Nowadays, climate change has turned into the leading environmental, technological, social and economical worldwide challenge [...]

A novel concept of wind farm fault detection by monitoring the wind speed in the wake region is proposed in this study. A wind energy dissipation model was coupled with a computational fluid dynamics solver to simulate the fluid field of a wind turbine array, and the wind velocity and direction in the simulation were exported for identifying wind turbine faults. The 3D steady Navier−Stokes equations were solved by using the cell center finite volume method with a second order upwind scheme and a k−ε turbulence model. In addition, the wind energy dissipation model, derived from energy balance and Betz’s law, was added to the Navier−Stokes equations’ source term. The simulation results indicate that the wind speed distribution in the wake region contains significant information regarding multiple wind turbine faults. A feature selection algorithm specifically designed for the analysis of wind flow was proposed to reduce the number of features. This algorithm proved to have better perform... [more]

This paper discusses the application of an in-cylinder catalyst in reducing the exhaust emissions from a diesel engine. This is an additional method of exhaust gas aftertreatment; yet the placement of a catalyst in the combustion chamber (i.e., the closest location to the process of combustion) allows a reduction of the emissions ‘at source’ (the catalyst applied on the glow plugs). For the investigations, we used an engine dynamometer to reproduce the traffic conditions of a homologation test carried out on a chassis dynamometer. We carried out the investigations on a Euro 4 1.3 JTD MultiJet diesel engine. The selection of the research object was followed by an analysis of the number of engines used in the EU meeting individual emission standards. We present results (measurement of carbon monoxide, hydrocarbons, nitrogen oxides, particle number, and carbon dioxide) related to the assessment of the applicability of the in-cylinder catalyst for three types of glow plugs: standard, catal... [more]

The paper undertakes the problem of proper structuring of multi-criteria decision support models. To achieve that, a methodological framework is proposed. The authors’ framework is the basis for the relevance analysis of individual criteria in any considered decision model. The formal foundations of the authors’ approach provide a reference set of Multi-Criteria Decision Analysis (MCDA) methods (TOPSIS, VIKOR, COMET) along with their similarity coefficients (Spearman correlation coefficients and WS coefficient). In the empirical research, a practical MCDA-based wind farm location problem was studied. Reference rankings of the decision variants were obtained, followed by a set of rankings in which particular criteria were excluded. This was the basis for testing the similarity of the obtained solutions sets, as well as for recommendations in terms of both indicating the high significance and the possible elimination of individual criteria in the original model. When carrying out the ana... [more]

Proton Exchange Membrane (PEM) water electrolysis system is one of the promising technologies to produce green hydrogen from renewable energy sources (wind and solar). However, performance and dynamic analysis of PEM water electrolysis systems are challenging due to the intermittent nature of such sources and involved multi-physical behaviour of the components and subsystems. This study proposes a generic dynamical model of the PEM electrolysis system represented in a modular fashion using Bond Graph (BG) as a unified modelling approach. Causal and functional properties of the BG facilitate the formal PEM electrolyser model to adapt and to fit the different configurations of the electrolyser ranging from laboratory scale to industrial scale. The system-specific key parameter values are identified optimally for a laboratory-scale electrolyser system running on a multi-source energy platform using experimental data. The mean absolute percentage error between simulation and experimental d... [more]

This study focuses on the use of cloud motion vectors (CMV) and fast radiative transfer models (FRTM) in the prospect of forecasting downwelling surface solar irradiation (DSSI). Using near-real-time cloud optical thickness (COT) data derived from multispectral images from the spinning enhanced visible and infrared imager (SEVIRI) onboard the Meteosat second generation (MSG) satellite, we introduce a novel short-term forecasting system (3 h ahead) that is capable of calculating solar energy in large-scale (1.5 million-pixel area covering Europe and North Africa) and in high spatial (5 km over nadir) and temporal resolution (15 min intervals). For the operational implementation of such a big data computing architecture (20 million simulations in less than a minute), we exploit a synergy of high-performance computing and deterministic image processing technologies (dense optical flow estimation). The impact of clouds forecasting uncertainty on DSSI was quantified in terms of cloud modifi... [more]

Coal is the second-largest source for electricity generation in the United States. However, the burning of coal produces dangerous gas emissions, such as carbon dioxide and Green House Gas (GHG) emissions. One alternative to decrease these emissions is biomass co-firing. To establish biomass as a viable option, the optimization of the biomass supply chain (BSC) is essential. Although most of the research conducted has focused on optimization models, the purpose of this paper is to incorporate machine-learning (ML) algorithms into a stochastic Mixed-Integer Linear Programming (MILP) model to select potential storage depot locations and improve the solution in two ways: by decreasing the total cost of the BSC and the computational burden. We consider the level of moisture and level of ash in the biomass from each parcel location, the average expected biomass yield, and the distance from each parcel to the closest power plant. The training labels (whether a potential depot location is ben... [more]

Using the thermalgravimetric technique, we investigated the non-isothermal combustion kinetics of abundant and low-cost date palm wastes (leaflet, rachis, fibers, and their composite) as potential biomass energy sources. The kinetic and thermodynamic parameters were determined by Flynn−Wall−Ozawa (FWO), Kissinger−Akahila−Sunose (KAS), and Starink methods. Thermogravimetric analysis results showed a major peak for the degradation of volatiles between 127−138 °C with average percentage mass loss of 68.04 ± 1.5, 65.57 ± 0.6, 62.97 ± 5.5, and 59.26 ± 3.2, for rachis, composite, leaflet, and fibers, respectively. The FWO model showed the lowest activation energy, Eα, of 157 ± 25.6, 158 ± 25.7, 164 ± 40.1, and 169 ± 51.8 kJ mol−1 for the composite, rachis, leaflet, and fibers, respectively. The positive enthalpy values confirmed an endothermic pyrolysis reaction. For all models, a minimal difference of 4.40, 5.57, 6.55, and 7.51 kJ mol−1 between activation energy and enthalpy for rachis, fib... [more]

Spectral induced polarization (SIP) is a non-intrusive geophysical method that collects chargeability information (the ability of a material to retain charge) in the time domain or its phase shift in the frequency domain. Although SIP is a temporal method, it cannot measure the dynamics of flow and solute/species transport in the subsurface over long times (i.e., 10−100 s of years). Data collected with the SIP technique need to be coupled with fluid flow and reactive-transport models in order to capture long-term dynamics. To address this challenge, PFLOTRAN-SIP was built to couple SIP data to fluid flow and solute transport processes. Specifically, this framework couples the subsurface flow and transport simulator PFLOTRAN and geoelectrical simulator E4D without sacrificing computational performance. PFLOTRAN solves the coupled flow and solute-transport process models in order to estimate solute concentrations, which were used in Archie’s model to compute bulk electrical conductivitie... [more]

Prediction of the timing and location of condensate build-up around the wellbore in gas condensate reservoirs is essential for the selection of appropriate methods for condensate recovery from these challenging reservoirs. The present work focuses on the use of a novel phase change tracking approach in monitoring the formation of condensate blockage in a gas condensate reservoir. The procedure entails the simulation of tight, low and high permeability reservoirs using global and local grid analysis in determining the size and timing of three common regions (Region 1, near wellbore; Region 2, condensate build-up; and Region 3, single-phase gas) associated with single and two-phase gas and immobile and mobile gas condensate. The results show that permeability has a significant influence on the occurrence of the three regions around the well, which in turn affects the productivity of the gas condensate reservoir studied. Predictions of the timing and location of condensate in reservoirs w... [more]

Due to rapid economic development and population growth, environmental pollution problems such as urban pollution and depletion of natural resources have become increasingly prominent. Municipal solid waste is part of these problems. However, waste is actually an improperly placed resource. As a part of green supply chain management, remanufacturing can turn waste products into remanufactured products for resale. Based on the development status of China’s remanufacturing industry, this paper establishes three Stackelberg game models, namely the free recycling model (model N), the government regulation model based on the reward−penalty mechanism (model G), and the government dual-intervention model (model GF). In this study, the standard solution method for the Stackelberg game method, namely the backward induction method, is applied to solve the dynamic game equilibrium. For comparison, a further numerical analysis is also carried. The research results show that: (1) in the closed-loop... [more]

Marine current energy is attracting more and more attention in the world as a reliable and highly predictable energy resource. However, conventional proportional integral (PI) control will be sensitive to the numerous challenges that exist in a marine current turbine system (MCTs) such as marine current disturbance, torque disturbance and other uncertain parameters. This paper proposes a fuzzy adaptive backstepping control (F-A-BC) approach for a marine current turbine system. The proposed F-A-BC strategy consisted of two parts. First, an adaptive backstepping control approach with the compensation of disturbance and uncertainty was designed to improve anti-interference of the MCT so that the maximum power point tracking (MPPT) was realized. Then, a fuzzy logic control approach was combined to adjust parameters of an adaptive backstepping control approach in real time. The effectiveness of the proposed controller was verified by the simulation of a direct-drive marine current turbine s... [more]

While urban maglev trains have the advantage of being optimized for urban environments where noise is low and dust is less generated, their driving efficiency is low when compared to rotary induction motors owing to the structural limitations of linear devices. To compensate for these disadvantages, various studies on train control schemes have been conducted. Representative control methods include improving the efficiency of using slip frequency by directly controlling the propulsion force using vector control. However, this method has limitations in its use as it relates to the normal force that affects the train’s levitation system. Therefore, in this study, mathematical analysis was conducted for each factor that mutually affects the control of the train. On this basis, the magnitude of the normal force related to the safety of the train is limited. Operating efficiency was improved by varying the slip frequency according to the operating conditions of the train. In addition, for v... [more]

This paper aims to indicate the linkages between crude oil prices and selected food price indexes (dairy, meat, oils, cereals, and sugar) and provide an empirical specification of the direction of the impact. This paper reviews the fuel−food price linkage models with consideration to the time series literature. This study adopts several methods, namely the Augmented Dickey−Fuller test, Granger causality test, the cointegration test, the vector autoregression model, and the vector error correction model, for studying the price transmission among the crude oil and five selected food groups. The data series covers the period between January 1990 and September 2020. The empirical results from the paper indicate that there are long-term relationships between crude oil and meat prices. The linkage of crude oil prices occurred with food, cereal, and oil prices in the short term. Furthermore, the linkages between the analyzed variables increased in 2006−2020.

Next-generation cellular networks are large-scale systems composed of numerous base stations interacting with many diverse users. One of the main challenges with these networks is their high energy consumption due to the expected number of connected devices. We handle this issue with a coalitional Model Predictive Control (MPC) technique for the case of next-generation cellular networks powered by renewable energy sources. The proposed coalitional MPC approach is applied to two simulated scenarios and compared with other control methods: the traditional best-signal level mechanism, a heuristic algorithm, and decentralized and centralized MPC schemes. The success of the coalitional strategy is considered from an energy efficiency perspective, which means reducing on-grid consumption and improving network performance (e.g., number of users served and transmission rates).

Renewable energy sources for the purpose of heating buildings cooperate perfectly with so-called low-temperature heating systems. Water loop surface heating systems had been thoroughly tested. In contrast, thermal performance of wall panels with heat pipes have not been fully recognized, yet. The determination of the thermal power as well as the control of panels thermal performance cannot be performed with the methods developed for water loop systems. In this paper, the novel heating panels with heat pipes were tested to analyze the possibility of controlling their performance by changing the mass flowrate of heating water and its temperature. Specific heating power of the investigated panels varies from 16.9 W/m2 to 93.8 W/m2 when supplying a water temperature ranging from 35 °C to 65 °C and mass flowrate from 10 g/s to 47.5 g/s. Investigations revealed that the thermal performance of the panels is more sensitive to the changes of temperature than to the changes of mass flowrate of s... [more]

The prediction of phase transformation of biomass ashes is challenging due to the highly variable composition of these fuels as well as the complex processes accompanying phase transformations. The AFT (Ash Fusion Temperature) model was performed in Statistica 13.1 software. This model was divided into three separate submodels, which were designed to predict the characteristic ash melting temperatures for raw and modified biomass. It is based on the chemical composition of fuel and ash as obtained using ash analysis standards. For the discussed models, several coefficients describing multiple regression parameters are presented. The AFT model discussed in this article is suitable for predicting ash fusion temperatures for biomass and allows for the prediction of the temperature with an average error of <±70.05 °C for IDT; <±51.98 °C for HT; <±47.52 °C for FT for raw biomass. For some of the additionally tested biomass, a value higher than the average difference between the mea... [more]

Experimental and numerical modeling of a heat pipe included with a phase change heat transfer was developed to assess the effects of three parameters of nanofluid, heat pipe inclination angles, and input heating power. Distilled water (DW) and 1-pyrene carboxylic-acid (PCA)-functionalized graphene nanofluid (with concentrations of 0.06 wt%) were used as working fluids in the heat pipe. A computational fluid dynamic (CFD) model was developed for evaluation of the heat transfer and two-phase flow through the steady-state process of the heat pipe. It was found that inclination significantly affects the heat transfer of the heat pipe. Maximum increment of thermal performance in the heat pipe reached 49.4% by using 0.06 wt% of PCA-functionalized graphene as working fluids. The result associated with this comparison indicates that the highest deviation is less than 6%, consequently confirming that the CFD model was successful in reproducing the heat and mass transfer processes in the DW and... [more]

The main point of this paper was to perform the design process for and verify the properties of an adaptive neural controller implemented for a real nonlinear object—an electric drive with an Induction Motor (IM). The controller was composed as a parallel combination of the classical Proportional-Integral (PI) structure, and the second part was based on Radial Basis Function Neural Networks (RBFNNs) with the on-line recalculation of the weight layer. The algorithm for the adaptive element of the speed controller contained two parts in parallel. The first of them was dedicated for the main path of the neural network calculations. The second realized the equations of the adaptation law. The stability of the control system was provided according to the Lyapunov theorem. However, one of the main issues described in this work is the optimization of the constant part of the analyzed parallel speed controller. For this purpose, the Grey Wolf Optimizer (GWO) was applied. A deep analysis of the... [more]

This study addresses the effect of nanoparticles’ conductivity and surface charge on the dielectric performance of insulating nanofluids. Dispersions of alumina and silicon carbide nanoparticles of similar size (~50 nm) and concentration (0.004% w/w) were prepared in natural ester oil. The stability of the dispersions was explored by dynamic light scattering. AC, positive and negative lightning impulse breakdown voltage, as well as partial discharge inception voltage of the nanofluid samples were measured and compared with the respective properties of the base oil. The obtained results indicate that the addition of SiC nanoparticles can lead to an increase in AC breakdown voltage and also enhance the resistance of the liquid to the appearance of partial discharge. On the other hand, the induction of positive charge from the Al2O3 nanoparticles could be the main factor leading to an improved positive Lightning Impulse Breakdown Voltage and worse performance at negative polarity.

Biological metal leaching is a technology used in the mining and biohydrometallurgy industries where microorganisms mediate the dissolution of metals and semi-metals from mineral ores and concentrates. The technology also has great potential for various types of metal-rich waste. In this study, bioleaching was used for sludge from water treatment. In addition to checking the applicability of the process to such a substrate, the influence of experimental conditions on the effectiveness of bioleaching of metals with sludge from water treatment was also determined, including sample acidification, addition of elemental sulfur, incubation temperature, and Acidithiobacillus thiooxidans-isolated strain. The measurement of metal concentration and, on this basis, the determination of bioleaching efficiency, as well as pH and oxygen redox potential (ORP), was carried out during the experiment at the following time intervals: 3, 6, 9, 12 days. After the experiment was completed, a mass balance wa... [more]

Gas hydrate saturation is an important index for evaluating gas hydrate reservoirs, and well logs are an effective method for estimating gas hydrate saturation. To use well logs better to estimate gas hydrate saturation, and to establish the deep internal connections and laws of the data, we propose a method of using deep learning technology to estimate gas hydrate saturation from well logs. Considering that well logs have sequential characteristics, we used the long short-term memory (LSTM) recurrent neural network to predict the gas hydrate saturation from the well logs of two sites in the Shenhu area, South China Sea. By constructing an LSTM recurrent layer and two fully connected layers at one site, we used resistivity and acoustic velocity logs that were sensitive to gas hydrate as input. We used the gas hydrate saturation calculated by the chloride concentration of the pore water as output to train the LSTM network. We achieved a good training result. Applying the trained LSTM re... [more]

The increase in domestic transportation in developing countries may adversely affect the energy efficiency of road transportation due to effective productivity and carbon dioxide emissions (CO2). When evaluating quantitatively the countries on the efficiency frontier, poor efficiency can still be seen sometimes due to the slack available in undesirable output measures. This paper uses desirable and undesirable output variables, such as passenger-kilometers (PKM), tones-kilometers (TKM), and carbon dioxide (CO2), to compute the weakly efficient decision-making units (DMUs). The data envelopment analysis (DEA) technology is used to assess the efficiencies of the decision-making units (DMUs), which are countries in our case. Then, the trade-off method with efficient binding surfaces is used to attain the optimal efficiencies of the weakly efficient DMUs. The marginal rates aid this trade-off analysis. Resultantly, such marginal trade-offs do not deteriorate the efficiency of the DMUs belo... [more]

In coal seams, depending on the composition of coal macerals, rank of coal, burial history, and migration of thermogenic and/or biogenic gas. In one ton of coal 1 to 25 m3 of methane can be accumulated. Accumulation of this gas is included in unconventional deposits. Exploitation of methane from coal seams is carried out with wells from mining excavations (during mining operations), wells drilled to abandoned coal mines, and wells from the surface to unexploited coal seams. Due to the low permeability of the coal matrix, hydraulic fracturing is also commonly used. Operations related to exploration (drilling works) and exploitation of methane from coal seams were analyzed. The preliminary analysis of the environmental threats associated with the exploration and exploitation of coalbed methane has made it possible to identify types of risks that affect the environment in various ways. The environmental risks were estimated as the product of the probability weightings of adverse events oc... [more]

This paper proposes a digital model predictive controller (DMPC) for a multi-input multi-output (MIMO) DC-DC converter interfaced with renewable energy resources in a hybrid system. Such MIMO systems generally suffer from cross-regulation, which seriously impacts the stability and speed of response of the system. To solve the contemporary issues in a MIMO system, a controller is required to attenuate the cross-regulation. Therefore, this paper proposes a controller, which increases speed of response and maintains stable output by regulating the load voltage independently. The inductor current and the capacitor voltage of the proposed converter are considered as the controlling parameters. With the aid of Forward Euler’s procedure, the future values are computed for the instantaneous values of controlling parameters. Cost function defines the control action by the predicted values that describe the system performance and establish optimal condition at which the output of the system is r... [more]