Rate of penetration (ROP) prediction is critical for the optimization of drilling parameters and ROP improvement during drilling. However, it is still challenging to accurately predict ROP based on traditional empirical formula methods. This is usually the case for the development of the Wushi 17-2 oilfield block in the South China Sea. The Liushagang Formation is complex and the ROP is relatively low and difficult to increase. Ordinary data-driven ROP prediction models are not applicable because they do not take into account the complexity of formation conditions. In this work, we characterize the formation with acoustic transit time and build a data-driven ROP prediction model based on a deep neural network approach. By using the exploratory well data of the Wushi 17-2 oilfield for training and testing, the matching degree of the established model with the real data can reach 82%. In addition, we have developed a drilling parameter optimization process based on the ROP prediction mod... [more]

In this study, we present a new approach for load forecasting (LF) using a recurrent fuzzy neural network (RFNN) for Kermanshah City. Imagine if there is a need for electricity in a region in the coming years, we will have to build a power plant or reinforce transmission lines, so this will be resolved if accurate forecasts are made at the right time. Furthermore, suppose that by building distributed generation plants, and predicting future consumption, we can conclude that production will be more than consumption, so we will seek to export energy to other countries and make decisions on this. In this paper, a novel combination of neural networks (NNs) and type-2 fuzzy systems (T2FSs) is used for load forecasting. Adding feedback to the fuzzy neural network can also benefit from past moments. This feedback structure is called a recurrent fuzzy neural network. In this paper, Kermanshah urban electrical load data is used. The simulation results prove the efficiency of this method for for... [more]

In this paper, we describe the relevance of diagnosing the lining condition of steel ladles in metallurgical facilities. Accidents with steel ladles lead to losses and different types of damage in iron and steel works. We developed an algorithm for recognizing thermograms of steel ladles to identify burnout zones in the lining based on the technology and design of neural networks. A diagnostic system structure for automated evaluating of the technical conditions of steel ladles without taking them out of service has been developed and described.

With the recent advancements in computational capacities and the widespread applications of machine learning in engineering problems, the role of numerical methods has been becoming more and more important to improve existing models or develop new models that can help researchers to better understand the underlying physics of combustion, their interaction with other physical phenomena such as turbulence, and their impacts on the performance of the related applications at both fundamental and practical levels [...]

In order to reduce the cost of data transmission, the meter data management system (MDMS) of the power operator usually delays time to obtain the power generation information of a solar photovoltaic (PV) power generation system. Although this approach solves the problem of data transmission cost, it brings more challenges to the solar PV power generation forecast. Because power operators usually need real-time solar PV power generation as a basis for the power dispatch, but considering the cost of communication, they cannot always provide corresponding historical power generation data in real time. In this study, an intelligent solar PV power generation forecasting mechanism combined with weather information is designed to cope with the issue of the absence of real-time power generation data. Firstly, the Pearson correlation coefficient analysis is used to find major factors with a high correlation in relation to solar PV power generation to reduce the computational burden of data fitt... [more]

This paper presents a method for use in estimating the state of charge (SOC) of lithium-ion batteries which is based on an electrochemical impedance equivalent circuit model with a controlled source. Considering that the open-circuit voltage of a battery varies with the SOC, an equivalent circuit model with a controlled source is proposed which the voltage source and current source interact with each other. On this basis, the radial basis function (RBF) neural network is adopted to estimate the uncertainty in the battery model online, and a non-linear observer based on the radial basis function of the RBF neural network is designed to estimate the SOC of batteries. It is proved that the SOC estimation error is ultimately bounded by Lyapunov stability analysis, and the error bound can be arbitrarily small. The high accuracy and validity of the non-linear observer based on the RBF neural network in SOC estimation are verified with experimental simulation results. The SOC estimation resul... [more]

The temperature rises hydrogen tanks during the fast-filling process could threaten the safety of the hydrogen fuel cell vehicle. In this paper, a 2D axisymmetric model of a type III hydrogen for the bus was built to investigate the temperature evolution during the fast-filling process. A test rig was carried out to validate the numerical model with air. It was found significant temperature rise occurred during the filling process, despite the temperature of the filling air being cooled down due to the throttling effect. After verification, the 2D model of the hydrogen tank was employed to study the temperature distribution and evolution of hydrogen during the fast-filling process. Thermal stratification was observed along the axial direction of the tank. Then, the effects of filling parameters were examined, and a formula was fitted to predict the final temperature based on the simulated results. At last, an effort was paid on trying the improve the temperature distribution by increas... [more]

In Canada, many mines have adopted the sublevel stoping method, such a blasthole stoping (BHS), to extract steeply deposited minerals. Sill pillars are usually kept in place in this mining method to support the weight of the overburden in underground mining. To prolong the mine’s life, sill pillars will be recovered, and sill pillar recovery could cause failures, fatality, and equipment loss in the stopes. In this paper, three sill pillar recovery schemes—SBS, SS1, and SS2—were proposed and conducted to assess the feasibility of recovering two sill pillars in a hard rock mine by developing a full-sized three-dimensional (3D) analysis model employing the finite element method (FEM). The numerical model was calibrated by comparing the model computed ground settlement with the in situ monitored ground settlement data. The rockburst tendency of the stope accesses caused by the sill pillar recovery was assessed by employing the tangential stress (Ts) criterion and burst potential index (BPI... [more]

Tectonic interpretation is critical to a coal mine’s safe production, and fault interpretation is an essential component of seismic tectonic interpretation. With the increasing necessity for accuracy in fault interpretation in coal mines, it is increasingly challenging to achieve greater accuracy only through traditional fault interpretation. The convolutional neural network (CNN) is a machine learning method established in recent years and it has been widely applied in coal mine fault interpretation because of its powerful feature-learning and classification capabilities. To improve the accuracy and efficiency of fault interpretation in coal mines, an automatic seismic fault identification method based on the convolutional neural network has been developed. Taking a mining area in eastern Yunnan province as an example, the CNN model realized automatic identification of faults with eight seismic attributes as feature inputs, and the model-training parameters were optimized and compared... [more]

Gentry biomass, as a residue from the mezcal production process, may be an interesting bioenergy alternative; however, its high ash content limits its application. In this study, pellets were generated with agave fiber mixed with Pinus species sawdust in the following six proportions (%): 100−0 (control), 80−20, 60−40, 40−60, 20−80 and 0−100 (control). The physical, chemical and energetic properties of the pellets were evaluated according to the UNE-EN ISO 17225-6, UNE EN ISO 17827-2, UNE-EN ISO 17828, UNE-EN ISO 18122, UNE-EN ISO 18123, UNE-EN ISO 18125, and UNE-EN ISO 18134-1 standards. The results showed significant statistical differences (p < 0.05) among the treatments tested. The percentage of volatile material and fixed carbon ranged from 86.53 to 89.96% and 4.17 to 8.16%, respectively; the ash content ranged from 0.27 to 10.06%, and the calorific value ranged from 17.33 to 18.03 MJ/kg. Bulk density ranged from 725.76 to 737.37 kg/m3 and the impact-strength index was in the r... [more]

Aiming at the poor precision problem in phase difference measurements with unknown frequencies in engineering practice, a new phase difference measurement method is proposed for Coriolis mass flowmeter based on correlation theories. Firstly, the signal frequency was estimated by using an adaptive notch filter, which was applied to filter the waves and determined the integer period of the sampling signals, and the non-integer period sampling signals needed to be extended. Then, the Hilbert transformation was conducted relative to the extended signals, and the correlation functions of these extended signals with the transformed signals can be computed. Finally, the formula of phase difference can be obtained by utilizing the sinusoidal function. Compared to traditional methods, such as the correlation method, the Hilbert transformation method, and sliding Goertzel algorithm, the proposed method is suitable for both integer period and non-integer period sampling signals, and its accuracy,... [more]

A seismic swarm is a series of earthquakes that occur in a small area over a short period of time. A sequence of earthquakes of this magnitude is unusual in Switzerland, and it is impossible to anticipate how it may unfold in the future.The seismic activity of such an event usually fades after a few days or weeks. Significantly greater earthquakes are likely to occur during the next several days, with up to a chance of 5 to 10%. For these reasons, the underground research facilities need tools to provide data on the impact of these events on their experiments. The paper presents the techniques implemented at The European Organization for Nuclear Research (CERN) to allow the tracking and monitoring of these unusual events. Additionally, the real effect of such an unusual event is presented together with the statistical approach to monitoring and effect evaluation. Considering the collision energy of the beams at 14 TeV, the energy stored in the magnets at 10 GJ (2400 kg of TNT), and the... [more]

Sustainable technologies, including clean energy in manufacturing and green and reverse logistics, generate conditions for industry development and future growth with the implementation of Industry 4.0 technologies and innovations in the context of sustainable development goals (SDGs). The objective of the article is to identify and analyse the potential of sustainable technologies in synergy with Industry 4.0 innovations and renewable energy initiatives in manufacturing and logistics in the context of SDGs. Qualitative analysis was performed on 105 enterprises of various business sizes, in several regions of Slovakia, within various industry sectors, and within geographical coverage. Based on the summarised results, we can state that more than 82% of surveyed enterprises implement the SDGs. Currently, more than 70% of enterprises prefer environmental aspects in business management. Based on the results, we find a significant relationship between the environmental management of the ent... [more]

Aiming at the problem that power load data are stochastic and that it is difficult to obtain accurate forecasting results by a single algorithm, in this paper, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-sample entropy (SE), the BP neural network (BPNN), and the Transformer model. Firstly, the power load data were decomposed into several power load subsequences with obvious complexity differences by using the CEEMDAN-SE. Then, BPNN and Transformer model were used to forecast the subsequences with low complexity and the subsequences with high complexity, respectively. Finally, the forecasting results of each subsequence were superimposed to obtain the final forecasting result. The simulation was taken from our proposed model and six forecasting models by using the load dataset from a certain area of Spain. The results showed that the MAPE of our proposed CEEMDAN-SE-BPNN-Tra... [more]

A neurofuzzy system is proposed for short-term electric load forecasting. The fuzzy rule base of ReNFuzz-LF consists of rules with dynamic consequent parts that are small-scale recurrent neural networks with one hidden layer, whose neurons have local output feedback. The particular representation maintains the local learning nature of the typical static fuzzy model, since the dynamic consequent parts of the fuzzy rules can be considered as subsystems operating at the subspaces defined by the fuzzy premise parts, and they are interconnected through the defuzzification part. The Greek power system is examined, and hourly based predictions are extracted for the whole year. The recurrent nature of the forecaster leads to the use of a minimal set of inputs, since the temporal relations of the electric load time-series are identified without any prior knowledge of the appropriate past load values being necessary. An extensive simulation analysis is conducted, and the forecaster’s performance... [more]

This paper aimed to research the interrelation between capital structure, corporate governance, equity ownership, and how they affect firm performance. The sample used consisted of 10 leading-energy-sector companies traded in the NYSE, most of which rank among the largest companies in the world by market capitalization, while the US-based ones are also Fortune 500 companies. Over the eleven-year period examined, from 2009 to 2019, a sampling frame of 110 data series was gathered and analyzed using panel data methodologies. The impact of the key parameters of capital structure, corporate governance, and equity ownership was tested using regression analysis (panel data method) on firm performance, measured by profitability. Our results support a significant relation among major capital structure and corporate governance parameters and firm performance, whereas no evidence was found to support a significant impact of equity ownership on the dependent variable found ascertained. Furthermor... [more]

Volcanic rock reservoirs have received extensive attention from scholars all over the world because of their geothermal, mineral, and oil and gas resources. Shear wave velocity is the essential information for AVO (amplitude variation with offset) analysis and the reservoir description of volcanic rocks. However, due to factors such as cost, technical reasons, and so on, shear wave velocity is not provided in many logging data. This paper proposes a shear wave velocity prediction method suitable for the conventional logging of volcanic rocks. Firstly, the Xu−White model is improved. The probability distributions formed by the prior information of the logging area are used to initialize the key petrophysical parameters in the model instead of the fixed parameter value to establish the statistical petrophysical model between the logging curve and shear wave velocity. Then, based on the Bayesian inversion method, the simulated P-wave velocity is matched with the actual P-wave logging data... [more]

A Bayesian data analysis workflow offers great advantages to the process of measurement and verification, including the estimation of savings uncertainty regardless of the chosen numerical model. However, it is still rarely used in practice, perhaps because practitioners are less familiar with the required tools. The present work documents a Bayesian methodology for the assessment of energy savings at the scale of a whole facility, following an energy-conservation measure. The first model, an energy signature commonly used in practice, demonstrates the steps of the Bayesian workflow and illustrates its advantages. The posterior distributions obtained by training this first model are used as prior distributions for a second, more complex model. This so-called “hidden Markov energy signature” model combines the energy signature with a hidden Markov model at an hourly resolution, and allows detection of occupancy. It has a large number of parameters and would likely not be identifiable wi... [more]

An umbilical cable is a compactly integrated cable consisting of electrical power cables, communication (electric signal) cables, and chemical transposition tubes. An umbilical cable is widely used in developing oil and gas resources of deep and ultra-deep water. With the increment of the length and the functional integration of umbilical cables, the crosstalk becomes a crucial issue in the cable design, and needs to be evaluated carefully before the fabrication and installation. Moreover, the twisted structure of communication cable cores has incurred extra difficulties to the crosstalk calculation. Nevertheless, it is not an easy task to model the complex twisted structure in existing models and methods of the crosstalk computation. In this regard, this paper proposes a numerical methodology for the crosstalk calculation considering the multi-conductor twisted structure of the cable cores. In the methodology, the four-wire twisted structure is modelled as a cascade of uniform multico... [more]

Our goal is to examine the efficiency of different intraday electricity markets and if any of their price prediction models are more accurate than others. This paper includes a comprehensive review of Germany, France, and Norway’s (NOR1) day-ahead and intraday electricity market prices. These markets represent different energy mixes which would allow us to analyze the impact of the energy mix on the efficiencies of these markets. To draw conclusions about extreme market conditions, (i) we reviewed the market data linked to COVID-19. We expected higher volatility in the lockdowns than before and therefore decrease in the efficiency of the prediction models. With our analysis, (ii) we want to draw conclusions as to whether a mix based mainly on renewable energies such as that in Norway implies lower volatilities even in times of crisis. This would answer (iii) whether a market with an energy mix like Norway is more efficient in highly volatile phases. For the analysis, we use data visual... [more]

The hybrid power system is a combination of renewable energy power plants and conventional energy power plants. This integration causes power quality issues including poor settling times and higher transient contents. The main issue of such interconnection is the frequency variations caused in the hybrid power system. Load Frequency Controller (LFC) design ensures the reliable and efficient operation of the power system. The main function of LFC is to maintain the system frequency within safe limits, hence keeping power at a specific range. An LFC should be supported with modern and intelligent control structures for providing the adequate power to the system. This paper presents a comprehensive review of several LFC structures in a diverse configuration of a power system. First of all, an overview of a renewable energy-based power system is provided with a need for the development of LFC. The basic operation was studied in single-area, multi-area and multi-stage power system configura... [more]

Deflection switched reluctance motors (DSRM) are prone to chattering at low speeds, which always affects the output efficiency of the DSRM and the mechanical loss of the motor. Combining the characteristics of a traditional reluctance motor with the strong nonlinear and high coupling of the DSRM, a control system for a DSRM based on a flexible neural network (FNN) is proposed in this paper. Based on the better robustness and fault tolerance of fuzzy PI control, the given speed signal is adjusted and converted into a torque control signal. As a result, the FNN control module possesses the strong self-learning ability and adaptive adjustment ability necessary to obtain the control voltage signal. Through simulations and experiments, it was verified that the control system can run stably on DSRM and shows good dynamic performance and anti-interference ability.

One of the most critical aspects of integrating renewable energy sources into the smart grid is photovoltaic (PV) power generation forecasting. This ensemble forecasting technique combines several forecasting models to increase the forecasting accuracy of the individual models. This study proposes a regression-based ensemble method for day-ahead PV power forecasting. The general framework consists of three steps: model training, creating the optimal set of weights, and testing the model. In step 1, a Random forest (RF) with different parameters is used for a single forecasting method. Five RF models (RF1, RF2, RF3, RF4, and RF5) and a support vector machine (SVM) for classification are established. The hyperparameters for the regression-based method involve learners (linear regression (LR) or support vector regression (SVR)), regularization (least absolute shrinkage and selection operator (LASSO) or Ridge), and a penalty coefficient for regularization (λ). Bayesian optimization is perf... [more]

The grid-connected inverter is a vital power electronic equipment connecting distributed generation (DG) systems to the utility grid. The quality of the grid-connected current is directly related to the safe and stable operation of the grid-connected system. This study successfully constructed a robust control system for a grid-connected inverter through a dynamic recurrent fuzzy-neural-network imitating sliding-mode control (DRFNNISMC) framework. Firstly, the dynamic model considering system uncertainties of the grid-connected inverter is described for the global integral sliding-mode control (GISMC) design. In order to overcome the chattering phenomena and the dependence of the dynamic information in the GISMC, a model-free dynamic recurrent fuzzy-neural-network (DRFNN) is proposed as a major controller to approximate the GISMC law without the extra compensator. In the DRFNN, a Petri net with varied threshold is incorporated to fire the rules, and only the parameters of the fired rul... [more]

Many environmental parameters affect the performance of solar photovoltaics (PV), such as dust and temperature. In this paper, three PV technologies have been investigated and experimentally analyzed (mono, poly, and flexible monocrystalline) in terms of the impact of dust and thermal energy on PV behavior. Furthermore, a modular neural network is designed to test the effects of dust and temperature on the PV power production of six PV modules installed at Sohar city, Oman. These experiments employed three pairs of PV modules (one cleaned daily and one kept dusty for 30 days). The performance of the PV power production was evaluated and examined for the three PV modules (monocrystalline, polycrystalline, and flexible), which achieved 30.24%, 28.94%, and 36.21%, respectively. Moreover, the dust reduces the solar irradiance approaching the PV module and reduces the temperature, on the other hand. The neural network and practical models’ performance were compared using different indicator... [more]