One of the main benefits of Building Information Modelling is the capability of improving the decision-making process thanks performing what-if tests on digital twins of the building to be realized. Pairing BIM models to Building Energy Models allows designers to determine in advance the energy consumption of the building, improving sustainability of the construction. The challenge is to consider as many elements involved in the energy balance as possible and shuffling their parameters within a certain range. In this work, the automatic creation of a relevant set of design options to be analyzed for searching the optimum has been carried out. Firstly, the usual workflow that would be applied manually has been automatically followed by running scripts and codes, depending just on the initial setup given by the user. Although the procedure is very resource consuming, the main advancement relies in the reduction of the manual intervention and the possibility of creating large datasets of... [more]

Inexpensive carbon sources offering an alternative to glucose are searched for to reduce costs of docosahexaenoic acid production by microalgae. The use of waste glycerol seems substantiated and prospective in this case. The objective of this study was to determine the production yield of heterotrophic microalgae Schizochytrium sp. biomass and the efficiency of docosahexaenoic acid production in various types of cultures with waste glycerol. Cultivation conditions were optimized using the Plackett−Burman method and Response Surface Methodology. The highest technological performance was obtained in the fed-batch culture, where the concentration of Schizochytrium sp. biomass reached 103.44 ± 1.50 g/dm3, the lipid concentration in Schizochytrium sp. biomass was at 48.85 ± 0.81 g/dm3, and the docosahexaenoic acid concentration at 21.98 ± 0.36 g/dm3. The highest docosahexaenoic acid content, accounting for 61.76 ± 3.77% of total fatty acids, was determined in lipid bodies of the Schizochytr... [more]

The 1-methylallyl (C4H71-3) allylic radical is an important intermediate species in oxidation of linear C4 unsaturated hydrocarbons (1-butene, 2-butene, and 1,3-butadiene). This study reports the first high-level quantum chemical calculations for an undisclosed reaction class of this radical at intermediate to high temperatures: direct H-atom abstraction from terminal methyl group by molecular oxygen. Moreover, we systematically calculated rate constants for primary, secondary, and tertiary H-atom abstraction from the C4, C5, and C6 allylic radicals, respectively. Our results can be further used as rate rules for kinetic model development of unsaturated hydrocarbon oxidation. All calculations were implemented using two different ab initio solvers: Gaussian and ORCA, three sets of ab initio methods, and two different kinetic solvers: MultiWell and PAPR. Temperature dependent rate constants and thermochemistry were carried out based on transition state theory and statistical thermodynami... [more]

Dry reforming of hydrocarbons, alcohols, and biological compounds is one of the most promising and effective avenues to increase hydrogen (H2) production. Catalytic dry reforming is used to facilitate the reforming process. The most popular catalysts for dry reforming are Ni-based catalysts. Due to their inactivation at high temperatures, these catalysts need to use metal supports, which have received special attention from researchers in recent years. Due to the existence of a wide range of metal supports and the need for accurate detection of higher H2 production, in this study, a systematic review and meta-analysis using ANNs were conducted to assess the hydrogen production by various catalysts in the dry reforming process. The Scopus, Embase, and Web of Science databases were investigated to retrieve the related articles from 1 January 2000 until 20 January 2021. Forty-seven articles containing 100 studies were included. To determine optimal models for three target factors (hydroca... [more]

This paper presents the results of research on the use of machine learning algorithms and electrical tomography in detecting humidity inside the walls of old buildings and structures. The object of research was a historical building in Wrocław, Poland, built in the first decade of the 19th century. Using the prototype of an electric tomograph of our own design, a number of voltage measurements were made on selected parts of the building. Many algorithmic methods have been preliminarily analyzed. Ultimately, the three models based on machine learning were selected: linear regression with SVM (support vector machine) learner, linear regression with least squares learner, and a multilayer perceptron neural network. The classical Gauss−Newton model was also used in the comparison. Both the experiments based on real measurements and simulation data showed a higher efficiency of machine learning methods than the Gauss−Newton method. The tomographic methods surpassed the point methods in meas... [more]

To enhance the oxidation strength and crosslinking yield of ultrahigh molecular weight polyethylene (UHMWPE), its composites were prepared by mixing 1%, 2%, and 3% (by wt.) of magnesium silicate hydrous (sepiolite) during this study. These composites were irradiated with 25 kGy and 50 kGy of irradiation doses in the open air. Subsequent to irradiation, the composites were characterized for updates in structure, oxidation strength, and degree of crosslinking while estimating the values of oxidation index (OI), crosslink density (Gx), and percent crystallinity (Xc), respectively. The reaction of modified sepiolite (SP) on irradiating UHMWPE/SP composites was evident from the origination of peaks at Si-O, O-Si-O, Si-O-Si, and Mg-OH and stretching vibration at 974 cm−1, 1014 cm−1, and 1080 cm−1, respectively. In addition, the moderate negative correlation of OI (−0.46) and strong positive correlation of Gx (0.87) with the absorbed dose, made obvious from correlation analysis, confirmed the... [more]

A method of fault tracking for relay protection devices is presented in this paper. Fault tracking means that after the failure of relay protection devices, the anomalies and warning information are obtained through data-mining technology, and then, the fault tracking algorithm is used to find the cause of failure. Let us take microcomputer protection as an example: Firstly, the common failure symptoms and the prior probability of failure causes can be collected through empirical field data. Then, the concept of an event set is proposed; thus, the causes set and the symptoms set of failure can be created. According to the causal relationship between the causes set of failure and symptoms set of failure, the reasoning chain and the corresponding Bayesian network model are built. Then, the probability of failure causes can be obtained through backward reasoning to continue the tracking analysis of failure causes for relay protection devices. Since the data used in modeling are all from s... [more]

The main objective of the paper is to present the development of digital economy and society in the Countries of the European Union in the light of the digital convergence of these markets. The analysis was based on the data on the Digital Economy and Society Index (DESI) from 2015 and 2020. Empirical studies have been conducted in two stages. First, basic measures of time series dynamics were used to determine the dynamics of changes in index values. On the other hand, in order to classify countries in terms of similarity of value and structure of the DESI, a cluster analysis was used as one of the basic methods of the so-called unsupervised statistical grouping. The studies presented confirm that the levels of indicators and the dynamics of changes in their value at the level of the synthetic DESI indicator, and in the five underlying areas of analysis, vary despite the fact that almost all are pro-growth, including for isolated clusters of EU-28 countries. When analyzing the DESI sy... [more]

High-fidelity simulations of turbulent flames are computationally expensive when using detailed chemical kinetics. For practical fuels and flow configurations, chemical kinetics can account for the vast majority of the computational time due to the highly non-linear nature of multi-step chemistry mechanisms and the inherent stiffness of combustion chemistry. While reducing this cost has been a key focus area in combustion modeling, the recent growth in graphics processing units (GPUs) that offer very fast arithmetic processing, combined with the development of highly optimized libraries for artificial neural networks used in machine learning, provides a unique pathway for acceleration. The goal of this paper is to recast Arrhenius kinetics as a neural network using matrix-based formulations. Unlike ANNs that rely on data, this formulation does not require training and exactly represents the chemistry mechanism. More specifically, connections between the exact matrix equations for kinet... [more]

The Xining Basin in Qinghai Province, China, is rich in mid−low temperature geothermal resources, but the reinjection of geothermal water has not yet started. In this paper, the physical properties of rocks sampled from the newly drilled geothermal well DR2018 were analyzed, and a series of core flooding experiments was conducted to assess the formation blockage risk during water reinjection. The experimental results show that the geothermal reservoir has a low porosity of 1.64−18.68% and a low permeability of 0.04−7.23 md. The rocks are weakly consolidated, and the movable clay and sand particles account for 0.18−23.42 wt %, which results in a significant rate and salinity sensitivity. Even at low water flow velocity of 0.31−1.64 cm/min, the core permeability will drop by 35−53% after 25-144PV injection. An obvious fluctuation and decline in core permeability can also be observed as the injected water salinity decreases. The blockage risk induced by the invasion of low-content scaling... [more]

It remains unclear to what extent remote sensing instruments can effectively improve the accuracy of short-term wind power forecasts. This work seeks to address this issue by developing and testing two novel forecasting methodologies, based on measurements from a state-of-the-art long-range scanning Doppler LiDAR. Both approaches aim to predict the total power generated at the wind farm scale with a five minute lead time and use successive low-elevation sector scans as input. The first approach is physically based and adapts the solar short-term forecasting approach referred to as “smart-persistence” to wind power forecasting. The second approaches the same short-term forecasting problem using convolutional neural networks. The two methods were tested over a 72 day assessment period at a large wind farm site in Victoria, Australia, and a novel adaptive scanning strategy was implemented to retrieve high-resolution LiDAR measurements. Forecast performances during ramp events and under va... [more]

This publication presents a multi-faceted analysis of the fuel consumption of motor vehicles and the way human impacts the environment, with a particular emphasis on the passenger cars. The adopted research methodology is based on the use of artificial neural networks in order to create a predictive model on the basis of which fuel consumption of motor vehicles can be determined. A database containing 1750 records, being a set of information on vehicles manufactured in last decade, was used in the process of training the artificial neural networks. The MLP (Multi-Layer Perceptron) 22-10-3 network has been selected from the created neural networks, which was further subjected to an analysis. In order to determine if the predicted values match the real values, the linear Pearson correlation coefficient r and coefficient of determination R2 were used. For the MLP 22-10-3 neural network, the calculated coefficient r was within range 0.93−0.95, while the coefficient of determination R2 assu... [more]

Currently, Lithium-ion batteries (LiB) are widely applied in energy storage devices in smart grids and electric vehicles. The state of charge (SOC) is an indication of the available battery capacity, and is one of the most important factors that should be monitored to optimize LiB’s performance and improve its lifetime. However, because the SOC relies on many nonlinear factors, it is difficult to estimate accurately. This paper presented the design of an effective SOC estimation method for a LiB pack Battery Management System (BMS) based on Kalman Filter (KF) and Artificial Neural Network (ANN). First, considering the configuration and specifications of the BMS and LiB pack, an ANN was constructed for the SOC estimation, and then the ANN was trained and tested using the Google TensorFlow open-source library. An SOC estimation model based on the extended KF (EKF) and a Thevenin battery model was developed. Then, we proposed a combined mode EKF-ANN that integrates the estimation of the E... [more]

This study optimizes pyrolysis conditions that will maximize energy recovery from the Beauty Leaf Tree (BLT; Calophyllum inophyllum L.) oil seed press cake. Response surface methodology (RSM) was used to determine the behavior of pyrolysis coproducts (solid, liquid and gas) at various temperatures and residence times. One significant discovery was that 61.7% of the energy (of the whole BLT oil seed) was still retained in the BLT oil seed cake after oil extraction. Controlled pyrolysis produced various proportions of biochar, bio-oil and syngas coproducts. Predictive models were developed to estimate both the mass and energy yields of the coproducts. In all experimental runs, the biochar component had the highest mass yield and energy content. Biochar mass yields were high at the lowest operating temperature used, but the energy yields based on a high heating value (HHV) of products were optimal at higher operating temperatures. From the RSM models, energy from the biochar is optimized... [more]

The rotor wake aerodynamic characterization is a fundamental aspect for the development and optimization of future rotary-wing aircraft. The paper is aimed at experimentally and numerically characterizing the blade tip vortices of a small-scale four-bladed isolated rotor in hover conditions. The investigation of the vortex decay process during the downstream convection of the wake is addressed. Two-component PIV measurements were carried out below the rotor disk down to a distance of one rotor radius. The numerical simulations were aimed at assessing the modelling capabilities and the accuracy of a free-wake Boundary Element Methodology (BEM). The experimental and numerical results were investigated by the Γ2 criterion to detect the vortex location. The rotor wake mean velocity field and the instantaneous vortex characteristics were investigated. The experimental/numerical comparisons show a reasonable agreement in the estimation of the mean velocity inside the rotor wake, whereas the... [more]

Variability, intermittency, and limited controllability are inherent characteristics of photovoltaic (PV) generation that result in inaccurate solutions to scheduling problems and the instability of the power grid. As the penetration level of PV generation increases, it becomes more important to mitigate these problems by improving forecasting accuracy. One of the alternatives to improving forecasting performance is to include a seasonal component. Thus, this study proposes using information on extraterrestrial radiation (ETR), which is the solar radiation outside of the atmosphere, in neural network models for day-ahead PV generation forecasting. Specifically, five methods for integrating the ETR into the neural network models are presented: (1) division preprocessing, (2) multiplication preprocessing, (3) replacement of existing input, (4) inclusion as additional input, and (5) inclusion as an intermediate target. The methods were tested using two datasets in Australia using four neu... [more]

This study assesses the performance of six global horizontal irradiance (GHI) clear sky models, namely: Bird, Simple Solis, McClear, Ineichen−Perez, Haurwitz and Berger−Duffie. The assessment is performed by comparing 1-min model outputs to corresponding clear sky reference 1-min Baseline Surface Radiation Network quality controlled GHI data from 13 South African Weather Services radiometric stations. The data used in the study range from 2013 to 2019. The 13 reference stations are across the six macro climatological regions of South Africa. The aim of the study is to identify the overall best performing clear sky model for estimating minute GHI in South Africa. Clear sky days are detected using ERA5 reanalysis hourly data and the application of an additional 1-min automated detection algorithm. Metadata for the models’ inputs were sourced from station measurements, satellite platform observations, reanalysis and some were modelled. Statistical metrics relative Mean Bias Error (rMBE),... [more]

The article aims to enhance understanding of how relations, behavior and attitude of the executives and contractors to achieving the objectives by energy supplying companies affect the efficiency of the latter’s overall development. There is offered a factor-reflexive approach to diagnosing the executives’ and contractors’ attitude to realizing the tasks of developing the energy supplying companies. This approach includes models to identify and analyze the factors that influenced the change in contractors’ behavior based on the combination of expert techniques, correlation and regression analysis and evaluation of the personnel response to organizational, resource and staffing projects development, through accumulating sufficient statistical information based on the enterprise’s features, relations and environment. This approach to diagnosing executives’ and contractors’ response to the implementation of tasks and the resulting changes takes into account the subjective component of the... [more]

Dynamic phenomena in electric power systems require fast and accurate algorithms for processing signals. The processing results include synchrophasor parameters, e.g., varying amplitude, phase or frequency of sinusoidal voltage or current signals. This paper presents a novel estimation method of synchrophasor parameters that comply with the requirements of IEEE/IEC standards. The authors analyzed an algorithm for measuring the phasor magnitude by means of a selected artificial neural network (ANN), an algorithm for estimating the phasor phase and frequency that makes use of the zero-crossing method. The original components of the presented approach are: the method of the synchrophasor magnitude estimation by means of a suitably trained and applied radial basic function (RBF); the idea of using two algorithms operating simultaneously to estimate the synchrophasor magnitude, phase and frequency that apply identical calculation methods are different in that the first one filters the input... [more]

An artificial neural network (ANN) based multi-frequency electrical impedance spectroscopy (EIS) technique is proposed to estimate the static state of charge (SOC) of lithium-ion (Li-ion) battery in this paper. The proposed ANN-based multi-frequency EIS technique firstly collects the data of AC independence and their corresponding static SOC. With battery discharging current and multi-frequency EIS results, an ANN model is built and trained to estimate SOC. The measurement data is obtained using the potentiostats/galvanostats device, and the ANN is trained using the neural network toolbox in MATLAB. According to the experimental results, the performance of the proposed ANN model is dependent on the number of neurons in the hidden layer. The proposed method is validated with a set of random discharging processes. The high accuracy of SOC estimation is able to be achieved with the average error reduced to 1.92% when the number of neurons in the hidden layer is 35. Therefore, the proposed... [more]

After the Fukushima Daiichi Accident, the safety features such as accident tolerant fuel (ATF) and diverse and flexible coping strategies (FLEX) for existing nuclear fleets are being investigated by the US Department of Energy under the Light Water Reactor Sustainability Program. This research is being conducted to quantify the risk-benefit of these safety features. Dynamic probabilistic risk assessment (DPRA)-based response-surface approach has been presented to quantify the FLEX and ATF benefits by estimating the risk associated with each option. ATFs with multilayered silicon carbide (SiC), iron-chromium-aluminum, and chromium-coated zirconium cladding were considered in this study. While these ATF candidates perform better than the current zirconium cladding (Zr), they may introduce additional failure modes in some operating conditions. The fuel failure analysis modules (FAMs) were developed to investigate ATF performance. The dynamic risk assessments were performed using RAVEN, a... [more]

Safety monitoring provides the detection of changes in systems or operations that may suggest any case of approaching a point close to exceeding the acceptable safety standards and indicates whether corrective/prevention actions have been taken. Safety information should be maintained within the scope of transport undertakings to ensure safety and be communicated to all responsible staff, depending on each person’s function in the processes. Regulatory authorities should continuously monitor the implementation of safety management processes and the processes performed by road transport service providers. Safety management, therefore, requires investment in development and modernisation to meet market needs resulting from the mobility of residents, the growth of transport, and the obligations of countries resulting from the transport and environmental policy pursued by the European Union. Along with changes in the transport system, a need to assess their significance for the transport s... [more]

Important industrial applications are based on magnetohydrodynamics (MHD), which concerns the flow of electrically conducting fluids immersed in external magnetic fields. Using the Finite Volume Method, we performed a 3D numerical study of the MHD flow of a conducting fluid in a circular duct. The flow considered was laminar and fully developed. Along the initial section of the duct, there were magnets placed around the duct producing magnetic fields in the radial direction. Two arrangements of magnetic field orientation were considered: fields pointing toward and away from the duct’s center alternately, and all fields pointing toward the duct’s center. For each arrangement of magnets, various intensities of magnetic fields were considered to evaluate two effects: the influence of the magnetic field on the flow velocity, and the influence of the flow velocity on magnetic field induction. It was found that for the second arrangement of magnets and Hartmann numbers larger than 10, the fl... [more]

The internal leakage of a hydraulic cylinder is an inevitable hydraulic system failure that seriously affects the working efficiency of the hydraulic system. Therefore, it is very important to accurately identify and predict leakage data in the hydraulic cylinder. In this paper, a model is proposed to simulate a small internal leakage of hydraulic cylinders, to convert the amount of leakage of hydraulic oil into strain signals through high-precision strain gauges and to train the collected strain signals using various neural networks to form a computational model and obtain prediction results from the model. The neural networks applied in this paper are convolutional neural networks, BP neural networks, T-S neural networks and Elman neural networks. The predicted results of the neural network are compared with the actual leakage amount. The results show that the prediction accuracy of the above four kinds of neural networks are all above 90%, of which the convolutional neural network i... [more]

This study evaluated the potential of artificial neural networks (ANNs) to predict the system-level performance of a thermoelectric generator (TEG), whose performance depends on various variables including engine load, engine rotation speed, and external load resistance. Therefore, a Python code was developed to determine an optimal ANN structure by tracking the training/prediction errors of the ANN as a function of the number of hidden layers and nodes of hidden layers. The optimal ANN was trained using 484 output current (I)−load resistance (R) datasets obtained under three different engine rotation speeds and five different engine loads. The prediction accuracy of the ANN was validated by comparing 88 I−R datasets reproduced by the ANN using experimental data that were not used for training. In the validation procedure, differences of only 3.49% and 2.59% were observed in the experimental and ANN-predicted output power obtained for the 1000 rpm−0.8 MPa brake mean effective pressure... [more]