Ecofriendly and green synthesis of carbon nanoparticles from rice bran: characterization and identification using image processing technique

Dhandapani, Kothandaraman; Venugopal, Krishnaveni; Kumar, Jothi Vinoth

doi:10.1007/s12588-019-09240-9

Cited by 31 publications

(12 citation statements)

References 22 publications

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“…8,9 CNDs have multipurpose applications in various elds, such as drug delivery, optoelectronic devices, sensing of metal ions, biological imaging, biomolecules, catalysis, solar cells, and the oxygen reduction reaction. [10][11][12][13][14][15][16] CNDs have been synthesized by various top-down methods, including laser ablation, ultrasonic treatment, arc-discharge, and electrochemical and chemical oxidation, and bottom-up methods such as hydrothermal carbonization, microwave irradiation, pyrolysis, and plasma treatment. 17 Among these synthesis methods, arc discharge and laser ablation processes need highly expensive instruments, whereas electrochemical oxidation and chemical oxidation need strong acids.…”

Section: Introductionmentioning

confidence: 99%

Cyan color-emitting nitrogen-functionalized carbon nanodots (NFCNDs) from Indigofera tinctoria and their catalytic reduction of organic dyes and fluorescent ink applications

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Cyan color-emitting nitrogen-functionalized carbon nanodots (NFCNDs) from Indigofera tinctoria and their catalytic reduction of organic dyes and fluorescent ink applications

et al. 2021

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“…Artificial intelligence (AI) has become an integral part in many fields like waste generation [6,7], automobiles, international business, educational institution [8], civil engineering applications [9,10], medical [11], image processing [12] and also, electrical power system applications [13]. AI analyze the system and make prediction based on previous available data.…”

Section: Introductionmentioning

confidence: 99%

Electric power load forecasting on a 33/11 kV substation using artificial neural networks

Veeramsetty

Deshmukh

2020

SN Appl. Sci.

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Estimation of electric power load on electric power substation is an essential task for system operator in order to operate the system in a reliable and optimal manner. In this paper, machine learning with artificial neural network is used for forecasting the load at a particular hour of the day on an electric power substation. Historical load data at each hour of the day for the period from September-2018 to November-2018 is taken from 33/11 kV substation near Kakatiya University in Warangal. A new artificial neural network architecture is developed based on the approach used to forecast the load. The developed model is simulated in MATLAB with available historical data to forecast the load on 33/11 kV electric power substation. Based on the analysis it is observed that the proposed architecture forecasts the load with better accuracy.Keywords Artificial neural networks · Electric power load forecasting · Machine learning · Mean square error · Mean absolute percentage error List of symbols L(D, t) Load at Dth day and tth hour L(D, t − 1) Load at Dth day and (t − 1)th hour L(D, t − 2) Load at Dth day and (t − 2)th hour L(D, t − 3) Load at Dth day and (t − 3)th hour L(t, D − 1) Load at (D − 1)th day and tth hour L(t, D − 2) Load at (D − 2)th day and tth hour L(t, D − 3) Load at (D − 3)th day and tth hour L(t, D − 4) Load at (D − 4)th day and tth hour MAPE Mean absolute percentage error MSE Mean square error y Target i Actual output y Predicted i Predicted output m Number of samples R Regression coefficient * Venkataramana Veeramsetty,

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“…As a result, an efficient short-term electric power load prediction model is very important and useful for smart grid operations and also improves decision making. 6 Artificial intelligence (AI) has become an integral part of many fields, such as civil engineering applications, 7,8 image processing, 9 and the medical sector. 10 AI techniques can help the power system operator to trade electricity at a maximum profit by predicting electrical load and electricity prices.…”

Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence (AI) has become an integral part of many fields, such as civil engineering applications, 7,8 image processing, 9 and the medical sector 10 . AI techniques can help the power system operator to trade electricity at a maximum profit by predicting electrical load and electricity prices.…”

Section: Introductionmentioning

confidence: 99%

Short‐term electric power load forecasting using factor analysis and long short‐term memory for smart cities

Veeramsetty¹,

Chandra²,

Salkuti

2020

Circuit Theory & Apps

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Electric load estimation is an important activity for electrical power system operators to operate the system stably and optimally. This paper develops a machine learning model with a long short-term memory and a factor analysis to predict the load at a specific hour of the day on an electrical power substation. Historical load data from the 33-/11-kV substation near Kakatiya University in Warangal are taken at each hour of the day for the period from September 2018 to November 2018. A new long short-term memory architecture with factor analysis is being designed based on the approach used to predict substation loads by simulation in Microsoft Azure Notebooks. Based on the study, it was found that the proposed design predicts loads with good accuracy.

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Ecofriendly and green synthesis of carbon nanoparticles from rice bran: characterization and identification using image processing technique

Cited by 31 publications

References 22 publications

Cyan color-emitting nitrogen-functionalized carbon nanodots (NFCNDs) from Indigofera tinctoria and their catalytic reduction of organic dyes and fluorescent ink applications

Cyan color-emitting nitrogen-functionalized carbon nanodots (NFCNDs) from Indigofera tinctoria and their catalytic reduction of organic dyes and fluorescent ink applications

Electric power load forecasting on a 33/11 kV substation using artificial neural networks

Short‐term electric power load forecasting using factor analysis and long short‐term memory for smart cities

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