Energy return on investment, energy payback time, and greenhouse gas emissions of coal seam gas (CSG) production in China: a case of the Fanzhuang CSG project

The development of photovoltaic (PV) energy has led to rising efficiencies, better reliability, and falling prices. A multicriteria analysis (MCA) of PV systems is proposed in this paper in order to evaluate the sustainability of alternative projects. The investigations are presented using multiple indicators: Energy Payback Time (EPBT), Energy Return on Investment (EROI), Greenhouse Gas per kilowatt-hour (GHG/kWh), Greenhouse Gas Payback Time (GPBT), Greenhouse Gas Return on Investment (GROI), Net Present Value (NPV), Discounted Payback Time (DPBT), and Discounted Aggregate Cost Benefit (D(B/C)A). PV energy is a relevant player in global electricity market and can have a key-role in sustainable growth.

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Linking greenhouse gas emissions footprint and energy return on investment in electricity generation planning

Journal of Cleaner Production ◽

10.1016/j.jclepro.2018.07.268 ◽

2018 ◽

Vol 200 ◽

pp. 911-921 ◽

Cited By ~ 10

Author(s):

Michael R.W. Walmsley ◽

Timothy G. Walmsley ◽

Martin J. Atkins

Keyword(s):

Greenhouse Gas Emissions ◽

Greenhouse Gas ◽

Return On Investment ◽

Electricity Generation ◽

Gas Emissions ◽

Energy Return On Investment ◽

Generation Planning

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Feasibility study on the production and consumption of wood pellets in Iran to meet return-on-investment and greenhouse gas emissions targets

Renewable Energy ◽

10.1016/j.renene.2019.10.140 ◽

2020 ◽

Vol 151 ◽

pp. 1-20 ◽

Cited By ~ 2

Author(s):

Vahid Nabavi ◽

Majid Azizi ◽

Asghar Tarmian ◽

Charles David Ray

Keyword(s):

Greenhouse Gas Emissions ◽

Greenhouse Gas ◽

Feasibility Study ◽

Return On Investment ◽

Wood Pellets ◽

Gas Emissions ◽

Production And Consumption

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The Combustion of Methane from Hard Coal Seams in Gas Engines as a Technology Leading to Reducing Greenhouse Gas Emissions—Electricity Prediction Using ANN

Energies ◽

10.3390/en13174429 ◽

2020 ◽

Vol 13 (17) ◽

pp. 4429

Author(s):

Marek Borowski ◽

Piotr Życzkowski ◽

Jianwei Cheng ◽

Rafał Łuczak ◽

Klaudia Zwolińska

Keyword(s):

Coal Seam ◽

Greenhouse Gas Emissions ◽

Greenhouse Gas ◽

Coal Mine ◽

Electricity Production ◽

Hard Coal ◽

Neural Network Models ◽

Gas Emissions ◽

Methane Drainage ◽

Geological Conditions

Greenhouse gases such as carbon dioxide and methane cause global warming and consequently climate change. Great efforts are being made to reduce greenhouse gas emissions with the objective of addressing this problem, hence the popularity of technologies conductive to reducing greenhouse gas emissions. CO2 emissions can be reduced by improving the thermal efficiency of combustion engines, for example, by using cogeneration systems. Coal mine methane (CMM) emerges due to mining activities as methane released from the coal and surrounding rock strata. The amount of methane produced is primarily influenced by the productivity of the coal mine and the gassiness of the coal seam. The gassiness of the formation around the coal seam and geological conditions are also important. Methane can be extracted to the surface using methane drainage installations and along with ventilation air. The large amounts of methane captured by methane drainage installations can be used for energy production. This article presents a quarterly summary of the hourly values of methane capture, its concentration in the methane–air mixture, and electricity production in the cogeneration system for electricity and heat production. On this basis, neural network models have been proposed in order to predict electricity production based on known values of methane capture, its concentration, pressure, and parameters determining the time and day of the week. A prediction model has been established on the basis of a multilayer perceptron network (MLP).

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