Annual Natural Gas Consumption Research Articles

Integrating geothermal energy in Hungary: A case study on sustainable urban heating and emissions mitigation through the district heating infrastructure

Hungary boasts exceptional geothermal capabilities, with an Earth's crust thermal gradient nearly double the global average. However, the current utilization of these resources is often one-sided and lacks careful consideration. This comprehensive study offers updated insights into Hungary's geothermal potential and its current energy utilization. By presenting a practical example and employing scenario-based analyses, this study demonstrates how basic calculations can guide investment decisions in geothermal infrastructure. The aim is to address the prevailing issue of cities and towns prioritizing fossil fuel-based energy sources, thereby overshadowing renewable energy alternatives, despite advancing technology. Notably, in the case of Székesfehérvár, it was determined that annual natural gas consumption could be reduced by an impressive 40–50%, leading to a substantial reduction of 5,000–9,200 tons of CO2 emissions, depending on the specific scenarios examined.

Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM

Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM

An investigation on potential use of ice thermal energy storage system as energy source for heat pumps

It has been crystal clear to everyone that ice storage systems provide significant advantages in reducing cooling costs, balancing energy supply and demand profiles, and shaving peak loads. As the main purpose of ice storage systems is for cooling purposes, separate heating systems, such as furnaces, heat pumps, electrical heaters, etc., are required for buildings with heating demands. This work offers to use an ice storage system in the heat pump as the energy source. Comprehensive thermodynamic and economic analyses are conducted to assess the proposed system. The proposed system is implemented in an office building in Istanbul, Turkey. Thermodynamic studies indicate that while the overall energetic and exergetic coefficient of performance (COP) values for the ice storage integrated air conditioning (AC) are calculated to be 4.57 and 2.04, respectively, they are 3.99 and 2.12 for the ice storage integrated heat pump. While the yearly operating cost for the traditional AC is 1.9 million TL, it is 0.5 million for the ice storage integrated AC. The annual natural gas consumption cost for heating is 295,000 TL, and 375,000 TL for the ice storage integrated heat pump. The payback period of the ice storage integrated AC and heat pump system is observed to be 4.5 years. It may provide almost 8 million TL savings at the end of 10 years. At the end of the present study, it is reached that the ice storage system has a promising potential to use in heat pumps as a low-temperature energy source. Also, it provides a more environmentally friendly heating operation as it does not require fossil fuels by removing the furnaces or provides higher COP for heat pumps.

Energy performance evaluation and modeling for an indoor farming facility

Indoor farming refers to a method of growing crops on vertically stacked layers in a soilless cultivation system (e.g., hydroponics) in a controlled indoor environment. In comparison with conventional open-field farms, indoor farming has advantages such as significantly improved productivity and water use efficiency, and reduction in food miles. However, indoor farming facilities are energy-intensive for maintaining favorable crop growing conditions.In this study, we evaluated the energy performance of indoor farming operations using both measurements and simulation results. Key performance parameters were first analyzed based on utility bills and continuous measurements from an indoor farming facility. Several operational issues were identified for the mechanical system. An energy model for an indoor farming facility was created using building energy simulation software EnergyPlus and calibrated based on measurements. A novel modeling approach simulating the unique mechanical system—misting systems—for indoor farming was developed using one of the advanced features in EnergyPlus— energy management system (EMS). The energy model was used to evaluate the effectiveness of energy-saving strategies to improve the energy efficiency of facility operations. These include simultaneous heating and cooling, hardly met temperature setpoints and improperly controlled dampers. These energy efficiency measures include fixing motorized damper control, eliminating simultaneous heating and cooling, and having wider ranges of temperature setpoints. Up to 48.1% of reduction in annual natural gas consumption was predicted with energy-saving measures. In addition, the limitations of using building energy simulation software for indoor farming modeling were discussed.

Performance research and application of the vapor pump boiler equipped with flue gas recirculation system

To improve the boiler thermal efficiency and reduce oxynitride emissions, a novel flue gas waste heat recovery system is proposed. The vapor pump boiler equipped with a flue gas recirculation system is first applied for practical engineering. The oxidizing air is preheated and humidified before the combustion process, which causes the dewpoint temperature of the flue gas to increase. Thus, more waste heat in the flue gas can be recovered by the boiler feedback water. Heat recovery efficiency of the system can reach 82.6%, resulting in a 7–8% increase in the boiler efficiency. In addition, the increases in oxidizing air humidity and flue gas recirculation both reduce oxynitride production. As a result, oxynitride emission decreases from 129 mg/m3 to 23 mg/m3, resulting in a reduction of 82.3%. The gas-fired boiler realizes ultra-low oxynitride emission. For a 1.8 MW boiler, annual natural gas consumption can be saved by 7–8%. Given current energy prices in Beijing, the proposed system is economical with a static payback period of 1.2 years. An overall system performance is provided as evidence of the potential benefits. The proposed system is proved to have advantages in energy savings, pollutant reduction, and economic benefits.

Experimental study and economic analysis of an absorption refrigeration system with new generator structure applied for pre-cooling in liquefied natural gas plant

In a variety of waste heat utilization technologies, absorption refrigeration can be driven by low-grade heat to provide cooling capacity, which is suitable for situations with both waste heat supply and refrigeration demand. In this paper, an ammonia-water absorption refrigeration prototype with a new generator structure is experimentally studied. Its economic analysis is carried out for pre-cooling in a liquefied natural gas plant. It is investigated that 1.8 kW cooling capacity at −10 °C is obtained, COP maintains between 0.29 and 0.35, and waste heat recovery ratio is improved by 150%. The proposed system is applied to a 30,000 Nm3 d−1 liquefied natural gas plant to replace the conventional compression refrigerator as the pre-cooling stage, and waste heat from exhaust gas and jacket coolant is recovered in the reboiler and stripping section, respectively, in order to provide cooling capacity for pre-cooling of both natural gas and refrigerant. Economic analysis shows that specific power consumption of the modified system is reduced by 30%, from 0.40 to 0.28 kWh Nm−3. Consequently, the annual natural gas consumption and operating costs are reduced by 17.3% and 9.4%, respectively, and the payback period is 2.2 years compared to conventional systems with compressor-based pre-cooling stages.

Long-Term Natural Gas Consumption Forecasting Based on Analog Method and Fuzzy Decision Tree

Classic forecasting methods of natural gas consumption extrapolate trends from the past to subsequent periods of time. The paper presents a different approach that uses analogues to create long-term forecasts of the annual natural gas consumption. The energy intensity (energy consumption per dollar of Gross Domestic Product—GDP) and gas share in energy mix in some countries, usually more developed, are the starting point for forecasts of other countries in the later period. The novelty of the approach arises in the use of cluster analysis to create similar groups of countries and periods based on two indicators: energy intensity of GDP and share of natural gas consumption in the energy mix, and then the use of fuzzy decision trees for classifying countries in different years into clusters based on several other economic indicators. The final long-term forecasts are obtained with the use of fuzzy decision trees by combining the forecasts for different fuzzy sets made by the method of relative chain increments. The forecast accuracy of our method is higher than that of other benchmark methods. The proposed method may be an excellent tool for forecasting long-term territorial natural gas consumption for any administrative unit.

Potential biomethane production from crop residues in China: Contributions to carbon neutrality

The Chinese government has set an ambitious goal of achieving carbon neutrality by 2060. Realizing negative greenhouse gas (GHG) emissions will require technologies like anaerobic digestion to recover biomethane from organic biomass. As the world's main grain-producing country, China has abundant crop residues, >85% of which were utilized as fertilizer, fodder, industrial material, biofuel, and base material in 2019. However, the potential contribution of crop residues to energy and reduced GHG emissions following anaerobic digestion have been scarcely evaluated. Based on crop yield and the field residue index, the annual quantity of crop residues in China from 2000 to 2019 was estimated. The annual crop residue yield increased between 2003 and 2014, and then remained constant at ~970 × 106 t/a, with an estimated 495 × 106 t/a being available for biomethane production. This corresponded to a potential biomethane yield of 82.25 × 109 m3/a, which could have met 29.2% of the annual natural gas consumption and 2.25% of the annual energy consumption in China in 2018 and 2019, respectively. Furthermore, the estimated potential GHG emission reduction was 197 × 106 t/a (CO2 equivalent), representing ~2% of China's GHG emissions in 2019. Nevertheless, to reach the expected goal, some crucial technical and equipment-oriented research and development should be promoted to adapt to the characteristics of China's crop residues and regional environment. Moreover, governmental support policies on subsidies would be required for the development of the biomethane industry, which should prioritize northern regions with relatively high quantities of crop residues.

Coupling spectral-dependent radiative cooling with building energy simulation

As a passive cooling strategy without energy consumption, radiative cooling has attracted considerable attention, especially in the building field. Building energy simulations have been conducted to identify the benefits of this technology in buildings. However, in existing studies, constant emissivity was used for radiative cooling materials in the building energy simulation programs, which can cause certain errors. To tackle this problem, this study developed a method to couple the spectral-dependent radiative cooling with building energy simulations in EnergyPlus. Compared with the existing constant-emissivity model, the proposed coupled model can further consider the influence of spectral-dependent emissivity, material surface temperature, and precipitable water on the radiative cooling power in EnergyPlus. Based on the results in a typical strip mall in New York, the radiative cooling power calculated by the proposed spectral-dependent model can be significantly different from that by the existing constant-emissivity model. However, since the energy saving from radiative cooling was relatively small compared with the total energy consumption, the differences in annual cooling electricity and heating natural gas consumption calculated by both models were not significant. Furthermore, case studies in five cities using the proposed model showed that using a broadband radiative cooling roof on a typical strip mall would reduce the annual electricity consumption for cooling, while increasing the annual natural gas consumption for heating. The coupling of spectral-dependent radiative cooling with building energy simulation would improve the accuracy of energy performance assessment for buildings with radiative cooling technology.

Forecasting annual natural gas consumption via the application of a novel hybrid model.

Accurate prediction of natural gas consumption (NGC) can offer effective information for energy planning and policy-making. In this study, a novel hybrid forecasting model based on support vector machine (SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed to predict annual NGC. An adaptive learning strategy based on sigmoid function is introduced to improve the performance of traditional artificial fish swarm algorithm (AFSA), which provides a dynamic adjustment for parameter moving step step and visual scope visual. IAFSA is used to obtain the optimal parameters of SVM. In addition, the annual NGC data of China is selected as an example to evaluate the prediction performance of the proposed model. Experimental results reveal that the proposed model in this study outperforms the benchmark models such as artificial neural network (ANN) and partial least squares regression (PLS). The mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE) values are as low as 0.512, 1.4958, and 1.0940. Finally, the proposed model is employed to predict NGC in China from 2020 to 2025.

The potential for expanding sustainable biogas production and some possible impacts in specific countries

AbstractCurrent food production practices tend to damage and deplete soil, diminish biodiversity, and degrade water supplies. For agriculture to become environmentally sustainable and simultaneously increase food output for a growing world population, fundamental changes in agricultural production systems are required. Renewable energy can reduce greenhouse gases (GHGs) but we also need simple, low‐cost approaches to remove atmospheric carbon and sequester it in stable forms. Recycling of digestate from the anaerobic digestion of agricultural and waste materials to soils can sequester atmospheric carbon and provide many other economic, social and environmental benefits. Biogasdoneright™ (BDR) is a set of practices that link biogas production with sustainable agriculture. The BDR approach to sustainable agriculture is being implemented on a large scale in Italy. In this paper, we examine the potential impact of implementing BDR in selected other countries. The biomethane potential in these countries, estimated conservatively, varies from about 10–30% of their current annual natural gas consumption. Biomethane from sequential (double) crops provides by far the greatest fraction of the biomethane potential. Double cropping also drives many of the environmental and economic benefits of BDR systems. Depending on where and how widely it is implemented, the production of biogas in BDR systems could have very significant national‐level impacts. For example, sufficient biomethane could be produced in Argentina to completely eliminate imports of natural gas, equivalent to about 28% of Argentina's 2017 trade deficit. In the USA, renewable biogas could generate electricity equal to nearly all of the electricity currently produced by domestic solar and wind resources. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd

Benchmarking of water consumption and waste management in multi-unit residential buildings (MURBs) in Toronto

Water and solid waste benchmarking and developing meaningful performance indicators can help government and building owners to make effective decisions on improving their buildings’ efficiency and sustainability. For this study, information on 120 multi-unit residential buildings (MURBs) has been collected and analyzed. The study entails the following steps: performing water and solid waste benchmarking for the 120 MURBs, developing meaningful performance indicators; determining performance ranking; and estimating different levels of savings (water, solid waste, cost, and GHG emissions). The most appropriate performance indicator and the benchmarking range for water and solid waste are as follows: water consumption normalized by square metre, 1.06–4.19 m3/m2; solid waste benchmarking normalized by number of units, 1.58–17.4 yd3/unit. Also, the MURBs domestic hot water system efficiencies are found to be very low (with overall system efficiency in the range of 35% to 45%). A relatively strong correlation is found between annual natural gas consumption and annual water consumption.

Forecasting annual natural gas consumption using socio-economic indicators for making future policies

Natural gas is a foreign-dependent source of energy in many countries and a rapid increase of its consumption is mainly associated with the increase of living standards and needs. In this work, Turkey was taken as a case study with high degree of foreign dependence of energy, and the future natural gas consumption was predicted by several different multiple regression models using socio-economic indicators as the descriptor variables. Among these, gross domestic product and inflation rate were found to be the only significant ones for this prediction. Next, three different projections for the future values of the significant descriptor variables were tested, and the natural gas consumption was predicted to rise gradually in the range 1.3 ± 0.2 billion m3 per year reaching to a consumption of 64.0 ± 3.5 billion m3 in the year 2025. It was then discussed that this additional natural gas can be compensated by utilizing local lignite sources or by starting a nuclear energy program although these two methods to reduce the future natural gas consumption have some conflictions with the general European energy matrix and environmental politics. Thus, it was concluded that resuming the wind and solar-based electricity generation programs can be considered as a more reasonable option.

The impact of British Columbia's carbon tax on residential natural gas consumption

We estimate the effect of British Columbia's (BC) carbon tax on per capita residential natural gas consumption using panel data regression and synthetic control models. We use province and state-level data documenting annual natural gas consumption from 1990 through 2014. Results from the panel data regression model suggest that the carbon tax substantially reduced residential natural gas consumption. Our preferred approach is the synthetic control method that we use to select a group of provinces and states against which BC's residential natural gas consumption trends can be compared. Using the synthetic control approach we find that the BC carbon tax reduced per capita residential natural gas consumption by approximately 7%.

Algorithmic tools for optimizing the temperature regime of evaporator at absorption-refrigeration units of ammonia production

We analyzed evaporators at absorption-refrigeration plants in a secondary condensation unit of ammonia production as control objects. Coordinates of the vectors of state, control and external perturbations were determined. The necessity of solving the problem on minimizing the cooling temperature of a circulating gas at evaporators in order to improve the energy efficiency of production was substantiated. Based on the analysis of industrial hardware-technological implementation of units for primary and secondary condensation, we elucidated features of operating conditions of evaporators, which predetermine the parametric uncertainty in the functioning of control objects. The main one among these uncertainties is associated with the control action related to the consumption of reflux. By using the method of mathematical modeling, based on the developed algorithm, we defined patterns of control action related to the consumption of reflux on the efficiency of heat exchange processes at evaporators in the absorption refrigeration units. We have established the extreme character of dependence of the heat flow (cooling capacity) and the temperature of cooling a circulating gas on the consumption of reflux. Maximum cooling capacity, and therefore the minimum temperature of cooling a circulating gas at a certain temperature head, are predetermined by the achievement of a critical regime of the bubbling boiling of a refrigerant. A further increase in the temperature head with an increase in the consumption of reflux contributes to the establishment of the transitional regime and reduces effectiveness of the heat exchange surface. We determined indicators of energy efficiency for ammonia production, namely, natural gas consumption under conditions of change in the control action related to the consumption of reflux and values of coordinates for the perturbation vector. The developed algorithmic tools make it possible to carry out the task on minimizing the cooling temperature of a circulating gas using a gradient-free technique of the step type applying the methods for a one-dimensional search for an extremum. It is shown that minimizing the cooling temperature of a circulating gas could reduce annual natural gas consumption by 500 thousand nm3 on average.

Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting

Along with the high growth rate of economy and fast increasing air pollution, clean energy, such as the natural gas, has played an important role in preventing the environment from discharge of greenhouse gases and harmful substances in China. It is very important to accurately forecast the demand of natural gas in China is for the government to formulate energy policies. This paper firstly proposes a combined forecasting model, name GM-S-SIGM-GA model, to forecast the demand of natural gas in China from 2011 to 2017, by constructing the grey model (GM(1,1)) and the self-adapting intelligent grey model (SIGM), respectively; then, it employs a genetic algorithm to determine the combined weight coefficients between these two models. Finally, using the tendency index (the annual changes of the share of natural gas consumption from the total energy consumption), which completely reveal the annual natural gas consumption share among the market, to successfully adjust the fluctuated changes for each data period. The natural gas demand data from 2002 to 2010 in China are used to model the proposed GM-S-SIGM-GA model, and the data from 2011 to 2017 are used to evaluate the forecasting accuracy. The experimental results demonstrate that the proposed GM-S-SIGM-GA model is superior to other single forecasting models in terms of the mean absolute percentage error (MAPE; 4.48%), the root mean square error (RMSE; 11.59), and the mean absolute error (MAE; 8.41), respectively, and the forecasting performances also receive the statistical significance under 97.5% and 95% confident levels, respectively.

Impact of future climate change on UK building performance

Global demand for dwelling energy and implications of changing climatic conditions on buildings confront the built environment to build sustainable dwellings. This study investigates the variability of future climatic conditions on newly built detached dwellings in the UK. Series of energy modelling and simulations are performed on ten detached houses to evaluate and predict the impact of varying future climatic patterns on five building performance indicators. The study identifies and quantifies a consistent declining trend of building performance which is in consonance with current scientific knowledge of annual temperature change prediction in relations to long term climatic variation. The average percentage decrease for the annual energy consumption was predicted to be 2.80, 6.60 and 10.56 for 2020s, 2050s and 2080s time lines respectively. A similar declining trend in the case of annual natural gas consumption was 4.24, 9.98 and 16.1, and that for building emission rate and heating demand were 2.27, 5.49 and 8.72 and 7.82, 18.43 and 29.46 respectively. The study further analyse future heating and cooling demands of the three warmest months of the year and ascertain future variance in relative humidity and indoor temperature which might necessitate the use of room cooling systems to provide thermal comfort.

A hybrid neuro-fuzzy simulation approach for improvement of natural gas price forecasting in industrial sectors with vague indicators

This study presents a hybrid neural network fuzzy mathematical programming approach for improvement of natural gas price estimation in industrial sector. It is composed of artificial neural network (ANN), fuzzy linear regression (FLR), and conventional regression (CR). The preferred FLR, ANN, and CR models are selected via mean absolute percentage of error. The intelligent approach of this study is then applied to estimate natural gas price in industrial sector. Domestic sector is also used to further show the flexibility and applicability of the hybrid approach. The economic indicators used in this paper are consumer price index, population, gross domestic and annual natural gas consumption. The stated indicators could be contaminated with noise and vagueness. Moreover, there is a need to develop a hybrid approach to deal with both noise and vagueness. The input data were divided into train and test datasets. A complete sensitivity analysis has been performed by changing train and test data to show the superiority of the proposed approach. The superiority of ANN for the domestic sector and FLR for the industrial sector was proved by error analysis. The results showed that different models may be selected as preferred model, in different cases and situations. The proposed approach of this study would help policy makers to effectively manage natural gas price in vague, noisy, and complex manufacturing sectors. This is the first study that presents a hybrid approach for estimating the natural gas price in industrial sector with possible noise, non-linearity, and uncertainty.

Forecasting Natural Gas Consumption Using Pso Optimized Least SquaresSupport Vector Machines

This paper proposes an effective model based on the least squares support vector machines (LSSVM) and the particle swarm optimization (PSO), termed PSO-LSSVM, for prediction of natural gas consumption, as an important energy resource. The salient feature of mapping nonlinear data into high dimension feature space, distinguishes LS-SVM as a powerful approach for forecasting and estimation. Optimization of the model’s parameters by a fast and efficient PSO algorithm results in an optimized model which is employed for prediction of annual natural gas consumption in Iran and Unites States. Promising results were obtained for prediction of Iranian gas consumption from 1998 to 2006 and U.S. gas consumption from 2001 to 2005. Besides, comparison to an optimized multi-layer preceptron (MLP) network, using error indices of MAPE and NMSE demonstrated the superior performance of the proposed PSO-LSSVM approach.

Optimization of Large Gas Pipeline Network--A Case Study in China

Abstract In China, annual natural gas consumption is over 67 billion cubic meters with an expected growth rate of 10% per year. Most of the gas is transported from well heads to markets over cross-country gas networks, which requires construction of the West to East Gas Network--one of the largest gas networks in the world. Presently, the network is comprised of four large-diameter pipelines and will include most major gas pipelines in China in the future. The network distributes approximately 30 x 109 m3 gas per year, of which 3% to 5% is burned to power the gas transportation. At current gas prices, gas transportation costs are roughly 350 million per year, which is a considerable cost that could be reduced by improvements in network design and operation. This paper reports on a study aimed at optimizing the network to minimize its energy consumption and cost. The large size and complex geometry of the network required breaking the study down into simple components, optimizing operation of the components locally, re-combining the optimized components into the network and optimizing the network globally. This four-step approach employed four different optimization methods, penalty function method, pattern search, enumeration and non-sequential dynamic programming, to solve the problem. The results show that cost savings, because of global optimization, are reduced with increased throughput. For example, increasing the gas rate from 67 - 90 million m3/d would reduce operational cost savings because of optimization from 23% - 1.15%. Moreover, the study shows that if the compressors were fully loaded at their maximum rating, the optimized operation would approach the one being presently practiced. Thus, the optimization is effective and much needed when the system does not work at its maximum capacity, a typical case in the present operations of Chinese gas networks.