Climate change 2014 - Synthesis Report

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

In this manuscript, a highly sensitive methane (CH4) sensor based on light-induced thermoelastic spectroscopy (LITES) using a 2.33 µm diode laser with high power is demonstrated for the first time. A quartz tuning fork (QTF) with an intrinsic resonance frequency of 32.768 kHz was used to detect the light-induced thermoelastic signal. A Herriot multi-pass cell with an effective optical path of 10 m was adopted to increase the laser absorption. The laser wavelength modulation depth and concentration response of this CH4-LITES sensor were investigated. The sensor showed excellent long term stability when Allan deviation analysis was performed. An adaptive Savitzky-Golay (S-G) filtering algorithm with χ2 statistical criterion was firstly introduced to the LITES technique. The SNR of this CH4-LITES sensor was improved by a factor of 2.35 and the minimum detection limit (MDL) with an integration time of 0.1 s was optimized to 0.5 ppm. This reported CH4-LITES sensor with sub ppm-level detection ability is of great value in applications such as environmental monitoring and industrial safety. 

Highly sensitive methane detection based on light-induced thermoelastic spectroscopy with a 2.33 µm diode laser and adaptive Savitzky-Golay filtering

Metal-Organic Frameworks (MOFs) as methane adsorbents: From storage to diluted coal mining streams concentration.

The energy consumption of buildings is very important for both economic and environmental reasons. Newly built buildings are characterized by higher insulation and airtightness of the building envelope, and are additionally equipped with technologies that minimize energy consumption in order to meet legal requirements. In existing buildings, the modernization process should be properly planned, taking into account available technologies and implementation possibilities. Hotel buildings are characterized by a large variability of energy demand, both on a daily and a yearly basis. Monitoring systems, therefore, provide the necessary information needed for proper energy management in the building. This article presents an energy analysis of the Turowka hotel located in Wieliczka (southern Poland). The historical hotel facility is being modernized as part of the project to adapt the building to the requirements of a sustainable building. The modernization proposal includes a trigeneration system with a multifunctional reverse regenerator and control module using neural algorithms. The main purpose is to improve the energy efficiency of the building and adapt it to the requirements of low-energy buildings. The implementation of a monitoring system enables energy consumption to be reduced and improves the energy performance of the building, especially through using energy management systems and control modules. The proposed retrofit solution considers the high energy consumption, structure of the energy demand, and limits of retrofit intervention on facades.

https://www.mdpi.com/1996-1073/13/8/1968/pdf

Energy Monitoring in a Heating and Cooling System in a Building Based on the Example of the Turówka Hotel

During the extraction of hard coal in Polish conditions, methane is emitted, which is referred to as ‘mine gas’. As a result of the desorption of methane, a greenhouse gas is released from coal seams. In order to reduce atmospheric emissions, methane from coal seams is captured by a methane drainage system. On the other hand, methane, which has been separated into underground mining excavations, is discharged into the atmosphere with a stream of ventilation air. For many years, Polish hard coal mines have been capturing methane to ensure the safety of the crew and the continuity of mining operations. As a greenhouse gas, methane has a significant potential, as it is more effective at absorbing and re-emitting radiation than carbon dioxide. The increase in the amount of methane in the atmosphere is a significant factor influencing global warming, however, it is not as strong as the increase in carbon dioxide. Therefore, in Polish mines, the methane–air mixture captured in the methane drainage system is not emitted to the atmosphere, but burned as fuel in systems, including cogeneration systems, to generate electricity, heat and cold. However, in order for such use to be possible, the methane–air mixture must meet appropriate quality and quantity requirements. The article presents an analysis of changes in selected parameters of the captured methane–air mixture from one of the hard coal mines in the Upper Silesian Coal Basin in Poland. The paper analyses the changes in concentration and size of the captured methane stream through the methane capturing system. The gas captured by the methane drainage system, as an energy source, can be used in cogeneration, when the methane concentration is greater than 40%. Considering the variability of CH4 concentration in the captured mixture, it was also indicated which pure methane stream must be added to the gas mixture in order for this gas to be used as a fuel for gas engines. The balance of power of produced electric energy in gas engines is presented. Possible solutions ensuring constant concentration of the captured methane–air mixture are also presented.

https://www.mdpi.com/1996-1073/13/1/44/pdf

Tests to Ensure the Minimum Methane Concentration for Gas Engines to Limit Atmospheric Emissions

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).

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

The paper presents an evaluation of work of two systems of local air cooling. Each of them consisted of a direct action refrigerator with evaporative water cooler. Taking effectiveness of utilization of cooling power into consideration, it was studied the effectiveness of air cooling by refrigerator TS-300B with evaporative water cooler CWW-420 and refrigerator DV-290 with evaporative water cooler RK-450. Evaluation of work of this cooling systems was carried out on the basis of established criteria. To determine this criteria, it was used the statistical methods of linear and nonlinear estimation of researches on the test stand. It was given linear and nonlinear statistical models evaluated an effectiveness of each of cooling systems and models evaluated an effectiveness of both cooling system. As a result of modification of the coefficients and exponents in these models, it was obtained another, more convenient in practical use, forms of criteria. Verification of all obtained criteria was made. It was made with reference to calculated evaporator thermal power of both refrigerators on the basis of measurements. The absolute and relative deviations from measured values for each criteria were described. The results of the comparison were presented in tables and graphs.

Use of statistical methods to the work of local air cooling systems in excavations

Legal requirements for the use of refrigerants increasingly restrict the use of high-global warming potential (GWP) refrigerants. As a result, there is a growing interest in natural refrigerants and in those belonging to the hydrofluoroolefins (HFO) class, which can be used on their own or in mixtures. One of them is the R1234ze(E) refrigerant, an alternative to the R134a refrigerant as well as being a component of numerous mixtures. The knowledge of thermodynamic and transport properties of refrigerants is required for the analysis and calculation of refrigeration cycles in refrigeration, air conditioning, or heating systems. The paper presents analytical equations for calculating the properties of the R1234ze(E) refrigerant in the state of saturation and in the subcooled liquid and superheated vapour regions that do not require numerical calculations and are characterised by small deviations. The Levenberg–Marquardt algorithm—one of the methods for non-linear least squares estimation—was used to develop them. A total of 26 equations were formulated. The formulated equations were statistically verified by determining absolute and relative deviations between the values obtained from CoolProp software and calculated values. The maximum relative deviation was not higher than 1% in any of them.

https://www.mdpi.com/1996-1073/13/12/3052/pdf

Functional Equations for Calculating the Properties of Low-GWP R1234ze(E) Refrigerant

Increasing emissions from mining areas and a high global warming potential of methane have caused gas management to become a vital challenge. At the same time, it provides the opportunity to obtain economic benefits. In addition, the use of combined heat and power (CHP) in the case of coalbed methane combustion enables much more efficient use of this fuel. The article analyses the possibility of electricity production using gas engines fueled with methane captured from the Budryk coal mine in Poland. The basic issue concerning the energy production from coalbed methane is the continuity of supply, which is to ensure the required amount and concentration of the gas mixture for combustion. Hence, the reliability of supply for electricity production is of key importance. The analysis included the basic characterization of both the daily and annual methane capture by the mine’s methane drainage system, as well as the development of predictive models to determine electricity production based on hourly capture and time parameters. To forecast electricity production, predictive models that are based on five parameters have been adopted. Models were prepared based on three time variables, i.e., month, day, hour, and two values from the gas drainage system-capture and concentration of the methane. For this purpose, artificial neural networks with different properties were tested. The developed models have a high value of correlation coefficient. but showed deviations concerning the very low values persisting for a short time. The study shows that electricity production forecasting is possible, but it requires data on many variables that directly affect the production capacity of the system.

R. Łuczak

Papers

Tests to Ensure the Minimum Methane Concentration for Gas Engines to Limit Atmospheric Emissions

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

Use of statistical methods to the work of local air cooling systems in excavations

Functional Equations for Calculating the Properties of Low-GWP R1234ze(E) Refrigerant

The Security of Energy Supply from Internal Combustion Engines Using Coal Mine Methane—Forecasting of the Electrical Energy Generation