ASSESSMENT OF SUSTAINABILITY INDICATORS OF TWO GAS- TURBINE PLANTS WITH NAPHTHA AND NAPHTHA-RFG MIXTURE AS FUELS

To enhance sustainability of any energy system exergy based sustainability indicators (exergy efficiency, waste exergy ratio, environmental effect factor and exergetic sustainability index) are used. In the present paper sustainability aspects of two GT based power plant are carried out using sustainability indicators. For this purpose, two GT1) configurations, case A (Naphtha based GT power plant) and case B (Naphtha-Residual fuel gas mixture GT 2) are taken up as case study. Results show that exergetic sustainability index obtained as for case A is higher as compared to case B

A review of process intensified CO2 capture in RPB for sustainability and contribution to industrial net zero

Carbon dioxide (CO2), a significant greenhouse gas released from power plants and industries, substantially impacts climate change; minimizing it and achieving carbon net zero is essential globally. In the direction of reducing CO2 emissions into the atmosphere, post-combustion carbon capture from large point CO2 emitters by chemical absorption involving the absorption of this gas in a capturing fluid is a commonly used and efficacious mechanism. Researchers have worked on the process using conventional columns. However, process intensification technology is required because of the high capital cost, the absorption column height, and the traditional columns’ low energy efficiency. Rotating packed bed (RPB) process intensification equipment has been identified as a suitable technology for enhanced carbon capture using an absorbing fluid. This article reviews and discusses recent model developments in the post-combustion CO2 capture process intensification using rotating packed beds. In the literature, various researchers have developed steady-state mathematical models regarding mass balance and energy balance equations in gas and liquid phases using ordinary or partial differential equations. Due to the circular shape, the equations are considered in a radial direction and have been solved using a numerical approach and simulated using different software platforms, viz. MATLAB, FORTRAN, and gPROMS. A comparison of various correlations has been presented. The models predict the mole fraction of absorbed CO2 and correspond well with the experimental results. Along with these models, an experimental data review on rotating packed bed is also included in this work

A Comprehensive Review on Audio based Musical Instrument Recognition: Human-Machine Interaction towards Industry 4.0

Over the last two decades, the application of machine technology has shifted from industrial to residential use. Further, advances in hardware and software sectors have led machine technology to its utmost application, the human-machine interaction, a multimodal communication. Multimodal communication refers to the integration of various modalities of information like speech, image, music, gesture, and facial expressions. Music is the non-verbal type of communication that humans often use to express their minds. Thus, Music Information Retrieval (MIR) has become a booming field of research and has gained a lot of interest from the academic community, music industry, and vast multimedia users. The problem in MIR is accessing and retrieving a specific type of music as demanded from the extensive music data. The most inherent problem in MIR is music classification. The essential MIR tasks are artist identification, genre classification, mood classification, music annotation, and instrument recognition. Among these, instrument recognition is a vital sub-task in MIR for various reasons, including retrieval of music information, sound source separation, and automatic music transcription. In recent past years, many researchers have reported different machine learning techniques for musical instrument recognition and proved some of them to be good ones. This article provides a systematic, comprehensive review of the advanced machine learning techniques used for musical instrument recognition. We have stressed on different audio feature descriptors of common choices of classifier learning used for musical instrument recognition. This review article emphasizes on the recent developments in music classification techniques and discusses a few associated future research problems

Role of big data in Agriculture-A Statistical Prospective

Not AvailableData are playing an important role making good planning and policies for agricultural growth and development. Population growth and climate change are worldwide trends that are increasing the importance of using big data science to improve agriculture. Add to that land degradation increasing marginal land and loss of biodiversity are better deals with study of big data science. Crop data can be break down into bits and bytes it will give better study about the crop development by using advance data analytics tools for betterment of agriculture. Here, talk about some important tools and techniques to handle and study the big data

Unveiling the combined effect of nano fertilizers and conventional fertilizers on crop productivity, profitability, and soil well-being

It is widely accepted that deficiency of macro (nitrogen) and micronutrients (zinc, copper etc.) affects the plant growth and development which cause a significant threat to crop production and food security. The Indian Farmers Fertilizer Cooperative (IFFCO) developed nano-urea (nano-N), nano-zinc (nano-Zn), and nano-copper (nano-Cu) liquid fertilizer formulations to enhance the crop yields, simultaneously addressing the nutrient deficiency, without causing toxicity. Therefore, this study was formulated to evaluate the effectiveness of nano-N (nano-urea), nano-Zn, and nano-Cu at varying N levels [0, 50, 75, and 100% of the recommended rates of nitrogen (RRN)] on maize-wheat and pearl millet-mustard systems during 2019–20 and 2020–21. The results exhibited that the application of nano-N + nano-Zn with 100% RRN exhibited significantly higher grain yields in maize (66.2–68.8%), wheat (62.6–61.9%), pearl millet (57.1–65.4%), and mustard (47.2–69.0%), respectively, over absolute control plots and combinations of three nano-fertilizers like nano-N + nano-Zn + nano-Cu applied plots. This was mainly attributed to the higher N and Zn uptake by the crops. However, 75% RRN with nano-N + nano-Zn also produced comparable yields. Thus, applying nano-N and nano-Zn via foliar applications, in conjunction with conventional urea, has the potential to reduce the required nitrogen fertilizer amount by up to 25%, while simultaneously maintaining equivalent yield levels. Similarly, 100% RRN and 75% RRN + nano-N + nano-Zn registered comparable profitability, soil mineral N, dehydrogenase activity (DHA), and soil microbial biomass carbon (SMBC), during both the study years. However, further research and field trials on nano fertilizers alone or in combination with conventional fertilizers are essential to fully unlock its benefits and ascertain its long-term effects which may offer a pathway to more efficient and eco-friendly crop nourishment

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This is an Extramural Research Projects funded by Agricultural Education Division of ICARNot AvailableAgriculture Education, ICA

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