Author
Y. Pydi Setty
Bio: Y. Pydi Setty is an academic researcher from National Institute of Technology, Warangal. The author has contributed to research in topics: Fluidized bed & Mass transfer coefficient. The author has an hindex of 10, co-authored 41 publications receiving 339 citations.
Papers
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TL;DR: In this article, experiments were carried out with Kodo millet and Fenugreek seeds using batch wall heated fluidized bed dryer and the exergy and energy analyses were performed changing wall temperature, air velocity, bed height and initial moisture content of bed material.
69 citations
TL;DR: In this paper, the results obtained from artificial neural networks are compared with those obtained using Tanks-in-series model with less percentage error, which indicates a better fit of artificial neural network to experimental data compared to various mathematical models.
61 citations
TL;DR: In this article, the authors deal with the synthesis of water-based stable colloidal nanoparticles and the study of their heat transfer characteristics and show that the heat transfer coefficient increases with an increase in the concentration of colloidal copper nanoparticles.
50 citations
TL;DR: In this paper, clarified cashew apple juice can be used as a potential substrate for microbial fuel cell generating open circuit voltage of 0.4V and maximum power density and current density of 31.58mW/m2, 350mV, and 350mA/m 2 respectively.
42 citations
TL;DR: In this article, the use of novel antimony-tin based anode material and manganese dioxide as cathode material is demonstrated in three different microbial fuel cells for studying their performance.
38 citations
Cited by
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TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).
13,246 citations
TL;DR: In this paper, experimental works on drying of tomatoes in a tray dryer covering different variables like power of heater and air flow velocity were performed using artificial neural network and empirical mathematical equations.
237 citations
TL;DR: In this paper, a review of the latest trends and advancements in microstructured reactors is presented, focusing on the fabrication, commercial aspects, design principles, and cutting-edge applications of microreactors.
183 citations
TL;DR: In this article, the authors present a comprehensive review of numerous significant applications of the ANN technique to solve problems of nonlinear function approximation, pattern detection, data interpretation, optimization, simulation, diagnosis, control, data sorting, clustering, and noise reduction in drying technology.
Abstract: Inspired by the functional behavior of the biological nervous system of the human brain, the artificial neural network (ANN) has found many applications as a superior tool to model complex, dynamic, highly nonlinear, and ill-defined scientific and engineering problems. For this reason, ANNs are employed extensively in drying applications because of their favorable characteristics, such as efficiency, generalization, and simplicity. This article presents a comprehensive review of numerous significant applications of the ANN technique to solve problems of nonlinear function approximation, pattern detection, data interpretation, optimization, simulation, diagnosis, control, data sorting, clustering, and noise reduction in drying technology. We summarize the use of the ANN approach in modeling various dehydration methods; e.g., batch convective thin-layer drying, fluidized bed drying, osmotic dehydration, osmotic-convective drying, infrared, microwave, infrared- and microwave-assisted drying processes, spray ...
151 citations
TL;DR: In this article, the authors summarized the important published works on nanofluid preparations, properties, experimental and numerical heat transfer behaviors, and two main categories were discussed in detail.
148 citations