scholarly journals Analysis of Natural Gas Consumption by the Industrial Sector of Bangladesh

2013 ◽  
Vol 27 ◽  
pp. 1-7
Author(s):  
Mahbubur Rahman ◽  
Mohammad Tamin ◽  
Lutfar Rahman
Keyword(s):  
Natural Gas ◽  
Chemical Engineering ◽  
Industrial Sector ◽  
Policy Makers ◽  
Usual Practice ◽  
Consumption Data ◽  
Depth Analysis ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
First Time

The natural gas consuming sectors in Bangladesh are: i) Power, ii) Fertilizer, iii) Industry, iv) Captive power, v) Domestic, vi) Commercial, and vii) Transportation (CNG). Broad sectoral consumptions are reported in various literatures and reports, however, further breakdown of the data are difficult to find, and neither reported. The combined consumption of fertilizer, industry and captive power sectors is a significant portion of national gas consumption. This paper presents for the first time an in-depth analysis of the industrial sector gas consumption. Data were collected for each type of industry, and grouped according to the United Nations Framework Convention for Climate Change (UNFCCC). Captive generation is included in the industrial sector consumption, unlike the usual practice of considering it under the power generation. It is noticed that garments, textile and leather industries together have shown remarkable growth in the last decade. All the industries are more or less related to the national GDP growth. Some are export oriented while others address the internal market. Therefore analysis presented here should be helpful for policy makers to prioritize the sectors in case preferential supply and tariff adjustments become necessary.DOI: http://dx.doi.org/10.3329/jce.v27i1.15846 Journal of Chemical Engineering, IEB Vol. ChE. 27, No. 1, June 2012: 1-7

Statistical models for disaggregation and reaggregation of natural gas consumption data

2014 ◽  
Vol 42 (5) ◽  
pp. 921-937 ◽  
Author(s):  
M. Brabec ◽  
O. Konár ◽  
M. Malý ◽  
I. Kasanický ◽  
E. Pelikán
Keyword(s):  
Natural Gas ◽  
Statistical Models ◽  
Consumption Data ◽  
Gas Consumption ◽  
Natural Gas Consumption

A Methodology to Quantify Residential Energy-Efficiency in a Heating-Dominated Climate

2011 ◽  
Author(s):  
Sean Casey ◽  
Marcus Bianchi ◽  
David Roberts ◽  
Moncef Krarti
Keyword(s):  
Energy Efficiency ◽  
Natural Gas ◽  
Inverse Modeling ◽  
Weather Data ◽  
Mechanical Equipment ◽  
Energy Assessment ◽  
Consumption Data ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
The Difference

A methodology is presented that uses readily available information such as energy consumption data, limited building characteristics, and local daily temperature data to identify energy-inefficient homes in a heating-dominated climate. Specifically, this methodology is applied to 327 owner-occupied, single-family homes in Boulder, Colorado, which are compared to simulated prototype homes. A home’s energy-efficiency is characterized by its construction properties, such as insulation R-values, infiltration rates, and mechanical equipment efficiencies. Previous research indicates a close relationship between these properties and inverse modeling parameters, such as the heating slope (HS) values from variable-base degree-day (VBDD) models. The methodology compares the HS values from VBDD models of monthly natural gas consumption data to simulated HS values of reference homes. The difference, ΔHS, is the primary criterion for quantifying a home’s energy-efficiency and energy retrofit potential. To validate the results of the methodology, the results from a detailed energy assessment of a field-test home are used. Using the natural gas consumption noted in the utility data and historical weather data for the dates of bill, a VBDD model is created and the HSfield-test is calculated. HSreference of a 2009-IECC reference home of identical size is calculated and the difference, ΔHS, is calculated. Using UA-values and mechanical efficiencies from the energy assessment report, the theoretical HS values are calculated for both the assessed home and the reference home. The difference, ΔHStheoretical, is calculated. Overall, a 24% difference is found between the ΔHS and ΔHStheoretical. While the accuracy can be improved, the implication is that the energy-efficiency of homes can be inferred from inverse modeling of utility data under a specific set of conditions.

scholarly journals Identifying Inefficient Single-Family Homes With Utility Bill Analysis

2010 ◽  
Author(s):  
Sean Casey ◽  
Moncef Krarti ◽  
Marcus Bianchi ◽  
David Roberts
Keyword(s):  
Energy Consumption ◽  
Natural Gas ◽  
Diagnostic Procedures ◽  
Simulation Software ◽  
Single Family ◽  
Consumption Data ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
A Site ◽  
International Energy Conservation Code

Differentiating between energy-efficient and inefficient single-family homes on a community scale helps identify and prioritize candidates for energy-efficiency upgrades. Prescreening diagnostic procedures can further retrofit efforts by providing efficiency information before a site-visit is conducted. We applied the prescreening diagnostic is applied to a simulated community of homes in Boulder, Colorado and analyzed energy consumption data to identify energy-inefficient homes. A home is defined as efficient if it is compliant with the prescriptive measures of the 2009 International Energy Conservation Code (IECC-2009) for Boulder, Colorado. Previous research indicates a correlation between building operational efficiency and the Heating Slope (HS) regression parameter resulting from the variable-base degree day method. We compared the HS values across a community of houses and those of an IECC-2009-compliant home to identify energy-inefficient homes on a community-scale. To simulate community-wide HS identification, we used DOE-2 energy simulation software for defined home archetypes and corresponding occupant behavior to artificially generate 567 sets of monthly natural gas consumption data Home archetypes were either compliant or incompliant at three conditioned areas; occupant effects were also simulated. Each simulation produced twelve months of natural gas use data. We used monthly energy consumption datasets to estimate the HS values with regression analysis and sorted the homes based on HS values.

EXPERIENCE, PROSPECTS AND PROBLEMS OF COOPERATION BETWEEN CHINA AND TURKMENISTAN IN THE FIELDOF NATURAL GAS

2019 ◽  
pp. 323-329
Author(s):  
Y. JIA
Keyword(s):  
Natural Gas ◽  
Central Asia ◽  
Oil And Gas ◽  
The European Union ◽  
Multilateral Cooperation ◽  
Industrial Chain ◽  
The Usa ◽  
Gas Consumption ◽  
Slow Progress ◽  
Natural Gas Consumption

Since 2007, the use of natural gas in China depends on the import, and with an increase in natural gas consumption, gas imports are also constantly growing. In 2018, Chinas natural gas imports approached 100 billion cubic meters, which is 70 times more than in 2006. In recent years, increasing attention has been paid to the use of natural gas in China. Turkmenistan is Chinas main source of pipeline gas imports, and China is Turkmenistans largest exporter of natural gas. In the framework of the traditional model of oil and gas cooperation, China and Turkmenistan are facing such problems as the uniform content of cooperation, lack of close ties in the field of multilateral cooperation and slow progress in the development of the entire industrial chain. Cooperation between China and Central Asia in the field of oil and gas is increasingly affecting the nerves of other countries, except the five countries of Central Asia, but including Russia, Afghanistan, Pakistan, India, Iran and other countries of the Middle East, Japan, South Korea, etc. and even the European Union and the USA. Despite the favorable trading environment for both parties, there are also problems in the domestic market of Turkmenistan and the risks of international competition.

Forecasting the daily natural gas consumption with an accurate white-box model

Energy ◽  
2021 ◽  
pp. 121036
Author(s):  
Nan Wei ◽  
Lihua Yin ◽  
Chao Li ◽  
Changjun Li ◽  
Christine Chan ◽  
...  
Keyword(s):  
Natural Gas ◽  
Box Model ◽  
Gas Consumption ◽  
Natural Gas Consumption
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