scholarly journals Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant

2020 ◽  
Vol 3 (3) ◽  
pp. 34
Author(s):  
Antoni Escobet ◽  
Teresa Escobet ◽  
Joseba Quevedo ◽  
Adoración Molina
Keyword(s):  
Natural Gas ◽  
Data Analytics ◽  
Liquefied Natural Gas ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Sensor Data ◽  
Gas Consumption ◽  
Using Data ◽  
Maintenance Plan ◽  
Remaining Time

This paper proposes a sensor-data-driven prognosis approach for the predictive maintenance of a liquefied natural gas (LNG) satellite plant. By using data analytics of sensors installed in the satellite plants, it is possible to predict the remaining time to refill the tank of the remote plants. In the proposed approach, the first task of data validation and correction is presented in order to transform raw data into reliable validated data. Then, the second task presents two methods for the prognosis of gas consumption in real time and the forecast of remaining time to refill the tank of the plant. The obtained results with real satellite plants showed good performance for direct implementation in a predictive maintenance plan.

Data-driven tools for assessing and combating COVID-19 outbreaks in Brazil based on analytics and statistical methods

2021 ◽  
Keyword(s):  
Data Analytics ◽  
Exploratory Data Analysis ◽  
Reproduction Number ◽  
Healthcare Systems ◽  
Regional Level ◽  
Data Driven ◽  
Effective Response ◽  
Reproduction Numbers ◽  
Exploratory Data ◽  
Using Data

The COVID-19 pandemic is one of the worst public health crises in Brazil and the world that has ever been faced. One of the main challenges that the healthcare systems have when decision-making is that the protocols tested in other epidemics do not guarantee success in controlling the spread of COVID-19, given its complexity. In this context, an effective response to guide the competent authorities in adopting public policies to fight COVID-19 depends on thoughtful analysis and effective data visualization, ideally based on different data sources. In this paper, we discuss and provide tools that can be helpful using data analytics to respond to the COVID-19 outbreak in Recife, Brazil. We use exploratory data analysis and inferential study to determine the trend changes in COVID-19 cases and their effective or instantaneous reproduction numbers. According to the data obtained of confirmed COVID-19 cases disaggregated at a regional level in this zone, we note a heterogeneous spread in most megaregions in Recife, Brazil. When incorporating quarantines decreed, effectiveness is detected in the regions. Our results indicate that the measures have effectively curbed the spread of the disease in Recife, Brazil. However, other factors can cause the effective reproduction number to not be within the expected ranges, which must be further studied.

Integrating Rule-Based Systems and Data Analytics Tools Using Open Standard PMML

2015 ◽  
Author(s):  
Yunpeng Li ◽  
Utpal Roy ◽  
Y. Tina Lee ◽  
Sudarsan Rachuri
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Analytics ◽  
Domain Knowledge ◽  
Prediction Models ◽  
Data Driven ◽  
Sensor Data ◽  
Formal Representation ◽  
Rule Based ◽  
Open Standard

Rule-based expert systems such as CLIPS (C Language Integrated Production System) are 1) based on inductive (if-then) rules to elicit domain knowledge and 2) designed to reason new knowledge based on existing knowledge and given inputs. Recently, data mining techniques have been advocated for discovering knowledge from massive historical or real-time sensor data. Combining top-down expert-driven rule models with bottom-up data-driven prediction models facilitates enrichment and improvement of the predefined knowledge in an expert system with data-driven insights. However, combining is possible only if there is a common and formal representation of these models so that they are capable of being exchanged, reused, and orchestrated among different authoring tools. This paper investigates the open standard PMML (Predictive Model Mockup Language) in integrating rule-based expert systems with data analytics tools, so that a decision maker would have access to powerful tools in dealing with both reasoning-intensive tasks and data-intensive tasks. We present a process planning use case in the manufacturing domain, which is originally implemented as a CLIPS-based expert system. Different paradigms in interpreting expert system facts and rules as PMML models (and vice versa), as well as challenges in representing and composing these models, have been explored. They will be discussed in detail.

scholarly journals Reducing Pollution Levels Generated by Short Sea Shipping. Use of Bayesian Networks to Analyse the Utilization of Liquefied Natural Gas as an Alternative Fuel

2019 ◽  
Vol 26 (1) ◽  
pp. 147-158
Author(s):  
Beatriz Molina Serrano ◽  
Nicoleta González Cancelas ◽  
Francisco Soler Flores
Keyword(s):  
Decision Making ◽  
European Union ◽  
Natural Gas ◽  
Alternative Fuels ◽  
Liquefied Natural Gas ◽  
Environmental Benefits ◽  
The European Union ◽  
Short Sea Shipping ◽  
Pollution Levels ◽  
Using Data

Abstract Pollution adjacent to the continent's shores has increased in the last decades, so it has been necessary to establish an energy policy to improve environmental conditions. One of the proposed solution was the search of alternative fuels to the commonly used in Short Sea Shipping to reduce pollution levels in Europe. Studies and researches show that liquefied natural gas could meet the European Union environmental requirements. Even environmental benefits are important; currently there is not significant number of vessels using it as fuel. Moreover, main target of this article is exposing result of a research in which a methodology to establish the most relevant variables in the decision to implement liquefied natural gas in Short Sea Shipping has been development using data mining. A Bayesian network was constructed because this kind of network allows to get graphically the relationships between variables and to determine posteriori values that quantify their contributions to decision-making. Bayesian model has been done using data from some European countries (European Union, Norway and Iceland) and database was generated by 35 variables classified in 5 categories. Main obtained conclusion in this analysis is that variables of transport and international trade and economy and finance are the most relevant in the decision-making process when implementing liquefied natural gas. Even more, it can be stablish that capacity of liquefied natural gas regasification terminals under construction and modal distribution of water cargo transportation continental as the most decisive variables because they are the root nodes in the obtained network.

Liquefied natural gas as a backup fuel for TPP

2021 ◽  
Vol 14 (2) ◽  
pp. 84-91
Author(s):  
S. N. Lenev ◽  
V. B. Perov ◽  
A. N. Vivchar ◽  
A. V. Okhlopkov ◽  
O. Y. Sigitov ◽  
...  
Keyword(s):  
Natural Gas ◽  
Power Plants ◽  
Large Scale ◽  
Gas Flow ◽  
National Policy ◽  
Liquefied Natural Gas ◽  
Fuel Oil ◽  
Gas Flows ◽  
Gas Industry ◽  
Gas Consumption

Major trends in the development of the gas industry point to a large-scale expansion of the liquefied natural gas (LNG) market, which continues to be a fast-growing segment compared to other energy sources. The national policy of the Russian Federation is aimed at developing the infrastructure of LNG complexes. This article analyses the world experience in the use of LNG complexes in gas consumption peak damping installations, which meet the conditions of LNG use as a backup fuel by PJSC Mosenergo branches (low-tonnage production combined with a large volume of LNG storage). It is shown that, in terms of the conditions of production and use of LNG at power plants, the most suitable are installations with 90–100% liquefaction of the incoming gas flow with an external refrigerating circuit using a mixed refrigerant or nitrogen, which provide the composition of regasified LNG almost identical to the composition of the source gas. The authors have formulated requirements for the development of energy-efficient LNG complexes at PJSC Mosenergo branches, including ensuring cycle energy consumption by expanding the network gas in the expander with utilization of refrigerating capacity in the liquefaction cycle, as well as cooling the compressed coolant of the refrigerating circuit by gas flows supplied further for combustion. The technological features of implementation of the LNG complex for production, storage and regasification of LNG as a reserve fuel for TPPs are reviewed. The study has shown that the most suitable power plant for the introduction of an LPG complex is TPP-22, for which a new fuel oil facility is being designed. Despite the current practice of using fuel oil and diesel fuel as backup fuels, LNG can have a competitive advantage through the use of secondary energy resources of TPPs. 

scholarly journals Big Data Analytics in Government: Improving Decision Making for R&D Investment in Korean SMEs

2019 ◽  
Vol 12 (1) ◽  
pp. 202
Author(s):  
Eun Sun Kim ◽  
Yunjeong Choi ◽  
Jeongeun Byun
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Driven ◽  
Medium Size ◽  
The Government ◽  
Using Data ◽  
High Level ◽  
D Investment

To expand the field of governmental applications of Big Data analytics, this study presents a case of data-driven decision-making using information on research and development (R&D) projects in Korea. The Korean government has continuously expanded the proportion of its R&D investment in small and medium-size enterprises to improve the commercialization performance of national R&D projects. However, the government has struggled with the so-called “Korea R&D Paradox”, which refers to how performance has lagged despite the high level of investment in R&D. Using data from 48,309 national R&D projects carried out by enterprises from 2013 to 2017, we perform a cluster analysis and decision tree analysis to derive the determinants of their commercialization performance. This study provides government entities with insights into how they might adjust their approach to Big Data analytics to improve the efficiency of R&D investment in small- and medium-sized enterprises.

scholarly journals Layer-Wise Relevance Propagation for Interpreting LSTM-RNN Decisions in Predictive Maintenance

2021 ◽  
Author(s):  
Haiyue Wu ◽  
Aihua Huang ◽  
John W. Sutherland
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Predictive Accuracy ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Sensor Data ◽  
Neural Network Models ◽  
Advanced Technique

Abstract Predictive maintenance (PdM) is an advanced technique to predict the time to failure (TTF) of a system. PdM collects sensor data on the health of a system, processes the information using data analytics, and then establishes data-driven models that can forecast system failure. Deep neural networks are increasingly being used as these data-driven models owing to their high predictive accuracy and efficiency. However, deep neural networks are often criticized as being “black boxes,” which owing to their multi-layered and non-linear structure provide little insight into the underlying physics of the system being monitored, and that are nontransparent and untraceable in their predictions. In order to address this issue, the layer-wise relevance propagation (LRP) technique is applied to analyze a long short-term memory (LSTM) recurrent neural network (RNN) model. The proposed method is demonstrated and validated for a bearing health monitoring study based on vibration data. The obtained LRP results provide insights into how the model “learns” from the input data and demonstrate the distribution of contribution/relevance to the neural network classification in the input space. In addition, comparisons are made with gradient-based sensitivity analysis to show the power of LRP in interpreting RNN models. The LRP is proved to have promising potential in interpreting deep neural network models and improving model accuracy and efficiency for PdM.

Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling

2018 ◽  
Vol 115 ◽  
pp. 41-53 ◽  
Author(s):  
Marcia Baptista ◽  
Shankar Sankararaman ◽  
Ivo. P. de Medeiros ◽  
Cairo Nascimento ◽  
Helmut Prendinger ◽  
...  
Keyword(s):  
Predictive Maintenance ◽  
Data Driven ◽  
Using Data ◽  
Arma Modeling

scholarly journals Energy Assessment of BelAZ-75131 Gas-diesel Mining Dump Trucks Operation at Kuzbass Open Casts

2020 ◽  
Vol 174 ◽  
pp. 03010
Author(s):  
Ilya Kuznetsov ◽  
Ivan Panachev ◽  
Georgiy Dubov ◽  
Sergey Nokhrin
Keyword(s):  
Natural Gas ◽  
Diesel Fuel ◽  
Liquefied Natural Gas ◽  
Dump Truck ◽  
Energy Estimation ◽  
Gaseous Fuels ◽  
Energy Assessment ◽  
Dump Trucks ◽  
Gas Consumption ◽  
The Cost

The parameters of the BelAZ-75131 heavy dump truck im- proved model using gas-diesel mixture are given in the paper. The moni- toring analysis of the conditions and operation indicators of BelAZ-75131 heavy dump truck using diesel and gaseous fuels is done. Numerical and percentage values of the replacement of diesel fuel with liquefied natural gas when transporting exploded rock mass are determined; the volume of diesel fuel and liquefied natural gas consumption has been established. The analytical dependence to calculate the cost per unit of energy during trans- portation by mining dump trucks with gas equipment is determined. The energy estimation of diesel and gas-diesel mining dump trucks operation is given.

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