Wind power is cleaner and less expensive compared to other alternative sources, and it has therefore become one of the most important energy sources worldwide. However, challenges related to the operation and maintenance of wind farms significantly contribute to the increase in their overall costs, and, therefore, it is necessary to monitor the condition of each wind turbine on the farm and identify the different states of alarm. Common alarms are raised based on data acquired by a supervisory control and data acquisition (SCADA) system; however, this system generates a large number of false positive alerts, which must be handled to minimize inspection costs and perform preventive maintenance before actual critical or catastrophic failures occur. To this end, a fault detection methodology is proposed in this paper; in the proposed method, different data analysis and data processing techniques are applied to real SCADA data (imbalanced data) for improving the detection of alarms related... [more]

Incorrect adjustment of railway traffic control devices (rtc) to the specificity of the area in terms of lightning density may result in their damage by lightning-caused electromagnetic pulses. An associated risk assessment, as the standard in the analyses of the strength of rtc devices, should be made. However, manufacturers of rtc devices typically do not perform tests with respect to such high overvoltage conditions. Particularly in the case of the EN 62305-2 standard, the manufacturers of new rail traffic control devices usually do not carry out such analyses. In this paper, an analysis of the damage vulnerability of rtc devices caused by lightning discharges for a selected structure, including a signal box equipped with digital rtc devices is presented. The necessity of conducting a lightning damage vulnerability analysis for rtc devices is demonstrated. A part of the railway track where lightning discharges occur was analyzed. The results of the risk analysis obtained with the us... [more]

The aim of this study was to optimize biomass and docosahexaenoic acid (DHA) production by Schizochytrium sp. grown on waste glycerol as an organic carbon source. Parameters having a significant effect on biomass and DHA yields were screened using the fractional Plackett−Burman design and the response surface methodology (RSM). Schizochytrium sp. growth was most significantly influenced by crude glycerin concentration in the growth medium (150 g/dm3), process temperature (27 °C), oxygen in the bioreactor (49.99% v/v), and the concentration of peptone as a source of nitrogen (9.99 g/dm3). The process parameter values identified as optimal for producing high DHA concentrations in the biomass were as follows: glycerin concentration 149.99 g/dm3, temperature 26 °C, oxygen concentration 30% (v/v), and peptone concentration 2.21 g/dm3. The dry cell weight (DCW) obtained under actual laboratory conditions was 66.69 ± 0.66 g/dm3, i.e., 1.27% lower than the predicted value. The DHA concentratio... [more]

Quantification in seismic probabilistic safety assessment (PSA) includes the convolution of hazard and fragility curves. Because it is difficult to find the closed-form integration for the convolution of two curves, numerical methods are widely used in practice. In practical applications, the number of ground motion level bins in numerical methods is limited, and it is not clear whether the limited number of bins leads to conservative or optimistic results. In this study, the effect of the number of bins on the quantification results with simplified assumptions is investigated. It is found that the quantification results mostly decrease as the number of bins increases. To enhance accuracy in the quantification results of seismic PSA, a method and a software tool that enable a sufficiently large number of bins to be used for the quantification of seismic PSA models are developed. The application of the developed software tool to an example seismic PSA model demonstrates how the quantifi... [more]

Mining companies are responsible for the impacts that result from their mining activities even after the mining period has ended. At the same time, at the European and international levels, there is a lack of a detailed operational methodology comprising environmental risks during and after closure of underground coal mines. The environmental risk aspects that need to be considered when planning the closure of an underground coal mine and post closure in the broader environmental context are the following: modification of water flow scheme, surface instability, mine gas emission on the surface, and water and soil pollution. In this study, we focus on assessing groundwater risk in the context of an underground coal mine closure and evaluating the selected risk mitigation strategies in terms of performance and cost. The results from this study could be used for developing a final closure groundwater assessment plan by selecting the most feasible treatment alternatives for different envir... [more]

Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings’ energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this study is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS−ANNs). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA) are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model’s optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90% correlation) can adequately optimize the ANN. In this regard, this tool’s prediction error declined by nearly 23%, 18%, and 36% by applying... [more]

Increasing traffic volumes in cities leads to common traffic congestions building up, especially during peak hours. To protect city dwellers from excessive fuel exhaust and traffic noise and to prevent drivers from time loss due to overloaded routes, it is important to inform them about real-time traffic conditions and possible delays in advance. Effectively influencing drivers’ decisions to divert from an original route choice in case of traffic hinderance is essential, and application of dynamic travel information in the form of variable message signs (VMSs) is believed to be effective in these terms. The paper examines drivers’ willingness to divert from an initial route choice due to the information provided on VMS boards. Their behavior was analyzed in terms of their response to everyday and artificially elongated travel times displayed on the VMSs. Maximum simulated elongation reached 200% and 300% of the initial state, depending on the characteristics of the pre-peak conditions.... [more]

To utilize the rapidly refreshed operating data of power systems fully and effectively, an integrated scheme for inter-area oscillatory stability assessment (OSA) is proposed in this paper using a compositive feature selection unit and random bits forest (RBF) algorithm. This scheme consists of offline, update, and online stages, and it can provide fast and accurate estimation of the oscillatory stability margin (OSM) by using the real-time system operating data. In this scheme, a compositive feature selection unit is specially designed to realize efficient feature selection, which can significantly reduce the data dimensionality, effectively alleviate feature redundancy, and provide accurate correlation information to system operators. Then, the feature set consisting of the selected pivotal features is used for the RBF training to build the mapping relationships between the OSM and the system operating variables. Moreover, to enhance the robustness of the scheme in the face of variab... [more]

Covid-19 affects the personal lives of millions and led to an economic crisis. Changed behavioral patterns and a reduction of industrial activity result in a reduction in power demand, and thus Covid-19 impacts the power systems around the world. Bottom-up mapping of the effect of Covid-19 on the energy demand is challenging, if not impossible. In order to analyze the impact of the pandemic on power demand, we instead propose a simplified approach based on an econometric analysis that quantifies the country-wide load reduction of Covid-19, using the number of active cases as well as the specific lockdown period as proxies. The time span covered is from 1 January 2016 to 31 August 2020. This long time span allows us to investigate the effect of Covid-19 on the power demand. We find that in Germany (DE) and Great Britain (GB) the power demand is reduced by about 1−1.7 MW per case, while in France the demand increased by 1 MW per case during times outside of the lockdown. On the other han... [more]

The article presents studies aimed at determining the relative risk of accidents at work in all age groups of employees and the trend of possible changes in risk as a function of time in the years 2008−2018. The studies were conducted on the basis of statistics of accidents at work in Poland in the years 2008−2018. The data were collected from statistical yearbooks published by the Polish Central Statistical Office. The database covered 732,460 accidents at work. A retrospective cohort analysis of the relative risks for each year and the entire population was carried out. Through sensitivity testing, changes in the summary effect resulting from the removal of a given study were determined. The group with the highest average relative risk of accidents at work (2.59) were employees aged 18−19 years. An increase in relative risk was observed among employees aged +60 years. The smallest relative risk (0.33) was determined among employees under 18 years of age, who in Poland are mainly trai... [more]

Mineral reactive surface area (RSA) is one of the key factors that control mineral reactions, as it describes how much mineral is accessible and can participate in reactions. This work aims to evaluate the impact of mineral RSA on numerical simulations for CO2 storage at depleted oil fields. The Farnsworth Unit (FWU) in northern Texas was chosen as a case study. A simplified model was used to screen representative cases from 87 RSA combinations to reduce the computational cost. Three selected cases with low, mid, and high RSA values were used for the FWU model. Results suggest that the impact of RSA values on CO2 mineral trapping is more complex than it is on individual reactions. While the low RSA case predicted negligible porosity change and an insignificant amount of CO2 mineral trapping for the FWU model, the mid and high RSA cases forecasted up to 1.19% and 5.04% of porosity reduction due to mineral reactions, and 2.46% and 9.44% of total CO2 trapped in minerals by the end of the... [more]

Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictio... [more]

Smart meters with automatic meter reading functionalities are becoming popular across the world. As a result, load measurements at various sampling frequencies are now available. Several methods have been proposed to infer device usage characteristics from household load measurements. However, many techniques are based on highly intensive computations that incur heavy computational costs; moreover, they often rely on private household information. In this paper, we propose a technique for the detection of appliance utilization patterns using low-computational-cost algorithms that do not require any information about households. Appliance utilization patterns are identified only from the system status behavior, represented by large system status datasets, by using dimensionality reduction and clustering algorithms. Principal component analysis, k-means, and the elbow method are used to define the clusters, and the minimum spanning tree is used to visualize the results that show the appe... [more]

Carbon capture and storage (CCS) has been increasingly looking like a promising strategy to reduce CO2 emissions and meet the Paris agreement’s climate target. To ensure that CCS is safe and successful, an efficient monitoring program that will prevent storage reservoir leakage and drinking water contamination in groundwater aquifers must be implemented. However, geologic CO2 sequestration (GCS) sites are not completely certain about the geological properties, which makes it difficult to predict the behavior of the injected gases, CO2 brine leakage rates through wellbores, and CO2 plume migration. Significant effort is required to observe how CO2 behaves in reservoirs. A key question is: Will the CO2 injection and storage behave as expected, and can we anticipate leakages? History matching of reservoir models can mitigate uncertainty towards a predictive strategy. It could prove challenging to develop a set of history matching models that preserve geological realism. A new Bayesian evi... [more]

Mechanical dewatering is a key process in the management of sewage sludge. However, the drainage efficiency depends on a number of factors, from the type and dose of the conditioning agent to the parameters of the drainage device. The selection of appropriate methods and parameters of conditioning and dewatering of sewage sludge is the task of laboratory work. This work can be accelerated through the use of artificial neural network (ANNs). The paper discusses the possibilities of using ANNs in predicting the dewatering efficiency of physically conditioned sludge. The input variables were only four parameters characterizing the conditioning methods and the dewatering method by centrifugation. These were the dose of the sludge skeleton builders (cement, gypsum, fly ash, and liquid glass), sonication parameters (sonication amplitude and time), and relative centrifugal force. Dewatering efficiency parameters such as sludge hydration and separation factor were output variables. Due to the... [more]

The most efficient energy management strategies for hybrid vehicles are the “Optimization-Based Strategies”. These strategies require a preliminary knowledge of the driving cycle, which is not easy to predict. This paper aims to combine Worldwide Harmonized Light-Duty Vehicles Test Cycle (WLTC) low section short trips with real traffic levels for vehicle energy and fuel consumption prediction. Future research can focus on implementing a new strategy for Hybrid Electric Vehicle (HEV) energy optimization, taking into account WLTC and Google Maps traffic levels. First of all, eight characteristic parameters are extracted from real speed profiles, driven in urban road sections in the city of Messina at different traffic conditions, and WLTC short trips as well. The minimum distance algorithm is used to compare the parameters and assign the three traffic levels (heavy, average, and low traffic level) to the WLTC short trips. In this way, for each route assigned from Google maps, vehicle’s e... [more]

Short-Term Load Prediction (STLP) is an important part of energy planning. STLP is based on the analysis of historical data such as outdoor temperature, heat load, heat consumer configuration, and the seasons. This research aims to forecast heat consumption during the winter heating season. By preprocessing and analyzing the data, we can determine the patterns in the data. The results of the data analysis make it possible to form learning algorithms for an artificial neural network (ANN). The biggest disadvantage of an ANN is the lack of precise guidelines for architectural design. Another disadvantage is the presence of false information in the analyzed training data. False information is the result of errors in measuring, collecting, and transferring data. Usually, trial error techniques are used to determine the number of hidden nodes. To compare prediction accuracy, several models have been proposed, including a conventional ANN and a wavelet ANN. In this research, the influence of... [more]

In October 2014, EU leaders agreed upon three key targets for the year 2030: a reduction of at least 40% in greenhouse gas emissions, a saving of at least a 27% share for renewable energy, and at least a 27% improvement in energy efficiency [...]

Buildings in hot climate areas are responsible for high energy consumption due to high cooling load requirements which lead to high greenhouse gas emissions. In order to curtail the stress on the national grid and reduce the atmospheric emissions, it is of prime importance that buildings produce their own onsite electrical energy using renewable energy resources. Photovoltaic (PV) technology is the most favorable option to produce onsite electricity in buildings. Installation of PV modules on the roof of the buildings in hot climate areas has a twofold advantage of acting as a shading device for the roof to reduce the cooling energy requirement of the building while producing electricity. A high ground coverage ratio provides more shading, but it decreases the efficiency of the PV system because of self-shading of the PV modules. The aim of this paper was to determine the optimal value of the ground coverage ratio which gives maximum overall performance of the roof-mounted PV system by... [more]

Seismic data and nuclear magnetic resonance (NMR) data are two of the highly trustable kinds of information in hydrocarbon reservoir engineering. Reservoir fluids influence the elastic wave velocity and also determine the NMR response of the reservoir. The current study investigates different pore types, i.e., micro, meso, and macropores’ contribution to the elastic wave velocity using the laboratory NMR and elastic experiments on coal core samples under different fluid saturations. Once a meaningful relationship was observed in the lab, the idea was applied in the field scale and the NMR transverse relaxation time (T2) curves were synthesized artificially. This task was done by dividing the area under the T2 curve into eight porosity bins and estimating each bin’s value from the seismic attributes using neural networks (NN). Moreover, the functionality of two statistical ensembles, i.e., Bag and LSBoost, was investigated as an alternative tool to conventional estimation techniques of... [more]

Concern over the asymmetric phenomena in the core region has increased considering safety issues that are highly possible to reduce the thermal margin significantly in nuclear power plants. Since the seized reactor coolant pump (RCP) accident of an advanced power reactor 1400 (APR1400) can be regarded as a representative core asymmetric event with respect to core inlet flow, the departure from nucleate boiling ratio (DNBR), which is a regulatory acceptance criterion in nuclear safety, should be evaluated with consideration of the uncertainty range of the core inlet flow reflecting the actual geometry. This study investigates the DNBR quantitatively in the entire fuel assemblies in the core using several codes for system behavior, computational flow dynamics, sub-channel analysis, and uncertainty evaluation. Based on the results from a system thermal-hydraulic analysis of a seized RCP accident of APR1400, this study presents the uncertainty range calculated by computational fluid dynami... [more]

It is necessary to monitor, acquire, preprocess, and classify microseismic data to understand active faults or other causes of earthquakes, thereby facilitating the preparation of early-warning earthquake systems. Accordingly, this study proposes the application of machine learning for signal−noise classification of microseismic data from Pohang, South Korea. For the first time, unique microseismic data were obtained from the monitoring system of the borehole station PHBS8 located in Yongcheon-ri, Pohang region, while hydraulic stimulation was being conducted. The collected data were properly preprocessed and utilized as training and test data for supervised and unsupervised learning methods: random forest, convolutional neural network, and K-medoids clustering with fast Fourier transform. The supervised learning methods showed 100% and 97.4% of accuracy for the training and test data, respectively. The unsupervised method showed 97.0% accuracy. Consequently, the results from machine l... [more]

Effectively transporting drilled cuttings to the surface is a vital part of the well construction process. Usually, mechanistic models are used to estimate the cuttings concentration during drilling. Based on the results from these model, operational parameters are adjusted to mitigate any nonproductive time events such as pack-off or lost circulation. However, these models do not capture the underlying complex physics completely and frequently require updating the input parameters, which is usually performed manually. To address this, in this study, a data-driven modeling approach is taken and evaluated together with widely used mechanistic models. Artificial neural networks are selected after several trials. The experimental data collected at The University of Tulsa−Drilling Research Projects (in the last 40 years) are used to train and validate the model, which includes a wide range of wellbore and pipe sizes, inclinations, rate-of-penetration values, pipe rotation speeds, flow rate... [more]

We investigate data-driven, simple-to-implement residential environmental models that can serve as the basis for energy saving algorithms in both retrofits and new designs of residential buildings. Despite the nonlinearity of the underlying dynamics, using Koopman operator theory framework in this study we show that a linear second order model embedding, that captures the physics that occur inside a single or multi zone space does well when compared with data simulated using EnergyPlus. This class of models has low complexity. We show that their parameters have physical significance for the large-scale dynamics of a building and are correlated to concepts such as the thermal mass. We investigate consequences of changing the thermal mass on the energy behavior of a building system and provide best practice design suggestions.

Wind energy and wind power forecast errors have a direct impact on operational decision problems involved in the integration of this form of energy into the electricity system. As the relationship between wind and the generated power is highly nonlinear and time-varying, and given the increasing number of available forecasting techniques, it is possible to use alternative models to obtain more than one prediction for the same hour and forecast horizon. To increase forecast accuracy, it is possible to combine the different predictions to obtain a better one or to dynamically select the best one in each time period. Hybrid alternatives based on combining a few selected forecasts can be considered when the number of models is large. One of the most popular ways to combine forecasts is to estimate the coefficients of each prediction model based on its past forecast errors. As an alternative, we propose using multivariate reduction techniques and Markov chain models to combine forecasts. Th... [more]