A few studies have been conducted recently in order to improve the aerodynamic performance of Darrieus vertical-axis wind turbines with straight blades (H-type VAWTs). The blade pitch angle control is proposed to enhance the performance of H-type VAWTs. This paper aims to investigate the performance of an H-type VAWT in terms of its power output and self-starting capability using an intelligent blade pitch control strategy based on a multi-layer perceptron artificial neural network (MLP-ANN) method. The performance of the proposed blade pitch controller is investigated by adding a conventional controller (PID) to the MLP-ANN controller (i.e., a hybrid controller). The dynamics of an H-type VAWT is mathematically modeled in a nonlinear state space for the stability analysis in the sense of Lyapunov. The effectiveness of the proposed pitch control system is validated by building an H-type VAWT prototype model that is extensively tested outdoors under different conditions for both fixed a... [more]

Circulating Fluidized Bed gasifiers are widely used in industry to convert solid fuel into liquid fuel. The Artificial Neural Network and neuro-fuzzy algorithm have immense potential to improve the efficiency of the gasifier. The main focus of this article is to implement the Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System modeling approach to estimate solid circulation rate at high pressure in the Circulating Fluidized Bed gasifier. The experimental data is obtained on a laboratory scale prototype in the Chemical Engineering laboratory at COMSATS University Islamabad. The Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System use four input features—pressure, single mean diameter, total valve opening and riser dp—and one output feature mass flow rate with multiple neurons in the hidden layers to estimate the flow of solid particles in the riser. Both Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System model worked on 217 data samples an... [more]

Wind direction and speed are the most important factors that determine the degree of damage caused by a jet fire. In this study, the metal hose used to extract/supply fuel was identified as the component with the highest risk for a jet fire occurring at an aerospace facility. A risk assessment was performed to evaluate the individual risk of a jet fire from the metal hose according to the wind direction and speed. HSE failure data was applied for calculating the jet fire probability including metal hose failure, ignition frequency, and jet fire frequency. Which was 3.0 × 10−4. The individual risk of different fatality probabilities was calculated according to the wind rose data for the aerospace facility. The individual risk from jet fire in the aerospace facility was calculated with a maximum risk of 3.35 × 10−5 and a minimum risk of 1.49 × 10−6. The individual risk satisfied HSE ALARP criteria. In addition, firewalls, extinguishing systems, and an emergency shut off system were enhan... [more]

This paper proposes enhanced prediction and control design methods for improving traffic flow with human-driven and automated vehicles. To achieve accurate prediction for the entire time horizon, data-driven and model-based prediction methods were integrated. The goal of the integration was to accurately predict the outflow of the traffic network, which was selected as the highway section in this paper. The proposed novel prediction method was used in the optimal design for calculating controlled inflows on highway ramps. The goal of the design was to reach the maximum outflow of the traffic network, even against disturbances on uncontrolled inflows of the network. The control design leads to an optimization problem based on the min−max principle, i.e., the traffic outflow is considered to be maximized by controlled inflows and to be minimized by uncontrolled inflows. The effectiveness of the prediction and the control methods through simulation examples are illustrated, i.e., traffic... [more]

This comparative research investigates the influence of a flexible magnetic flux and a chemical change on the freely fluid motion of a (MHD) magneto hydrodynamic boundary layer incompressible nanofluid across an exponentially expanding sheet. Water and ethanol are used for this analysis. The temperature transmission improvement of fluids is described using the Buongiorno model, which includes Brownian movement and thermophoretic distribution. The nonlinear partial differential equalities governing the boundary layer were changed to a set of standard nonlinear differential equalities utilizing certain appropriate similarity transformations. The bvp4c algorithm is then used to tackle the transformed equations numerically. Fluid motion is slowed by the magnetic field, but it is sped up by thermal and mass buoyancy forces and thermophoretic distribution increases non-dimensional fluid temperature resulting in higher temperature and thicker boundary layers. Temperature and concentration, on... [more]

Load forecasting (LF) is essential in enabling modern power systems’ safety and economical transportation and energy management systems. The dynamic balance between power generation and load in the optimization of power systems is receiving increasing attention. The intellectual development of information in the power industry and the data acquisition system of the smart grid provides a vast data source for pessimistic load forecasting, and it is of great significance in mining the information behind power data. An accurate short-term load forecasting can guarantee a system’s safe and reliable operation, improve the utilization rate of power generation, and avoid the waste of power resources. In this paper, the load forecasting model by applying a fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network (ILSTM-NN), and then establish short-term load forecasting using this novel model. Sparrow Search Algorithm is a novel swarm intelligence optimization algor... [more]

A model reference adaptive control and fuzzy neural network (FNN) synchronous motion compensator for a gantry robot is presented in this paper. This paper proposes the development and application of gantry robots with MRAC and FNN online compensators. First, we propose a model reference adaptive controller (MRAC) under the cascade control method to make the reference model close to the real model and reduce tracking errors for the single axis. Then, a fuzzy neural network compensator for the gantry robot is proposed to compensate for the synchronous errors between the dual servo motors to improve precise movement. In addition, an online parameter training method is proposed to adjust the parameters of the FNN. Finally, the experimental results show that the proposed method improves the synchronous errors of the gantry robot and demonstrates the methodology in this paper. This study also successfully integrates the hardware and successfully verifies the proposed methods.

Exchangeability means the possibility of the fuel changing, with conservation of the required energy and environmental criteria. The assessment of fuel exchangeability should be realized by a suitable method, which must reliably present the possibility of the exchangeability of fuels, or reject it. In the presented paper, research on the exchangeability of solid fuels in the field of heating production is surveyed by the case study. Based on the available published knowledge from previous studies on fuel exchangeability, the statistical method was chosen for evaluation. The application of this method is useful. For example, by evaluating the exchangeability of natural gas, the manuscript will describe its application for the field of solid fuels in heat production. The research evaluated and analyzed the sample of 12 fuels. For each fuel sample, 35 gas attributes were measured, which were classified into separate flue gas attribute groups: ash content, combustion heat, heating capacity... [more]

The effects of the diameter and location of an inserted wire on methane−air flame characteristics in a micro-burner, with a backward-facing step, were investigated numerically. Our goal was to shed light on the parameters that the authors had not already considered in the previous study. To do so, the effects of the studied parameters on the flame location and distribution of temperature, H, and OH species, were scrutinized. It was shown that increasing the inserted wire’s diameter and relocating the inserted wire towards the outlet had polynomial and linear effects on the flame location in the burner, respectively. Although changing these two parameters did not have any obvious effects on the maximum temperature of the auxiliary axis in the burner or the external wall, effects on the peak values of the hot-flame critical chemical species of OH and H were recognized. Furthermore, it was shown that the temperature distribution on the outer surface of the burner was more influenced by th... [more]

From the breakdown of the Kaplan rotor of a hydrogenerator unit and the monitored data collected during its operation before such a failure, this work presents a post-occurrence data analysis in which a previously developed hybrid method based on unsupervised machine learning techniques is applied to detect and diagnose failure before a unit shutdown. In addition to demonstrating the efficiency and capacity of the developed method in an application with real data, the conducted analysis seeks to shed light on the events that occurred at the considered hydroelectric power plant, helping to understand the failure mode evolution and outcome. The results of the fault detection and diagnosis process clearly demonstrated how the evolution of failure modes took place in the analyzed equipment. The detection of potential failures far in advance would support adequate maintenance planning and mitigating actions that could prevent unit breakdown and the consequent damage and financial losses.

A convolutional neural network (CNN) autoencoder model has been developed to generate 3D realizations of time-averaged velocity in the wake of the wind turbines at the Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility. Large-eddy simulations (LES) of the SWiFT site are conducted using an actuator surface model to simulate the turbine structures to produce training and validation datasets of the CNN. The simulations are validated using the SpinnerLidar measurements of turbine wakes at the SWiFT site and the instantaneous and time-averaged velocity fields from the training LES are used to train the CNN. The trained CNN is then applied to predict 3D realizations of time-averaged velocity in the wake of the SWiFT turbines under flow conditions different than those for which the CNN was trained. LES results for the validation cases are used to evaluate the performance of the CNN predictions. Comparing the validation LES results and CNN predictions, we show that the d... [more]

The introduction of electromobility contributes to an increase in energy efficiency and lower air pollution. European countries have not been among the world’s leading countries in this statistic. In addition, there have been different paces in the implementation of electromobility in individual countries. The main purpose of this paper is to determine the directions of change and the degrees of concentration in electromobility in European Union (EU) countries, especially after the economic closure as a result of the COVID-19 pandemic. The specific objectives are to indicate the degree of concentration of electromobility in the EU and changes in this area, especially during the COVID-19 pandemic; to determine the dynamics of changes in the number of electric cars in individual EU countries, showing the variability in this aspect, while also taking into account the crisis caused by COVID-19; to establish the association between the number of electric cars and the parameters of the econo... [more]

Vehicle speed prediction plays a critical role in energy management strategy (EMS). Based on the adaptive particle swarm optimization−least squares support vector machine (APSO-LSSVM) algorithm with BP neural network (BPNN), a novel closed-loop vehicle speed prediction system is proposed. The database of a vehicle internet platform was adopted to construct a speed prediction model based on the APSO-LSSVM algorithm. Furthermore, a BPNN is established according to the local high-precision nonlinear fitting relationship between the predicted value and error so as to correct the prediction value. Then, the results are returned to the APSO-LSSVM model for calculating the minimum fitness function, thus obtaining a closed-loop prediction system. Finally, equivalent fuel consumption minimization strategy (ECMS) based EMS was performed. According to the simulation results, the RMSE performance is 0.831 km/h within 5 s, which is over 20% higher than other performances. Additionally, the training... [more]

A new EGS arrangement, Robust EGS (REGS), is studied for its potential benefits for wide-spread applications for clean, carbon-free, electrical energy generation. Numerical simulations are carried out to prove the key benefit of REGS in a simple, but effective, geologic heat exchanger arrangement with large, stabilized fracture aperture and controlled flow zones. The numerical model results show the estimated potential energy capacity and the converted value to electrical energy generation over a 30-year operation time period for two simple REGS arrangements. The results may assist EGS investors and drilling companies in deciding whether the investment and operation can be made profitable for the wide-scale application of REGS for green energy generation.

In this paper, a novel short-term load forecasting method amalgamated with quantile regression random forest is proposed. Comprised with point forecasting, it is capable of quantifying the uncertainty of power load. Firstly, a bespoke 2D data preprocessing taking advantage of empirical mode decomposition (EMD) is presented. It can effectively assist subsequent point forecasting models to extract spatial features hidden in the 2D load matrix. Secondly, by exploiting multimodal deep neural networks (DNN), three short-term load point forecasting models are conceived. Furthermore, a tailor-made multimodal spatial−temporal feature extraction is proposed, which integrates spatial features, time information, load, and electricity price to obtain more covert features. Thirdly, relying on quantile regression random forest, the probabilistic forecasting method is proposed, which exploits the results from the above three short-term load point forecasting models. Lastly, the experimental results d... [more]

Here, we determined the kinetic parameters of SO2 adsorption on unburned carbons from lignite fly ash and activated carbons based on hard coal dust. The model studies were performed using the linear and non-linear regression method for the following models: pseudo first and second order, intraparticle diffusion, and chemisorption on a heterogeneous surface. The quality of the fitting of a given model to empirical data was assessed based on: R2, R, Δq, SSE, ARE, χ2, HYBRID, MPSD, EABS, and SNE. It was clearly shown that the linear regression more accurately reflects the behaviour of the adsorption system, which is consistent with the first-order kinetic reaction—for activated carbons (SO2 + Ar) or chemisorption on a heterogeneous surface—for unburned carbons (SO2 + Ar and SO2 + Ar + H2O(g) + O2) and activated carbons (SO2 + Ar + H2O(g) + O2). Importantly, usually, each of the approaches (linear/non-linear) indicated a different mechanism of the studied phenomenon. A certain universality... [more]

This work reports a numerical investigation of microcombustion in an undulate microchannel, using premixed hydrogen and air to understand the effect of the burner design on the flame in order to obtain stability of the flame. The simulations were performed for a fixed equivalence ratio and a hyperbolic temperature profile imposed at the microchannel walls in order to mimic the heat external losses occurred in experimental setups. Due to the complexity of the flow dynamics combined with the combustion behavior, the present study focuses on understanding the effect of the fuel inlet rate on the flame characteristics, keeping other parameters constant. The results presented stable flame structure regardless of the inlet velocity for this type of design, meaning that a significant reduction in the heat flux losses through the walls occurred, allowing the design of new simpler systems. The increase in inlet velocity increased the flame extension, with the flame being stretched along the mic... [more]

This study presents an optimization platform based on the climatic data provided by the National Renewable Energy Laboratory (NREL) to determine the optimum solar panel orientation. Our optimization model is simpler to use than the clearness index model since there is no need to calculate the extraterrestrial insolation on a horizontal flat plate and the shape factor. This optimization approach is based on the hourly climatic data. It determines the optimum tilt angle and azimuth angle of a solar panel for the maximum power generation, considering the diurnal variation of climatic conditions. The hourly evaluation of insolation allows setting up a solar panel azimuth angle that responds to the peak power demand. The main data that impacts the solar panel performance consists of the solar direct normal incident (DNI), direct horizontal incident (DHI), global horizontal incident (GHI), ambient temperature, wind speed, and ground albedo, all of which were obtained from the NREL database f... [more]

According to the BP Statistical Review of World Energy 2020 [...]

This study uses deep learning algorithms to predict the rotational speed of the turbine generator in an oscillating water column-type wave energy converter (OWC-WEC). The effective control and operation of OWC-WECs remain a challenge due to the variation in the input wave energy and the significantly high peak-to-average power ratio. Therefore, the rated power control of OWC-WECs is essential for increasing the operating time and power output. The existing rated power control method is based on the instantaneous rotational speed of the turbine generator. However, due to physical limitations, such as the valve operating time, a more refined rated power control method is required. Therefore, we propose a method that applies a deep learning algorithm. Our method predicts the instantaneous rotational speed of the turbine generator and the rated power control is performed based on the prediction. This enables precise control through the operation of the high-speed safety valve before the en... [more]

This paper introduces a novel, transfer-learning-based approach to include physics into data-driven normal behavior monitoring models which are used for detecting turbine anomalies. For this purpose, a normal behavior model is pretrained on a large simulation database and is recalibrated on the available SCADA data via transfer learning. For two methods, a feed-forward artificial neural network (ANN) and an autoencoder, it is investigated under which conditions it can be helpful to include simulations into SCADA-based monitoring systems. The results show that when only one month of SCADA data is available, both the prediction accuracy as well as the prediction robustness of an ANN are significantly improved by adding physics constraints from a pretrained model. As the autoencoder reconstructs the power from itself, it is already able to accurately model the normal behavior power. Therefore, including simulations into the model does not improve its prediction performance and robustness... [more]

The reliability of the power supply depends on the reliability of the structure of the grid. Grid networks are exposed to varying weather events, which makes them prone to faults. There is a growing concern that climate change will lead to increasing numbers and severity of weather events, which will adversely affect grid reliability and electricity supply. Predictive models of electricity reliability have been used which utilize computational intelligence techniques. These techniques have not been adequately explored in forecasting problems related to electricity outages due to weather factors. A model for predicting electricity outages caused by weather events is presented in this study. This uses the back-propagation algorithm as related to the concept of artificial neural networks (ANNs). The performance of the ANN model is evaluated using real-life data sets from Pietermaritzburg, South Africa, and compared with some conventional models. These are the exponential smoothing (ES) an... [more]

Driver drowsiness is one of the leading causes of traffic accidents. This paper proposes a new method for classifying driver drowsiness using deep convolution neural networks trained by wavelet scalogram images of electrocardiogram (ECG) signals. Three different classes were defined for drowsiness based on video observation of driving tests performed in a simulator for manual and automated modes. The Bayesian optimization method is employed to optimize the hyperparameters of the designed neural networks, such as the learning rate and the number of neurons in every layer. To assess the results of the deep network method, heart rate variability (HRV) data is derived from the ECG signals, some features are extracted from this data, and finally, random forest and k-nearest neighbors (KNN) classifiers are used as two traditional methods to classify the drowsiness levels. Results show that the trained deep network achieves balanced accuracies of about 77% and 79% in the manual and automated... [more]

With the implementation of new policies supporting renewable natural gas production from organic wastes, Canada began replacing traditional disposal methods with highly integrated biogas production strategies. Herein, data from published papers, Canadian Biogas Association, Canada’s national statistical agency, and energy companies’ websites were gathered to gain insight into the current status of anaerobic digestion plants in recovering energy and resource from organic wastes. The availability of materials prepared for recycling by companies and local waste management organizations and existing infrastructures for municipal solid waste management were examined. Governmental incentives and discouragements in Canada and world anaerobic digestion leaders regarding organic fraction municipal solid waste management were comprehensively reviewed to identify the opportunities for developing large-scale anaerobic digestion in Canada. A range of anaerobic digestion facilities, including water... [more]

Renewable energy is being adopted worldwide, and the proportion of offshore wind turbines is increasing. Offshore wind turbines operate in harsh weather conditions, resulting in various failures and high maintenance costs. In this paper, a condition diagnosis model for condition monitoring of an offshore wind turbine has been developed. The generator, main bearing, pitch system, and yaw system were selected as components subject to the condition monitoring by considering the failure rate and downtime of the wind turbine. The condition diagnosis model works by comparing real-time and predictive operating data of the wind turbine, and about four years of Supervisory Control and Data Acquisition (SCADA) data from a 2 MW wind turbine was used to develop the model. A deep neural network and an artificial neural network were used as machine learning to predict the operational data in the condition diagnosis model, and a confusion matrix was used to measure the accuracy of the failure determi... [more]