Records with Keyword: Natural Gas
Understanding Continuance Usage of Natural Gas: A Theoretical Model and Empirical Evaluation
Victor Fernández-Guzmán, Edgardo R. Bravo
September 21, 2018 (v1)
Keywords: continuance usage, expectation-confirmation, Natural Gas
The adoption of natural gas increased notably last years, and there is some recognition that it improves the quality of life of inhabitants. While initial acceptance is an essential first step, the continued use is relevant to the long-term success of any technology. However, the literature on energy has focused on adoption and has devoted less attention to models that explain continuance usage. Accordingly, this study developed a model to explain continuance usage, grounded in Expectation-Confirmation Model (ECM). Unlike adoption models, confirmation of previous expectations and satisfaction with the experience of use have a relevant role in this phenomenon. Data was gathered through a questionnaire to 435 users of the service in a Latin American metropolis, and structural equations model was used for analysis. The results show that constructs of the ECM (perceived usefulness, disconfirmation, and satisfaction) influences on continuance intention. While the price impacts as expected,... [more]
Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †
Gregory D. Merkel, Richard J. Povinelli, Ronald H. Brown
September 21, 2018 (v1)
Keywords: artificial neural networks, deep learning, Natural Gas, short term load forecasting
Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE).
Combining Petroleum Coke and Natural Gas for Efficient Liquid Fuels Production
Ikenna J Okeke, Thomas A Adams II
August 28, 2018 (v1)
This work explores the technical feasibility and economic profitability of converting petroleum coke (petcoke) and natural gas to liquid fuels via Fischer-Tropsch synthesis. Different petcoke conversion strategies were examined to determine the conversion pathway which can be competitive with current market prices with little or no adverse environmental impacts. Three main design approaches were considered: petcoke gasification only, combined petcoke gasification and natural gas reforming through traditional processing steps, and combined petcoke gasification and natural gas reforming by directly integrating the gasifier’s radiant cooler with the gas reformer. The designs investigated included scenarios with and without carbon capture and sequestration, and with and without CO2 emission tax penalties. The performance metrics considered included net present value, life cycle greenhouse gas emissions, and the cost of CO2 avoided. The design configuration that integrated natural gas refor... [more]
Combining Biomass, Natural Gas, Carbonless Heat to produce liquid fuels
Leila Hoseinzade, Thomas A Adams II
August 15, 2018 (v1)
Keywords: Biomass, Carbonless Heat, Natural Gas, Polygeneration
In this study, a new Biomass-Gas-Nuclear heat-To-Liquid fuel (BGNTL) process is presented which uses high-temperature nuclear heat as the heat source for steam methane reforming (SMR). This process co-produces liquid fuels (Fischer-Tropsch liquids, methanol and DME) and power. The BGNTL process was simulated using a combination of different software packages including gPROMS, MATLAB, ProMax, and Aspen Plus. This included the use of a rigorous multi-scale model for the nuclear-heat-powered SMR reactor which was developed in a prior work in gPROMS. Energy efficiency and cradle-to-grave life cycle inventory and life-cycle impact analyses of greenhouse gas (GHG) emissions were accomplished to analyze the environmental impacts of the BGNTL system. Plant performance was compared with a base case Biomass-Gas-To-Liquid (BGTL) process at the same size. In both processes, a carbon capture and storage (CCS) option is considered. It has been found that both processes result in negative total life... [more]
Aspen Plus Simulation of Biomass-Gas-and-Nuclear-To-Liquids (BGNTL) Processes (Using CuCl Route)
James Alexander Scott, Thomas Alan Adams II
August 7, 2018 (v1)
These are Aspen Plus simulation files for a Biomass-Gas-and-Nuclear-To-Liquids chemical plant (a conceptional design), which uses the Copper-Chloride route for hydrogen production. This is a part of a larger work (see linked LAPSE record for pre-print and associated publication in Canadian J Chem Eng). Process sections and major units in this simulation include: Gasification, Integrated-Gasification-Methane-Reforming, Pre-Reforming, Water Gas Shift, Autothermal Reforming, Syngas Blending and Upgrading, Solid Oxide Fuel Cell power islands, Fischer-Tropsch Synthesis, Methanol Synthesis, Dimethyl Ether Synthesis, Heat Recovery and Steam Generation, CO2 Compression for Sequestration, Cooling Towers, and various auxiliary units for heat and pressure management. See the linked work for a detailed description of the model.
Biomass-Gas-and-Nuclear-To-Liquids (BGNTL) Processes Part I: Model Development and Simulation
James Alexander Scott, Thomas Alan Adams II
August 7, 2018 (v1)
New polygeneration processes for the co-production of liquid fuels (Fischer-Tropsch liquids, methanol, and dimethyl ether) and electricity are presented. The processes use a combination of biomass, natural gas, and nuclear energy as primary energy feeds. Chemical process models were created and used to simulate candidate versions of the process, using combinations of models ranging from complex multi- scale models to standard process flowsheet models. The simulation results are presented for an Ontario, Canada case study to obtain key metrics such as efficiency and product conversions. Sample Aspen Plus files are provided in the supplementary material to be used by others.
Biomass-Gas-and-Nuclear-To-Liquids Aspen Plus Simulations
Leila Hoseinzade, Thomas A. Adams II
June 12, 2018 (v1)
Aspen Plus simulation for eight different chemical processes. Each simulation corresponds to a process which convert biomass, natural gas, and in some cases, nuclear energy, into either dimethyl ether (DME) or Fischer-Tropsch liquids (synthetic gasoline and diesel). Some processes contain carbon capture and sequestration (CCS) steps.

The processes may include various technologies such as biomass gasification, steam methane reforming, integrated gasification and natural gas reforming, integrated high temperature gas-cooled reactors and natural gas reforming, water gas shift reaction, FT synthesis, DME synthesis, MEA or MDEA based carbon capture, gas combustion turbines, gas cleaning, and other processing steps. Nuclear energy, when used, is integrated into the system via a high temperature helium coolant as an energy carrier from certain kinds of Gen IV nuclear reactors.

The eight processes are: BGNTL-FT (biomass-gas-nuclear-to-liquids with FT synthesis), BGNTL-FT-CCS (the same w... [more]
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