By: Mir Sayed Shah Danish, Zahra Nazari, Tomonobu Senjyu
This study investigates the transformation of energy models to align with
machine learning requirements as a promising tool for optimizing the operation
of combined cycle power plants (CCPPs). By modeling energy production as a
function of environmental and control variables, this methodology offers an
innovative way to achieve energy-efficient power generation in the context of
the data-driven application. This study focuses on developing ... more
This study investigates the transformation of energy models to align with
machine learning requirements as a promising tool for optimizing the operation
of combined cycle power plants (CCPPs). By modeling energy production as a
function of environmental and control variables, this methodology offers an
innovative way to achieve energy-efficient power generation in the context of
the data-driven application. This study focuses on developing a thorough
AI-coherent modeling approach for CCPP optimization, preferring an
interdisciplinary perspective and coming up with a comprehensive, insightful
analysis. The proposed numerical model using Broyden Fletcher Goldfarb Shanno
(BFGS) algorithm enhances efficiency by simulating various operating scenarios
and adjusting optimal parameters, leading to a high yield power generation of
2.23% increase from 452 MW to 462.1 MW by optimizing the environmental factors.
This study deals with data-driven modeling based on historical data to make
predictions without prior knowledge of the system's parameter, demonstrating
several merits in identifying patterns that can be difficult for human analysts
to detect, high accuracy when trained on large datasets, and the potential to
improve over time with new data. The proposed modeling approach and methodology
can be expanded as a valuable tool for forecasting and decision-making in
complex energy systems.
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By: Fatih Gulec, Baris Atakan, Falko Dressler
A number of transmission models for airborne pathogens transmission, as
required to understand airborne infectious diseases such as COVID-19, have been
proposed independently from each other, at different scales, and by researchers
from various disciplines. We propose a communication engineering approach that
blends different disciplines such as epidemiology, biology, medicine, and fluid
dynamics. The aim is to present a unified framework u... more
A number of transmission models for airborne pathogens transmission, as
required to understand airborne infectious diseases such as COVID-19, have been
proposed independently from each other, at different scales, and by researchers
from various disciplines. We propose a communication engineering approach that
blends different disciplines such as epidemiology, biology, medicine, and fluid
dynamics. The aim is to present a unified framework using communication
engineering, and to highlight future research directions for modeling the
spread of infectious diseases through airborne transmission. We introduce the
concept of mobile human ad hoc networks (MoHANETs), which exploits the
similarity of airborne transmission-driven human groups with mobile ad hoc
networks and uses molecular communication as the enabling paradigm. In the
MoHANET architecture, a layered structure is employed where the infectious
human emitting pathogen-laden droplets and the exposed human to these droplets
are considered as the transmitter and receiver, respectively. Our
proof-of-concept results, which we validated using empirical COVID-19 data,
clearly demonstrate the ability of our MoHANET architecture to predict the
dynamics of infectious diseases by considering the propagation of
pathogen-laden droplets, their reception and mobility of humans.
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