Coal Power _Final.ppt
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Coal power generation was first started in 1882 in New York by Thomas Edison. Coal is the largest source of energy generation worldwide, accounting for 37% of global energy. Coal forms from decaying plants over millions of years under certain conditions and the energy extracted from coal today comes from the energy plants absorbed from the sun. In 2015, coal accounted for 29.2% of global electricity generation while in Sri Lanka in 2016, coal made up 35% of electricity generation. The impacts of coal power plants include air pollution from GHG emissions, water pollution from cooling water and waste water, solid waste generation from ash and industrial/municipal waste, and noise and vibration from machine operation and maintenance.
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![INTRODUCTION
• Coal power generation was started on 1882 In New York
by Thomas Edison. The first thermal energy source in
the world is coal[1] and coal is the largest energy
generation source having share of 37% of the world. [2]
• Coal is the sedimentary rock which formed because of
the decaying of plants before millions of years ago
under favorable conditions to formation of coal. The
energy get from coal today comes from the energy that
plants absorbed from the sun and locked in to the coal.
• In 2015, world energy consumption for electricity
generation was Oil- 32.94%, Coal- 29.2%, Natural Gas-
23.85%, Hydro- 6.79%, Nuclear-4.44%, Renewables-
5.78%(Wind-1.44%, Solar-0.45% and Other
Renewables-0.89%) [3]
• In 2016, generation mix of Sri Lanka was Oil- 31%,
Coal- 35%, Hydro- 25%, Other Renewables-9%.[4]](https://image.slidesharecdn.com/coalpowerfinal-220916045548-febd60d2/85/Coal-Power-_Final-ppt-1-320.jpg)
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detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Embedded machine learning-based road conditions and driving behavior monitoring
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Casting-Defect-inSlab continuous casting.pdf
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Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
22CYT12-Unit-V-E Waste and its Management.ppt
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Eric Nizeyimana's document 2006 from gicumbi to ttc nyamata handball play
this notes have been created
by Eric36 at nyamata ttc
after readings
pe book for y2sme .
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- 1. INTRODUCTION • Coal power generation was started on 1882 In New York by Thomas Edison. The first thermal energy source in the world is coal[1] and coal is the largest energy generation source having share of 37% of the world. [2] • Coal is the sedimentary rock which formed because of the decaying of plants before millions of years ago under favorable conditions to formation of coal. The energy get from coal today comes from the energy that plants absorbed from the sun and locked in to the coal. • In 2015, world energy consumption for electricity generation was Oil- 32.94%, Coal- 29.2%, Natural Gas- 23.85%, Hydro- 6.79%, Nuclear-4.44%, Renewables- 5.78%(Wind-1.44%, Solar-0.45% and Other Renewables-0.89%) [3] • In 2016, generation mix of Sri Lanka was Oil- 31%, Coal- 35%, Hydro- 25%, Other Renewables-9%.[4]
- 2. INTRODUCTION • Air Pollution - GHG emission(SOx, NOx, CO2,etc.), VOC, CO, Dust, Coal Handling • Water pollution – Cooling water, Waste water, contamination • Marine Pollution - Cooling water, Waste water, contamination, Coal Transport • Noise & Vibration - Machine Operation and maintenance • Generation of Solid Waste – Ash, Industrial waste, municipal waste Impacts of coal power plant
- 3. 1. Paul Breeze, “Coal – Fired Generation”, July 2015, Elsevier Science, First Edition, Chapter 3. 2. https://www.iea.org/tcep/power/coal 3. WEC, World energy resources 2016. 2016, World Energy Council. p. 1028. 4. Maheshi Danthurebandara, Leelananda Rajapaksha, “Environmental consequences of different electricity generation mixes in Sri Lanka by 2050”, Journal of Cleaner Production ,2018 5. The Local Air Pollution Cost of Coal Storage and Handling: Evidence From U.S. Power. September 26, 2018 6. Water quality assessment around a coal-fired power plant in southern coast of Java, Indonesia. 7. CASE STUDY: The Lakwijaya Coal Power Plant - Environmental Foundation 8. Study on emission of hazardous trace elements in a REFERENCE