Showing papers by "Motilal Nehru National Institute of Technology Allahabad published in 2022"

Performance evolution of different controllers for frequency regulation of a hybrid energy power system employing chaotic crow search algorithm

Abstract: The work described herein compares the performance of different optimized controllers, viz. proportional-integral, proportional-integral-derivative (PID) with filter, two-degree-of-freedom (2DOF)-PID, 3DOF-PID, fractional-order-PID, cascade PI-PID, tilt-integral-derivative (TID), and cascade-TID (CC-TID) controllers in frequency regulation of a hybrid energy distributed power system (HEDPS). The HEDPS is integrated with a multi-unit hydrothermal power plant for ensuring stable power supply. Crow search algorithm has been adopted with chaotic mapping (CCSA) for fine-tuning of the controller settings mentioned above. Extensive analysis has been presented to confirm the superiority of the CC-TID controller compared to other prevalent controllers of state-of-art in terms of different performance specifications. The tuning competence of the CCSA has been demonstrated over conventional CSA and other available optimization techniques. To enhance the mastery of the controller, disturbance-observer (Dob) is developed to estimate fast-changing disturbance profiles and subsequently refines the control law. The controller's robustness is affirmed under random perturbations, presence of nonlinearities, and variation of parameters. The effect of integration of a geothermal power plant on the system performance has also been outlined. The efficacy of Dob-aided CC-TID controller in frequency regulation is validated thereof.

Modification in the physical properties of nanocrystalline ZnO thin films by Sn/Ni co-doping for transparent conductive oxide applications

Abstract: In this paper, the modification in the physical properties of spin-coated ZnO thin films upon Sn/Ni co-doping has been presented. All the films possess the hexagonal wurtzite structure of ZnO with varying surface morphologies. Among all the films, the optical transmittance beyond 550 nm wavelength is maximum for the 1 at% Ni-doped ZnO (1NZO) and (1 at% Sn + 1 at% Ni) co-doped ZnO (1T1NZO) films. The optical band gap for the pristine ZnO film is 3.26 eV, which decreases upon doping and co-doping. The PL spectra of the films exhibit the UV emission corresponding to excitonic transitions and the blue and green emissions related to native defects in the ZnO structure. The room-temperature electrical conductivity is found maximum in the 1T1NZO film. The existence of 1T1NZO film with improved optical transmittance and high electrical conductivity supports its use in transparent conductive oxide applications. • All the films possess a hexagonal wurtzite structure of ZnO. • Morphologies of the films depend on the Sn, Ni co-doping contents. • Optical transmittance beyond 550 nm wavelength improves upon doping and co-doping. • The optical band gap values of the films lie in the range 3.26–3.03 eV. • The 1T1NZO film exhibits improved transmittance and high electrical conductivity.

Maximum correlation based mutual information scheme for intrusion detection in the data networks

Abstract: For an efficient detection of intrusion in the network, the system requires sensitive information. This information contains various features depending upon the network. The selection of useful features and relevant information from the dataset has always been difficult for the user. These features bound the performance of the system and also restrict them in making accurate decisions. This irrelevant feature plays a major role in making confusion between the normal data pattern against the attack data pattern. This article proposes a novel Maximum correlation-based mutual information technique for efficient feature selection (MCMIFS) in a data network. The proposed method is utilized with Kernel Extreme Learning Machine (KELM) based multiclass classifier for effective intrusion detection. The performance of the proposed scheme is evaluated using standard intrusion detection datasets with feature ranging from 42 to 155. The hybrid of MCMIFS with KELM reveals the effectiveness of the proposed scheme by improving the accuracy of detection, i.e., 99.97%, decreasing the FPR, i.e., 0.19, by decreasing the computational complexity. The proposed Intrusion Detection Scheme (IDS) results are analyzed and compared with the other existing techniques.

Antimonene-gold based twin-core SPR sensor with a side-polished semi-arc groove dual sensing channel: an investigation with 2D material

Abstract: We propose surface plasmon resonance (SPR) based single-side polished photonic crystal fiber (SSP-PCF) sensor for low as well as high refractive index (RI) sensing. To achieve this, an active metal gold (Au) is deposited on the PCF's flat narrow channels to form a dual-sensing channel. Following that, a thin nanolayer antimonene is deposited on Au, as its buckled honeycomb lattice structure aids in the trapping of numerous biomolecules. For the sensing range of 1.27 to 1.39, numerical results show that the wavelength sensitivity (WS) and amplitude sensitivity (AS) mounted on 77,000 nmRIU−1 and 1320.41 RIU−1, respectively, with wavelength resolution (RW), and amplitude resolution (RA), as high as 1.298 × 10–6 RIU, and 8.6 × 10–7 RIU. The promising results obtained from the proposed SSP-PCF sensor offers improved refractive index sensing with a fine figure of merit (FOM), i.e., 311.74 RIU−1 for the sensing range of 1.27 to 1.39, which covers most known analytes such as proteins, cancer cells, glucose, viruses, DNA/RNA, medicinal drugs, halogenated organic acids. Further, the proposed sensor's design requires a simple fabrication procedure.

Corporate Social Responsibility Measures: A Brief Review:

Abstract: Studies are being conducted to measure the impact of corporate social responsibility (CSR) on different dimensions of performance of the firm. Therefore, it becomes imperative to measure CSR activi...

Life cycle assessment and techno-economic analysis of algae-derived biodiesel: current challenges and future prospects

Abstract: For sustainability assessment of algal biodiesel production, life cycle analysis (LCA) offers a quantitative measure. In this chapter, various literature available on LCA studies of algae biodiesel production are evaluated, depicting that some comprehensive studies compiling various techniques. LCA outputs are dependent on various inputs such as the algae species, bioreactor type, and biochemical reaction conditions. Nevertheless, the paucity in the systematic framework for LCA in the biofuel production process also affected the LCA outputs. In addition, the system boundaries, temporal units, choice of allocation, land utilization, and biogenic carbon source have to be redefined; along with the other unconsidered variables (till now) such as infrastructure construction, systematic maintenance, transportation, waste management, and process intensification, and the overall cost involved should also be included in the LCA method. This chapter covers an inclusive and critical overview of the LCA and techno-economic and policy analyses for the complete algal biodiesel procedure that can assist in future-related studies.

Fabrication of flexible ternary polymer blends comprising polypyrrole, polyvinylalcohol, and poly(4‐styrenesulfonic acid): Study of structural, morphological, and dielectric properties

Abstract: A new ternary polymer blend was synthesized and characterized in the present work. A series of ternary polymer blends comprising water soluble polypyrrole, polyvinyl alcohol, and poly(4-styrenesulfonic acid) were prepared by solution casting method. The structural, electrical, and thermal properties of prepared ternary blends were investigated. X-ray diffraction studies indicated the semicrystalline nature of the polymer blends and chemically interacted polymer chain were revealed by Fourier transform infrared spectra. The morphological phase transformations were observed from scanning electron microscope and atomic force microscopy images. Thermogravimetric analysis explained the thermal degradation process. The ternary blends also possess excellent mechanical stability observed through force versus extension plot. The dielectric constant value of ternary blends was obtained in the range of 600–800 at low frequency range. The highest conductivity obtained was between 10−1 to 10−3 S cm−1 for ternary blends at higher frequency range. The overall results explains the charge transportation and relaxation process in the ternary blends due to presence of an electric field. The excellent dielectric constant values of ternary blends will be efficient for various electronics applications.

Multiobjective optimization of ultrasound intensified and ionic liquid catalyzed in situ algal biodiesel production considering economic, environmental and safety indicators

Abstract: The current study focuses on the multiobjective optimization of an ultrasound intensified and ionic liquid catalyzed in situ transesterification of wet microalgae for renewable biodiesel production. The process is developed and simulated in Aspen Plus V10 simulator and an excel based multiobjective optimization (EMOO) programme for the elitist non-dominated sorting genetic algorithm-II is used for optimization. Total Annual Cost, i.e. TAC (representing economics), organic waste (representing cleaner production), individual risk, i.e. IR (representing risk to the human capital), and CO2 emission (representing environment) are chosen as the objectives for the constrained optimization of this process. The results show that the TAC reduces with the increase in the generation of organic waste, CO2 emission and IR. This article contemplates and articulates the reasons for the obtained trade-offs between objectives. The quantitative trade-offs between objectives aid to the better decision making about the process design and operation while satisfying economic, environmental and safety concerns. Finally, net flow method (NFM) has been implemented for the identification of best suitable solution in the Pareto-optimal fronts. Simultaneous optimization of all four objectives resulted in the impressive savings in TAC (∼15%), organic waste (∼33%), IR (∼9%), and CO2 emission (∼37%).

Understanding the science of fungal endophthalmitis - AIOS 2021 Sengamedu Srinivas Badrinath Endowment Lecture

Abstract: Fungal endophthalmitis is a potentially blinding condition. It is more often reported from Asia, including India. The incidence is lower than bacterial endophthalmitis. But it is relatively more challenging to treat than bacterial endophthalmitis. Many eyes may need therapeutic keratoplasty and/or evisceration. The current mainstays of treatment are vitrectomy irrespective of the presenting vision, intravitreal antifungal agents, and systemic therapy; additionally, the patients could require prolonged treatment with repeat vitreous surgeries and intravitreal injections. Difficulty in clinical diagnosis, delay in microbiological culture, and limited options of antifungal drugs make the treatment more difficult and less rewarding. Three common fungi causing endophthalmitis are Aspergillus, Fusarium, and Candida. The former two are molds, often identified in exogenous endophthalmitis, postoperative and traumatic; the latter is yeast and is more often identified in endogenous endophthalmitis. A faster diagnosis with newer molecular microbiological technologies might help institute treatment earlier than it is currently possible. A target trial using big data from different regions of the world might emulate a randomized clinical trial to design a definite treatment strategy. Given fewer antifungal drugs, one must be mindful of antifungal stewardship to prevent resistance to the existing drugs.

Dielectric resonator-based two-port filtennas with pattern and space diversity for 5G IoT applications

Abstract: Abstract This paper describes a multiple-input multiple-output (MIMO) antenna for low millimeter (mm)-wave applications based on dielectric resonators. This is the first time that a filtering response is used in conjunction with an MIMO antenna operating at a low mm-wave frequency. The antenna is simulated using an asymmetrical U-shaped aperture and a microstrip line feed. The suggested filtenna has two distinguishing characteristics: (i) the diversity parameters of the proposed MIMO are increased by including pattern and spatial diversity, and (ii) the proposed feed mechanism of a dielectric resonator provides the filtering response. Between the two ports, a metallic plate tilts the radiation pattern by 45°. The anti-parallel locations of the ports increase the isolation value by >30 dB. To validate the performance of the suggested antenna, the proposed filtenna was built and confirmed. The proposed antenna operates between the frequencies 27.9 and 28.5 GHz. Within the operating frequency range, the observed gain is ~4.5 dBi. On the contrary, the gain suppression level beyond the operational frequency range is ~15 dB. The stable radiation properties and high diversity parameter values of the suggested filtenna make it an effective solution for 5G Internet of Things sensing applications.

Strategic consideration as feedstock resource for biofuel production as a holistic approach to control invasive plant species

Abstract: Biofuel, the source of energy for the future, is a renewable source of energy. Generally, it is extracted from plant biomass as biooil, biogas, biodiesel, and bioethanol. Invasive species have been contemplated as a promising source of feedstock for meeting future biofuel production demand. Invasive plant species demonstrate suitable characteristics for biofuel crops due to their rapid growth, abundance, and marginal habitat. The consideration of the invasive plants as feedstock aids to check the negative consequences of invasive plants in the environment and helps to bridge the energy gap by being sustainable source of bioenergy. This chapter discusses the potential of invasive plant species to meet the feedstock demand for biofuel postconventional-oil peaking. Further, it compiles the ideas from literature to suggest a suitable strategy required for producing invasive plants as biofuel crops.

Emerging commercial opportunities for conversion of waste to energy: aspect of gasification technology

Abstract: In the last few decades, waste production and its proper management is a matter of concern for many developing countries like India. Although various waste-to-energy (WTE) generation processes are adopted to produce different by-products including electricity, heat, compost, and biofuels. This chapter deals with the different major WTE technologies that is, incineration, pyrolysis, gasification, liquefaction, fermentation, and anaerobic decomposition used across the world. Several emerging technologies in the field of WTE generation, such as hydrothermal carbonization, dendro liquid energy, microbial electrolysis cells, and microbial fuel cells, are also briefly discussed here. Furthermore, a critical assessment of the hazardous solid waste management for power generation using gasification technology has been made. The various gasification technologies, such as fixed bed, fluidized bed, entrained bed, rotary kiln, and plasma gasification along with the chemical reaction undergone during the conversation of feedstock, have also been discussed in detail. The analysis reveals that gasification technology is a feasible option for solid waste management and able to meet existing emission limits and has a notable impact on the reduction of landfill disposal options.

Nano-reduction of gold and silver ions: A perspective on the fate of microbial laccases as potential biocatalysts in the synthesis of metals (gold and silver) nano-particles

Abstract: • Potential involvements of microbial laccases in the synthesis of silver and gold nanoparticles have been comprehensively assessed. • Treasured roles of microbes and associated enzymes in synthesis of gold and silver nanoparticles have also been presented. • As potential green biocatalysts for the synthesis of metal nanoparticles, microbial laccases may be promisingly used. • Methodologies as well as involved possible mechanisms have been discussed in details in order to disclose the effectiveness of microbial laccases in the synthesis of gold and silver nanoparticles. • Different characterization results of synthesized gold and silver nanoparticles based on UV–Vis spectra, XRD, SEM, TEM and other techniques have also been discussed. • Mechanistic evaluation also shows a hope for the effectiveness of microbial laccases in the synthesis of other metal nanoparticles. Nanoparticles of metals have momentous place in the field of biological as well as pharmaceutical chemistry due to which in the present scenario of the research, this field is of auspicious interest. Synthesis of metal nanoparticles via microbial assistance is a burning field for their green synthesis. In this direction, microbial enzymes play significant role, out of which microbial laccases may also be a talented biocatalyst for the synthesis of metal nanoparticles considering its efficacy and interesting promising biological applications. A very little works are known on the role of microbial laccases in the synthesis of metal nanoparticles but after effective scrutiny of their reported works on the synthesis of gold and silver nanoparticles, its fate as potential biocatalyst in the synthesis of metals nanoparticles is being automatically established. Thus, this perspective commendably appraises the active applicability of microbial laccases in the synthesis of gold and silver nanoparticles by reducing their ions in suitable reaction environment.

A Convolutional Neural Network Based Framework for Health Monitoring of a Welded Joint Steel Frame Structure

Abstract: This study presents a Convolutional Neural Network (CNN) based Deep-learning framework for the health monitoring of a welded joint steel plane frame structure. The deep learning algorithm model is trained to extract local features from the vibration-based time-frequency scalogram images and using those features to distinguish the undamaged and two different damage cases of the steel plane frame structure. The performance and robustness of the framework are tested for an unseen image set corresponding to the training classes. The proposed deep learning framework can successfully classify the undamaged and damaged classes with high testing accuracy that signifies its efficiency as an automation tool for the health monitoring of steel plane frame structures subjected to damage near joints.

Synthesis and Modelling of A Novel Multi-Input Multi-Output System Topology Implemented on Non-Inverting Buck-Boost Converter for Renewable Energy Applications

Abstract: The increasing use of hybrid energy sources in power supply system is generating a necessity of power electronic converters that can effectively engage multiple inputs simultaneously. The Multi-Input Multi-Output (MIMO) converter proposed in this paper handles several inputs from renewable sources and redistributes the energy thus obtained to outputs. The presented topology is that of a Non-Inverting Buck-Boost (NI-BB) Converter which is supplied by Photovoltaic Cell (PV) source, a fuel cell and a DC Battery. The converter operation has been simulated on MATLAB-SIMULINK. The transient and steady state modelling of the converter has been derived.