With the continuous optimization of the steel production process and the increasing emergence of smelting methods, it has become difficult to monitor and control the production process using the traditional steel management model. The regulation of steel smelting processes by means of machine learning has become a hot research topic in recent years. In this study, through the data mining and correlation analysis of the main equipment and processes involved in steel transfer, a network algorithm was optimized to solve the problems of standard back propagation (BP) networks, and a steel temperature forecasting model based on improved back propagation (BP) neural networks was established for basic oxygen furnace (BOF) steelmaking, ladle furnace (LF) refining, and Ruhrstahl−Heraeus (RH) refining. The main factors influencing steel temperature were selected through theoretical analysis and heat balance principles; the production data were analyzed; and the neural network was trained and tes... [more]

The scaling of oil pipelines over time leads to issues including diminished flow rates, wasted energy, and decreased efficiency. To take appropriate action promptly and avoid the aforementioned issues, it is crucial to determine the precise value of the scale within the pipe. Non-invasive gamma attenuation systems are one of the most accurate detection methods. To accomplish this goal, the Monte Carlo N Particle (MCNP) algorithm was used to simulate a scale thickness measurement system, which included two sodium iodide detectors, a dual-energy gamma source (241 Am and 133 Ba radioisotopes), and a test pipe. Water, gas, and oil were all used to mimic a three-phase flow in the test pipe, with the volume percentages ranging from 10% to 80%. Moreover, a scale ranging in thickness from 0 to 3 cm was inserted into the pipe, gamma rays were shone on the pipe, and on the opposite side of the pipe, photon intensity was measured by detectors. There were 252 simulations run. Fifteen time and freq... [more]

The rapid increase in demand for renewable energy has led to a need for more efficient and effective ways to produce biogas from palm oil mill effluent (POME), which is rich in biological and chemical oxygen demand (BOD and COD). Despite its potential as a source of biogas, POME is not always effectively utilized in biogas production due to a lack of optimization of the treatment process. This study aims to address this issue by identifying the critical parameters affecting biogas production from POME and optimizing the process for maximum biogas yield and COD removal. This study employed comparative analysis and response surface methodology to optimize the performance of palm oil mill effluent (POME)-based biogas plants in Malaysia. Historical data from three commercial POME-based biogas plants in Malaysia were analyzed to identify the most critical parameters for biogas yield and COD removal. Response surface methodology, using Box−Behnken design and Design-Expert software, was then... [more]

In order to improve the problem of low oxygen supply efficiency during aerobic composting and prolong composting maturity, a genetic algorithm was used to optimize the initial weights and thresholds of the standard BP neural network and obtain the optimal parameters, and then a clonal selection algorithm was used to optimize the mutation operator in the genetic algorithm and duplicate the operator. A CGA-BP neural network based on an aeration oxygen supply prediction model was constructed, and the aeration oxygen supply predicted by the model was used to ferment the compost and accelerate the process of compost maturation. The results show that compared with the standard BP neural network algorithm and the GA-BP neural network algorithm, this model has accurate prediction performance in predicting aeration oxygen supply, with a prediction accuracy of 99.26%. The aeration oxygen supply predicted based on the CGA-BP model can effectively promote the composting maturity process and meet t... [more]

The valve is a key control component in the oil and gas transportation system, which, due to the environment, transmission medium, and other factors, is susceptible to internal leakage, resulting in valve failure. Conventional testing methods cannot judge the service life of valves. Therefore, it is important to carry out valve life prediction research for oil and gas transmission safety. In this work, a valve service life prediction method based on the PCA-PSO-LSSVM algorithm is proposed. The main factors affecting valve service life are obtained by principal component analysis (PCA), the least squares support vector machine (LSSVM) is used to predict the valve service life, the parameters are optimized by using particle swarm optimization (PSO), and the valve service life prediction model is established. The results show that the predicted valve service life based on the PCA-PSO-LSSVM algorithm is closer to the actual value, with an average relative error (MRE) of 16.57% and a root m... [more]

Steel surface defect detection is an important issue when producing high-quality steel materials. Traditional defect detection methods are time-consuming and uneconomical and require manually designed prior information or extra supervisors. Surface defects have different representations and features at different scales, which make it challenging to automatically detect the locations and defect types. This paper proposes a real-time steel surface defect detection technology based on the YOLO-v5 detection network. In order to effectively explore the multi-scale information of the surface defect, a multi-scale explore block is especially developed in the detection network to improve the detection performance. Furthermore, the spatial attention mechanism is also developed to focus more on the defect information. Experimental results show that the proposed network can accurately detect steel surface defects with approximately 72% mAP and satisfies the real-time speed requirement.

With the development of the Internet of Things (IoT) and virtual reality (VR) technology, the demand for high-precision gesture intelligent analysis of a human−machine interaction module for IoT−VR systems is increasing. Therefore, random forest (RF) and convolution neural network (CNN) algorithms are used in this study to build an intelligent gesture recognition model. The experiments were conducted to test the application performance of the design model. The test results show that the qualification rate of the analytical model designed in this study is significantly higher than that of the comparative model. When the threshold is determined to be 43.26 mm, the analytical qualification rates of the RF-CNN (the method of combining RF with CNN algorithms), faster regions with CNN features (Faster-RCNN), and RF models are 82.41%, 76.10%, and 59.10%, respectively. The calculation time of the RF−CNN model is between the two comparative models. From the test data, it can be observed that th... [more]

Visible blue light emitting diodes (LED) have been studied to inactivate Escherichia coli (E. coli) O157:H7 in agar gels. The LED array was optimized to attain uniform light illumination, and the light intensity distribution was visualized through optical simulation. The uniformity of LED light intensity was assessed, and the evenly spaced array showed the best uniformity with a Petri factor of 0.99. Microbial populations in agar gels prepared with and without a dye were analyzed after light irradiation. Each segment of the gels with different heights was taken to measure microbial reduction, and the results indicated that optical properties, such as opaqueness, played an important role in microbial reduction. The agar gel without and with a dye showed a maximum reduction of <3.4 and 5 log CFU/g) existed mainly in the bottom layer of the sample, despite the average contamination being <5 log CFU/g. This study provides a suitable approach for designing the LED photoinactivation pr... [more]

The electronic health record (EHR) surveillance process relies on wireless security administered in application technology, such as the Internet of Things (IoT). Automated supervision with cutting-edge data analysis methods may be a viable strategy to enhance treatment in light of the increasing accessibility of medical narratives in the electronic health record. EHR analysis structured data structure code was used to obtain data on initial fatality risk, infection rate, and hazard ratio of death from EHRs for prediction of unexpected deaths. Patients utilizing EHRs in general must keep in mind the significance of security. With the rise of the IoT and sensor-based Healthcare 4.0, cyber-resilience has emerged as a need for the safekeeping of patient information across all connected devices. Security for access, amendment, and storage is cumulatively managed using the common paradigm. For improving the security of surveillance in the aforementioned services, this article introduces an e... [more]

In this work, an artificial neural network is used to recognize timestamps of evolution. Timestamps are associated with outliers determined during the recognition of the genome attractors of organisms. The aim of this work is to present a new method of penetrating deep into evolution using the recognized timestamps. To achieve this aim, the neural networks of different number of layers were implemented in order to check the influence of the number of layers on the visibility of the timestamps. Moreover, the teaching process was repeated 10 times for each implemented neural network. The recognition of each organism evolution was also repeated 10 times for each taught neural network to increase the reliability of the results. It is presented, among other findings, that during the recognition of the timestamps of evolution not only the number of homologous comparisons and the lengths of compared sequences are important but also the distribution of similarities between sequences. It is als... [more]

An interval type-2 fuzzy set and fuzzy Petri net combined risk assessment model for chemical production was proposed to solve the problems of disorganized hierarchy and poorly targeted measures, as well as the requirement for complex equipment associated with chemical production risk assessment. First, four different types of risk databases were established according to the production process of cyclohexane. Considering the intrinsic relationship between the risk factors in the fault database, the interval type-2 fuzzy set was used to improve the semantic transformation accuracy and calculate the confidence in the risk factors. The fuzzy Petri net model was used to simulate the dynamic development of accidents, and the parallel relationship between risk factors was intuitively described. Thereafter, the external relationship between risk factors was analyzed, and the net structure of each layer was divided to build a multilevel model. Finally, the catalyst activation process during cyc... [more]

The pressure drop and conveying stability caused by the bend significantly affect the pneumatic conveying process of stiff shotcrete, which is the key to solving the problem of long-distance transportation. In this paper, the effects of different air velocities (32 m/s, 36 m/s, 40 m/s), water-cement ratios (0.1, 0.2, and 0.3), and bending-diameter ratios (4, 12, and 20) on the pressure drop of the elbow R1 and conveying stability R2 are studied using the response surface method. The conveying stability is characterized by the pressure variation coefficient (C.V). The response surface graph aids in the intuitive analysis of the effects of these variables. The results show that the impact of air velocity on R1 and R2 is exceptionally significant, and the interaction of each factor on the response value is analyzed. The response value and the quadratic polynomial regression equation between the various factors are obtained in addition to the flow characteristics of stiff shotcrete under d... [more]

A landslide is a type of natural disaster that has the highest frequency, the widest distribution and the heaviest losses worldwide; landslides seriously threaten human life and property and major engineering facilities. Therefore, it is important to improve landslide displacement prediction technology to avoid and mitigate landslide disasters. A landslide displacement prediction method based on a chaotic Gaussian mutation sparrow search algorithm-optimised BP neural network (CG-SSA-BP) is proposed to address the problems of the traditional sparrow search algorithm (SSA)-optimised BP (SSA-BP) neural network; it tends to fall into local optima, and it has slow convergence and a low prediction accuracy for landslide displacement prediction. This paper takes the Baishui River landslide in the Three Gorges reservoir area as the research object, and the double exponential smoothing (DES) method is used to decompose the landslide displacement into a trend term and a periodic term to solve th... [more]

To boost the operational performance of a non-aqueous DES electrolyte-based vanadium-iron redox flow battery (RFB), our previous work proposed a double-layer porous electrode spliced by carbon paper and graphite felt. However, this electrode’s architecture still needs to be further optimized under different operational conditions. Hence, this paper proposes a multi-layer artificial neural network (ANN) model to predict the relationship between vanadium-iron RFB’s performance and double-layer electrode structural characteristics. A training dataset of ANN is generated by three-dimensional finite-element numerical simulations of the galvanostatic discharging process. In addition, a genetic algorithm (GA) is coupled to an ANN regression training process for optimizing the model parameters to elevate the accuracy of ANN prediction. The novelty of this work lies in this modified optimal method of a double-layer electrode for non-aqueous RFB driven by a machine learning (ML) model coupled wi... [more]

The rise of artificial intelligence (AI)-based image analysis has led to novel application possibilities in the field of solvent analytics. Using convolutional neural networks (CNNs), better and more automated analysis of optically visible phenomena becomes feasible, broadening the spectrum of non-invasive measurements. These so-called smart sensors have attracted increasing attention in pharmaceutical and chemical process engineering; their additional sensor data enables more precise process control as additional process parameters can be monitored. This contribution presents an approach to analyzing single rising droplets to determine their physical properties; for example, geometrical parameters such as diameter, projection area and volume. Additionally, the rising velocity is determined, as well as the density and interfacial tension of the rising liquid droplet, determined from the force balance. Thus, a method was developed for analyzing liquid−liquid properties suitable for real... [more]

Anomaly detection for core temperature has great significance in maintaining the safety of nuclear power plants. However, traditional auto-encoder-based anomaly detection methods might extract the latent space features with redundancy, which may lead to missing and false alarms. To address this problem, the idea of feature disentangling is introduced under the auto-encoder framework in this paper. First, a feature disentangling auto-encoder (DAE) is proposed where a latent space disentangling loss is designed to disentangle the features. We further propose an incrementally feature disentangling auto-encoder (IDAE), which is the improved version of DAE. In the IDAE model, an incremental feature generation strategy is developed, which enables the model to evaluate the disentangling degree to adaptively determine the feature dimension. Furthermore, an iterative training framework is designed, which focuses on the parameter training of the newly incremented feature, overcoming the difficul... [more]

Although carbon fiber-reinforced polymer (CFRP) composites have excellent intrinsic mechanical properties, they are vulnerable to impact loads because of their weak inter-laminar fracture toughness, which results in delamination damage. This study presents a novel hybrid nanofiller combination of multi-walled carbon nanotubes (MWCNT) and alumina nanoparticles (Al2O3) to determine improvements in the impact resistance of CFRP laminate. The projectile impact experimental study is carried out on 140 mm × 140 mm × 1.5 mm CFRP laminate with spherical and conical nose shape projectiles. The numerical study of the test plate consisting of six layers is performed, in which each layer is modeled using a shell element and connected through tiebreak contact. Using the Cowper−Symonds equation to determine the dynamic mechanical properties, the numerical validation is established considering the strain rate effect. The results, such as residual velocity, damage area, ballistic limit velocity and de... [more]

In this experiment, chrysophanol analysis in Senna occidentalis (aerial parts) extract obtained by optimizing ultrasound-assisted extraction (UAE) variables (temperature, time, and liquid-to-solid ratio) using response surface methodology (RSM) was performed by employing the HPLC-UV method. For UAE process optimization, a highly significant quadratic model (p < 0.001) was projected to attain maximum chrysophanol yield. The extraction temperature, time, and liquid-to-solid ratio for the best UAE method were determined to be 49.3 °C, 57.7 min, and 18.7 mL/g, respectively. The optimized extract was subjected to a chrysophanol analysis utilizing HPLC-UV (fitted with a Pinnacle C18 column), and a gradient mobile phase composed of 0.5% formic acid (solvent A), acetonitrile (solvent B), methanol (solvent C), at a flow rate of 1.0 mL/min, and an optimum wavelength of 279 nm, respectively. It furnished a compact and intense peak of chrysophanol at Rt = 23.809 min. The experimental value (20.... [more]

Numerical testing is an ideal testing method in the research on the mechanical behaviors of jointed rock. However, there are few systematic studies focused on the comparison between the two-dimensional (2D) and the three-dimensional (3D) simulation effects on rock mechanical behaviors, particularly those of jointed rock. In this paper, a particle flow model was established by utilizing PFC2D and PFC3D to represent the rock materials, and the rock (especially jointed rock) mechanical behaviors (uniaxial compressive strength UCS, tensile strength TS, crack initiation stress level Kσ, and the pattern of crack initiation) between 2D and 3D models were compared and analyzed. As expected, the result shows that the UCS and TS showed an increasing tendency with the increase in the joint angle (β) for both the 2D and the 3D models, and the strength of the 3D model was less than that of the 2D model under uniaxial compression but was greater than that of the 2D model under uniaxial tension. The... [more]

Conventional flighted rotary drums usually have flights parallel to the rotating axis, which cannot facilitate the axial motion of the materials in the drum. Here, a new type of horizontal rotary drum with inclined flights and beads was designed. Inclined flights are used to facilitate the axial movement of beads and material, while beads are used as fillers to increase the gas-liquid contact area and to crush the solid materials. We simulated the drum and studied the axial motion of fillers using the discrete element method (DEM). To improve the mass and heat transfer performance, we optimized the distribution of beads in the active phase. The effects of the rotational speed, joint angle, and inlet flow rate in the drum were investigated systematically. The individual effects were evaluated in terms of the mass of particles in the active phase (MAP) and passive phase (MPP), the percentage of the active phase occupied by the particles (OAR), and the axial speed (AS). The response surfa... [more]

The tremendous and fast green transition in Denmark has initiated the large-scale grid-integration of renewable energy sources, electrification of energy consumption, and establishment of PtX and Energy Islands, setting goals for transmission grid development—such as the establishment of new connections—and for grid reconstruction—such as the extensive substitution of overhead lines (OHLs) with underground cables (UGCs). The share of UGCs in the Danish transmission grid is increasing. Presence of UGC has resulted in that resonances of the harmonic impedance characteristics of the transmission grid are brought within the harmonic order range coinciding with the harmonic emission sources and causing systemwide increase of the harmonic voltage distortion in the 400 kV transmission grid. The transformation of the 400 kV transmission grid has given rise to the need to predict harmonic voltage distortion using simulation models to secure an adequate power quality and support investment decis... [more]

After the Paris Agreement was signed in 2015, many countries worldwide focused on the hydrogen economy, aiming for eco-friendly and renewable energy by moving away from the existing carbon economy, which has been the primary source of global warming. Hydrogen is the most common element on Earth. As a light substance, hydrogen can diffuse quickly; however, it also has a small risk of explosion. Representative explosion accidents have included the Muskingum River Power Plant Vapor Cloud Explosion accident in 2007 and the Silver Eagle Refinery Vapor Cloud Explosion accident in 2009. In addition, there was an explosion in a hydrogen tank in Gangneung, Korea, in May 2019, and a hydrogen refueling station (HRS) in Norway exploded in 2018. Despite this risk, Korea is promoting the establishment of HRSs in major urban centers, including downtown areas and public buildings, by using the Regulatory Sandbox to install HRSs. This paper employed the Hydrogen Risk Assessment Model (HyRAM) of Sandia... [more]

Currently, fossil fuels are used to produce fuels and electricity, which are finite sources and have a negative impact on the natural environment. An excellent alternative to these fuels is biofuels, such as bioethanol from waste forest biomass. Pine needles are one of the most important available forest biomass materials with s significant impact on local understory vegetation. Forest waste biomass, which is a rich source of lignocellulose, can be used in various ways, such as for the eco-economical production of bioethanol. The aim of this study was to analyze the possibilities of bioethanol production from pine needle biomass obtained from forest land following different soil preparations and logging residue management. The pine needle dry matter yield, chemical components of pine needle biomass (cellulose, hemicellulose, lignin), and the amount of ethanol yield per hectare were evaluated. The highest average yield pine needle equal to 6.17 Mg∙ha−1 was observed. Bioethanol yield per... [more]

Statistical characteristics of the wind speed in the Samaria region of Israel have been analyzed by processing 11 years of wind data provided by the Israeli Meteorological Service, recorded at a 10 m height above the ground. The cumulative mean wind speed at a measurement height was shown to be 4.53 m/s with a standard deviation of 2.32 m/s. The prevailing wind direction was shown to be characterized by a cumulative mean azimuth of 226° with a standard deviation of 79.76°. The results were extrapolated to a 70 m height in order to estimate wind characteristics at the hub height of a medium-scale wind turbine. Moreover, Weibull distribution parameters were calculated annually, monthly, and seasonally, demonstrating a good match with histogram-based statistical representations. The shape parameter of the Weibull distribution was shown to reside within a narrow range of 1.93 to 2.15, allowing us to assume a Rayleigh distribution, thus simplifying wind turbine energy yield calculations. Th... [more]

The use of heat pumps for heating and cooling of buildings is increasing, offering an efficient and eco-friendly thermal energy supply. However, their complexity and system integration require attention to detail, and minor design or operational errors can significantly impact a project’s success. Therefore, it is essential to have a thorough understanding of the system’s intricacies and demands, specifically detailed system knowledge and precise models. In this article, we propose a method using artificial neural networks to develop heat pump models from measured data. The investigation focuses on an operational heat pump plant for heating and cooling a cluster of municipal buildings in Stavanger, Norway. The work showcases that the network configurations can provide process insights and knowledge when detailed system information is unavailable. Model A predicts the heat pump response to temperature setpoint and inlet conditions. Except for some challenges during low-demand cooling mo... [more]