The accurate determination of methane adsorption isotherms in coals is crucial for both the evaluation of underground coalbed methane (CBM) reserves and design of development strategies for enhancing CBM recovery. However, the experimental measurement of high-pressure methane adsorption isotherms is extremely tedious and time-consuming. This paper proposed the use of an ensemble machine learning (ML) method, namely the gradient boosting decision tree (GBDT), in order to accurately estimate methane adsorption isotherms based on coal properties in the Qinshui basin, China. The GBDT method was trained to correlate the adsorption amount with coal properties (ash, fixed carbon, moisture, vitrinite, and vitrinite reflectance) and experimental conditions (pressure, equilibrium moisture, and temperature). The results show that the estimated adsorption amounts agree well with the experimental ones, which prove the accuracy and robustness of the GBDT method. A comparison of the GBDT with two com... [more]

Nonuniform proppant distribution is fairly common in hydraulic fractures, and different closure behaviors of the propped and unpropped fractures have been observed in lots of physical experiments. However, the modeling of partially propped fracture closure is rarely performed, and its effect on gas production is not well understood as a result of previous studies. In this paper, a fully coupled fluid flow and geomechanics model is developed to simulate partially propped fracture closure, and to examine its effect on gas production in fractured shale reservoirs. Specifically, an efficient hybrid model, which consists of a single porosity model, a multiple porosity model and the embedded discrete fracture model (EDFM), is adopted to model the hydro-mechanical coupling process in fractured shale reservoirs. In flow equations, the Klinkenberg effect is considered in gas apparent permeability, and adsorption/desorption is treated as an additional source term. In the geomechanical domain, th... [more]

The goal of the paper is to develop an online forecasting procedure to be adopted within the H2020 InteGRIDy project, where the main objective is to use the photovoltaic (PV) forecast for optimizing the configuration of a distribution network (DN). Real-time measurements are obtained and saved for nine photovoltaic plants in a database, together with numerical weather predictions supplied from a commercial weather forecasting service. Adopting several error metrics as a performance index, as well as a historical data set for one of the plants on the DN, a preliminary analysis is performed investigating multiple statistical methods, with the objective of finding the most suitable one in terms of accuracy and computational effort. Hourly forecasts are performed each 6 h, for a horizon of 72 h. Having found the random forest method as the most suitable one, further hyper-parameter tuning of the algorithm was performed to improve performance. Optimal results with respect to normalized root... [more]

Most electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated level and at high frequency rates. This data opens the possibility of developing new forecasting models with a potential positive impact on electricity systems. We present a general methodology that can process and forecast many smart-meter time series. Instead of using traditional and univariate approaches, we develop a single but complex recurrent neural-network model with long short-term memory that can capture individual consumption patterns and consumptions from different households. The resulting model can accurately predict future loads (short-term) of individual consumers, even if these were not included in the original training set. This entails a great potential for large-scale applications as once the single network is trained, accurate individual forecast for n... [more]

This study proposes super efficiency (SE) as an efficient analytical method for evaluating the performance of energy research projects. Because the SE method is based on data envelopment analysis (DEA), it is free from the difficulty of weighting output, allows for the use of variables with diverse standards of measurement, and is capable of providing ranking information that regular DEA (CCR, BCC) analysis techniques cannot. To analyze the feasibility of the DEA-SE method, an efficiency evaluation was performed for energy research projects using both the weighting method as an existing method and the SE method. When the results were compared and analyzed, skewing toward particular output types was observed in the weighting method, owing to problems inherent in the method itself and in the weighting of subordinate variables that make up the total performance score. Therefore, adopting DEA-SE will redress the known problems of the weighting method by minimizing the problems of weighting... [more]

An artificial neural network (ANN)-based supplementary frequency controller is designed for a doubly fed induction generator (DFIG) wind farm in a local power system. Since the optimal controller gain that gives highest the frequency nadir or lowest peak frequency is a complicated nonlinear function of load disturbance and system variables, it is not easy to use analytical methods to derive the optimal gain. The optimal gain can be reached through an exhaustive search method. However, the exhaustive search method is not suitable for online applications, since it takes a long time to perform a great number of simulations. In this work, an ANN that uses load disturbance, wind penetration, and wind speed as the inputs and the desired controller gain as the output is proposed. Once trained by a proper set of training patterns, the ANN can be employed to yield the desired gain in a very efficient manner, even when the operating condition is not included in the training set. Therefore, the p... [more]

Vehicle integrated thermal management system (VTMS) is an important technology used for improving the energy efficiency of vehicles. Physics-based modeling is widely used to predict the energy flow in such systems. However, physics-based modeling requires several experimental approaches to get the required parameters. The experimental approach to obtain these parameters is expensive and requires great effort to configure a separate experimental device and conduct the experiment. Therefore, in this study, a neural network (NN) approach is applied to reduce the cost and effort necessary to develop a VTMS. The physics-based modeling is also analyzed and compared with recent NN techniques, such as ConvLSTM and temporal convolutional network (TCN), to confirm the feasibility of the NN approach at EPA Federal Test Procedure (FTP-75), Highway Fuel Economy Test cycle (HWFET), Worldwide harmonized Light duty driving Test Cycle (WLTC) and actual on-road driving conditions. TCN performed the best... [more]

The purpose of this study is to reduce the risk of leakage of CO2 geological storage by injecting the dissolved CO2 solution instead of the supercritical CO2 injection. The reservoir simulation method is used in this study to evaluate the contributions of the different trapping mechanisms, and the safety index method is used to evaluate the risk of CO2 leakage. The function of the dissolved CO2 solution injection is performed by a case study of a deep saline aquifer. Two scenarios are designed in this study: the traditional supercritical CO2 injection and the dissolved CO2 solution injection. The contributions of different trapping mechanisms, plume migrations, and the risk of leakage are evaluated and compared. The simulation results show that the risk of leakage via a natural pathway can be decreased by the approach of injecting dissolved CO2 solution instead of supercritical CO2. The amount of the CO2 retained by the safe trapping mechanisms in the dissolved CO2 solution injection s... [more]

Seismic reflection coefficient inversion in the joint time-frequency domain is a method for inverting reflection coefficients using time domain and frequency domain information simultaneously. It can effectively improve the time-frequency resolution of seismic data. However, existing research lacks an analysis of the factors that affect the resolution of inversion results. In this paper, we analyze the influence of parameters, such as the length of the time window, the size of the sliding step, the dominant frequency band, and the regularization factor of the objective function on inversion results. The SPGL1 algorithm for basis pursuit denoising was used to solve our proposed objective function. The applied geological model and experimental field results show that our method can obtain a high-resolution seismic reflection coefficient section, thus providing a potential avenue for high-resolution seismic data processing and seismic inversion, especially for thin reservoir inversion and... [more]

This work deals with the use of the Clarke transformation for the theoretical derivation of circuit models for the analysis of asymmetrical transients in three-phase circuits. Asymmetrical transients occur when only one or two phases of a three-phase power system are involved in a switch operation. Such a condition is critical from a theoretical viewpoint since the Clarke transformation is based on the assumption of circuit symmetry between the three phases. If the symmetry assumption is not met, the equivalent circuits in the transformed variables α, β, and 0 are not uncoupled. The literature concerning numerical approaches for asymmetrical transient analysis is very rich, but a comprehensive and rigorous analytical investigation of circuit models within the framework of the Clarke transformation is still lacking. Contrary to numerical approaches, analytical solutions provide deeper insight into the phenomenon and allow for theoretical interpretation and better understanding of the tr... [more]

This paper presents the use of scaling and dimensional analysis to assess the viability of conventional modelling of immiscible displacement occurring when water is injected into the oil-saturated, porous rock—a conventional secondary oil-recovery method. A brief description of the laboratory tests of oil displacement with water performed on long core sets taken from wells operating on a Polish oil reservoir was presented. A dimensionless product generator based on dimensional analysis and Buckingham Π theorem was used to generate all possible combinatorial sets of dimensionless products for physical variables describing the phenomenon. The mathematical model of the phenomenon was transformed to its dimensionless form, using a selected set of the products. The results of the laboratory tests were analyzed as functions of the products. Statistically verified quantities describing both dependent and independent experiment variables were subject to a regression analysis to study dependenc... [more]

Electrical load forecasting plays a crucial role in the proper scheduling and operation of power systems. To ensure the stability of the electrical network, it is necessary to balance energy generation and demand. Hence, different very short-term load forecast technologies are being designed to improve the efficiency of current control strategies. This paper proposes a new forecaster based on artificial intelligence, specifically on a recurrent neural network topology, trained with a Levenberg−Marquardt learning algorithm. Moreover, a sensitivity analysis was performed for determining the optimal input vector, structure and the optimal database length. In this case, the developed tool provides information about the energy demand for the next 15 min. The accuracy of the forecaster was validated by analysing the typical error metrics of sample days from the training and validation databases. The deviation between actual and predicted demand was lower than 0.5% in 97% of the days analysed... [more]

Predicting flow patterns that develop on the roof of high-rise buildings is critical for the development of urban wind energy. In particular, the performance and reliability of devices largely depends on the positioning strategy, a major unresolved challenge. This work aims at investigating the effect of variations in the turbulent inflow and the geometric model on the flow patterns that develop on the roof of tall buildings in the realistic configuration of the University of Birmingham’s campus in the United Kingdom (UK). Results confirm that the accuracy of Large Eddy Simulation (LES) predictions is only marginally affected by differences in the inflow mean wind speed and turbulence intensity, provided that turbulence is not absent. The effect of the presence of surrounding buildings is also investigated and found to be marginal to the results if the inflow is turbulent. The integral length scale is the parameter most affected by the turbulence characteristics of the inflow, while gu... [more]

The objective of this study is to investigate the influence of winglet pitching as an aero-brake on the performance of a model wind turbine by wind tunnel experiments. Time-resolved particle image velocimetry, force sensor, and datalogger were used to characterize the coupling between wake statistics, aerodynamic loads, and rotation speed. Results highlighted that, for a winglet with 4% of the rotor diameter length, the increase of its pitching angle can significantly reduce the turbine rotation speed up to ∼28% and thrust coefficient of ∼20%. The winglet pitching induced minor influence on the velocity deficit in the very near wake regions, while its influence on accelerating the wake recovery become clear around three diameters downstream the turbine rotor. The turbulence kinetic energy exhibited a distinctive increase under large pitching angles in the near wake region at the turbine hub height due to the strong vertical flow fluctuations. Further investigation on the spectra of wak... [more]

Energy has a strategic role in the economic and social development of countries. In the last few decades, energy demand has been increasing exponentially across the world, and predicting energy demand has become one of the main concerns in many countries. The residential and commercial sectors constitute about 34.7% of global energy consumption. Anticipating energy demand in these sectors will help governments to supply energy sources and to develop their sustainable energy plans such as using renewable and non-renewable energy potentials for the development of a secure and environmentally friendly energy system. Modeling energy consumption in the residential and commercial sectors enables identification of the influential economic, social, and technological factors, resulting in a secure level of energy supply. In this paper, we forecast residential and commercial energy demands in Iran using three different machine learning methods, including multiple linear regression, logarithmic m... [more]

The present study develops a novel optimization method for designing a Stirling engine by combining a variable-step simplified conjugate gradient method (VSCGM) and a neural network training algorithm. As compared with existing gradient-based methods, like the conjugate gradient method (CGM) and simplified conjugate gradient method (SCGM), the VSCGM method is a further modified version presented in this study which allows the convergence speed to be greatly accelerated while the form of the objective function can still be defined flexibly. Through the automatic adjustment of the variable step size, the optimal design is reached more efficiently and accurately. Therefore, the VSCGM appears to be a potential and alternative tool in a variety of engineering applications. In this study, optimization of a low-temperature-differential gamma-type Stirling engine was attempted as a test case. The optimizer was trained by the neural network algorithm based on the training data provided from thr... [more]

In this paper, a tracking control approach is developed based on an adaptive reinforcement learning algorithm with a bounded cost function for perturbed nonlinear switched systems, which represent a useful framework for modelling these converters, such as DC−DC converter, multi-level converter, etc. An optimal control method is derived for nominal systems to solve the tracking control problem, which results in solving a Hamilton−Jacobi−Bellman (HJB) equation. It is shown that the optimal controller obtained by solving the HJB equation can stabilize the perturbed nonlinear switched systems. To develop a solution to the translated HJB equation, the proposed neural networks consider the training technique obtaining the minimization of square of Bellman residual error in critic term due to the description of Hamilton function. Theoretical analysis shows that all the closed-loop system signals are uniformly ultimately bounded (UUB) and the proposed controller converges to optimal control la... [more]

Failure of a pipeline carrying gaseous hydrogen can have several effects, some of which can pose a significant threat of harm to people and damage to buildings in its immediate proximity. This paper presents a probabilistic risk assessment procedure for the estimation of damage to people and buildings endangered by high-pressure hydrogen pipeline explosions. Such a procedure provides an evaluation of annual probability of damage to people and buildings under an extreme event using a combination of the conditional probability of damage triggered by an explosion and the probability that the explosion occurs as a consequence of the pipeline failure. The release of hydrogen is simulated using the LimitState:SLAB model and the size of the hydrogen-air cloud in the flammability range is evaluated, then overpressure and impulse generated by the blast are evaluated through the Netherland Organization for Applied Scientific Research (TNO) model, while explosion effects on people and buildings a... [more]

To comprehensively understand the effectiveness of external factors on flow characteristics and realize particle flow distribution evenly in bulk layers is an essential prerequisite for improving the performance of heat transfer in vertical sinter cooling beds (VSCBs). The numerical discrete element method (DEM) was applied to investigate external geometric and operational factors, such as the aspect ratio, geometry factor, half hopper angle, normalized outlet scale, and discharge velocity. Using the Taguchi method, a statistical analysis of the effect of design factors on response was performed. In this study, we focused more on external factors than granular properties, be remodelling the external factors was more useful and reliable for actual production in industries. The results showed that the most important factor was the aspect ratio, followed by the geometry factor, normalized outlet scale, half hopper angle, and discharge velocity for the dimensionless height of mass flow. In... [more]

Motor-driven equipment (ME) is one of the key components in an air-conditioning system, which contributes to the vast majority of the total energy consumption by air-conditioning systems. Distinguishing variable- and constant-speed equipment is important since the energy simulation models of the two types differ. Traditionally, types of ME are known in advance, and energy consumption data are consequently analyzed. However, in the application scenarios of energy consumption data mining, precedent information on the ME type could be missing. Thus, this study applies this process in reverse, providing new insight into energy consumption data of ME to recognize variable-speed ME in an air-conditioning system. The energy consumption data of ME in an air-conditioning system implemented in a commercial building were collected and numerically analyzed. A proposed simple parameter, coefficient of the median, and several numerical parameters were calculated and used to distinguish variable- fro... [more]

The paper deals with the impact of the technological break duration during the cold storage cycle on the energy demand of the cold store for vegetables and fruit and the temperature distribution in the ground under the cold store. The studied facility was a two-chamber vegetable cold store located in southern Poland used to store carrots (Daucus carota) for nine months a year. The experiments were conducted for 12 months (01.05.2017−30.04.2018). The technological break during this period lasted three months (from 1 July 2018 to 30 September 2018). Continuous measurements (with 1-h frequency) were made in order to determine the boundary conditions for numerical analysis. The measured parameters included indoor air temperature, outdoor air temperature, ground temperature under the building and in its vicinity. There were 22 measuring points andPT100 sensors were used. The numerical analysis was based on the elementary balances method. WUFIplus® software was used as a calculation supporti... [more]

This paper presents a new approach method for online rotor and stator resistance estimation of induction motors using artificial neural networks for the sensorless drive. In this method, the rotor resistance is estimated by a feed-forward neural network with the learning rate as a function. The stator resistance is also estimated using the two-layered neural network with learning rate as a function. The speed of the induction motor is also estimated by the neural network. Therefore, the accurate estimation of the rotor and stator resistance improved the quality of the sensorless induction motor drive. The results of simulation and experiment show that the estimated speed tracks the real speed of the induction motor; simultaneously, the error between the estimated rotor and stator resistance using neural network and the normal rotor and stator resistance is very small.

The current work is focused on investigating the potential of data-driven post-processing techniques, including proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) for flame dynamics. Large-eddy simulation (LES) of a V-gutter premixed flame was performed with two Reynolds numbers. The flame transfer function (FTF) was calculated. The POD and DMD were used for the analysis of the flame structures, wake shedding frequency, etc. The results acquired by different methods were also compared. The FTF results indicate that the flames have proportional, inertial, and delay components. The POD method could capture the shedding wake motion and shear layer motion. The excited DMD modes corresponded to the shear layer flames’ swing and convect motions in certain directions. Both POD and DMD could help to identify the wake shedding frequency. However, this large-scale flame oscillation is not presented in the FTF results. The negative growth rates of the decomposed mode confi... [more]

Today’s spread of power distribution networks, with the installation of a significant number of renewable generators that depend on environmental conditions and on users’ consumption profiles, requires sophisticated models for monitoring the power flow, regulating the electricity market, and assessing the reliability of power grids. Such models cannot avoid taking into account the variability that is inherent to the electrical system and users’ behavior. In this paper, we present a solution for the generation of a compressed surrogate model of the electrical state of a realistic power network that is subject to a large number (on the order of a few hundreds) of uncertain parameters representing the power injected by distributed renewable sources or absorbed by users with different consumption profiles. Specifically, principal component analysis is combined with two state-of-the-art surrogate modeling strategies for uncertainty quantification, namely, the least-squares support vector ma... [more]

Solar radiation data is essential for the development of many solar energy applications ranging from thermal collectors to building simulation tools, but its availability is limited, especially the diffuse radiation component. There are several studies aimed at predicting this value, but very few studies cover the generalizability of such models on varying climates. Our study investigates how well these models generalize and also show how to enhance their generalizability on different climates. Since machine learning approaches are known to generalize well, we apply them to truly understand how well they perform on different climates than they are originally trained. Therefore, we trained them on datasets from the U.S. and tested on several European climates. The machine learning model that is developed for U.S. climates not only showed low mean absolute error (MAE) of 23 W/m2, but also generalized very well on European climates with MAE in the range of 20 to 27 W/m2. Further investiga... [more]