Gas Consumption Research Articles

Reassessing determinants of urban energy intensity in China: insights from controllable and uncontrollable factors.

This study adopts a new approach to reassess the factors influencing urban energy intensity in China. Initially, the factors impacting energy intensity are classified into controllable and uncontrollable categories. Subsequently, employing a single-factor multi-stage method combined with the Adaboost method, 289 Chinese cities are categorized based on uncontrollable factors to eliminate the influence of inherent differences on energy intensity. Finally, panel data regression analyses are conducted using data from 289 Chinese cities between 2005 and 2016, individually for each city type, to evaluate the extent to which controllable factors contribute to energy intensity. The findings indicate that (1) heightened energy prices, an increased share of electricity consumption, and a greater proportion of centralized heating significantly influence the reduction of energy intensity across all city types; (2) to optimize energy consumption, each city type should adopt specific strategies. For instance, cities located in resource-rich heating regions with low economic outputs can reduce their energy intensity by increasing electricity consumption, while cities with high economic outputs can decrease their energy intensity by increasing natural gas consumption. The findings of this study carry substantial implications for the Chinese government in shaping targeted energy policies tailored to different city types.

Theoretical and experimental study on total heat recovery of condensing gas boiler flue gas by gas engine-driven compression heat pump

How to improve the utilization efficiency of natural gas and achieve the goal of carbon reduction has gradually become the focus of people’s attention. The exhaust temperature of the condensing gas boiler is between 50℃ and 80℃, which still contains some heat that is not used, so it is necessary to recover this part of the heat. This paper mainly studies the total heat recovery and utilization of waste heat from low-temperature flue gas of condensing gas boiler. In the research field of gas boiler flue gas waste heat recovery, the use of gas compression heat pump to recover gas boiler flue gas waste heat is still very few. Based on the principle of reducing the cold source temperature, a low-temperature flue gas recovery scheme of a gas engine-driven compression heat pump coupled condensing gas boiler is proposed. The low-temperature chilled water produced by gas engine-driven compression heat pump is used to recover flue gas waste heat from condensing gas boiler and gas engine, which can improve the utilization efficiency of natural gas and eliminate the “white smoke phenomenon” simultaneously. In addition, the waste heat of flue gas is used as the low-temperature heat source of gas engine-driven compression heat pump to ensure the stable operation of the heat pump in the low-temperature environment. The flue gas waste heat recovery technology has been improved compared with other flue gas waste heat recovery technologies in terms of primary energy utilization efficiency, system stability, corrosion resistance of flue gas heat exchangers, and investment recovery cycle. The experimental results show that when the water inlet temperature of the flue gas heat exchanger is 15℃, the overall power of the unit can reach 645 kW, and the final exhaust temperature is below 25℃. The overall primary energy utilization rate of the GEHP&CB can reach 110 %, which is 10 %∼20 % higher than that of the condensing gas boiler. The payback period of the increased initial investment is 1.4 heating seasons. Due to the improvement of natural gas utilization efficiency significantly reduces natural gas consumption and pollutant discharge.

Energetical effect of electrochromic glazing on the double-skin façade of the building in different climates

Today, energy has an essential role in the world’s future, and managing its consumption can be very influential and crucial in human life. Most of the energy consumption in any country is related to building. The aim of writing this article is to investigate the effect of combining electrochromic glazing and double skin facades. For this purpose, a double skin facade building with simple glass and a building in which electrochromic is used in the second layer of the facade were compared. These buildings were designed by Design Builder. The results demonstrated significant reductions in energy consumption in the second case. To study the effect of weather on the performance of this system, four cities in Iran with diverse climates have been selected. The energy consumption of cooling, heating, lighting, electricity, and gas utilization of the same building in different climates was studied. Using electrochromic glazing in the double-skin facade reduced the annual electrical consumption of the entire building by up to 3.5% for hot_humid weather. Also, the total annual gas consumption of the building was reduced up to 22.7% for hot and dry weather.

Performance enhancement of microwave discharge cathode by external gas injection system

The effect of external gas injection near the plume on the performance of a microwave discharge cathode is investigated. Experiments were conducted under two gas injection conditions: gas addition and gas distribution. Gas distribution tended to lead to better performance than gas addition. In the gas distribution experiments, the total gas consumption was kept constant, the internal gas flow rate was reduced, and external gas was supplied. The electron current increased from 0.26 to 0.49 A at 35 V when the microwave power was 15 W. When the sum of the internal and external gas flow rates was held constant, external gas injection lowered the neutralization costs and increased the gas utilization efficiency. The neutralization current was successfully increased by 48 % at 12 W of microwave power compared with 8 W of microwave power with the same level of naturalization cost and gas utilization efficiency. The electron temperature and plasma density in the plume, as measured using a Langmuir probe, suggested that an increase of ionization in the plume by the external gas injection enhanced the cathode's performance.

Situ-Oracle: A Learning-Based Situation Analysis Framework for Blockchain-Based IoT Systems

The decentralized nature of blockchain enables data traceability, transparency, and immutability as complementary security features to the existing Internet of Things (IoT) systems. These Blockchain-based IoT (BIoT) systems aim to mitigate security risks such as malicious control, data leakage, and dishonesty often found in traditional cloud-based, vendor-specific IoT networks. As we steadily advance into the era of situation-aware IoT, the use of machine learning (ML) techniques has become essential for synthesizing situations based on sensory contexts. However, the challenge to integrate learning-based situation awareness with BIoT systems restricts the full potential of such integration. This is primarily due to the conflicts between the deterministic nature of smart contracts and the non-deterministic nature of machine learning, as well as the high costs of conducting machine learning on blockchain. To address the challenge, we propose a framework named Situ-Oracle. With the framework, a computation oracle of the blockchain ecosystem is leveraged to provide situation analysis as a service, based on Recurrent Neural Network (RNN)-based learning models tailored for the Situ model, and specifically designed smart contracts are deployed as intermediary communication channels between the IoT devices and the computation oracle. We used smart homes as a case study to demonstrate the framework design. Subsequently, system-wide evaluations were conducted over a physically constructed BIoT system. The results indicate that the proposed framework achieves better situation analysis accuracy (above 95%) and improves gas consumption as well as network throughput and latency when compared to baseline systems (on-chain learning or off-chain model verification). Overall, the paper presents a promising approach for improving situation analysis for BIoT systems, with potential applications in various domains such as smart homes, healthcare, and industrial automation.

How to better match predicted loads of district heating system: A novel control approach focused on coupling source and network by data-driven methods

Deconstructing the load forecast into appropriate control parameters is essential for effective intelligent heating control. Drawing from the predicted load, a novel heating control approach is proposed, emphasizing the integration of source and network. The heating regulation of the energy station is achieved through controlling the supply water temperature, while the thermal entrance of the heating network is regulated by adjusting the flow rate using valve control. The heat transfer mechanism influencing the regulation of District Heating Systems (DHSs) operation and the alignment of supply and demand are analyzed to deconstruct the four key factors: load, flow, load-flow ratio, and supply water temperature. Multiple Linear Regression (MLR) method is employed to formulate the parameter equations relating to supply water temperature and valve opening. Given the uncertainty interference in DHS in practice, the model undergoes periodic dynamic revisions. The model’s validity is confirmed by the empirical data from three different heating seasons. The average absolute deviation of the calculated supply water temperature and valve opening from the actual DHS is 0.42℃ and 1.12%, respectively. When implemented in an actual system, the approach effectively decreased gas consumption by 29.3% while effectively fulfilling heating requirements.

Monitor and reduce emissions to meet compliance and sustainability goals

In today’s climate-conscious world, companies across the globe continue to push to optimise energy use and reduce or eliminate their greenhouse gas emissions. Investments aimed at emission monitoring and reduction are driven by the need to optimise consumption of electricity, gas, oil, and water to meet emissions reduction targets and sustainability goals. Tracking and reducing emissions can improve process uptime through predictive maintenance and reduce product losses across the process. Methane emissions are in particular focus as methane is more than 25 times as potent as carbon dioxide at trapping heat in the atmosphere. The U.S. Environmental Protection Agency stated that a third of methane emissions in North America come from the industrial sector, and similar figures come from the European Commission. It is no wonder that government and industry are laser-focussed on improving their approach to the reduction of methane emissions. As companies drive their own corporate standards to achieve net zero emissions, there is also a need to comply with existing and upcoming governmental emissions standards and regulations, and report emissions on a periodic basis. The trend to move fugitive emissions monitoring from periodic to continuous provides operators with a greater insight into emissions sources which drives efficiencies through the reduction of lost product and a more predictive approach to maintenance. Investors are increasingly focussed on environmental, social, and governance (ESG) reporting and favouring companies that can demonstrate strong ESG performance. Fugitive emission leaks can translate into billions of dollars of lost productivity annually, and serious occupational accidents.

Implications of Western Australia’s updated domestic gas policy

In August 2023, Western Australia (WA) updated its domestic gas reservation policy to restrict onshore gas exports through the existing pipeline network, primarily affecting the Perth Basin. While the policy update aimed to secure domestic gas supplies amid projected shortfalls, it may complicate the outlook for the WA gas market through restricted access to capital, reduced incentives for substantial investments in large-scale developments, and the elimination of a potential future gas demand source for the state. WA’s domestic gas consumption is expected to peak by the end of this decade and then steadily decline as part of the state’s net-zero emission ambitions. In contrast, the liquefied natural gas (LNG) market especially in Australia’s main buying partners in Asia is set to grow into the 2040s, offering lenders demand certainty. This paper assesses the potential implications of WA’s updated domestic gas policy. The state’s domestic gas market is projected to require 8–10 tcf of gas over 2023–50. Currently producing and under-development fields are expected to supply approximately 5 tcf during this period, leaving around a 2 tcf gap to be filled by the onshore resource base and potential new Domestic Market Obligations (DMOs). The paper reiterates the importance of gas in WA’s energy transition and the need to ensure future supply failing which prices could rise to LNG netback or trigger further intervention.

Towards a Sustainable Laser Powder Bed Fusion Process via the Characterisation of Additively Manufactured Nitinol Parts

Is additive manufacturing (AM) a sustainable process? Can the process be optimised to produce sustainable AM parts and production techniques? Additive manufacturing offers the production of parts made of different types of materials in addition to the complex geometry that is difficult or impossible to produce by using the traditional subtractive methods. This study is focused on the optimisation of laser powder bed fusion (L-PBF), one of the most common technologies used in additive manufacturing and 3D printing. This research was carried out by modulating the build layer thickness of the deposited metal powder and the input volumetric energy density. The aim of the proposed strategy is to save the build time by maximizing the applied layer thickness of nitinol powder while retrieving the different AM part properties. The saving in the process time has a direct effect on the total cost of the produced part as a result of several components like electric energy, inert gas consumption, and labour. Nickel-rich nitinol (52.39 Ni at.%) was selected for investigation in this study due to its extremely high superplastic and shape memory properties in addition to the wide application in various industries like aerospace, biomedical, and automotive. The results obtained show that significant energy and material consumption can be found by producing near full dens AM parts with limited or no alteration in chemical and mechanical properties.

Evaluation of fault-sealing: a case study on Xiangguosi underground gas storage, SW China

China vigorously implements the dual carbon strategy, and natural gas consumption continues to grow rapidly. It is urgent to improve the natural gas “production, supply, storage, and sales” system and accelerate the construction of underground gas storage (UGS). UGS requires “storage, injection, and extraction”. Faults are the primary factor of “storage”, the evaluation of fault-sealing is crucial for the safe construction and operation of UGS. Existing researches start from the perspective of the hydrocarbon reservoir, develop an evaluation of the basic characteristics of faults, lithological configuration of the fault’s two blocks, analysis of production performance, Shale Smear Factor (SSF), Shale Gouge Ratio (SGR), properties of fault-filling materials, 3D tectonic model, normal pressure on the fault plane, fault shear index, and fault plugging coefficient. This paper systematically analyzes the adaptability of existing methods and proposes an index to evaluate the fault-sealing capability of UGS. Taking the first UGS in SW China—Xiangguosi UGS as a case, using this workflow to evaluate the fault-sealing capability of 5 major faults. It is believed that the evaluation of fault-sealing on Xiangguosi UGS is good. This conclusion supports the design of construction key indicators and is the important basis point of the construction of the “signpost” UGS. It is of guiding significance and provides a reference for the evaluation of fault-sealing for the rebuilding of depleted gas reservoirs to UGSs.

Prediction of energy consumption for manufacturing small and medium-sized enterprises (SMEs) considering industry characteristics

There has been a growing demand for energy consumption statistics in the manufacturing industry to establish national energy and greenhouse gas policies. Despite its importance, the Korean government faces significant challenges in collecting energy data at a facility level in a precise and timely manner. To address the lack of timely data, this paper employs machine-learning models to predict the annual total energy consumptions of each manufacturing facility. We first designed four prediction models that take into account the characteristics and energy consumption behaviors of industry sub-sectors. As input variables, these prediction models mainly included electricity consumption, employee size, energy types, gas consumption and other accessible data. Finally, we conducted numerical experiments on approximately 100,000 facilities and evaluated the prediction performance of various machine-learning algorithms such as linear regression, decision tree regression, random forest regression, gradient boost regression, and extreme gradient boosting regression. The numerical experiments provided insights into which model and algorithm offer the best prediction performance for each industry sub-sector. In addition, we identified the important variables for predicting total energy consumption, revealing that not only electricity but also various other energy sources and variables representing industry-specific characteristics play a crucial role in improving prediction performance.

Rapid nucleation and growth of tetrafluoroethane hydrate enhanced by bubble and gas cycling

The application of hydrate technology is predominantly limited by the rapid nucleation and stable formation of hydrates, which could be promoted by the disturbance of bubbles. In this study, the effect of bubble generation and different bubble sizes on the nucleation and growth characteristics of R134a hydrates were investigated. The impact of three methods (depressurization-induced R134a vaporization, depressurization combined with stirring and gas cycling bubbling) of bubble effect and size on the nucleation, growth characteristics, and stability of R134a hydrate formation were explored. The research results indicate that utilizing only the depressurization method to induce R134a vaporization and bubble formation leads to a gas consumption rate of 19.6 mmol/min, which is 868 times higher than that in a pure water system. Furthermore, refining bubble size through stirring under depressurization conditions further augments the gas conversion rate by 8.6 %, and the optimal temperature for R134a hydrate formation is identified to be as low as 4 °C. Importantly, utilizing gas cycling as a means to generate bubbles enables uniform and rapid nucleation and growth of hydrates. Within a short 10 min, R134a hydrates was rapidly and stably formed, achieving a formation rate 35 times faster compared to the boiling-condensation method. In comparison to stirring-depressurization, the gas cycling method further increases the gas consumption rate by 10 %. The drawback of slow nucleation and growth rates in hydrates, as well as the need to enhance their stability, is addressed by the gas cycling method.

Direct growth of highly oriented GaN thin films on silicon by remote plasma CVD

We report on low-temperature (500 °C) and low-pressure (0.3 mbar) direct growth of GaN thin films on silicon (100) substrates using remote plasma chemical vapour deposition (RP-CVD). In the custom-designed reactor, an RF inductively coupled plasma is generated remotely from the substrate’s area to facilitate the decomposition of group-V precursor, N2 with added H2, while group-III precursor trimethylgallium (TMGa), is directly injected into the growth chamber mixed with H2 carrier gas. Growth parameters such as RF power, process pressure and gas flow rates have been optimized to achieve a film growth rate of about 0.6 µm h−1. Several characterization techniques were used to investigate the plasma and the properties of the grown thin films in terms of their crystallinity, morphology, topography, and composition. The films are highly textured with a preferential orientation along the c-axis of the wurtzite structure. They present a small roughness in the nanometer range and a columnar microstructure with a grain size of one hundred nanometer, and a gallium polarity (+c plane oriented). Rutherford backscattering spectrometry and nuclear reaction analysis show that the chemical composition is homogeneous through the depth of the layer, with a III/V ratio close to 1, a very low content of oxygen (below the detection limit ∼1%) and a carbon content up to 11%. It was shown that the increase of plasma power helps to reduce this carbon contamination down to 8%. This research paves the way for a growth method compatible with cost reduction of III–V thin film production achieved through reduced gas consumption facilitated by RP-CVD operation at low pressure.

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.

Damping accumulative NDAGM(1,N, α) power model and its applications

PurposeIn this essay, a new NDAGM(1,N,α) power model is recommended to resolve the hassle of the distinction between old and new information, and the complicated nonlinear traits between sequences in real behavior systems.Design/methodology/approachFirstly, the correlation aspect sequence is screened via a grey integrated correlation degree, and the damped cumulative generating operator and power index are introduced to define the new model. Then the non-structural parameters are optimized through the genetic algorithm. Finally, the pattern is utilized for the prediction of China’s natural gas consumption, and in contrast with other models.FindingsBy altering the unknown parameters of the model, theoretical deduction has been carried out on the newly constructed model. It has been discovered that the new model can be interchanged with the traditional grey model, indicating that the model proposed in this article possesses strong compatibility. In the case study, the NDAGM(1,N,α) power model demonstrates superior integrated performance compared to the benchmark models, which indirectly reflects the model’s heightened sensitivity to disparities between new and old information, as well as its ability to handle complex linear issues.Practical implicationsThis paper provides a scientifically valid forecast model for predicting natural gas consumption. The forecast results can offer a theoretical foundation for the formulation of national strategies and related policies regarding natural gas import and export.Originality/valueThe primary contribution of this article is the proposition of a grey multivariate prediction model, which accommodates both new and historical information and is applicable to complex nonlinear scenarios. In addition, the predictive performance of the model has been enhanced by employing a genetic algorithm to search for the optimal power exponent.

Analysis and Forecasting of Energy Data and GDP Based on Pearson Correlation Analysis-Random Forest Algorithm

The relationship between energy data and GDP is an important economic issue because energy is the foundation that supports modern economic development. This paper examines the relationship between energy data and GDP and uses Pearson correlation analysis to calculate the relationship between each variable in energy data and annual GDP for 52 regions. The results show that total electricity consumption is the factor with the highest degree of influence on GDP, followed by natural gas consumption, biomass consumption, hydroelectricity consumption, LPG consumption and geothermal energy consumption. This indicates that there is an extremely strong correlation between local energy data and GDP. To further explore this correlation, this paper attempts to predict the GDP of the locality based on the local energy data using a machine learning approach.The Random Forest algorithm is chosen as the machine learning model, and the training, validation and test sets are divided according to the ratio of 6:2:2. By using MSE, RMSE, MAE, MAPE and R to evaluate the model, it is found that the random forest machine learning algorithm can predict the local GDP well.The predicted value of GDP and the actual value of GDP are very close to each other, meanwhile, the value of MSE is 62.779.These findings show that the local energy data are closely related to economic development. The impact of energy factors on the economy should be taken into account when formulating economic policies. In addition, machine learning algorithms can provide strong support for economic forecasting and provide more accurate information for decision makers.

Predicting Liquid Natural Gas Consumption via the Multilayer Perceptron Algorithm Using Bayesian Hyperparameter Autotuning

Reductions in energy consumption and greenhouse gas emissions are required globally. Under this background, the Multilayer Perceptron machine-learning algorithm was used to predict liquid natural gas consumption to improve energy consumption efficiency. Setting hyperparameters remains challenging in machine-learning-based prediction. Here, to improve prediction efficiency, hyperparameter autotuning via Bayesian optimization was used to identify the optimal combination of the eight key hyperparameters. The autotuned model was validated by comparing its predictive performance with that of a base model (with all hyperparameters set to the default values) using the coefficient of variation of root-mean-square error (CvRMSE) and coefficient of determination (R2) based on the Measurement and Verification Guideline evaluation metrics. To confirm the model’s industrial applicability, its predictions were compared with values measured at a small-to-medium-sized food factory. The optimized model performed better than the base model, achieving a CvRMSE of 12.30% and an R2 of 0.94, and achieving a predictive accuracy of 91.49%. By predicting energy consumption, these findings are expected to promote the efficient operation and management of energy in the food industry.

The Effect of Energy Consumption Towards Economic Growth: The Case of 11 Asian Countries

This research aims to determine and analyze the effect of Coal Consumption per capita (Kwh), Oil Consumption per capita (Kwh), Gas Consumption per capita (Kwh), and Renewable energy consumption per capita (Kwh) towards GDP per capita (current US$). This research uses GDP Per Capita (current USD) data sourced from world banks with a period from 1994 to 2021 and Data for Coal Consumption per capita (Kwh), Oil Consumption per capita (Kwh), Gas Consumption per capita (Kwh), and Renewable energy consumption per capita (Kwh) from Our Wold within a period from 1994 to 2021. The data used is data from 11 Asian countries, which is Mainland China, The Republic of Korea, Japan, India, Bangladesh, Pakistan, Thailand, Indonesia, Malaysia, Philippines, and Viet Nam. The analysis method used is Panel Data Econometrics, with Ordinary Least Square and Fixed Effect Model. The result from this research shows that Oil Consumption per capita (Kwh), Coal consumption per capita (Kwh), Gas Consumption per capita (Kwh), and Renewable energy consumption per capita (Kwh) have a positive significant effect towards GDP per capita (current USD).

An Extensive Review on Gas Hydrates: Recent Patents, Properties, Formation, Detection, Production, Importance, and Challenges

Introduction:: Gas Hydrates, or Clathrate Hydrates, have been the subject of increasing scientific and industrial attention due to their potential as an alternative energy source, their role in climate change, and their association with geohazards. The growth of new indigenous gas supply sources could impart a significant positive ripple effect on a country's economy, ecological balance, and energy landscape. This burgeoning interest has led to a surge in research and development, resulting in numerous patents related to the extraction, processing, and utilization of gas Hydrates. Objectives:: This review paper aims to provide an up-to-date, comprehensive overview of the properties, formation, detection, production, importance, challenges, and patent landscape of Gas hydrates. The integration of patented technologies into the field underscores the importance of intellectual property in shaping the future of energy, environment, and economic development. Methods:: Patented technologies in this field are contributing to making this resource more accessible and commercially viable. Moreover, the development of gas hydrates as an energy source could act as a safeguard for manufacturing jobs that are sensitive to gas prices, with proprietary technologies enhancing the efficiency and sustainability of the production process. Results:: On the environmental front, an uptick in the consumption of natural gas, known for its cleaner combustion, could herald positive change. Patented innovations in clean and efficient extraction and utilization methods for Gas Hydrates are instrumental in reducing the environmental impact. From the standpoint of energy security, a larger domestic slice of the energy pie, complemented by an extensive array of gas supply alternatives, could equip the nation to better navigate the unpredictable terrain of future energy scenarios. Conclusion:: The strategic patenting of key technologies in the exploration, production, and application of Gas Hydrates ensures competitive advantage and fosters innovation, driving forward the energy industry's evolution.

Role of Economic Expansion, Energy Utilization and Urbanization on Climate Change in Egypt based on Artificial Intelligence

This study employs machine-learning algorithms (ML), specifically Random Forest (RF) and Gradient Boosting (GB), to assess the impact of various factors, including Gross Domestic Product (GDP) growth, urbanization, and energy consumption, on carbon dioxide emissions (CO2). The research underscores the RF algorithm's superior accuracy in determining independent variables' influence on CO2 emissions compared to GB. Furthermore, the study reveals that natural gas is the most significant contributor to CO2 emissions in Egypt, accounting for 49.7% of the total, followed closely by oil at 46.7%. The effect of other variables on CO2 emissions is relatively minimal. The findings also establish a strong positive correlation between the consumption of natural gas, oil, and coal and CO2 emissions in Egypt. Additionally, a negative relationship is observed between GDP growth, suggesting a positive trend in environmentally friendly economic expansion and urbanization on CO2 emissions in Egypt. This unique scenario, where urban expansion appears to have an inverse relationship with CO2 emissions, sets Egypt apart from many other countries and signifies a favorable environmental outcome.