There are many petrochemical industries that need adequate knowledge of multiphase flow phenomena inside pipes. In such industries, measuring the void fraction is considered to be a very challenging task. Thus, various techniques have been used for void fraction measurements. For determining more accurate multiphase flow measurements, this study employed dual non-intrusive techniques, gamma-ray and electrical capacitance sensors. The techniques using such sensors are considered non-intrusive as they do not cause any perturbation of the local structure of the phases’ flow. The first aim of this paper is to analyze both techniques separately for the void fraction data obtained from practical experiments. The second aim is to use both techniques’ data in a neural network model to analyze measurements more efficiently. Accordingly, a new system is configured to combine the two techniques’ data to obtain more precise results than they can individually. The simulations and analyzing procedur... [more]

Thedistribution of aggregates in the cross-section of the drum, in the case of equipment used for the production of asphalt mixtures, essentially influences the performance of rotary dryers. In the research carried out in this article, the optimization of the distribution of particles in the active region of the drum was pursued by modifying the geometric shape of the flights, taking into account at the same time the influence of the technological parameters of operation such as drum rotation speed and drum filling degree. The studies were performed using the discrete element method (DEM), and the obtained results revealedthat flight geometry strongly influences drying efficiency. The efficiency of rectangular double-angled type flights is strongly influenced by the drum rotation speed, especially at high levels of filling degree (20%), with the mass of aggregates exposed to drying increasing by 41.11% when drum rotation speed increases from 10 rpm to 15 rpm. Similarly, hook-type fligh... [more]

This paper proposes a milli-reactor design method incorporating reactor runaway criteria. Based on Computational Fluid Dynamic (CFD) simulation, neural networks are applied to obtain the optimal reactor structure according to the target reaction requirements. Varma’s theory, the critical Nusselt number for stable operation of the flow reactor, is derived. Inserts of the multi-blade structure are designed and investigated to enhance mixing and heat transfer performance. The flow field and heat transfer capacities are obtained by CFD calculations in the range of Re 50−1800. The internal components increase the heat transfer performance up to 21 times, and the pressure drop up to 16 times. The inclined angle of the blade is recommended to be 45°, which can effectively improve heat transfer without generating excessive pressure drop. By partial least squares regression (PLS) analysis, Re and the number of blades are the most critical factors affecting heat transfer, and the five blades and... [more]

Virus-like particles (VLPs) are versatile vaccine carriers for conferring broad protection against influenza by enabling high-level display of multiple hemagglutinin (HA) strains within the same particle construct. The insect cell-baculovirus expression vector system (IC-BEVS) is amongst the most suitable platforms for VLP expression; however, productivities vary greatly with particle complexity (i.e., valency) and the HA strain(s) to be expressed. Understanding the metabolic signatures of insect cells producing different HA-VLPs could help dissect the factors contributing to such fluctuations. In this study, the metabolic traces of insect cells during production of HA-VLPs with different valences and comprising HA strains from different groups/subtypes were assessed using targeted metabolic analysis and metabolic flux analysis. A total of 27 different HA-VLP variants were initially expressed, with titers varying from 32 to 512 HA titer/mL. Metabolic analysis of cells during the produc... [more]

For the purpose of studying the dynamic and inner flow features of an open inlet channel axial flow pump unit, in the present study, numerical calculations using the SST k-ω turbulence model are applied to an open inlet channel axial flow pumping unit based on the NS equation, and experimental validation is then performed. The experimental output indicates that the designed working conditions are Q = 350 L/s, head H = 5.065 m, efficiency η = 79.56%, and the maximum operating head is H = 9.027 m, which is about 1.78 times that of the design head; further, the pump device can operate in a wide range of working conditions. In addition, the design working conditions are within the range of high-efficiency operating conditions. The calculated values and the experimental comparison are all within a 5.0% margin of error; further, the numerical calculations are reliable. The hydraulic loss of the inlet channel under the design condition Q = 350 L/s is 0.0676 m, which satisfies the relationship... [more]

Fault diagnosis is crucial for realizing safe process operation when a fault occurs. Multivariate statistical process control (MSPC) has widely been adopted for fault detection in real processes, and contribution plots based on MSPC are a well-known fault diagnosis method, but it does not always correctly diagnose the causes of faults. This study proposes a new fault diagnosis method based on the causality between process variables and a monitored index for fault detection, which is referred to as a causal plot. The proposed causal plot utilizes a linear non-Gaussian acyclic model (LiNGAM), which is a data-driven causal inference algorithm. LiNGAM estimates a causal structure only from data. In the proposed causal plot, the causality of a monitored index of fault detection methods, in addition to process variables, is estimated with LiNGAM when a fault is detected with the monitored index. The process variables having significant causal relationships with the monitored indexes are iden... [more]

Aluminum 6061 is a precipitation-hardened alloy, containing magnesium and silicon as its major alloying element, and it is widely used in industries due to its remarkable mechanical properties and weld ability. Several types of machining manners are researched for aluminum 6061 alloys, and micro-abrasive jet blasting (μ-AJM) is one of the most developed processes for improving surface roughness. When operating μ-AJM on aluminum alloys, sufficient consideration of environmental variables such as particle types, nozzle diameter, pressure, standoff distance, and injection time is a prerequisite for the appropriate degree of roughness. To find the optimal conditions, a statistical method of analysis of variables was used to investigate the maximum value for depth, diameter, width, etc., of the blasting surface. According to experimental results, multiple factors have a great influence on the condition of surface roughness. The blasting processes were conducted as square and cylindrical exp... [more]

In this paper, the heat transfer and flow characteristics of a helically coiled tube with twisted elliptical in a low Reynolds number (Re = 500−3000) flow were investigated numerically. The working fluid flowed in a laminar regime. Numerical results were compared with empirical correlations in the existing literature, demonstrating the accuracy of the analysis in this study. Firstly, we investigated the effects of geometric parameters and Reynolds number on the heat transfer and flow characteristics. The results showed that lower twist pitch length and semi-major axis length could induce sufficient fluid mixing and a larger temperature gradient near the tube wall, enhancing the heat exchange and producing larger friction resistance. Overall, the heat transfer performances were improved by about 1.04−1.21 times and 1.02−1.23 times for different semi-major axis lengths and different twist pitch lengths, respectively, compared to the smooth helical coil. Secondly, by changing the working... [more]

The recombinant adeno-associated virus (rAAV) is a viral vector technology for gene therapy that is considered the safest and most effective way to repair single-gene abnormalities in non-dividing cells. However, improving the viral titer productivity in rAAV production remains challenging. The first step to this end is to effectively monitor the process state variables (cell density, GLC, GLN, LAC, AMM, and rAAV viral titer) to improve the control performance for an enhanced productivity. However, the current approaches to monitoring are expensive, laborious, and time-consuming. This paper presents an extended Kalman filter (EKF) approach used to monitor the rAAV production using the online viable cell density measurements and estimating the other state variables measured at a low frequency. The proposed EKF uses an unstructured mechanistic kinetic model applicable in the upstream process. Three datasets were used for parameter estimation, calibration, and testing, and the data were c... [more]

Used cooking oil (UCO) is a hydrocarbon that causes significant environmental pollution globally. Oil is a difficult pollutant to remove from the environment due to its hydrophobic nature. Bioremediation is preferred over chemical and physical remediation approaches as it is more economical and environmentally friendly. Hence, the search for a microbe with outstanding degrading capability is imperative due to the nature of hydrocarbons as pollutants. The current study evaluates the ability of glyphosate degrader isolates, Burkholderia vietnamiensis AQ5-12 and Burkholderia sp. AQ5-13, to degrade UCO as the sole carbon source. The physico-cultural conditions affecting the activities of the isolates were studied using one-factor-at-a-time (OFAT) and response-surface methodology (RSM). The temperature, agitation speed, UCO concentration, nitrogen supply, yeast extract concentration, and inoculum size were optimised during a 7-day incubation period. Based on the OFAT results, the highest de... [more]

Effective high slope risk assessment plays an important role in the safety management and control of the open-pit coal mining process. Traditional slope stability risk assessment methods rarely consider the time factor or evaluate the dynamic change of high slope in an open-pit mine at a certain time in a sensitivity assessment. This paper develops an interval trapezoidal fuzzy soft set method to achieve the high slope dynamic risk evaluation. The proposed dynamic interval trapezoidal fuzzy soft set method for risk assessment of high slope in an open-pit coal mine is developed by integrating the time points and weights of slope risk factors. The extended interval trapezoidal fuzzy soft set was used to calculate the weights of risk factors at different times, and the Fuzzy Analytical Hierarchy Process (FAHP) method was applied to determine the weights of risk factors. The weight change of different risk factors with time can be easily achieved with the proposed method. As a case study,... [more]

During the global energy crisis, it is essential to improve the energy efficiency of pumps by adjusting the pump’s control strategy according to the operational states. However, monitoring the pump’s operational states with the help of external sensors brings both additional costs and risks of failure. This study proposed an interpolation method based on PN curves (power−speed curves) containing information regarding motor shaft power, speed, and flow rate to achieve high accuracy in predicting the pump’s flow rates without flow sensors. The impact factors on the accuracy of the estimation method were analyzed. Measurements were performed to validate the feasibility and robustness of the PN curve interpolation method and compared with the QP and back-propagation neural network (BPNN) methods. The results indicated that the PN curve interpolation method has lower errors than the other two prediction models. Moreover, the average absolute errors of the PN curve interpolation method in th... [more]

Due to the opaqueness of rock and the limitation of detection technology, it is impossible to accurately describe the crack growth process and determine the law of rock breakage. Based on smoothed particle hydrodynamics and the finite element method (SPH-FEM), a numerical model for high-speed water jet breaking was established in this work to simulate the fragmentation process of rock impacted by a high-speed water jet, and to study the effects of different jet angles on the propagation of microscopic cracks inside the rock. Additionally, we further analyzed the jet impact angle on the microscopic crack propagation trend of the rock by applying confining pressure to the rock. Theoretical and experimental analyses showed that the inclination angle of the jet determined the direction of axial crack propagation in the tension-type center. When the inclination angle of the jet exceeded 20°, the ability of water jet erosion was insufficient, and the efficiency of rock fragmentation was low.... [more]

Predicting surface settlement in deep foundation pit engineering plays a central role in the safety of foundation pit construction. Recently, static or dynamic methods are usually applied to predict ground settlement in deep foundation pit projects. In this work, we propose a model combining wavelet noise reduction and radial basis neural network (XW-RBF) to reduce noise interference in monitoring data. The results show that the XW-RBF model predicts an average relative error of 0.77 and a root average square error of 0.13. The prediction performance is better than the original data prediction results with noise structure and has higher prediction accuracy. The noise data caused by the interference of construction and the surrounding environment in the original data can be removed via the wavelet noise reduction method, with the discreteness of the original data reducing by 30%. More importantly, our results show that the XW-RBF model can reflect the law of data change to predict the f... [more]

The genus Achillea has significant medical potential due to the presence of highly bioactive compounds in its chemical composition. To take advantage of plants’ biomedical potential, it is of great importance to use a proper extraction process. This study aimed to determine and compare the preliminary chemical composition of five different Achillea species extracted with two conventional (infusion and maceration) and two non-conventional (ultrasound-assisted extraction (UAE) and Subcritical water extraction (SWE)) techniques. The extracts were prepared using the previously described procedures for infusion, maceration, UAE and SWE extraction. For all extracts, the extraction yield (dry extract (DE)) was determined. The analyzed extracts were preliminarily chemically characterized spectrophotometrically in terms of total phenolic content (TPC) and total flavonoid content (TFC). The obtained results showed that non-conventional techniques delivered higher values of TPC and TFC than conve... [more]

In this study, bacteria from the genus of Azotobacter, Bacillus and Pseudomonas were isolated from the roots of Phaseolus vulgaris and used as plant growth-promoting bacteria for Sinapis alba L., Brassica napus L., Amaranthus retroflexus L., Linum usitatissimum L., Panicum miliaceum L. and Rumex patientia L. plants. The results showed that all three bacteria had different effects on plants growth considering both sterile and non-sterile soil. Bacillus sp. induced the greatest influence in terms of the root length of Sinapis alba L. grown in sterile soil (with 28%), while considering non-sterile soil, Pseudomonas sp. increased the root and shoot length by 11.43% and 25.15%, respectively, compared to the blank sample. Azotobacter sp. exerted the highest beneficial influence on Brassica napus L. growth in non-sterile soil, since the root and shoot lengths were stimulated with 27.64% and 52.60%, respectively, compared to uninoculated plants. Bacillus sp. had a positive effect on the growth... [more]

The base of solar collector systems is usually installed in soil that contains moisture. In cold regions, due to the low ambient temperature, the moisture in the soil freezes, creating a risk of frost heave. This study analyzed the frost heave mechanism of power transmission and transformation foundation, clarified the factors affecting soil frost heave and the way to solve soil layer frost heave, and proposed the use of heat transfer elements to pre-frozen soil layers to prevent the foundation of the solar collector system from freezing. A numerical model of the ground heat exchange pipes in soil was established. The effects of different soil types, soil moisture content, and the effective radius and operating time on the heat transfer performance of the system were investigated by the verified numerical model. The results show that the heat pipe pre-freezing technology can reduce the drop in soil temperature, thereby increasing the temperature difference between the ground heat excha... [more]

The loss mechanism of shear and upstream blockage caused by the interaction of the purged flow and ingested gas needs to be systematically studied to optimize the flow near the rim. In order to study the causes and influence factors of blocking and shearing effects and quantify their losses reasonably, the three-dimensional unsteady numerical method validated by the experiment data was adopted to study the turbine with three kinds of sealing structures. The block and shear loss are quantified by integrating the dissipation coefficient in the volume where the specific aerodynamic loss occurs. The result indicated that there was a larger radial velocity and smaller tangential velocity of the purged flow relative to the main flow caused the blocking effect. Therefore, its loss is affected by the seal flow and seal structure. The shear effect is mainly affected by the tangential velocity gradient and the axial velocity gradient near the cavity exit. The contribution of the tangential veloc... [more]

The long-term demand forecast for annual national electricity and energy consumption plays a vital role in future strategic planning, power system installation programming, energy investment planning, and next-generation unit construction. Three machine learning algorithms of BP-NN, MLR, and LS-SVM were chosen for training forecasting models, with the data on population, GDP, mean temperature, sunshine, rainfall, and frost days in 1993−2019 serving as the input variables. The total data were divided by 70% into the training set (1993−2011) and 30% into the test set (2012−2019), in chronological order. RMSE, MAPE, and MaxError were adopted as the performance criteria. The statistical results show that the gross population of the UK increases year by year from 1993 to 2020. The GDP generally increases before 2007 but has a decline, and then varies with a large amplitude afterward. The electricity and energy consumption of the UK generally increase from 1993 and reach a peak around 2005.... [more]

With the increase in industrial accidents induced by natural disasters, the study of earthquake risk assessment has been widely considered by scholars. However, the cascade evolution of Natech (natural−technological) disasters has not been thoroughly studied, especially in chemical parks with complex technological processes. From the perspective of scenario deduction, combined with cross-impact analysis and a damping interpretation structural model, this paper analyzes the evolution process of cascade disaster in a chemical industrial park after the Wenchuan earthquake. At the same time, a visual network risk assessment model is constructed to identify the impact of earthquake cascade disasters on the park. The simulation results show that the scenario-driven risk assessment method proposed in this paper can directly reflect the coupling relationship and propagation path among the derived events and realize dynamic, intuitive and structured disaster expression to deal with the earthqua... [more]

State of Charge (SoC) estimation is one of the most significant and difficult techniques to promote the commercialization of electric vehicles (EVs). Suffering from various interference in vehicle driving environment and model uncertainties due to the strong time-variant property and inconsistency of batteries, the existing typical SoC estimators such as coulomb counting and extended Kalman filter cannot perform their theoretically optimal efficacy in practical applications. Aiming at enhancing the robustness of SoC estimation and improving accuracy under the real driving conditions with noises and uncertainties, this paper proposes a framework consisting of (1) an adaptive-κ nonlinear diffusion filter to reduce the noise in current measurement, (2) a self-learning strategy to estimate and remove the zero-drift, (3) a coulomb counting algorithm to realize open-loop SoC estimation, (4) an H∞ filter to implement closed-loop robust estimation, and (5) a data fusion unite to achieve the fi... [more]

For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are... [more]

The storage of large numbers of batteries and accumulators is associated with an increased risk of their ignition, which results in the release of significant amounts of hydrogen into the environment. The aim of the study was to reconstruct hazardous zones after hydrogen and liquefied propane−butane (reference gas) release for different industrial processes with the use of numerical methods. Two numerical tools (Fire Dynamics Simulator and Ansys software) were applied for the three-dimensional reconstruction of flammable gas release. Propane−butane was produced from aerosol packages, and hydrogen was produced during battery charging. Emission was analyzed in an industrial building, and both emissions were independent processes. The obtained results indicated that the hazardous zones correspond to the lower explosive level concentrations for both analyzed gasses. Moreover, the high-resolution computational fluid dynamic (CFD) model for flammable gas emissions provided noninvasive and di... [more]

Process measurements are contaminated by random and/or gross measuring errors, which degenerates performances of data-based strategies for enhancing process performances, such as online optimization and advanced control. Many approaches have been proposed to reduce the influence of measuring errors, among which expectation maximization (EM) is a novel and parameter-free one proposed recently. In this study, we studied the EM approach in detail and argued that the original EM approach is not feasible to rectify measurements contaminated by persistent biases, which is a pitfall of the original EM approach. So, we propose a modified EM approach here to circumvent this pitfall by fixing the standard deviation of random error mode. The modified EM approach was evaluated by several benchmark cases of process data rectification from literatures. The results show advantages of the proposed approach to the original EM in solving efficiency and performance of data rectification.

In recent years, Fourier Transform InfraRed (FTIR) spectrometry has been widely used to estimate different characteristics and contents of materials in many fields. Even though numerous works have been published in this area, it has still been difficult to suggest a global method that can predict the properties of crude oils from different resources based solely on FTIR data. In this study, we compare the application of several methods in order to predict particular important properties (i.e., viscosity, density, total sulfur content, total acid number, etc.) of crude oil samples from seven different Canadian oil fields. We employed chemometric methods such as Partial Least Squares regression (PLS) and principal component regression (PCR) and compared the results to the performance of neural networks (NN) with a different number of layers. These methods were evaluated by calculating the coefficient of determination (R2) and prediction root mean squared errors (RMSE). Although less comp... [more]