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Records with Subject: Numerical Methods and Statistics
Showing records 26 to 50 of 2073. [First] Page: 1 2 3 4 5 6 Last
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]
Synthesis, Molecular Docking, Molecular Dynamics Studies, and In Vitro Biological Evaluation of New Biofunctional Ketoprofen Derivatives with Different N-Containing Heterocycles
Stanimir Manolov, Dimitar Bojilov, Iliyan Ivanov, Gabriel Marc, Nadezhda Bataklieva, Smaranda Oniga, Ovidiu Oniga, Paraskev Nedialkov
July 13, 2023 (v1)
Keywords: 1,2,3,4-tetrahydroisoquinoline, 1,2,3,4-tetrahydroquinoline, hybrid molecules, in vitro biological activity, ketoprofen, molecular docking, molecular dynamics, piperidine, pyrrolidine
Herein, we report the synthesis of four new hybrid molecules between ketoprofen or 2-(3-benzoylphenyl)propanoic acid and N-containing heterocyclic compounds, such as piperidine, pyrrolidine, 1,2,3,4-tetrahydroquinoline, and 1,2,3,4-tetrahydroisoquinoline. The obtained hybrid compounds were fully characterized using 1H- and 13C-NMR, UV-Vis, and HRMS spectra. Detailed HRMS analysis is provided for all novel hybrid molecules. The compounds were assessed for their in vitro anti-inflammatory and antioxidant activity. The lipophilicity of the hybrids was determined, both theoretically (cLogP) and experimentally (RM). The affinity of the compounds to the human serum albumin was assessed in silico by molecular docking study using two software, and the stability of the predicted complexes was evaluated by molecular dynamics study. All novel hybrids have shown very good HPSA activity, statistically close when compared to the reference—quercetin. The molecular docking confirmed the obtained in vi... [more]
A Novel Energy-Intensity Model Based on Time Scale for Quasi-Continuous Production in Iron and Steel Industry
Biao Lu, Yongkang Hao, Hao Wang, Demin Chen, Xingyin Wang, Ning Li
July 13, 2023 (v1)
Keywords: energy-intensity model, quasi-continuous production, time scale, workpiece valid weight
Energy intensity is an important assessment indicator of energy consumption. Unfortunately, the traditional energy intensity model (TEIM) has obvious limitations when applied to quasi- continuous production process, especially for small time scales (STS). Therefore, a novel energy intensity model (NEIM) has been established in this study. The NEIM includes three main stages. Firstly, the statistical period and time scale have been determined. Secondly, the concept of workpiece valid weight has been proposed for a given time scale. Then the specific calculation method has also been established. Thirdly, a NEIM has been suggested according to the definition of energy intensity. The application results for a reheating furnace show that the NEIM’s effectiveness has been verified via comparison with the TEIM for large time scale (LTS) and critical time scale (CTS), whereas the NEIM still has validity at STS. Additionally, calculation results for the NEIM reflect more trend information at LT... [more]
Optimization of Levenberg Marquardt Algorithm Applied to Nonlinear Systems
Xinyi Huang, Hao Cao, Bingjing Jia
July 7, 2023 (v1)
Keywords: algorithm optimization, BP neural network, CSTR control system, LM algorithm, nonlinear systems
As science and technology advance, industrial manufacturing processes get more complicated. Back Propagation Neural Network (BPNN) convergence is comparatively slower for processing nonlinear systems. The nonlinear system used in this study to evaluate the optimization of BPNN based on the LM algorithm proved the algorithm’s efficacy through a MATLAB simulation analysis. This paper examined the application impact of the enhanced approach using the Continuous stirred tank reactor (CSTR) control system as an example. The study’s findings demonstrate that the LM optimization algorithm’s identification error exceeds 10-5. The research’s suggested control approach for reactant concentration CA in CSTR systems provides a better tracking effect and a stronger anti-interference capacity. Compared to the PI control method, the overall control effect is superior. As a result, the optimization model for nonlinear systems has a greatly improved processing accuracy. With some data support for the a... [more]
Numerical Experiments on Performance Comparisons of Conical Type Direct-Acting Relief Valve—With or without Conical Angle in Valve Element and Valve Seat
Huiyong Liu, Qing Zhao
July 7, 2023 (v1)
Keywords: AMESIM, CTDARV, performance comparisons
This paper conducts numerical experiments on performance comparisons of CTDARV—with or without conical angle in the valve element and valve seat. The working principles of three kinds of CTDARV are introduced. The simulation models of three kinds of CTDARV are established by utilizing AMESIM. Numerical experiments on CTDARV, with or without a conical angle in the valve element and the valve seat, are conducted and the performance comparisons of three kinds of CTDARV are obtained. The results show that: (1) When all parameters of VED, VSD, VEM, SS, CAVE&CAVS, and OD have the same value, respectively, CA-VE has the highest stable pressure, CA-VE&VS has the highest stable displacement, CA-VS has the lowest stable pressure, and CA-VE has the lowest stable displacement. The stable pressure of CA-VE is significantly higher than that of CA-VS and CA-VE&VS. The stable displacement of CA-VE&VS is significantly higher than that of CA-VE and CA-VS, and the stable displacement of CA-VE and CA-VS h... [more]
Data-Driven Synthesis of a Geometallurgical Model for a Copper Deposit
Yuyang Mu, Juan Carlos Salas
July 7, 2023 (v1)
Keywords: cluster analysis, copper deposit, geometallurgy, Machine Learning, unsupervised learning
Geometallurgy integrates aspects of geology, metallurgy, and mine planning in order to improve decision making in mining schedules. A geometallurgical model is a 3D space that is typically synthesized from early-stage small-scale samples and is composed of several metallurgical units, or domains. This work explores the synthesis of a geometallurgical model for a copper deposit using a purely data-driven unsupervised approach. To this end, a dataset of 1112 drill samples is used, which are clustered using different methods, namely, k-means, hierarchical clustering (AGG), self-organizing maps (SOM), and DBSCAN. Two cluster validity indices (Silhouette and Calinski−Harabasz) are used to select the final model. To validate the potential of the proposed approach, a simulated economic evaluation is conducted. Results demonstrate that k-means exhibits a better performance in terms of modeling and that using the obtained geometallurgical model for mining scheduling increases the project’s Net... [more]
An Artificial Intelligence Method for Flowback Control of Hydraulic Fracturing Fluid in Oil and Gas Wells
Ruixuan Li, Hangxin Wei, Jingyuan Wang, Bo Li, Xue Zheng, Wei Bai
July 7, 2023 (v1)
Keywords: Artificial Intelligence, deep learning neural network, hydraulic fracture, process control
Hydraulic fracturing is one of the main ways to increase oil and gas production. However, with existing methods, the diameter of the nozzle cannot be easily adjusted. This therefore results in ‘sand production’ in flowback fluid, affecting the application of hydraulic fracturing. This is because it is difficult to identify the one-dimensional series signal of fracturing fluid collected on site. In order to avoid ‘sand production’ in the flowback fluid, the nozzle should be properly controlled. Aiming to address this problem, a novel augmented residual deep learning neural network (AU-RES) is proposed that can identify the characteristics of multiple one-dimensional time series signals and effectively predict the diameter of the nozzle. The AU-RES network includes three parts: signal conversion layer, residual and convolutional layer, fully connected layer (including regression layer). Firstly, a spatial conversion algorithm for multiple one-dimensional time series signals is proposed,... [more]
A Novel Hybrid Approach for Modeling and Optimisation of Phosphoric Acid Production through the Integration of AspenTech, SciLab Unit Operation, Artificial Neural Networks and Genetic Algorithm
Marko Pavlović, Jelena Lubura, Lato Pezo, Milada Pezo, Oskar Bera, Predrag Kojić
July 7, 2023 (v1)
Keywords: artificial neural network, Aspen, Genetic Algorithm, multi-objective optimization, phosphoric acid, UCEGO filter
The purpose of the study was to identify and predict the optimized parameters for phosphoric acid production. This involved modeling the crystal reactor, UCEGO filter (as a detailed model of the filter is not available in Aspen Plus or other simulation software), and acid separator using Sci-Lab to develop Cape-Open models. The simulation was conducted using Aspen Plus and involved analyzing 10 different phosphates with varying qualities and fractions of P2O5 and other minerals. After a successful simulation, a sensitivity analysis was conducted by varying parameters such as capacity, filter speed, vacuum, particle size, water temperature for washing the filtration cake, flow of recycled acid and strong acid from the separator below the filter, flow of slurry to reactor 1, temperature in reactors, and flow of H2SO4, resulting in nearly one million combinations. To create an algorithm for predicting process parameters and the maximal extent of recovering H3PO4 from slurry, ANN models we... [more]
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