grey box techniques for modelling the lithium-ion battery. The approaches, advantages and disadvantages of black box and grey box type battery modelling are analysed. In addition, analysis has been carried out for extracting parameters of a lithium-ion battery model using evolutionary algorithms.

This comprehensive review paper examines the technological advancements towards smart energy management in smart cities. It provides an overview of the concept of smart energy management, the challenges faced by cities in managing their energy consumption, and the need for technological advancements to overcome these challenges. The advancements are categorized based on their applications, such as smart grids, smart buildings, and smart transportation, and their benefits are discussed, including increased efficiency, reduced costs, and better sustainability. The paper also presents case studies of successful implementation of smart energy management technologies and discusses the challenges faced during implementation and how they were overcome. In addition, the paper highlights potential research areas and emerging technologies, including block chain, edge computing, IoT, big data analytics, energy harvesting technologies, machine learning, and distributed energy resources (DERs). The importance of technological advancements for smart energy management in smart cities is emphasized, and recommendations for future research and development in the field are provided. Overall, this review paper contributes to the ongoing development of smart cities and provides valuable insights for researchers, industry professionals, and policymakers working towards a more sustainable future.

The aim of this paper is to investigate the effects of partial shading on energy output of different Solar Photovoltaic Array (SPVA) configurations and to mitigate the losses faced in Solar Photovoltaic (SPV) systems by incorporating bypass diodes. Owing to the practical difficulty of conducting experiments on varied array sizes, a generalized MATLAB M-code has been developed for any required array size, configuration, shading patterns, and number of bypass diodes. The proposed model which also includes the insolation-dependent shunt resistance can provide sufficient degree of precision without increasing the computational effort. All the configurations have been analyzed and comparative study is made for different random shading patterns to determine the configuration less susceptible to power losses under partial shading. Inferences have been drawn by testing several shading scenarios.

The adsorption behavior of rice husk for cadmium ions from aqueous solutions has been investigated as a function of appropriate equilibrium time, adsorbent dose, temperature, adsorbate concentrations and pH in a batch system. Studies showed that the pH of aqueous solutions affected cadmium removal with the result that removal efficiency increased with increasing solution pH. The maximum adsorption was 98.65% at solution pH 6, contact time 60 min and initial concentration of 25 mg/L. The experimental data were analysed by the Langmuir, Freundlich and Temkin models of adsorption. The characteristic parameters for each isotherm and related correlation coefficients have been determined. Thermodynamic parameters such as , and have also been evaluated and it has been found that the sorption process was feasible, spontaneous and exothermic in nature. The kinetics of the sorption were analysed using the pseudo-first order and pseudo-second order kinetic models. Kinetic parameters, rate constants, equilibrium sorption capacities and related correlation coefficients for each kinetic model were calculated and discussed. It was shown that the adsorption of cadmium could be described by the pseudo-second order equation, suggesting that the adsorption process is presumably a chemisorption. The rice husk investigated in this study showed good potential for the removal of cadmium from aqueous solutions. The goal for this work is to develop inexpensive, highly available, effective metal ion adsorbents from natural waste as alternative to existing commercial adsorbents.

Water is one of the most important components in the environment. This is essential for all forms of life and leisurely plays a vital role in the world economy. The discharge of large amount of dye wastewater from different industries such as textile, leather, pulp, rubber pharmaceuticals, food processing, electroplating cosmetics, plastic, paper industries, etc. to the aquatic system constitutes the major hazards to the living environment. Hence, the rapid removal of these dyes from wastewater before their discharge is an important necessity of day to day life as well as for environmental safety. Several traditional treatment methods were available for the removal of dyes from water/ wastewater such as chemical coagulation, filtration, flocculation, ozonation, oxidation, photocatalytic degradation, ion exchange, biodegradation, electrolysis and adsorption. Among all these treatment methods, adsorption process using activated carbon is one of the most important, effective and reliable method for the removal of dyes from aquatic system. However, the widespread application of activated carbon is restricted because of its high cost. Therefore, the attention has moved to select the low-cost and efficient adsorbents which are alternative to the existing activated carbon. Some of the natural materials, agricultural wastes, industrial wastes and biosorbents have been reported as an effective low-cost adsorbent for the removal of dyes from aquatic system by many researchers. The current review paper explains the detailed survey on the dye removal methods, and scope for the improvement can be done on the removal of dyes from industrial wastewater.

Switched reluctance machines have emerged as an important technology in industrial automation; they represent a real alternative to conventional variable speed drives in many applications. This paper reviews the technology status and trends in switched reluctance machines. It covers the various aspects of modeling, design, simulation, analysis, and control. Finally, it discusses the impact of switched reluctance machines technology on intelligent motion control.

Activated carbon, prepared from an agricultural waste, cashew nut shell (CNS) was utilized as an adsorbent for the removal of methylene blue (MB) dye from aqueous solution. Batch adsorption study was carried out with variables like pH, adsorbent dose, initial dye concentration and time. The response surface methodology (RSM) was applied to design the experiments, model the process and optimize the variable. A 24 full factorial central composite design was successfully employed for experimental design and analysis of the results. The parameters pH, adsorbent dose, initial dye concentration, and time considered for this investigation play an important role in the adsorption studies of methylene blue dye removal. The experimental values were in good agreement with the model predicted values. The optimum values of pH, adsorbent dose, initial dye concentration and time are found to be 10, 2.1846 g/L, 50 mg/L and 63 min for complete removal of MB dye respectively.

This letter presents the implementation of a pair of parallel coupled-line resonators (PCRs) for isolation enhancement in planar microstrip patch array antennas. Each PCR consists of three coupled lines separated by a small coupling distance. The attempted configuration provides band-reject characteristics at the design frequency of 3.5 GHz. Two such PCRs are replicated to provide higher order rejection that enhances the bandstop characteristics. The designed PCR is deployed in a two-element microstrip patch antenna array, and the mutual coupling characteristics are studied. The proposed PCR-based decoupling unit cell provides additional 12–26.2-dB coupling reduction with an enhancement of antenna gain up to 1.25 dB. The prototype antenna is fabricated, and the simulation results are validated using experimental measurements.

This article presents the optimization of process parameters in friction stir welding (FSW) of Aluminum Alloy AA 5083 with multiple responses based on orthogonal array with grey relational analysis. The L9 orthogonal array of Taguchi experimental design is used for optimizing the FSW process parameters on tensile strength of FSW welds and total input power required for the process. The process parameters considered for optimization are the Rotational speed of the tool in rpm, transverse speed in mm/min, and the axial force in KN. The objective of this article is to find the optimum levels of the process parameters in which it yields maximum tensile strength and consumes minimum power. Based on the grey relational grade, optimum levels of parameters have been identified, and significant contribution of parameters is determined by ANOVA. The optimum levels of the process parameters are determined and validated by the confirmation run.

The present study explains the preparation and application of sulfuric acid–treated orange peel (STOP) as a new low-cost adsorbent in the removal of methylene blue (MB) dye from its aqueous solution. The effects of temperature on the operating parameters such as solution pH, adsorbent dose, initial MB dye concentration, and contact time were investigated for the removal of MB dye using STOP. The maximum adsorption of MB dye onto STOP took place in the following experimental conditions: pH of 8.0, adsorbent dose of 0.4 g, contact time of 45 min, and temperature of 30°C. The adsorption equilibrium data were tested by applying both the Langmuir and Freundlich isotherm models. It is observed that the Freundlich isotherm model fitted better than the Langmuir isotherm model, indicating multilayer adsorption, at all studied temperatures. The adsorption kinetic results showed that the pseudo-second-order model was more suitable to explain the adsorption of MB dye onto STOP. The adsorption mechanism results showed that the adsorption process was controlled by both the internal and external diffusion of MB dye molecules. The values of free energy change (ΔG o) and enthalpy change (ΔH o) indicated the spontaneous, feasible, and exothermic nature of the adsorption process. The maximum monolayer adsorption capacity of STOP was also compared with other low-cost adsorbents, and it was found that STOP was a better adsorbent for MB dye removal.

Industrial Wireless Sensor Networks (WSNs) are becoming increasingly popular due to their enhanced scalability and low cost of deployment. However, they also present new challenges, such as energy optimization and network maintenance, which industrial users must address. In order to meet the challenges, Machine Learning techniques have been used to create an enhanced energy optimization model for Industrial WSNs. This model utilizes knowledge-based learning to identify and optimize the energy consumption of the nodes, allowing Industrial WSNs to consume the least amount of energy for the given tasks. In addition, the model also evaluates the effectiveness of feedback control schemes and predicts the best possible outcomes for its application in Industrial WSNs to ensure higher efficiency and longer network lifetime. The model also enables the exploration of potential trade-offs between power consumption and communication performance to ensure a better energy-efficient solution. The proposed EEOM obtained 64.72% transmission energy consumption, 35.28% transmission energy saving, 67.27% received energy consumption, 32.73% received energy storage, 52.16% idle-mode energy consumption, 47.84% idle-mode energy storage, 66.31% sleep-mode energy consumption, and 33.69% sleep-mode energy storage. It also obtained 90.44% prevalence threshold, 90.33% critical success index, 93.93% Delta-P, 90.06% MCC and 92.17% FMI rates. It also provides the ability to identify the best selection of nodes and paths for data transmission to reduce network traffic. When applied in conjunction with manual intervention, these automated knowledge-based techniques will make Industrial WSNs more reliable, efficient, and energy-cost effective.

The ability of nano-silversol-coated activated carbon (NSSCAC) to adsorb Pb2+ from aqueous solution has been investigated through batch experiments. The adsorption of lead onto NSSCAC has been found to depend on adsorbent dose, initial concentration and contact time. The experiments were carried out at natural solution pH. Equilibrium data fitted well with the Langmuir model and Freundlich model with a maximum adsorption capacity of 23.81 mg of Pb/g of NSSCAC. The experiments showed that the highest removal rate was 92.42% for Pb2+ under optimal conditions. The sorption of Pb2+ on NSSCAC was rapid during the first 30 min and the equilibrium attained within 60 min. The kinetic processes of Pb2+ adsorption on NSSCAC were described by applying pseudo-first-order and pseudo-second-order kinetic models. The kinetic data for the adsorption process obeyed a pseudo-second-order kinetic model, suggesting that the adsorption process is chemisorption. The NSSCAC investigated in this study showed good potential for the removal of Pb2+ from aqueous solution.

The integration of renewable energy sources into smart grids offers a promising solution for building sustainable and reliable energy systems. However, optimizing hybrid renewable energy systems remains a crucial area of research. The study presents a comprehensive approach combining artificial intelligence algorithm techniques with metaheuristic optimization algorithms for anticipating and managing renewable energy sources in smart grid environments. With precision, recall, and accuracy scores of 0.92, 0.93, and 0.92, respectively, the proposed Hybrid LSTM-RL model beats current algorithms in correctly forecasting energy demand patterns. With an accuracy of 0.91 for various load balancing measures, the RL-SA algorithm efficiently measures load balancing. With mean squared error (MSE), mean absolute error (MAE), R-squared score, root mean square error (RMSE), and mean absolute percentage error (MAPE) values of 345.12, 15.07, 0.78, 18.57, and 7.83, respectively, the CNN-PSO algorithm also turns out to be the most successful at forecasting the generation of renewable energy. These discoveries help hybrid renewable energy systems in smart grid settings advance, enabling effective, dependable, and economical energy production and distribution. The suggested solution also has the potential to be used in rural and off-grid settings. Overall, this research offers a useful method for maximizing the production of renewable energy and acts as a spark for additional studies into energy management systems.