The melting effect, a type of heat transferal process, is a fascinating mechanism of thermo-physics. It is related to phase change issues that occur in several industrial mechanisms. Glass treatment, polymer synthesis, and metal processing are among these. In view of this, the current investigation explicates the flow of a dusty nanofluid through a stretching cylinder in a porous medium by considering the effect of the melting heat transfer phenomenon. Using the required similarity transformations, the governing partial differential equations (PDEs) showing the energy transference and fluid motion in both the liquid and dust phases were translated into ordinary differential equations (ODEs). The numerical solutions for the acquired ODEs were developed using the Runge−Kutta−Fehlberg method of fourth−fifth order (RKF-45) and the shooting process. Graphical representations were used to interpret the effects of the governing parameters, including the porosity parameter, the Eckert number,... [more]

The bi-reforming of methane (BRM) is a promising process which converts greenhouse gases to syngas with a flexible H2/CO ratio. As there are many factors that affect this process, the coupled effects of multi-parameters on the BRM product are investigated based on Gibbs free energy minimization. Establishing a reliable model is the foundation of process optimization. When three input parameters are changed simultaneously, the resulting BRM products are used as the dataset to train three artificial neural network (ANN) models, which aim to establish the BRM prediction model. Finally, the trained ANN models are used to predict the BRM products when the conditions vary in and beyond the training range to test their performances. Results show that increasing temperature is beneficial to the conversion of CH4. When the molar flow of H2O is at a low level, the increase in CO2 can enhance the H2 generation. While it is more than 0.200 kmol/h, increasing the CO2 flowrate leads to the increase... [more]

Ammonia as a non-carbon fuel is expected to play an important role in the future, but it is difficult to be effectively utilized at this stage due to its flame retardancy and other characteristics. Therefore, we propose to use gliding arc plasma combined with a swirl burner to enhance the combustion performance of ammonia. The electrical characteristics, electron density, gas rotational temperature and the distribution of key active species in the burner were studied via optical emission spectroscopy (OES). With the increase of equivalence ratio (EQR), the width of the Hα line decreases significantly, indicating that the electron density shows a downward trend, even as the gas rotational temperature shows an upward trend. When the equivalence ratio was 0.5, the gas rotational temperature increases by about 320 K compared with the pure air condition. During pure air discharge, there will still be obvious NO emission due to the plasma reaction, but with the addition of NH3, the NO conten... [more]

The P−U characteristic curve of the photovoltaic (PV) cell is a single peak curve with only one maximum power point (MPP). However, the fluctuation of the irradiance level and ambient temperature will cause the drift of MPP. In the maximum power point tracking (MPPT) algorithm of PV systems, BP neural network (BPNN) has an unstable learning rate and poor performance, while the genetic algorithm (GA) tends to fall into local optimum. Therefore, a novel PV fuzzy MPPT algorithm based on an adaptive genetic simulated annealing-optimized BP neural network (AGSA-BPNN-FLC) is proposed in this paper. First, the adaptive GA is adopted to generate the corresponding population and increase the population diversity. Second, the simulated annealing (SA) algorithm is applied to the parent and offspring with a higher fitness value to improve the convergence rate of GA, and the optimal weight threshold of BPNN are updated by GA and SA algorithm. Third, the optimized BPNN is employed to predict the MPP... [more]

Stirling regenerator is one of the emerging heat exchanger systems in the area of cryogenic cooling. Many kinds of research have been conducted to study the efficiency of Stirling regenerators. Therefore, the principles and related knowledge of Stirling refrigerators must be thoroughly understood to design a regenerator with excellent performance for low-temperature and cryogenic engineering applications. In this study, an experimental setup is developed to estimate the pressure drop of the oscillating flow through two different wire-mesh regenerators, namely, 200 mesh and 300 mesh, for various operating frequencies ranging from 3 (200 RPM) to 10 Hz (600 RPM). Transient, axisymmetric, incompressible, and laminar flow governing equations are solved numerically, and source terms are added in the governing equations with the help of the porous media model and the Ergun semiempirical correlation, assuming that the wire meshes are cylindrical particles arranged uniformly. Simulation results... [more]

Dispersed bubbly flow is important to understand when working in a wide variety of hydrodynamic engineering areas; the main objective of this work is to numerically study bubble-induced instability. Surface tension and bubble-induced turbulence effects are considered with the momentum and k-ω transport equations. Steady dispersed bubbly flow is generated at the inlet surface using time-step and user-defined functions. In order to track the interface between the liquid and gas phases, the volume of fraction method is used. Several calculation conditions are considered in order to determine the effects of bubble diameter, bubble distribution, bubble velocity and bubble density on flow instability and void fraction. The void fraction of the domain is set to no more than 0.5% under different bubbly (micro/small) flow conditions; and the order of magnitude of the Reynolds number is 106. Results from the simulation indicate that velocity fluctuation induced by bubble swarm increases with inc... [more]

Particle and gas flow characteristics are numerically simulated by means of a proposed gas−particle second-order moment two-fluid model with particle kinetic−friction stress model in a bubbling fluidized bed. Anisotropic behaviors of gas−solid two-phase stresses and their interactions are fully considered by the two-phase Reynolds stress model and their closure correlations. The dispersion behaviors of the non-spherical expand graphite and spherical heavy particles are predicted by using the parameters of distributions of particle velocity, porosity, granular temperature, and dominant frequency. Compared to particles density 2700 kg/m3, ultralight particles exhibit the higher voidages with big bubbles and larger axial-averaged velocity of particles and stronger dispersion behaviors. Maximum granular temperature is approximately 3.0 times greater than that one, and dominant frequency for axial porosity fluctuations is 1.5 Hz that is 1/3 time as larger as that heavy particle.

Microwave heating is a process in which the electric, magnetic, and temperature fields are coupled with each other and are characterised by strong non-linearity, high time variability, and infinite dimensionality. This paper proposes a method for predicting the microwave heating temperature distribution of the TE10 mode, because the traditional numerical calculation method is not conducive to designing microwave controllers. First, the spatial distribution of the main electromagnetic mode TE10 waves in a rectangular waveguide was analysed using the principal mode analysis method. An expression for the transient dissipated power and a heat balance equation with infinite-dimensional characteristics were constructed. Then, the microwave heating model was decomposed into electromagnetic and temperature field submodels. A time discretization approach was used to approximate the transient constant dielectric constant. The heating medium was meshed to solve the electric field strength and tra... [more]

With the application of complex fracturing and other complex technologies, external extrusion has become the main cause of casing damage, which makes non-API high-extrusion-resistant casing continuously used in unconventional oil and gas resources exploitation. Due to the strong sensitivity of string ovality, uneven wall thickness, residual stress, and other factors to high anti-collapse casing, the API formula has a big error in predicting the anti-collapse strength of high anti-collapse casing. Therefore, Bayesian regularization artificial neural network (BRANN) is used to predict the external collapse strength of high anti-collapse casing. By collecting full-scale physical data, including initial defect data, geometric size, mechanical parameters, etc., after data preprocessing, the casing collapse strength data set is established for model training and blind measurement. Under the classical three-layer neural network, the Bayesian regularization algorithm is used for training. Thro... [more]

Welding is widely used to manufacture and repair steel structures such as piping and pressure vessels. Welding induces deformation and residual stress, which influences the mechanical performance of the structural members. Noting patch welding, which is applied to repair steel structures, a series of patch welding experiments and numerical analyses were carried out. The features of out-of-plane deformation and residual stress by patch welding were examined by changing the patch size. The out-of-plane deformation showed different modes in the patch joints. The magnitude of the out-of-plane deformation depended on the patch size. The tensile residual stress at the weld toe increased with the enlargement of the patch size. The costs for the different sizes of patch welding were estimated for choosing the patch size reasonably. The patch size should be determined by considering the mechanical influences of welding and the economic viewpoints of the welding process.

Countries all over the world are making their contribution to the common goal of energy saving and emission reduction. Solar energy is gaining more attention as a renewable energy source. Polysilicon is an important raw material for solar panels and the production of polysilicon is a vital part of the photovoltaic industry. Polysilicon production is a typical process industry enterprise, and its production process is continuous and highly energy intensive. Therefore, it is necessary to forecast and analyze the consumption of polysilicon production plants. To address the problem that it is difficult to predict future consumption based on historical data alone due to the time-series, massive, nonlinear, and complex nature of data in polysilicon workshops. This study proposes a combined workshop energy consumption prediction model based on Bayesian estimation of time-series decomposition and convolutional neural network (TSD-CNN). The method uses a time-series decomposition method to mode... [more]

This paper associates with stability analysis of linear impulsive stochastic delay systems (ISDSs). Although many conclusions about the stability of ISDSs have been obtained based on Lyapunov’s method, relatively few research theories about delay-dependent stability with less conservativeness have been established. Therefore, we introduce an appropriate Lyapunov-Krasovskii functional (LKF) to work out this problem, and a novel delay-dependent exponential stability theorem is first deduced. On the other hand, when mean-square stability is considered, we present delay-dependent stability conditions, it is of interest to note that the proposed conditions do not depend on the size of delays in the diffusion term, which solves the problems of determining the mean-square stability of ISDSs for which the diffusion term delays are not available. In the end, two numerical examples are carried out to verify the feasibility of our conclusions.

This paper addresses the difficulty of evaluating operating status in widely used gear pumps. A method for constructing hydraulic pump health indicators and evaluating health status is proposed based on LSTM−VAE. In this study, the vibration signal data source of gear pumps was assessed in the accelerated life test. Firstly, the normalized feature vectors of the whole-life operation data of gear pumps were extracted by wavelet packet decomposition and amplitude feature extraction. Combining an LSTM algorithm with a VAE algorithm, a method for constructing hydraulic pump health indicators based on LSTM−VAE is proposed. By learning the feature vectors of gear pumps in varying health conditions, a one-dimensional HI curve of the gear pumps was obtained. Then, LSTM was used to predict the HI curve of gear pumps. According to the volume efficiency of the gear pumps, the health status of gear pumps is divided into four states: health, sub-health, deterioration, and failure. The health status... [more]

Mammography is a low-dose X-ray imaging technique that can detect breast tumors, cysts, and calcifications, which can aid in detecting potential breast cancer in the early stage and reduce the mortality rate. This study employed a multilayer convolutional neural network (MCNN) to screen breast lesions with mammographic images. Within the region of interest, a specific bounding box is used to extract feature maps before automatic image segmentation and feature classification are conducted. These include three classes, namely, normal, benign tumor, and malignant tumor. Multiconvolution processes with kernel convolution operations have noise removal and sharpening effects that are better than other image processing methods, which can strengthen the features of the desired object and contour and increase the classifier’s classification accuracy. However, excessive convolution layers and kernel convolution operations will increase the computational complexity, computational time, and traini... [more]

The coal gasifier is the core unit of the coal gasification system. Due to its exposure to high temperatures, high pressures, and aggressive media, it is highly susceptible to serious accidents in the event of failure. Therefore, it is important for the gasifier to perform failure-risk assessment to understand its safety status and provide safety measures. Bayesian networks (BNs) for risk analysis of process systems has received a lot of attention due to its powerful inference capability and its ability to reflect complex relationships between risk factors. However, the acquisition of basic probability data in a Bayesian network is always a great challenge. In this study, an improved Bayesian network integrated with a trapezoidal intuitionistic fuzzy number-based similarity aggregation method (TpIFN-SAM) is proposed for the failure-risk assessment of process systems. This approach used the TpIFN-SAM to collect and aggregate experts’ opinions for obtaining the prior probabilities of the... [more]

As the oil and petrochemical products pass through the oil pipeline, the sediment scale settles, which can cause many problems in the oil fields. Timely detection of the scale inside the pipes and taking action to solve it prevents problems such as a decrease in the efficiency of oil equipment, the wastage of energy, and the increase in repair costs. In this research, an accurate detection system of the scale thickness has been introduced, which its performance is based on the attenuation of gamma rays. The detection system consists of a dual-energy gamma source (241 Am and 133 Ba radioisotopes) and a sodium iodide detector. This detection system is placed on both sides of a test pipe, which is used to simulate a three-phase flow in the stratified regime. The three-phase flow includes water, gas, and oil, which have been investigated in different volume percentages. An asymmetrical scale inside the pipe, made of barium sulfate, is simulated in different thicknesses. After irradiating t... [more]

Radix (SMR) is a widely-used herbal medicine for the treatment of various blood stasis-related diseases (mainly circulatory system). It has been extensively studied in the field of pharmacology over the last few decades. In addition, several reviews concerning the effect of SMR are available. The purpose of this study was to review the pharmacological activities of SMC based on the 10th revision of the international disease classification (ICD-10). After an analysis of the literatures in the Medline database between January 1988 and August 2018, 691 eligible articles were chosen and 971 results were obtained (395 in vitro, 536 in vivo, and 40 human). The extracted data were categorized into the disease chapters of the ICD-10 and the major chapters were: IX Diseases of the circulatory system, II Neoplasms, XI Diseases of the digestive system, XIX Injury, poisoning and certain other consequences of external causes, IV Endocrine, nutritional, and metabolic diseases, VI Diseases of the ner... [more]

This article aims to review, analyze, and classify the published research applications of the Data Envelopment Analysis (DEA) window analysis technique. The number of filtered articles included in the study is 109, retrieved from 79 journals in the web of science (WoS) database during the period 1996−2019. The papers are classified into 15 application areas: energy and environment, transportation, banking, tourism, manufacturing, healthcare, power, agriculture, education, finance, petroleum, sport, communication, water, and miscellaneous. Moreover, we present descriptive statistics related to the growth of publications over time, the journals publishing the articles, keyword terms used, length of articles, and authorship analysis (including institutional and country affiliations). To the best of the authors knowledge, this is the first survey reviewing the literature of the DEA window analysis applications in the 15 areas mentioned in the paper.

When a liquid flows, it has an internal resistance to flow. Viscosity is the property that measures this resistance, which is a fundamental characteristic parameter of liquids. The monitoring of viscosity is essential for quality control in many industrial areas, such as the pharmaceutical, chemical, and energy-related industries. Several instruments measure the viscosity of a liquid, the most used being the capillary viscometers. These instruments are complex, associated with high cost and expensive prices. This represents a challenge in several industries, where accurate viscosity knowledge is essential in designing various industrial equipment and processes. Using image processing and machine learning algorithms is a promising alternative to the current measurement methods. This work aims to extract characteristic information from videos of droplets of different samples using image processing algorithms. An Artificial Neural Network model utilizes the extracted characteristics to cl... [more]

To solve the trajectory tracking control problem for a class of nonlinear systems with time-varying parameter uncertainties and unknown control directions, this paper proposed a neural sliding mode control strategy with prescribed performance against event-triggered disturbance. First, an enhanced finite-time prescribed performance function and a compensation term containing the Hyperbolic Tangent function are introduced to design a non-singular fast terminal sliding mode (NFTSM) surface to eliminate the singularity in the terminal sliding mode control and speed up the convergence in the balanced unit-loop neighborhood. This sliding surface guarantees arbitrarily small overshoot and fast convergence speed even when triggering mistakes. Meanwhile, we utilize the Nussbaum gain function to solve the problem of unknown control directions and unknown time-varying parameters and design a self-recurrent wavelet neural network (SRWNN) to handle the uncertainty terms in the system. In addition,... [more]

Power lithium battery is an important core component of electric vehicles (EV), which provides the main power and energy for EV. In order to improve the estimation accuracy of the state of charge (SOC) of the electric vehicle battery (E-cell), the extended Kalman filter (EKF) algorithm, and backpropagation neural network (BPNN) are used to build the SOC estimation model of the E-cell, and the self-learning characteristic of BP neural network is used to correct the error and track the SOC of the E-cell. The results show that the average error of SOC estimation of BP-EKF model is 0.347%, 0.0231%, and 0.0749%, respectively, under the three working conditions of constant current discharge, pulse discharge, and urban dynamometer driving schedule (UDDS). Under the influence of different initial value errors, the average estimation errors of BP-EKF model are 0.2218%, 0.0976%, and 0.5226%. After the noise interference is introduced, the average estimation errors of BP-EKF model under the three... [more]

Yeasts are considered an extraordinary alternative source of natural carotenoids and pigmented terpenoids with multiple applications. Production of carotenoids by yeast fermentation technology has many benefits; it is cost-effective, easily scalable, and safe. The aim of this research is the isolation of yeasts from natural resources and selection of the most potent bioagent for carotenoid production. Additionally, an upgraded carotenoid extraction protocol we established, which implies the testing of four methods for cell lysis (hydrochloric acid treatment, ultrasound treatment, milling treatment, and osmotic pressure treatment), three extraction methods (conventional extraction, ultrasound extraction, and conventional + ultrasound extraction), and three extraction solvents (acetone, isopropanol/methanol (50:50), and ethanol). For the first time, the obtained results were further modeled by an artificial neural network (ANN). Based on the obtained maximal carotenoid yield (253.74 ± 9.... [more]

In this work, 2099 experimental data of binary systems composed of CO2 and ionic liquids are studied to predict solubility using a multilayer perceptron. The dataset includes 33 different types of ionic liquids over a wide range of temperatures, pressures, and solubilities. The main objective of this work is to propose a procedure for the prediction of CO2 solubility in ionic liquids by establishing four stages to determine the model parameters: (1) selection of the learning algorithm, (2) optimization of the first hidden layer, (3) optimization of the second hidden layer, and (4) selection of the input combination. In this study, a bound is set on the number of model parameters: the number of model parameters must be less than the amount of predicted data. Eight different learning algorithms with (4,m,n,1)-type hidden two-layer architectures (m = 2, 4, …, 10 and n = 2, 3, …, 10) are studied, and the artificial neural network is trained with three input combinations with three combinat... [more]

The classification effect of hyperspectral remote sensing images is greatly affected by the problem of dimensionality. Feature extraction, as a common dimension reduction method, can make up for the deficiency of the classification of hyperspectral remote sensing images. However, different feature extraction methods and classification methods adapt to different conditions and lack comprehensive comparative analysis. Therefore, principal component analysis (PCA), linear discriminant analysis (LDA), and locality preserving projections (LPP) were selected to reduce the dimensionality of hyperspectral remote sensing images, and subsequently, support vector machine (SVM), random forest (RF), and the k-nearest neighbor (KNN) were used to classify the output images, respectively. In the experiment, two hyperspectral remote sensing data groups were used to evaluate the nine combination methods. The experimental results show that the classification effect of the combination method when applying... [more]

This paper focuses on the use of AI in various MEMS (Micro-Electro-Mechanical System) biosensor types. Al increases the potential of Micro-Electro-Mechanical System biosensors and opens up new opportunities for automation, consumer electronics, industrial manufacturing, defense, medical equipment, etc. Micro-Electro-Mechanical System microcantilever biosensors are currently making their way into our daily lives and playing a significant role in the advancement of social technology. Micro-Electro-Mechanical System biosensors with microcantilever structures have a number of benefits over conventional biosensors, including small size, high sensitivity, mass production, simple arraying, integration, etc. These advantages have made them one of the development avenues for high-sensitivity sensors. The next generation of sensors will exhibit an intelligent development trajectory and aid people in interacting with other objects in a variety of scenario applications as a result of the active de... [more]