In industrial processes, process data often exhibit complex characteristics, such as nonstationarity and nonlinearity, which brings challenges to process monitoring. In this study, a monitoring strategy for nonstationary processes is proposed based on cointegration theory and multiple order moments. Considering the nonstationarity presented in some variables, cointegration analysis (CA) is applied to obtain long-term equilibrium relationships among these nonstationary variables, which are then combined with stationary variables to form a new stationary dataset. For the purpose of process monitoring, a new monitoring index that contains multiple order moments is proposed to capture different statistical features of a previously obtained stationary data set. Moving windows are applied to capture changes of local statistical characteristics to implement online monitoring. Case studies on simulation data and an industrial dataset are presented to illustrate the effectiveness of the propose... [more]

In the context of hydrogen production from biomass or organic waste with dark fermentation, this study analysed 55 studies (339 experiments) in the literature looking for the effect of operating parameters on the process performance of dark fermentation. The effect of substrate concentration, pH, temperature, and residence time on hydrogen yield, productivity, and content in the biogas was analysed. In addition, a linear regression model was developed to also account for the effect of nature and pretreatment of the substrate, inhibition of methanogenesis, and continuous or batch operating mode. The analysis showed that the hydrogen yield was mainly affected by pH and residence time, with the highest yields obtained for low pH and short residence time. High hydrogen productivity was favoured by high feed concentration, short residence time, and low pH. More modest was the effect on the hydrogen content. The mean values of hydrogen yield, productivity, and content were, respectively, 6.4... [more]

A radial basis function neural network (RBFNN), with a strong function approximation ability, was proven to be an effective tool for nonlinear process modeling. However, in many instances, the sample set is limited and the model evaluation error is fixed, which makes it very difficult to construct an optimal network structure to ensure the generalization ability of the established nonlinear process model. To solve this problem, a novel RBFNN with a high generation performance (RBFNN-GP), is proposed in this paper. The proposed RBFNN-GP consists of three contributions. First, a local generalization error bound, introducing the sample mean and variance, is developed to acquire a small error bound to reduce the range of error. Second, the self-organizing structure method, based on a generalization error bound and network sensitivity, is established to obtain a suitable number of neurons to improve the generalization ability. Third, the convergence of this proposed RBFNN-GP is proved theor... [more]

In this paper, we study the bifurcations of non-linear dynamical systems. We continue to develop the analytical approach, permitting the prediction of the bifurcation of dynamics. Our approach is based on implicit (approximate) amplitude-frequency response equations of the form FΩ,A;c̲ =0, where c̲ denotes the parameters. We demonstrate that tools of differential geometry make possible the discovery of the change of differential properties of solutions of the equation FΩ,A;c̲ =0. Such qualitative changes of the solutions of the amplitude-frequency response equation, referred to as metamorphoses, lead to qualitative changes of dynamics (bifurcations). We show that the analytical prediction of metamorphoses is of great help in numerical simulation.

Many models for accurately predicting the performance of gasket plate heat exchangers were developed in the last decades, grouped in three categories: empirical, semi-analytical or theoretical/numerical, with the view to saving materials and energy through correct design of industrial equipment. This work addresses one such model, namely Lévêque correlation modified by Martin and by Dović, which is promising due to the correct assumption of the flow in sine duct channels and the consideration of energy losses caused by flow reversal at plate edges and the flow path changing when entering the chevron angle. This model was validated by our own experimental data under industrial conditions for vegetable oils processing, both in laminar flow (Re = 8−42) and fully developed turbulent flow (Re = 446−1137). Moreover, in this study, particular values for constants/parameters of the model were determined for the corrugation inclination angle relative to vertical direction equal to 30°. Through... [more]

Switched-mode power supply (SMPS) has been of vital importance majorly in power management of industrial equipment with much-improved efficiency and reliability. Given the diverse range on loading and operating conditions of SMPS, several anomalies can occur in the device resulting to over-voltage, overloading, erratic atmospheric conditions, etc. Electrical over-stress (EOS) is one of the commonly used causes of failure among power electronic devices. Since there is a limitation for the SMPS in terms of input voltage and current (two methods of controlling an SMPS), the device has been subjected to an accelerated aging test using EOS. This study presents a two-fold approach to evaluate the overall state of health of SMPS using an integration of extended Kalman filter (EKF) and deep neural network. Firstly, the EKF algorithm would assist in fusing fault features to acquire an comprehensive degradation trend. Secondly, the degradation pattern of the SMPS has been monitored for four diff... [more]

Compaction is a critical step in asphalt pavement construction. The objective of this study is to analyze the mesoscale mechanical behaviors of coarse aggregates in asphalt mixtures during gyratory compaction through experiments and numerical simulation using the Discrete Element Method (DEM). A novel granular sensor (SmartRock) was embedded in an asphalt mixture specimen to collect compaction response data, including acceleration, stress, rotation angle and temperature. Moreover, the irregularly shaped coarse aggregates were regenerated in the DEM model, and numerical simulations were conducted to analyze the evolution of aggregate interaction characteristics. The findings are as follows: (1) the measured contact stress between particles changes periodically during gyratory compaction, and the amplitude of stress tends to be stable with the increase of compaction cycles; (2) the contact stress of particles is influenced by the shape of aggregates: flat-shaped particles are subjected t... [more]

The aim of the present study was to determine how thermal stimulation via electromagnetic microwave radiation impacts the yields of biogas and methane produced by methane fermentation of five selected energy crop species in anaerobic reactors. The resultant performance was compared with that of reactors with conventional temperature control. The highest biogas production capacity was achieved for maize silage and Virginia mallow silage (i.e., 680 ± 28 dm3N/kgVS and 506 ± 16 dm3N/kgVS, respectively). Microwave radiation as a method of heating anaerobic reactors provided a statistically-significantly boost in methane production from maize silage (18% increase). Biomethane production from maize silage rose from 361 ± 12 dm3N/kgVS to 426 ± 14 dm3N/kgVS. In the other experimental variants, the differences between methane concentrations in the biogas were non-significant.

Recurrent Neural Networks (RNNs) have been widely applied in various fields. However, in real-world application, because most devices like mobile phones are limited to the storage capacity when processing real-time information, an over-parameterized model always slows down the system speed and is not suitable to be employed. In our proposed temperature control system, the RNN-based control model processes the real-time temperature signals. It is necessary to compress the trained model with acceptable loss of control performance for further implementation in the actual controller when the system resource is limited. Inspired by the layer-wise neuron pruning method, in this paper, we apply the nonlinear reconstruction error (NRE) guided layer-wise weight pruning method on the RNN-based temperature control system. The control system is established based on MATLAB/Simulink. In order to compress the model size to save the memory capacity of temperature controller devices, we first prove the... [more]

Highway tunnels are one of the paramount infrastructure systems that affect the welfare of communities. They are vulnerable to higher limits of deterioration, yet there are limited available funds for maintenance and rehabilitation. This state of circumstances entails the development of a deterioration model to forecast the performance condition behavior of critical tunnel elements. Accordingly, this research paper proposes an integrated deterioration prediction model for five highway tunnel elements, namely, cast-in-place tunnel liners, concrete interior walls, concrete portal, concrete ceiling slab, and concrete slab on grade. The developed deterioration model is envisioned in two fundamental components, which are model calibration and model assessment. In the first component, an integrated model of Gaussian process regression and a grey wolf optimization algorithm (GWO-GPR) is introduced for deterioration behavior prediction of highway tunnel elements. In this regard, the grey wolf... [more]

Natural gas pipelines have attracted increasing attention in the energy industry thanks to the current demand for green energy and the advantages of pipeline transportation. A novel deep learning method is proposed in this paper, using a coupled network structure incorporating the thermodynamics-informed neural network and the compressor Boolean neural network, to incorporate both functions of pipeline transportation safety check and energy supply predictions. The deep learning model is uniformed for the coupled network structure, and the prediction efficiency and accuracy are validated by a number of numerical tests simulating various engineering scenarios, including hydrogen gas pipelines. The trained model can provide dispatchers with suggestions about the number of phases existing during the transportation as an index showing safety, while the effects of operation temperature, pressure and compositional purity are investigated to suggest the optimized productions.

The purity of graphite often affects its application in different fields. In view of the low efficiency of manual recognition and the omission of features extracted by single convolution neural network, this paper proposes a method for identifying graphite purity using a multi-model weighted fusion mechanism. The ideas suggested in this paper are as follows. On the self-built small sample data set, offline expansion and online enhancement are carried out to improve the generalization ability of the model and reduce the overfitting problem of deep convolution neural networks. Combined with transfer learning, a dual-channel convolution neural network is constructed using the optimized Alex Krizhevsky Net (AlexNet) and Alex Krizhevsky Net 50 (AlexNet50) to extract the deep features of the graphite image. After the weighted fusion of the two features, the Softmax classifier is used for classification. Experimental results show that recognition accuracy after weighted fusion is better than... [more]

Oil and gas pipelines are lifelines for a country’s economic survival. As a result, they must be closely monitored to maximize their performance and avoid product losses in the transportation of petroleum products. However, they can collapse, resulting in dangerous repercussions, financial losses, and environmental consequences. Therefore, assessing the pipe condition and quality would be of great significance. Pipeline safety is ensured using a variety of inspection techniques, despite being time-consuming and expensive. To address these inefficiencies, this study develops a model that anticipates sources of failure in oil pipelines based on specific factors related to pipe diameter and age, service (transported product), facility type, and land use. The model is developed using a multilayer perceptron (MLP) neural network, radial basis function (RBF) neural network, and multinomial logistic (MNL) regression based on historical data from pipeline incidents. With an average validity of... [more]

Safe and stable operation plays an important role in the chemical industry. Fault detection and diagnosis (FDD) make it possible to identify abnormal process deviations early and assist operators in taking proper action against fault propagation. After decades of development, data-driven process monitoring technologies have gradually attracted attention from process industries. Although many promising FDD methods have been proposed from both academia and industry, challenges remain due to the complex characteristics of industrial data. In this work, classical and recent research on data-driven process monitoring methods is reviewed from the perspective of characterizing and mining industrial data. The implementation framework of data-driven process monitoring methods is first introduced. State of art of process monitoring methods corresponding to common industrial data characteristics are then reviewed. Finally, the challenges and possible solutions for actual industrial applications a... [more]

The conventional elastic least-squares reverse time migration (LSRTM) generally inverts the parameter perturbation of the model rather than the reflectivity of reflected P- and S-modes, which leads to difficulty in directly interpreting the physical properties of the subsurface media. However, an accurate velocity model that is needed by the separation of seismic records of conventional LSRTM is usually unavailable in real data, which limits its application. In this study, we introduce a new practical correlative LSRTM (CLSRTM) scheme based on wave mode decomposition without amplitude and phase distortion, which frees from separation of seismic records. In this study, we deduced the migration and the de-migration operators using the decoupled P- and S-wave equations in heterogeneous media, which needs no extra wavefield decomposition in simulated data. To accelerate the convergence and improve the efficiency of the inversion, we adopted an analytical step-length formula that can be inc... [more]

Predicting the time series of 10.7-cm solar radio flux is a challenging task because of its daily variability. This paper proposed a non-linear method, a convolutional and recurrent neural network combined model to achieve end-to-end F10.7 forecasts. The network consists of a one-dimensional convolutional neural network and a long short-term memory network. The CNN network extracted features from F10.7 original data, then trained the feature signals in the long short-term memory network, and outputted the predicted values. The F10.7 daily data during 2003−2014 are used for the testing set. The mean absolute percentage error values of approximately 2.04%, 2.78%, and 4.66% for 1-day, 3-day, and 7-day forecasts, respectively. The statistical results of evaluating the root mean square error, spearman correlation coefficient shows a superior effect as a whole for the 1−27 days forecast, compared with the ordinary single neural network and combination models.

The adaptability of vehicle-mounted heating systems that include burner and stove remarkably influences the system efficiency, heat flux uniformity, and pollutants emission. In this work, the performance of a rotary cup burner assembly with three different cooking stoves was investigated using ANSYS Fluent software based on five factors of thermal efficiency, heat transfer intensity, heating uniformity, CO emissions, and flue gas outlet temperature. The Eulerian-Lagrangian method was used to perform the diesel spray, and the shear stress transfer k-ω turbulence model and the probability density function model were employed to simulate the turbulent combustion. Based on the simulation results, the performance pentagon of the above five factors was constructed to evaluate the comprehensive performance of the new rotary cup burner system. The rotary cup burner had a good performance when it is used in two staple food stoves and a subsidiary food stove. In staple food stove A, its higher f... [more]

Aiming at the problem of low positioning accuracy caused by modal aliasing and noise interference in DC cable fault location analysis of a VSC-HVDC system, a double-ended fault location method for flexible DC cables based on improved local mean decomposition is proposed. Firstly, the local mean decomposition (LMD) is used to decompose the six-mode voltage signal to obtain the product function (PF) component; then, to overcome the problem that the instantaneous frequency function of the LMD is limited by the extreme value, the Hilbert transform is performed on the PF1 to obtain the instantaneous frequency curve, and the arrival time of the voltage traveling wave head is determined from the mutation information. Finally, the fault distance is obtained by using the principle of double-ended traveling wave fault location. Different fault conditions are simulated, analyzed, and compared with wavelet transform and Hilbert−Huang transform. The results show that the proposed method has a posit... [more]

Wind resistance is one of the most important safety targets for high-rise buildings, especially slab-shaped ones with relatively large length−width ratios. In this study, the characteristics of wind pressure on a reduced-scale model of a slab-shaped high-rise building were analyzed experimentally. The experiment was conducted using the DTC Initium electronic scanning pressure measurement system in the wind tunnel at the Xiamen University of Technology, China. The spatial distribution and time-frequency characteristics of the wind pressure signals were analyzed with various wind flow directions under uniform and boundary-layer inflow conditions. The results show that both of the inflow conditions and the wind directions have significant influences on the magnitude and distribution characteristics of the wind pressure on the building surfaces. The wavelet transform-based analysis shows that the wind pressure on the building surfaces presents obvious intermittent characteristics, with the... [more]

In modern electric vehicles, electrical failure has become a critical problem that reduces the lifetime of traction motors. Moreover, traction motors with high-voltage and high-speed systems for a high power density have been aggravating the shaft voltage problems. This study identifies that direct-oil-cooling systems exacerbate this problem. To address this, an analytical method for calculating parasitic capacitance is proposed to determine the effects of cooling oil in a traction motor with a direct-oil-cooling system. Capacitance equivalent circuits are configured based on whether the slot is submerged in the cooling oil. In addition, an electric field decomposition method is applied to analyze the distortion of the electric field by the structure of the conduction parts in the motor. The results indicate that the parasitic capacitances of the traction motor are increased by the influence of the cooling oil resulting in an increase in the shaft voltage.

This paper is aimed at the investigation of the acoustic and spectral characteristics of underwater electric sparks generated between two plate electrodes, using synchronized gas bubble injection. There are two purposes served by discharge initiation in the bubble. Firstly, it creates a favorable condition for electrical breakdown. Secondly, the gas bubble provides an opportunity for the direct spectroscopy of plasma light emission, avoiding water absorption. The effect of water absorption on captured spectra was studied. It was observed that the emission intensity of the Ha line and a shockwave amplitude generated by discharge strongly depend on the size of the gas bubble in the moment of the discharge initiation. It was found that the plasma in the underwater spark channel does not correspond to a source of black-body radiation. This study can be also very useful for understanding the difference between discharges produced directly in a liquid and discharges produced in gas/vapor bub... [more]

Pollution of the environment with heavy metals requires finding solutions to eliminate them from aqueous flows. The current trends aim at exploiting the advantages of the adsorption operation, by using some low-cost sorbents from agricultural waste biomass, and with good retention capacity of some heavy metal ions. In this context, it is important to provide tools that allow the modeling and optimization of the process, in order to transpose the process to a higher operating scale of the biosorption process. This paper capitalizes on the results of previous research on the biosorption of heavy metal ions, namely Pb(II), Cd(II), and Zn(II) on soybean biomass and soybean waste biomass resulting from biofuels extraction process. The data were processed by applying a methodology based on Artificial Neural Networks (ANNs) and evolutionary algorithms (EAs) capable of evolving ANN parameters. EAs are represented in this paper by the Differential Evolution (DE) algorithm, and a simultaneous tr... [more]

This paper aims to propose a new algorithm for designing thermal vapor compressors (TVCs) using given operation parameters. First, an axisymmetric model was used to simulate a TVC, and the results were compared with those from published experimental results. A simulation set was designed to analyze the TVC dimensions, and then statistically-significant parameters (p-value < 0.05) were chosen for the subsequent studies. Thereafter, three parametric lengths were defined and a model presenting entrainment ratio (ER) was developed using a set of simulation results. The obtained characteristic equation allows us to scale (up or down) the TVC to different capacities, calculate the real-time sizes or predict the performance. It was found that the critical “TVC/primary nozzle” throat diameter ratio is constant in every scale-up study, depending on operation conditions. By establishing a characteristic graph, the approach was expanded for a broader algorithm. The comparative results revealed... [more]

The effect of the cam profile on the nonlinear dynamics phenomenon of the follower is studied at three involutes’ profiles for the cam. The value of the Lyapunov exponent parameter is calculated at different internal distances of the follower guide from inside and at different cam speeds. The effect of the Lyapunov exponent value on the contact stress is studied based on the clearance between the follower and its guides. The contact between the cam and the square grooving key and between the cam and the follower has been taken into consideration at different locations. The finite element method is used to calculate the contact stress numerically using the SolidWorks program. The nonlinear response of the follower is calculated analytically using the Newton−Euler equations of rigid body dynamics of translation and rotation motions while the follower position is tracked experimentally using a high-speed 3-D camera device. The contact stress is checked and verified using photo-elastic app... [more]

Mammals and birds have quicker heart rates compared to other species. Mammalian cardiomyocytes have T-tubule membranes that facilitate rapid changes in Ca2+ concentrations. In contrast, bird cardiomyocytes do not possess T-tubule membranes, which raises the question of how birds achieve fast heartbeats. In this study, we compared the changes in Ca2+ concentration in cardiomyocytes isolated from adult quails and rats to elucidate the mechanism resulting in rapid heart rates in birds. Cardiomyocytes isolated from quails were significantly narrower than those isolated from rats. When Ca2+ concentration changes in the entire cardiomyocytes were measured using Fura-2 acetoxymethyl ester (AM), the time to peak was statistically longer in quails than in rats. In contrast, the decay time was markedly shorter in quails than in rats. As a result, the total time of Ca2+ concentration change was shorter in quails than in rats. A spatiotemporal analysis of Ca2+ concentration changes in quail cardio... [more]