This article presents the application of the radioisotope absorption method in the study of two-phase water−air flow in a horizontal pipe. The measurement principle and the test stand are briefly described. The main part of the article presents the signal analysis methods applied to data obtained from scintillation detectors. Because these signals are mostly stochastic waveforms, they were analyzed statistically using the cross-correlation function (CCF), and methods that are a combination of CCF and differential methods: CCF/ASDF and CCF/AMDF, where ASDF is the average square difference function, and AMDF is the average magnitude difference function. Examples of the results of gas phase velocity measurement for four types of flow are presented. It was found that the CCF/ASDF and CCF/AMDF methods allow more accurate results of measurements of the dispersed phase to be obtained than the CCF method.

Electricity has a crucial function in contemporary civilization. The power grid must be stable to ensure the efficiency and dependability of electrical equipment. This implies that the high-voltage equipment at the substation must be reliably operated. As a result, the appropriate and dependable use of systems to monitor the operating status of high-voltage electrical equipment has recently gained attention. Partial discharge (PD) analysis is one of the most promising solutions for monitoring and diagnosing potential problems in insulation systems. Noise is a major challenge in diagnosing and detecting defects when using this measurement. This study aims to denoise PD signals using a data decomposition method, improved complete ensemble empirical mode decomposition with adaptive noise algorithm, combined with statistical significance test to increase noise reduction efficiency and to derive and visualize the Hilbert spectrum of the input signal in time-frequency domain after filtering... [more]

This paper explores the current attitudes of managers and executives working in the energy sector towards the Data-Driven Organizational Model implied by Big Data. The aim is to explore and understand the current mindset of senior decision makers, since their success depends as much on cognitive and behavioral processes as on their technical competences. We adopt a grounded-theory approach, developing models of understanding and belief abductively, driven by the data obtained from participants through a reflection guide. We find that managers differ significantly in their understanding and engagement with their challenges; they display interest but differ in their commitment and enthusiasm; they identify a lack of strategy and skills as current barriers; and they are currently unwilling to trust data, treating evidence according to their own prior commitments. This is a significant barrier to establishing the Data-Driven Organizational Model. These findings raise concerns, and the pape... [more]

This study deals with wave-induced unsteady pressure on a ship moving with a constant forward speed in regular head waves. Two different numerical methods are applied to solve wave−ship interaction problems: a Rankine panel method which adopts velocity potential, and a Cartesian-grid method which solves the momentum and mass conservation equations under the assumption of inviscid and incompressible fluids. Before comparing l1ocal pressure distributions, the computational methods are validated for global quantities, such as ship motion responses and added resistance, by comparison with available experimental data. Then, the computational results and experimental data are compared for hydrodynamic pressure, particularly focusing on the magnitude of the first-harmonic component in different sections and vertical locations. Furthermore, the Cartesian-grid method is used to simulate the various wave-amplitude conditions, and the characteristics of the zeroth-, first-, and second-harmonic co... [more]

In this article, we focus on the development of a multiscale modeling and recurrent neural network (RNN) based optimization framework of a plasma etch process on a three-dimensional substrate with uniform thickness using the inductive coupled plasma (ICP). Specifically, the gas flow and chemical reactions of plasma are simulated by a macroscopic fluid model. In addition, the etch process on the substrate is simulated by a kinetic Monte Carlo (kMC) model. While long time horizon optimization cannot be completed due to the computational complexity of the simulation models, RNN models are applied to approximate the fluid model and kMC model. The training data of RNN models are generated by open-loop simulations of the fluid model and the kMC model. Additionally, the stochastic characteristic of the kMC model is presented by a probability function. The well-trained RNN models and the probability function are then implemented in computing an open-loop optimization problem, in which a moving... [more]

Coprecipitation-adsorption plays a significant role during coagulation-flocculation-sedimentation (C/F/S) of antimony (Sb) in water. This work uses a Box−Behnken statistical experiment design (BBD) and response surface methodology (RSM) to investigate the effects of major operating variables such as initial Sb(III, V) concentration (100−1000 µg/L), ferric chloride (FC) dose (5−50 mg/L), and pH (4−10) on redox Sb species. Experimental data of Sb(III, V) removal were used to determine response function coefficients. The model response value (Sb removal) showed good agreement with the experimental results. FC showed promising coagulation behavior of both Sb species under optimum pH (6.5−7.5) due to its high affinity towards Sb species and low residual Fe concentration. However, a high dose of 50 mg/L of FC is required for the maximum (88−93%) removal of Sb(V), but also for the highest (92−98%) removal of low initial concentrations of Sb(III). Furthermore, BBD and RSM were found to be reli... [more]

A novel variant of NSN P systems, called numerical spiking neural P systems with a variable consumption strategy (NSNVC P systems), is proposed. Like the spiking rules consuming spikes in spiking neural P systems, NSNVC P systems introduce a variable consumption strategy by modifying the form of the production functions used in NSN P systems. Similar to the delay feature of the spiking rules, NSNVC P systems introduce a postponement feature into the production functions. The execution of the production functions in NSNVC P systems is controlled by two, i.e., polarization and threshold, conditions. Multiple synaptic channels are used to transmit the charges and the production values in NSNVC P systems. The proposed NSNVC P systems are a type of distributed parallel computing models with a directed graphical structure. The Turing universality of the proposed NSNVC P systems is proved as number generating/accepting devices. Detailed descriptions are provided for NSNVC P systems as number... [more]

In the present study, a Box−Behnken design of response surface methodology (RSM) was employed to optimize the processing factors (force: 100, 150, and 200 kN; speed: 3, 5, and 7 mm/min; and temperature: 40, 60, and 80 °C) for extracting pumpkin seeds oil under uniaxial compression. The design generated 15 experiments including twelve combinations of factors and three replicates at the center point. The responses: oil yield (%), oil expression efficiency (%), and energy (J) were calculated, and the regression models determined were statistically analyzed and validated. The optimum factors combination: 200 kN, 4 mm/min and 80 °C predicted the oil yield of 20.48%, oil expression efficiency of 60.90%, and energy of 848.04 J. The relaxation time of 12 min at the optimum factors increased the oil efficiency to 64.53%. The lower oil point force was determined to be 57.32 kN for estimating the maximum oil output. The tangent curve and generalized Maxwell models adequately (R2 = 0.996) describe... [more]

The development of efficient methods for process performance verification has drawn a lot of attention in the research community. Viability theory is a mathematical tool to identify the trajectories of a dynamical system which remains in a constraint set. In this paper, viability theory is investigated for this purpose in the case of nonlinear processes that can be represented in Linear Parameter Varying (LPV) form. In particular, verification algorithms based on the use of invariance and viability kernels and capture basin are proposed. The difficulty with the application of this theory is the computation of these sets. A Lagrangian method has been used to approximate these sets. Because of simplicity and efficient computations, zonotopes are adopted for set representation. Two new sets called Safe Work Area (SWA) and Required Performance (RP) are defined and an algorithm is proposed to use these concepts for the verification purpose. Finally, two application examples based on well-kn... [more]

In the process of butadiene rubber production, internal leakage occurs in heat exchangers due to excessive pressure difference. It leads to the considerable flow of organic matters into the circulating water system. Since these organic matters are volatile and prone to explode in the cold water tower, internal leakage is potentially dangerous for the enterprise. To prevent this phenomenon, a novel intelligent early warning and risk assessment method (DYN-EW-QRA) is proposed in this paper by combining dynamic simulations (DYN), long short-term memory (LSTM), and quantitative risk assessment (QRA). First, an original internal leakage mechanism model of a heat exchanger network is designed and simulated by DYN to obtain datasets. Second, the potential relationships between variables that have a direct impact on the hazards of the accident are deeply learned by LSTM to predict the internal leakage trends. Finally, the QRA method is used to analyze the range and destructive power of potenti... [more]

The peripheral nervous system is the bridge of communication between the central nervous system and other body systems. Autologous nerve grafting is the mainstream method for repair of nerve lesions greater than 20 mm. However, there are several disadvantages and limitations of autologous nerve grafting, thus prompting the need for fabrication of nerve conduits for clinical use. In this study, we successfully fabricated astragaloside (Ast)-containing polyurethane (PU) nerve guidance conduits via digital light processing, and it was noted that the addition of Ast improved the hydrophilicity of traditional PU conduits by at least 23%. The improved hydrophilicity not only led to enhanced cellular proliferation of rat Schwann cells, we also noted that levels of inflammatory markers tumor necrosis factor-alpha (TNF-α) and cyclooxygenase-2 (COX-2) significantly decreased with increasing concentrations of Ast. Furthermore, the levels of neural regeneration markers were significantly enhanced... [more]

Alzheimer’s disease (AD) is the most common cognitive disorder in the elderly population. However, effective pharmacological agents targeting AD have not been developed. The processed Polygoni multiflori Radix (PPM) and its main active substance, 2,3,5,4′-tetrahydroxystilbene-2-O-β-glucoside (TSG), has received considerable attention, majorly due to its neuroprotective activities against multiple biological activities within the human body. In this study, we provide new evidence on the therapeutic effect of PPM and TSG during cognitive impairment by evaluating the ameliorative potential of PPM and TSG in scopolamine-induced amnesia in ICR mice. PPM (100 or 200 mg/kg) was orally administered during the experimental period (days 1−15), and scopolamine was intraperitoneally injected to induce cognitive deficits during the behavioural test periods (days 8−15). The administration of PPM and TSG significantly improved memory loss and cognitive dysfunction in behavioural tests and regulated t... [more]

In this study, aluminum foams reinforced with different steel elements were produced by the AluLight process. The master alloy AlMgSi0.6 was used as the matrix material, titanium hydride (TiH2) powder was used as a foaming agent, and steel wire, cylindrical steel mesh and flat steel mesh were used as reinforcing elements. Reinforcements were placed inside the mold, along with the precursors, and samples were manufactured by gas releasing particles in a semi-solid state. To examine the effect of the reinforcements on specific energy absorption, quasi-static uniaxial compression tests were carried out, with a constant separation rate of 1 mm/s. From the tested results, the energy absorption per unit volume and specific energy absorption efficiency were calculated and then also statistically evaluated. The results showed that examined shapes of reinforcements affect differently specific energy absorption and its efficiency, compared to non-reinforced aluminum foams. The best result obtain... [more]

Biorefinery, which utilizes carbon-neutral biomass as a resource, is attracting attention as a significant alternative in a modern society confronted with climate change. In this study, spent coffee grounds (SCGs) were used as the feedstock for lactic acid fermentation. In order to improve sugar conversion, alkali pretreatment was optimized by a statistical method, namely response surface methodology (RSM). The optimum conditions for the alkali pretreatment of SCGs were determined as follows: 75 °C, 3% potassium hydroxide (KOH) and a time of 2.8 h. The optimum conditions for enzymatic hydrolysis of pretreated SCGs were determined as follows: enzyme complex loading of 30-unit cellulase, 15-unit cellobiase and 50-unit mannanase per g biomass and a reaction time of 96 h. SCG hydrolysates were used as the carbon source for Lactobacillus cultivation, and the conversions of lactic acid by L. brevis ATCC 8287 and L. parabuchneri ATCC 49374 were 40.1% and 55.8%, respectively. Finally, the maxi... [more]

Uncertainty and variability are inherent to pile design and consequently, there have been considerable researches in quantifying the reliability or probability of failure of structures. This paper aims at examining and comparing the applicability and adaptability of Minimax Probability Machine Regression (MPMR), Emotional Neural Network (ENN), Group Method of Data Handling (GMDH), and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the reliability analysis of pile embedded in cohesionless soil and proposes an AI-based prediction method for bearing capacity of pile foundation. To ascertain the homogeneity and distribution of the datasets, Mann−Whitney U (M−W) and Anderson−Darling (AD) tests are carried out, respectively. The performance of the developed soft computing models is ascertained using various statistical parameters. A comparative study is implemented among reliability indices of the proposed models by employing First Order Second Moment Method (FOSM). The results of FOSM sho... [more]

Drift deposition of emerging and carcinogenic contaminant dicamba (3,6-dichloro-2-methoxy benzoic acid) has become a major health and environmental concern. Effective removal of dicamba in aqueous medium becomes imperative. This study investigates the adsorption of a promising adsorbent, MIL-101(Cr) metal-organic framework (MOF), for the removal of dicamba in aqueous solution. The adsorbent was hydrothermally synthesized and characterized using N2 adsorption-desorption isotherms, Brunauer, Emmett and Teller (BET), powdered X-ray diffraction (XRD), Fourier Transformed Infrared (FTIR) and field emission scanning electron microscopy (FESEM). Adsorption models such as kinetics, isotherms and thermodynamics were studied to understand details of the adsorption process. The significance and optimization of the data matrix, as well as the multivariate interaction of the adsorption parameters, were determined using response surface methodology (RSM). RSM and artificial neural network (ANN) were... [more]

In this study, a quantitative method for classifying the frost geometry is first proposed to substantiate a numerical model in predicting frost properties like density, thickness, and thermal conductivity. This method can recognize the crystal shape via linear programming of the existing map for frost morphology. By using this method, the frost conditions can be taken into account in a model to obtain the corresponding frost properties like thermal conductivity, frost thickness, and density for specific frost crystal. It is found that the developed model can predict the frost properties more accurately than the existing correlations. Specifically, the proposed model can identify the corresponding frost shape by a dimensionless temperature and the surface temperature. Moreover, by adopting the frost identification into the numerical model, the frost thickness can also be predicted satisfactorily. The proposed calculation method not only shows better predictive ability with thermal condu... [more]

This study aimed to determine the levels and possible sources of heavy metals (HMs) in the sediments of Chalan beel (a large lake-like aquatic ecosystem) area located in the northwestern part of Bangladesh. The mean concentrations (mg kg−1) of two HMs, Cd (6.22) and Pb (51.39) exceeded the world normal averages (WNA), whereas the mean concentrations (mg kg−1) of Ni (60.46), Zn (10.75), Mn (8.64) and Cu (4.71) were below the WNA. The sediments showed significant enrichment with Cd, Pb and Ni in the studied area. The geo-accumulation index values of Cd (3.72) and Pb (0.76) were significantly higher in the sediments. The contamination factor and potential ecological risk index values of Cd and Pb revealed that Chalan beel was extremely and moderately contaminated by these heavy metals, respectively. Analysis of dye complexes used in handlooms around the Chalan beel areas revealed that mean concentrations of Cd and Pb exceeded the WNA. Furthermore, analyses of principal component, cluster... [more]

Orange peel, which is a by-product of oranges, contains carbohydrates that can be converted into sugars and used in the fermentation process. In this study, the thermal alkaline pretreatment process was chosen because of its simplicity and lesser reaction time. In addition, the reaction factors were optimized using response surface methodology. The determined optimal conditions were as follows: 60.1 g/L orange peels loading, 3% KOH and 30 min. Under the optimal conditions, glucan content (GC) and enzymatic digestibility (ED) were found to be 32.8% and 87.8%, respectively. Enzymatic hydrolysis was performed with pretreated and non-pretreated orange peels using three types of enzyme complex (cellulase, cellobiase and xylanase). The minimum concentrations of enzyme complex required to obtain maximum ED were 30 FPU (filter paper unit), 15 CBU (cellobiase unit), and 30 XNU (xylanase unit) based on 1 g-biomass. Additionally, ED of the treated group was approximately 3.7-fold higher than that... [more]

Immobilized Clostridium beijerinckii TISTR 1461 was used to enhance the butanol production efficiency from sugarcane molasses. Lotus stalk (LS) pieces were used as carriers for cell immobilization. Sugarcane molasses containing 50 g/L of sugar supplemented with 1 g/L of yeast extract was found to be an appropriate medium for bacterial cell immobilization on the LS pieces. Carrier size (4, 12 and 20 mm in length) and carrier loading (1:15, 1:30 and 1:45, w/v) were optimized for high levels of butanol production using response surface methodology (RSM). The batch fermentation was carried out under anaerobic conditions in 1 L screw-capped bottles at 37 °C and an agitation rate of 150 rpm. It was found that the optimum conditions for the butanol production were the carrier size of 4 mm and carrier loading of 1:31 (w/v). Under these conditions, the butanol concentration (PB) was 12.89 g/L, corresponding to the butanol productivity (QB) of 0.36 g/L∙h and butanol yield (YB/S) of 0.36 g/g. The... [more]

PV (photovoltaic) systems are receiving the spotlight in Korea due to the Renewable Energy 3020 Implementation Plan (RE3020), which has the goal of reaching 20% for the proportion of renewable energy generation by 2030. Accordingly, the actual performance evaluation of PV systems to achieve the RE3020 has become more important. PV efficiency is mainly determined by various weather conditions (e.g., solar radiation) that affect the power generation of PV systems. However, the efficiency is also affected by changes in module surface temperature. In particular, the efficiency decreases when the module surface temperature rises. That is, the actual PV efficiency falls short of the rated efficiency. The estimation of module surface temperature is critical for evaluating the actual performance of PV systems. Many studies have been conducted to calculate the surface temperature. However, most of the previous studies focused on calculations of current surface temperatures using current environ... [more]

Predicting future energy demand will allow for better planning and operation of electricity providers. Suppliers will have an idea of what they need to prepare for, thereby preventing over and under-production. This can save money and make the energy industry more efficient. We applied a multiple regression model and three Convolutional Neural Networks (CNNs) in order to predict Florida’s future electricity use. The multiple regression model was a time series model that included all the variables and employed a regression equation. The univariant CNN only accounts for the energy consumption variable. The multichannel network takes into account all the time series variables. The multihead network created a CNN model for each of the variables and then combined them through concatenation. For all of the models, the dataset was split up into training and testing data so the predictions could be compared to the actual values in order to avoid overfitting and to provide an unbiased estimate... [more]

The adoption of electric vehicles (EVs) have proven to be a crucial factor to decreasing the emission of greenhouse gases (GHG) into the atmosphere. However, there are various hurdles that impede people from purchasing EVs. For example, long charging time, short driving range, cost and insufficient charging infrastructures available, etc. This article reports the public perception of EV-adoption using statistical analyses and proposes some recommendations for improving EV-adoption in Qatar. User perspectives on EV-adoption barriers in Qatar were investigated based on survey questionnaires. The survey questionnaires were based on similar studies done in other regions of the world. The study attempted to look at different perspectives of the adoption of EV, when asked to a person who is aware of EVs (technical respondents—people studying/working at universities/research centers and policy makers) or a person who may or may not be aware of EVs (non-technical respondents—people working in... [more]

Monitoring the performance of a photovoltaic (PV) system when environmental parameters are not available is very difficult. Comparing the energy datasets of the arrays belonging to the same PV plant is one strategy. If the extension of a PV plant is limited, all the arrays are subjected to the same environmental conditions. Therefore, identical arrays produce the same energy amount, whatever the solar radiation and cell temperature. This is valid for small- to medium-rated power PV plants (3−50 kWp) and, moreover, this typology of PV plants sometimes is not equipped with a meteorological sensor system. This paper presents a supervision methodology based on comparing the average energy of each array and the average energy of the whole PV plant. To detect low-intensity anomalies before they become failures, the variability of the energy produced by each array is monitored by using the Bollinger Bands (BB) method. This is a statistical tool developed in the financial field to evaluate the... [more]

The problem of forecasting hourly solar irradiance over a multi-step horizon is dealt with by using three kinds of predictor structures. Two approaches are introduced: Multi-Model (MM) and Multi-Output (MO). Model parameters are identified for two kinds of neural networks, namely the traditional feed-forward (FF) and a class of recurrent networks, those with long short-term memory (LSTM) hidden neurons, which is relatively new for solar radiation forecasting. The performances of the considered approaches are rigorously assessed by appropriate indices and compared with standard benchmarks: the clear sky irradiance and two persistent predictors. Experimental results on a relatively long time series of global solar irradiance show that all the networks architectures perform in a similar way, guaranteeing a slower decrease of forecasting ability on horizons up to several hours, in comparison to the benchmark predictors. The domain adaptation of the neural predictors is investigated evaluat... [more]