Records with Subject: Numerical Methods and Statistics
Showing records 1 to 25 of 2055. [First] Page: 1 2 3 4 5 Last
Numerical Study on High Throughput and High Solid Particle Separation in Deterministic Lateral Displacement Microarrays
Maike S. Wullenweber, Jonathan Kottmeier, Ingo Kampen, Andreas Dietzel, Arno Kwade
September 21, 2023 (v1)
Keywords: Computational Fluid Dynamics, deterministic lateral displacement, discrete element method, high throughput, immersed boundary method, particle concentration, particle separation
Deterministic lateral displacement (DLD) is a high-resolution passive microfluidic separation method for separating micron-scale particles according to their size. Optimizing these microsystems for larger throughputs and particle concentrations is of interest for industrial applications. This study evaluates the limitations of the functionality of the DLD separation principle under these specific conditions. For this reason, different particle volume fractions (up to 11%) and volumetric flow rates (corresponding to Reynolds numbers up to 50) were varied within the DLD microsystem and tested in different combinations. Resolved two-way coupled computational fluid dynamics/discrete element method (CFD-DEM) simulations including spherical particles were performed. The results show a general increase in the critical diameter with increasing volume fraction and decreasing separation efficiency. The largest tested Reynolds number (Re = 50) results in the highest separation efficiency, particu... [more]
Fused Data-Driven Approach for Early Warning Method of Abnormal Conditions in Chemical Process
Xiaomiao Song, Fabo Yin, Dongfeng Zhao
September 21, 2023 (v1)
Keywords: abnormal conditions, chemical process, data-driven
The utilization of data-driven methods in chemical process modeling has been extensively acknowledged due to their effectiveness. However, with the increasing complexity and variability of chemical processes, predicting and warning of anomalous conditions have become challenging. Extracting valuable features and constructing relevant warning models are critical problems that require resolution. This research proposed a novel fused method that integrates K-means density-based spatial clustering of applications with noise (DBSCAN) clustering and bi-directional long short-term memory multilayer perceptron (Bi-LSTM-MLP) to enable early warning of abnormal conditions in chemical processes. The paper applied the proposed method to analyze the early warning using actual process data from Eastman Tennessee and the atmospheric pressure reduction unit as an example. In the TE model and example, the root mean square error (RMSE) of this method is 0.006855 and 0.052546, respectively, which is quit... [more]
Optimal Discrete Element Parameters for Black Soil Based on Multi-Objective Total Evaluation Normalized-Response Surface Method
Zhipeng Wang, Tong Zhu, Youzhao Wang, Feng Ma, Chaoyue Zhao, Xu Li
September 21, 2023 (v1)
Keywords: black soil, discrete element methodology, multi-objective homogenization method, parameter calibration, response surface methodology, stacking angle
The lack of accurate black soil simulation model parameters in the design and optimization of soil remediation equipment has led to large errors in simulation results and simulation outcomes, which to some extent restricts the development of soil remediation equipment. Accurate discrete element parameters can improve the efficiency of soil remediation equipment. To improve the reliability of the discrete element contact parameters for black soil, a set of optimal discrete element contact parameters was found that could comprehensively represent a variety of particle sizes and minimize error. In this paper, the best discrete element contact parameters were selected by using a multi-indicator total evaluation normalization method combined with the response surface method, combined with black soil solid and simulated stacking tests. First, the physical parameters of the black soil and the accumulation angle were determined. Next, Plackett−Burman tests were carried out for each grain size... [more]
Multi-Objective Optimization of Drilling GFRP Composites Using ANN Enhanced by Particle Swarm Algorithm
Mohamed S. Abd-Elwahed
September 21, 2023 (v1)
Keywords: artificial neural network, drilling process, glass fiber reinforced polymer, Optimization, Particle Swarm Optimization, response surface analysis, sustainable machining
This paper aims to optimize the quality characteristics of the drilling process in glass fiber-reinforced polymer (GFRP) composites. It focuses on optimizing the drilling parameters with drill point angles concerning delamination damage and energy consumption, simultaneously. The effects of drilling process parameters on machinability were analyzed by evaluating the machinability characteristics. The cutting power was modeled through drilling parameters (speed and feed), drill point angle, and laminate thickness. The response surface analysis and artificial neural networks enhanced by the particle swarm optimization algorithm were applied for modeling and evaluating the effect of process parameters on the machinability of the drilling process. The most influential parameters on machinability properties and delamination were determined by analysis of variance (ANOVA). A multi-response optimization was performed to optimize drilling process parameters for sustainable drilling quality cha... [more]
Triaxial Compression Strength Prediction of Fissured Rocks in Deep-Buried Coal Mines Based on an Improved Back Propagation Neural Network Model
Yiyang Wang, Bin Tang, Wenbin Tao, Anying Yuan, Tianguo Li, Zhenyu Liu, Fenglin Zhang, An Mao
September 21, 2023 (v1)
Keywords: fissured rock specimen, improved BP neural network prediction model, numerical tests, triaxial compression tests
In deep coal mine strata, characterized by high ground stress and extensive fracturing, predicting the strength of fractured rock masses is crucial for stability analysis of the surrounding rock in coal mine strata. In this study, rock samples were obtained from construction sites in deep coal mine strata and intact, as well as fissured, rock specimens were prepared and subjected to triaxial compression tests. A numerical model based on the discrete element method was then established and the micro-parameters were calibrated. A total of 288 triaxial compression tests on the rock specimens under different conditions of confining pressure, loading rate, fissure dip angle, and fissure length, were conducted to obtain the triaxial compressive strength of the fractured rock specimens under different conditions. To address the limitations of traditional back propagation (BP) neural networks in solving stochastic problems, a modified BP neural network model was developed using a random factor... [more]
Method for Dynamic Prediction of Oxygen Demand in Steelmaking Process Based on BOF Technology
Kaitian Zhang, Zhong Zheng, Liu Zhang, Yu Liu, Sujun Chen
September 21, 2023 (v1)
Keywords: basic oxygen furnace mode, Big Data, dynamic prediction, oxygen demand, steelmaking
Oxygen is an important energy medium in the steelmaking process. The accurate dynamic prediction of oxygen demand is needed to guarantee molten steel quality, improve the production rhythm, and promote the collaborative optimization of production and energy. In this work, a analysis of the mechanism and of industrial big data was undertaken, and we found that the characteristic factors of Basic Oxygen Furnace (BOF) oxygen consumption were different in different modes, such as duplex dephosphorization, duplex decarbonization, and the traditional mode. Based on this, a dynamic-prediction modeling method for BOF oxygen demand considering mode classification is proposed. According to the characteristics of BOF production organization, a control module based on dynamic adaptions of the production plan was researched to realize the recalculation of the model predictions. A simulation test on industrial data revealed that the average relative error of the model in each BOF mode was less than... [more]
Industrial Data-Driven Processing Framework Combining Process Knowledge for Improved Decision Making—Part 1: Framework Development
Émilie Thibault, Jeffrey Dean Kelly, Francis Lebreux Desilets, Moncef Chioua, Bruno Poulin, Paul Stuart
September 21, 2023 (v1)
Keywords: data processing, data reconciliation, framework, industrial data, operating regime, steady-state detection
Data management systems are increasingly used in industrial processes. However, data collected as part of industrial process operations, such as sensor or measurement instruments data, contain various sources of errors that can hamper process analysis and decision making. The authors propose an operating-regime-based data processing framework for industrial process decision making. The framework was designed to increase the quality and take advantage of available process data use to make informed offline strategic business operation decisions, i.e., environmental, cost and energy analysis, optimization, fault detection, debottlenecking, etc. The approach was synthesized from best practices derived from the available framework and improved upon its predecessor by putting forward the combination of process expertise and data-driven approaches. This systematic and structured approach includes the following stages: (1) scope of the analysis, (2) signal processing, (3) steady-state operatin... [more]
Predicting Shear Wave Velocity Using a Convolutional Neural Network and Dual-Constraint Calculation for Anisotropic Parameters Incorporating Compressional and Shear Wave Velocities
Jiaqi Liu, Zhixian Gui, Gang Gao, Yonggen Li, Qiang Wei, Yizhuo Liu
September 21, 2023 (v1)
Keywords: anisotropic parameters, CNN, dual constraints encompassing both compressional and shear wave velocities, shear wave velocity prediction
As the exploration of unconventional reservoirs progresses, characterizing challenging formations like tight sandstone becomes increasingly complex. Anisotropic parameters play a vital role in accurately characterizing these unconventional reservoirs. In light of this, this paper introduces an approach that uses a dual-constraint anisotropic rock physics model based on compressional and shear wave velocities. The proposed method aims to enhance the precision of anisotropic parameter calculations, thus improving the overall accuracy of reservoir characterization. The paper begins by applying a convolutional neural network (CNN) to predict shear wave velocity, effectively resolving the issue of incomplete shear wave logging data. Subsequently, an anisotropic rock physics model is developed specifically for tight sandstone. A comprehensive analysis is conducted to examine the influence of quartz, clay porosity aspect ratio, and fracture density on compressional and shear wave velocities.... [more]
Research on the Optimization of the Operating Parameters of Methane Carbon Dioxide Reforming Using the Response Surface Methodology
Xing Huang, Zhengguo Lv, Xin Yao, Yang Liu, Yuhe Wang, Sijia Zhu
September 20, 2023 (v1)
Keywords: BBD, chemkin simulation, methane carbon dioxide reforming, parameter optimization, response surface methodology
In order to reduce the production cost of the methane carbon dioxide reforming reaction, and improve its actual production efficiency, in this paper, the optimal working parameters of the methane carbon dioxide reforming reaction are studied. The influence of different factors on methane conversion is studied via a single-factor numerical simulation analysis and the response surface methodology. Firstly, a numerical model of the methane carbon dioxide reforming reaction is established using Ansys Chemkin Pro software to analyze the influence of single factors (reactor temperature, reaction pressure, gas velocity) on methane conversion rate; secondly, the response surface model with the methane conversion rate as the response value is established using the BBD (Box−Behnken design) method; and finally, the order of influence of each variable on methane conversion and the optimal reaction conditions are determined using the response surface method. The factors are listed in order of their... [more]
Prediction of Refractive Index of Petroleum Fluids by Empirical Correlations and ANN
Georgi Nikolov Palichev, Dicho Stratiev, Sotir Sotirov, Evdokia Sotirova, Svetoslav Nenov, Ivelina Shishkova, Rosen Dinkov, Krassimir Atanassov, Simeon Ribagin, Danail Dichev Stratiev, Dimitar Pilev, Dobromir Yordanov
September 20, 2023 (v1)
Keywords: ANN, empirical correlation, intercriteria analysis, Petroleum, refractive index
The refractive index is an important physical property that is used to estimate the structural characteristics, thermodynamic, and transport properties of petroleum fluids, and to determine the onset of asphaltene flocculation. Unfortunately, the refractive index of opaque petroleum fluids cannot be measured unless special experimental techniques or dilution is used. For that reason, empirical correlations, and metaheuristic models were developed to predict the refractive index of petroleum fluids based on density, boiling point, and SARA fraction composition. The capability of these methods to accurately predict refractive index is discussed in this research with the aim of contrasting the empirical correlations with the artificial neural network modelling approach. Three data sets consisting of specific gravity and boiling point of 254 petroleum fractions, individual hydrocarbons, and hetero-compounds (Set 1); specific gravity and molecular weight of 136 crude oils (Set 2); and speci... [more]
Cadmium Elimination via Magnetic Biochar Derived from Cow Manure: Parameter Optimization and Mechanism Insights
Yi Wen, Dingxiang Chen, Yong Zhang, Huabin Wang, Rui Xu
September 20, 2023 (v1)
Keywords: artificial neural network, cadmium removal, cow manure, magnetic biochar, response surface methodology
Designing an efficient and recyclable adsorbent for cadmium pollution control is an urgent necessity. In this paper, cow manure, an abundant agricultural/animal husbandry byproduct, was employed as the raw material for the synthesis of magnetic cow manure biochar. The optimal preparation conditions were found using the response surface methodology model: 160 °C for the hydrothermal temperature, 600 °C for the pyrolysis temperature, and Fe-loading with 10 wt%. The optimal reaction conditions were also identified via the response surface methodology model: a dosage of 1 g·L−1, a pH of 7, and an initial concentration of 100 mg·L−1. The pseudo-second-order model and the Langmuir model were used to fit the Cd(II) adsorption, and the adsorption capacity was 612.43 mg·g−1. The adsorption was dominated by chemisorption with the mechanisms of ion-exchange, electrostatic attraction, pore-filling, co-precipitation, and the formation of complexations. Compared to the response surface methodology m... [more]
Special Issue: Neural Networks, Fuzzy Systems and Other Computational Intelligence Techniques for Advanced Process Control
Jie Zhang, Meihong Wang
September 20, 2023 (v1)
Computational intelligence (CI) techniques have developed very fast over the past two decades, with many new methods emerging [...]
Thermal Behavior Prediction of Sludge Co-Combustion with Coal: Curve Extraction and Artificial Neural Networks
Chaojun Wen, Junlin Lu, Xiaoqing Lin, Yuxuan Ying, Yunfeng Ma, Hong Yu, Wenxin Yu, Qunxing Huang, Xiaodong Li, Jianhua Yan
September 20, 2023 (v1)
Keywords: artificial neural networks (ANN), prediction, sludge co-combustion, thermal behavior, thermogravimetric curve extraction (TCE)
Previous studies on the co-combustion of sludge and coal have not effectively utilized the characteristics of the combustion process to predict thermal behavior. Therefore, focusing on these combustion process characteristics is essential to understanding and predicting thermal behavior during the co-combustion of sludge and coal. In this paper, we use thermogravimetric analysis to study the co-combustion of coal and sludge at different temperatures (300−460 °C, 460−530 °C, and 530−600 °C). Our findings reveal that the ignition improves, but the combustion worsens with more sludge. Then, we further employ curve extraction based on temperature and image segmentation to extract the DTG (weight loss rate) curves. We successfully predicted the DTG curves for different blends using nonlinear regression and curve extraction, achieving an excellent R2 of 99.7%. Moreover, the curve extraction method predicts DTG better than artificial neural networks for two samples in terms of R2 (99.7% vs. 9... [more]
Failure Risk Prediction Model for Girth Welds in High-Strength Steel Pipeline Based on Historical Data and Artificial Neural Network
Ke Wang, Min Zhang, Qiang Guo, Weifeng Ma, Yixin Zhang, Wei Wu
September 20, 2023 (v1)
Keywords: failure risk, girth welds, pipeline, sample selection
Pipelines are the most economical and sensible way to transport oil and gas. Long-distance oil and gas pipelines consist of many steel pipes or pipe fittings joined by welded girth welds, so girth welds are an essential part of pipelines. Owing to the limitations of welding conditions and the complexity of controlling weld quality in the field, some defects are inevitably present in girth welds and adjacent weld areas. These defects can lead to pipeline safety problems; therefore, it is necessary to perform failure risk assessment of pipeline girth welds. In this study, an artificial neural network model was proposed to predict the failure risk of pipeline girth welds with defects. Firstly, many pipeline girth weld failure cases, pipeline excavation, and inspection data were collected and analyzed to determine the main factors influencing girth weld failure. Secondly, a spatial orthogonal optimization method was used to select training samples for the artificial neural network model to... [more]
A Mechanistic Model Based on Statistics for the Prediction of a Converter’s End-Point Molten Steel Temperature
Fang Gao, Dazhi Wang, Yanping Bao, Xin Liu, Lidong Xing, Lihua Zhao
September 20, 2023 (v1)
Keywords: converter, endpoint temperature, heat loss coefficient, model, multiple linear regression
With the high efficiency and automation of converter smelting, it is becoming increasingly important to predict and control the endpoint temperature of the converter. Based on the heat balance, a model for predicting the molten pool temperature in a converter was established. Moreover, the statistical method of multiple linear regression was used to calculate the converter heat loss coefficient, greatly improving the prediction accuracy of the mechanistic model. Using the model, the oxidation process for each element in the molten pool, the melting processes of scrap, and the flux were also calculated. The model could better approximate the actual smelting process. Data from a 130 t converter were collected to validate the model. When the error ranges were limited to ±20 and ±15 °C, the model hit rates were 96 and 86.7%, respectively.
A Fast Density Peak Clustering Method for Power Data Security Detection Based on Local Outlier Factors
Zhuo Lv, Li Di, Cen Chen, Bo Zhang, Nuannuan Li
August 3, 2023 (v1)
Keywords: anomaly detection, density, distance, local outlier factors, power data, principal component analysis
The basic work of power data research is anomaly detection. It is necessary to find a method suitable for processing current power system data. Research proposes an algorithm of fast density peak clustering with Local Outlier Factor (LOF). The algorithm has poor performance in processing datasets with irregular shapes and significant local density changes, and has the disadvantage of strong dependence on truncation distance. This study provides the decision rules for outliers incorporating the idea of LOF. The improved algorithm can fully consider the characteristics of power data and reduce the dependence on truncation distance. In anomaly detection based on the simulation of real power data, the classification accuracy of the improved CFSFDP algorithm is 4.87% higher than that of the traditional algorithm, and the accuracy rate is 97.41%. The missed and false detection rates of the LOF-CFSFDP algorithm are decreased by 2.23% and 2.64%, respectively, compared to the traditional algori... [more]
Artificial Neural Networks (ANNs) for Vapour-Liquid-Liquid Equilibrium (VLLE) Predictions in N-Octane/Water Blends
Esteban Lopez-Ramirez, Sandra Lopez-Zamora, Salvador Escobedo, Hugo de Lasa
August 3, 2023 (v1)
Keywords: Artificial Neural Networks, hydrocarbon/water blends, Machine Learning, vapour-liquid-liquid equilibrium
Blends of bitumen, clay, and quartz in water are obtained from the surface mining of the Athabasca Oil Sands. To facilitate its transportation through pipelines, this mixture is usually diluted with locally produced naphtha. As a result of this, naphtha has to be recovered later, in a naphtha recovery unit (NRU). The NRU process is a complex one and requires the knowledge of Vapour-Liquid-Liquid Equilibrium (VLLE) thermodynamics. The present study uses experimental data, obtained in a CREC-VL-Cell, and Artificial Intelligence (AI) for vapour-liquid-liquid equilibrium (VLLE) calculations. The proposed Artificial Neural Networks (ANNs) do not require prior knowledge of the number of vapour-liquid phases. These ANNs involve hyperparameters that are used to obtain the best ANN model architecture. To accomplish this, this study considers (a) R2 Coefficients of Determination and (b) ANN training requirements to avoid data underfitting and overfitting. Results demonstrate that temperature has... [more]
A Thermal Analysis of LASER Beam Welding Using Statistical Approaches
Ariel Flores Monteiro de Oliveira, Elisan dos S. Magalhães, Luiz E. dos S. Paes, Milton Pereira, Leonardo R. R. da Silva
August 3, 2023 (v1)
Keywords: Compute Unified Device Architecture (CUDA-C), heat transfer simulation, LASER welding, parallel computing, parameter optimization, Taguchi’s design
Implementing input parameters that match the experimental weld shape is challenging in LASER beam welding (LBW) simulation because the computed heat input and spot for temperature acquisition strongly affect the outcomes. Therefore, this study focuses on investigating the autogenous LBW of AISI 1020 using a three-dimensional heat transfer model that assumes a modified Gaussian heat flux distribution depending on LASER power (Qw), radius (R), and penetration (hp). The influence of such variables on the simulated weld bead was assessed through analysis of variance (ANOVA). The ANOVA returns reliable results as long as the data is normally distributed. The input radius exerts the most prominent influence. Taguchi’s design defined the studied data reducing about 65% of the simulations compared to a full factorial design. The optimum values to match the computed outcomes to lab-controlled experiments were 2400 W for power (80% efficiency), 0.50 mm for radius, and 1.64 mm for penetration. Mo... [more]
A Methodology for Consolidation Effects of Inventory Management with Serially Dependent Random Demand
Mauricio Huerta, Víctor Leiva, Fernando Rojas, Peter Wanke, Xavier Cabezas
August 3, 2023 (v1)
Keywords: allocation rules, ARMA models, copula method, dedicated facilities, mathematical programming, R software, regular transshipment, statistical methods
Most studies of inventory consolidation effects assume time-independent random demand. In this article, we consider time-dependence by incorporating an autoregressive moving average structure to model the demand for products. With this modeling approach, we analyze the effect of consolidation on inventory costs compared to a system without consolidation. We formulate an inventory setting based on continuous-review using allocation rules for regular transshipment and centralization, which establishes temporal structures of demand. Numerical simulations demonstrate that, under time-dependence, the demand conditional variance, based on past data, is less than the marginal variance. This finding favors dedicated locations for inventory replenishment. Additionally, temporal structures reduce the costs of maintaining safety stocks through regular transshipments when such temporal patterns exist. The obtained results are illustrated with an example using real-world data. Our investigation pro... [more]
A New Fast Calculating Method for Meshing Stiffness of Faulty Gears Based on Loaded Tooth Contact Analysis
Zhe Liu, Haiwei Wang, Fengxia Lu, Cheng Wang, Jiachi Zhang, Mingjian Qin
August 3, 2023 (v1)
Keywords: fault degradation, faulty gear, finite element method, loaded tooth contact analysis, mesh stiffness
Gear transmission systems are widely used in various fields. The occurrence of gear cracks, tooth pitting, and other faults will lead to the dynamic characteristics deterioration of the transmission system. In order to calculate the meshing stiffness of faulty gear pairs more effectively and precisely, this article improves the loaded tooth contact analysis (LTCA) method by analyzing the influence of different fault types on gear deformation, including bending-shearing deformation and contact deformation, which combines the accuracy of the finite element method (FEM) and the rapidity of the analytical method (AM). The improved LTCA method can model the fault areas accurately and optimize the deformation coordination equation under the actual meshing situation of the faulty gear tooth, making it suitable for calculating the meshing stiffness of faulty gears. Based on the calculation results of the finite element method, the accuracy of the improved meshing stiffness calculation method h... [more]
A Study of a Domain-Adaptive LSTM-DNN-Based Method for Remaining Useful Life Prediction of Planetary Gearbox
Zixuan Liu, Chaobin Tan, Yuxin Liu, Hao Li, Beining Cui, Xuanzhe Zhang
August 3, 2023 (v1)
Keywords: domain adaptation, LSTM-DNN network, planetary transmission, remaining useful life prediction
Remaining Useful Life (RUL) prediction is an important component of failure prediction and health management (PHM). Current life prediction studies require large amounts of tagged training data assuming that the training data and the test data follow a similar distribution. However, the RUL-prediction data of the planetary gearbox, which works in different conditions, will lead to statistical differences in the data distribution. In addition, the RUL-prediction accuracy will be affected seriously. In this paper, a planetary transmission test system was built, and the domain adaptive model was used to Implement the transfer learning (TL) between the planetary transmission system in different working conditions. LSTM-DNN network was used in the data feature extraction and regression analysis. Finally, a domain-adaptive LSTM-DNN-based method for remaining useful life prediction of Planetary Transmission was proposed. The experimental results show that not only the impact of different oper... [more]
Non-Carcinogenic Risk Assessment for Heavy Metals in the Soil and Rice in the Vicinity of Dabaoshan Mine, South China
Huarong Zhao, Kangming Shi, Jianqiao Qin, Zikang Ren, Guoliang Yang
August 3, 2023 (v1)
Keywords: Dabaoshan mine, hazard quotient, heavy metal, normal QQ plot, paddy soil, rice
Heavy-metal pollution has attracted wide attention in recent years. The problem of heavy-metal pollution in the vicinity of the Dabaoshan mine, the largest polymetallic mine in South China, has attracted widespread attention. In this study, 38 samples of rice and paddy soil near the Dabaoshan mine were collected. The physical and chemical properties of the soil, including Cu, Cd, Zn, Pb, and Ni levels in the soil and rice, were analyzed. The heavy-metal baseline in paddy soil was analyzed by a normal Q−Q plot. The bioaccumulation factor of the rice was calculated. The non-carcinogenic risk of heavy metals was evaluated by calculating the hazard quotient (HQ). Threshold values of Cu, Cd, Zn, Pb, and Ni were 35.01, 0.51, 70.94, 59.78, and 16.34 mg/kg, respectively. The threshold values of Cu, Zn, and Pb were higher than the background value and lower than the secondary value of China’s soil environmental quality standard. The threshold value of Cd was higher than both the background valu... [more]
Research on a Photovoltaic Power Prediction Model Based on an IAO-LSTM Optimization Algorithm
Liqun Liu, Yang Li
August 3, 2023 (v1)
Keywords: Aquila optimization algorithm, neural networks, PV power prediction
With the rapid popularization and development of renewable energy, solar photovoltaic power generation systems have become an important energy choice. Convolutional neural network (CNN) models have been widely used in photovoltaic power forecasting, with research focused on problems such as long training times, forecasting accuracy and insufficient speed, etc. Using the advantages of swarm intelligence algorithms such as global optimization, strong adaptability and fast convergence, the improved Aquila optimization algorithm (AO) is used to optimize the structure of neural networks, and the optimal solution is chosen as the structure of neural networks used for subsequent prediction. However, its performance in processing sequence data with time characteristics is not good, so this paper introduces a Long Short-Term Memory (LSTM) neural network which has obvious advantages in time-series analysis. The Cauchy variational strategy is used to improve the model, and then the improved Aquil... [more]
Supercritical CO2 Extraction of Seed Oil from Psophocarpus tetragonolobus (L.) DC.: Optimization of Operating Conditions through Response Surface Methodology and Probabilistic Neural Network
Padej Pao-la-or, Boonruang Marungsri, Kakanang Posridee, Ratchadaporn Oonsivilai, Anant Oonsivilai
August 3, 2023 (v1)
Keywords: oleic acid, probabilistic neural network, Psophocarpus tetragonolobus (L.) DC., response surface methodology, supercritical fluid extraction
For the treatment of menopausal symptoms, nutraceuticals and herbal remedies are thought to be more natural and safer than hormones. Attention has been paid to the winged bean (Psophocarpus tetragonolobus (L.)) DC. seed oil. They are constituted of phytosterols, which may be effective in preventing menopausal symptoms. The purpose was to determine the optimal conditions for supercritical fluid extraction of oleic-rich oil from winged bean seeds. To optimize the condition, the response surface methodology (RSM) and probabilistic neural network (PNN) were utilized. In this research, PNN was used to improve RSM estimation by reducing the number of calculations. The optimized extraction conditions for winged bean seed oil entailed a CO2 flow rate of 21.3 L/h, a pressure of 30 MPa, a temperature of 55 °C, and an extraction time of 90 min. Under these conditions, the extraction process yielded a maximum oil yield of 36.27%. Ultimately, winged bean seed oil included a greater proportion of un... [more]
Droplet Based Estimation of Viscosity of Water−PVP Solutions Using Convolutional Neural Networks
Mohamed Azouz Mrad, Kristof Csorba, Dorián László Galata, Zsombor Kristóf Nagy, Hassan Charaf
August 2, 2023 (v1)
Keywords: convolutional neural networks, Polyvinylpyrrolidone, viscosity, viscosity estimation, water–PVP
The viscosity of a liquid is the property that measures the liquid’s internal resistance to flow. Monitoring viscosity is a vital component of quality control in several industrial fields, including chemical, pharmaceutical, food, and energy-related industries. In many industries, the most commonly used instrument for measuring viscosity is capillary viscometers, but their cost and complexity pose challenges for these industries where accurate and real-time viscosity information is vital. In this work, we prepared fourteen solutions with different water and PVP (Polyvinylpyrrolidone) ratios, measured their different viscosity values, and produced videos of their droplets. We extracted the images of the fully developed droplets from the videos and we used the images to train a convolutional neural network model to estimate the viscosity values of the water−PVP solutions. The proposed model was able to accurately estimate the viscosity values of samples of unseen chemical formulations wi... [more]
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