scholarly journals Machine Learning in Estimating CO2 Emissions from Electricity Generation

2021 ◽  
Author(s):  
Marco Rao
Keyword(s):  
Machine Learning ◽  
Co2 Emission ◽  
Electricity Generation ◽  
Combined Cycle ◽  
Support Vector ◽  
Turbine Plant ◽  
Gas Turbine Plant ◽  
Using Data ◽  
Competing Technologies ◽  
Significant Application

In the last decades, there has been an outstanding rise in the advancement and application of various types of Machine learning (ML) approaches and techniques in the modeling, design and prediction for energy systems. This work presents a simple but significant application of a ML approach, the Support Vector Machine (SVM) to the estimation of CO2 emission from electricity generation. The CO2 emission was estimate in a framework of Cost-Effectiveness Analysis between two competing technologies in electricity generation using data for Combined Cycle Gas Turbine Plant (CCGT) provided by IEA for Italy in 2020. Respect to other application of ML techniques, usually developed to address engineering issues in energy generation, this work is intended to provide useful insights in support decision for energy policy.

Machine learning based detection of volcano seismicity using the spatial pattern of amplitudes

2020 ◽  
Author(s):  
Yuta Maeda ◽  
Yoshiko Yamanaka ◽  
Takeo Ito ◽  
Shinichiro Horikawa
Keyword(s):  
Machine Learning ◽  
Large Amplitude ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Seismic Events ◽  
Amplitude Ratios ◽  
Volcano Seismicity ◽  
Using Data ◽  
Summit Region ◽  
Manual Classification

Summary We propose a new algorithm, focusing on spatial amplitude patterns, to automatically detect volcano seismic events from continuous waveforms. Candidate seismic events are detected based on signal-to-noise ratios. The algorithm then utilizes supervised machine learning to classify the existing candidate events into true and false categories. The input learning data are the ratios of the number of time samples with amplitudes greater than the background noise level at 1 s intervals (large amplitude ratios) given at every station site, and a manual classification table in which ‘true'' or ‘false'' flags are assigned to candidate events. A two-step approach is implemented in our procedure. First, using the large amplitude ratios at all stations, a neural network model representing a continuous spatial distribution of large amplitude probabilities is investigated at 1 s intervals. Second, several features are extracted from these spatial distributions, and a relation between the features and classification to true and false events is learned by a support vector machine. This two-step approach is essential to account for temporal loss of data, or station installation, movement, or removal. We evaluated the algorithm using data from Mt. Ontake, Japan, during the first ten days of a dense observation trial in the summit region (November 1–10, 2017). Results showed a classification accuracy of more than 97 per cent.

A Study of Cross-National Differences in Happiness Factors Using Machine Learning Approach

2015 ◽  
Vol 25 (09n10) ◽  
pp. 1699-1702 ◽  
Author(s):  
Theresia Ratih Dewi Saputri ◽  
Seok-Won Lee
Keyword(s):  
Machine Learning ◽  
Information Gain ◽  
Development Project ◽  
Support Vector ◽  
National Differences ◽  
Dimensionality Reduction Technique ◽  
Machine Learning Approach ◽  
National Happiness ◽  
Using Data ◽  
Cross National

National happiness has been actively studied throughout the past years. The happiness factor varies due to different human perspectives. The factors used in this work include both physical needs and the mental needs of humanity, for example, the educational factor. This work identified more than 90 features that can be used to predict the country happiness. Due to numerous features, it is unwise to rely on the prediction of national happiness by manual analysis. Therefore, this work used a machine learning technique called Support Vector Machine (SVM) to learn and predict the country happiness. In order to improve the prediction accuracy, dimensionality reduction technique which is the information gain was also used in this work. This technique was chosen due to its ability to explore the interrelationships among a set of variables. Using data of 187 countries from the UN Development Project, this work is able to identify which factor needed to be improved by a certain country to increase the happiness of their citizens.

DADICC: Intelligent system for anomaly detection in a combined cycle gas turbine plant

2008 ◽  
Vol 34 (4) ◽  
pp. 2267-2277 ◽  
Author(s):  
A ARRANZ ◽  
A CRUZ ◽  
M SANZBOBI ◽  
P RUIZ ◽  
J COUTINO
Keyword(s):  
Anomaly Detection ◽  
Gas Turbine ◽  
Intelligent System ◽  
Combined Cycle ◽  
Turbine Plant ◽  
Gas Turbine Plant

Attempt of Early Stuck Detection Using Unsupervised Deep Learning With Probability Mixture Model

2021 ◽  
Author(s):  
Tomoya Inoue ◽  
Yujin Nakagawa ◽  
Ryota Wada ◽  
Keisuke Miyoshi ◽  
Shungo Abe ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Detection ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Models ◽  
Vector Machines ◽  
Unsupervised Deep Learning ◽  
Drilling Operations ◽  
Using Data

Abstract The early detection of a stuck pipe during drilling operations is challenging and crucial. Some of the studies on stuck detection have adopted supervised machine learning approaches with ordinal support vector machines or neural networks using datasets for “stuck” and “normal”. However, for early detection before stuck occurs, the application of ordinal supervised machine learning has several concerns, such as limited stuck data, lack of an exact “stuck sign” before it occurs, and the various mechanisms involved in pipe sticking. This study acquires surface drilling data from various wells belonging to several agencies, examines the effectiveness of multiple learning models, and discusses the possibility of the early detection of pipe sticking before it occurs. Unsupervised machine learning using data on the normal activities is a possible advanced method for early stuck detection, which is adopted in this study. In addition, as a countermeasure to another concern that even normal activities involve various operations, we apply unsupervised learning with multiple learning models.

CALCULATION OF THE DRAINAGE SYSTEM OF LEAVING FLUE GASES FROM THE TURBINE THROUGH THE COOLING TOWER

2018 ◽  
Vol 8 (1) ◽  
pp. 135-138
Author(s):  
Anatoly A. KUDINOV ◽  
Yulia E. DEMINA
Keyword(s):  
Power Plant ◽  
Cooling Tower ◽  
Drainage System ◽  
Combined Cycle ◽  
Flue Gases ◽  
Simultaneous Reduction ◽  
Turbine Plant ◽  
Circulating Water ◽  
Effi Ciency ◽  
Gas Turbine Plant

The article presents result of a research a system of the venting of exhaust gases of the recovery boiler the gas turbine plant through the natural draft cooling tower in the environment. The use of this scheme allows the fl ue gases to lower the temperature of the circulating water at the outlet of the cooling tower to provide a deeper vacuum in the condenser steam turbine combined cycle power plant with simultaneous reduction of capital to build chimneys. As a result of the application of this scheme, an increase in the absolute electric effi ciency of turbines is achieved. As stated in Article method of calculating the removal of exhaust fl ue gas systems with a perforated distributor ring allows to determine the level of engineering design and volume requirements of these systems.

scholarly journals Increasing the Energy Efficiency of the Power Supply System on Liquefied Natural Gas through the Use of a Combined Cycle Gas Turbine Plant

2018 ◽  
Vol 179 ◽  
pp. 01016
Author(s):  
S V Zaika ◽  
D A Uglanov ◽  
E A Shatokhin ◽  
S S Zaika ◽  
A A Shimanov
Keyword(s):  
Power Supply System ◽  
Combined Cycle ◽  
Technical Solution ◽  
Additional Energy ◽  
Turbine Plant ◽  
Industrial Enterprises ◽  
Main Pipelines ◽  
Loss Compensation ◽  
Piping Systems ◽  
Gas Turbine Plant

The use of LNG for the production of additional energy is topical today. Very often LNG is used for gasification of settlements and industrial enterprises. LNG makes it possible to gasify objects remote from main pipelines for long distances by creating an LNG reserve directly from the consumer, avoiding the construction of expensive piping systems. However, in this case, there are large losses of cryoproduct and its low-potential heat. In this article, we propose a technical solution for compensating for the loss of cryoproducts during storage using an installation consisting of two circuits, one of which is a loss compensation loop and the other is a circuit for generating additional energy. The conducted researches showed that the use of this system allowed to return the previously used for liquefying electric power in the amount of 24%.

The Optimum Pressure Ratio for a Combined Cycle Gas Turbine Plant

1995 ◽  
Vol 209 (4) ◽  
pp. 259-264 ◽  
Author(s):  
J H Horlock
Keyword(s):  
Gas Turbine ◽  
Pressure Ratio ◽  
Combined Cycle ◽  
Maximum Efficiency ◽  
Specific Work ◽  
Optimum Pressure ◽  
Turbine Plant ◽  
Gas Turbine Plant ◽  
Overall Efficiency ◽  
Gas Turbine Cycle

A graphical method of calculating the performance of gas turbine cycles, developed by Hawthorne and Davis (1), is adapted to determine the pressure ratio of a combined cycle gas turbine (CCGT) plant which will give maximum overall efficiency. The results of this approximate analysis show that the optimum pressure ratio is less than that for maximum efficiency in the higher level (gas turbine) cycle but greater than that for maximum specific work in that cycle. Introduction of reheat into the higher cycle increases the pressure ratio required for maximum overall efficiency.

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