Nanomaterial-enabled fiber optic gas sensor for fossil fuel energy systems

2004 ◽  
Author(s):  
Hai Xiao ◽  
Alexander Bourandas ◽  
Aniruddha Kulkarni ◽  
Junhang Dong ◽  
Scott W. Teare
Keyword(s):  
Gas Sensor ◽  
Fossil Fuel ◽  
Fiber Optic ◽  
Energy Systems ◽  
Fossil Fuel Energy

Solar Thermal Energy

2018 ◽  
Author(s):  
E. L. Wolf
Keyword(s):  
Solar Cells ◽  
Thermal Energy ◽  
Fossil Fuel ◽  
Rankine Cycle ◽  
Carnot Cycle ◽  
The Sun ◽  
Solar Thermal Energy ◽  
Heat Engines ◽  
Direct Use ◽  
Fossil Fuel Energy

The Sun’s spectrum on Earth is modified by the atmosphere, and is harvested either by generating heat for direct use or for running heat engines, or by quantum absorption in solar cells, to be discussed later. Focusing of sunlight requires tracking of the Sun and is defeated on cloudy days. Heat engines have efficiency limits similar to the Carnot cycle limit. The steam turbine follows the Rankine cycle and is well developed in technology, optimally using a re-heat cycle of higher efficiency. Having learned quite a bit about how the Sun’s energy is created, and how that process might be reproduced on Earth, we turn now to methods for harvesting the energy from the Sun as a sustainable replacement for fossil fuel energy.

The Problem of Energy

2014 ◽  
Vol 31 (5) ◽  
pp. 3-20 ◽  
Author(s):  
John Urry
Keyword(s):  
Social Theory ◽  
21St Century ◽  
Fossil Fuel ◽  
Energy Systems ◽  
Social Thought ◽  
Human Intervention ◽  
The West ◽  
The Past ◽  
And Control ◽  
Global Growth

Energy forms and their extensive scale are remarkably significant for the ways that societies are organized. This article shows the importance of how societies are ‘energized’ and especially the global growth of ‘fossil fuel societies’. Much social thought remains oblivious to the energy revolution realized over the past two to three centuries which set the ‘West’ onto a distinct trajectory. Energy is troubling for social thought because different energy systems with their ‘lock-ins’ are not subject to simple human intervention and control. Analyses are provided here of different fossil fuel societies, of coal and oil, with the latter enabling the liquid, mobilized 20th century. Consideration is paid to the possibilities of reducing fossil fuel dependence but it is shown how unlikely such a ‘powering down’ will be. The author demonstrates how energy is a massive problem for social theory and for 21st-century societies. Developing post-carbon theory and especially practice is far away but is especially urgent.

Fiber optic gas sensor with nanocrystalline ZnO

2014 ◽  
Vol 20 (1) ◽  
pp. 48-52 ◽  
Author(s):  
B. Renganathan ◽  
A.R. Ganesan
Keyword(s):  
Gas Sensor ◽  
Fiber Optic

scholarly journals Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics

2019 ◽  
Vol 9 (20) ◽  
pp. 4417 ◽  
Author(s):  
Sana Mujeeb ◽  
Turki Ali Alghamdi ◽  
Sameeh Ullah ◽  
Aisha Fatima ◽  
Nadeem Javaid ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Power Systems ◽  
Prediction Model ◽  
Wind Power ◽  
Fossil Fuel ◽  
Energy Systems ◽  
Accurate Estimation ◽  
Wind Power Forecasting ◽  
Power Forecasting

Recently, power systems are facing the challenges of growing power demand, depleting fossil fuel and aggravating environmental pollution (caused by carbon emission from fossil fuel based power generation). The incorporation of alternative low carbon energy generation, i.e., Renewable Energy Sources (RESs), becomes crucial for energy systems. Effective Demand Side Management (DSM) and RES incorporation enable power systems to maintain demand, supply balance and optimize energy in an environmentally friendly manner. The wind power is a popular energy source because of its environmental and economical benefits. However, the uncertainty of wind power makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. The proposed model has two stages. In the first stage, Wavelet Packet Transform (WPT) is used to decompose the past wind power signals. Other than decomposed signals and lagged wind power, multiple exogenous inputs (such as, calendar variable and Numerical Weather Prediction (NWP)) are also used as input to forecast wind power. In the second stage, a new prediction model, Efficient Deep Convolution Neural Network (EDCNN), is employed to forecast wind power. A DSM scheme is formulated based on forecasted wind power, day-ahead demand and price. The proposed forecasting model’s performance was evaluated on big data of Maine wind farm ISO NE, USA.

Carbon nanotubes coated fiber optic ammonia gas sensor

2011 ◽  
Author(s):  
S. Manivannan ◽  
L. R. Shobin ◽  
A. M. Saranya ◽  
B. Renganathan ◽  
D. Sastikumar ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Gas Sensor ◽  
Fiber Optic ◽  
Ammonia Gas ◽  
Coated Fiber
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