Records with Subject: Numerical Methods and Statistics
Showing records 1 to 25 of 2073. [First] Page: 1 2 3 4 5 Last
Discrete Meta-Simulation of Silage Based on RSM and GA-BP-GA Optimization Parameter Calibration
Gonghao Li, Juan Ma, Xiang Tian, Chao Zhao, Shiguan An, Rui Guo, Bin Feng, Jie Zhang
February 10, 2024 (v1)
Keywords: BP neural network, discrete element method, Genetic Algorithm, parameter calibration, response surface method, silage
The EDEM software (Altair EDEM 2022.0 professional version 8.0.0) was used to create a discrete element model of silage to address the lack of silage evidence parameters and contact parameters between silage and conveying equipment when using the discrete element method to simulate and analyze crucial aspects of silage conveying and feeding. Physical tests and simulations were used to calibrate the significant parameters, and the silage stacking angle obtained from simulation and tests was then validated. The response value of the stacking angle (38.65°) obtained from the physical examination was used as the response value. The response surface (RSM) finding and the GA finding based on the genetic algorithm (GA) artificial neural network (BP) model were used to compare the significance parameters. The PB and steepest climb tests were used to screen the significant factors. Results indicate that the static friction coefficient between silage and silage, the rolling friction coefficient... [more]
Advancing Fault Prediction: A Comparative Study between LSTM and Spiking Neural Networks
Rute Souza de Abreu, Ivanovitch Silva, Yuri Thomas Nunes, Renan C. Moioli, Luiz Affonso Guedes
February 10, 2024 (v1)
Keywords: generalized stochastic Petri net (GSPN), industrial processes, LSTM networks, spiking neural networks (SNNs), system fault prediction
Predicting system faults is critical to improving productivity, reducing costs, and enforcing safety in industrial processes. Yet, traditional methodologies frequently falter due to the intricate nature of the task. This research presents a novel use of spiking neural networks (SNNs) in anticipating faults in syntactical time series, utilizing the generalized stochastic Petri net (GSPN) model. The inherent ability of SNNs to process both time and space aspects of data positions them as a prime instrument for this endeavor. A comparative evaluation with long short-term memory (LSTM) networks suggests that SNNs offer comparable robustness and performance.
Statistical Process Control Using Control Charts with Variable Parameters
Tadeusz Sałaciński, Jarosław Chrzanowski, Tomasz Chmielewski
February 10, 2024 (v1)
Keywords: control charts, process stability, statistical process control (SPC)
An extremely important issue in quality management is monitoring and diagnosing processes, and, subsequently, supervising them using so-called control charts. In typical production processes, charts with constant parameters are commonly used, such as x-R, x-s, CUSUM, EWMA and others, which, in most cases, are effective tools for process stability evaluation. Charts considered untypical (in statistical process control) are those with variable sample sizes, variable sampling intervals and/or variable control limits. Such charts are used when process analysis based on standard, well-known charts may lead to serious errors. Modern control charts are a response to the requirements of Industry 4.0 and are an excellent tool for supervising production processes. Their use together with Cp and Cpk indices and other process capability indices is a starting point for process improvement. The methodology of nonstandard charts is inadequately recognized and rarely used in practice. The theory of th... [more]
An Efficient and Accurate Approach to Electrical Boundary Layer Nanofluid Flow Simulation: A Use of Artificial Intelligence
Amani S. Baazeem, Muhammad Shoaib Arif, Kamaleldin Abodayeh
February 10, 2024 (v1)
Keywords: boundary layer flow, consistency, explicit scheme, neural network, stability
Engineering and technological research groups are becoming interested in neural network techniques to improve productivity, business strategies, and societal development. In this paper, an explicit numerical scheme is given for both linear and nonlinear differential equations. The scheme is correct to second order. Additionally, the scheme’s consistency and stability are guaranteed. Backpropagation of Levenberg−Marquardt, the effect of including an induced magnetic field in a mathematical model for electrical boundary layer nanofluid flow on a flat plate, is quantitatively investigated using artificial neural networks. Later, the model is reduced into a set of boundary value problems, which are then resolved using the suggested scheme and a shooting strategy. The outcomes are also contrasted with earlier studies and the MATLAB solver bvp4c for validation purposes. In addition, neural networking is also employed for mapping input to outputs for velocity, temperature, and concentration p... [more]
Methods of Partial Differential Equation Discovery: Application to Experimental Data on Heat Transfer Problem
Tatiana A. Andreeva, Nikolay Y. Bykov, Yakov A. Gataulin, Alexander A. Hvatov, Alexandra K. Klimova, Alexander Ya. Lukin, Mikhail A. Maslyaev
February 10, 2024 (v1)
Keywords: best subset selection, data-driven models, discovering partial differential equations, genetic evolutionary algorithm, heat transfer equation, inverse problems, submerged horizontal cylindrical heat source, viscous liquid convection
The paper presents two effective methods for discovering process models in the form of partial differential equations based on an evolutionary algorithm and an algorithm for the best subset selection. The methods are designed to work with sparse and noisy data and implement various numerical differentiation techniques, including piecewise local approximation using multidimensional polynomial functions, neural network approximation, and an additional algorithm for selecting differentiation steps. To verify the algorithms, the experiment is carried out on pulsed heating of a viscous liquid (glycerol) by a submerged horizontal cylindrical heat source. Temperature measurements are taken only at six points, which makes the data very sparse. The noise level ranges from 0.2 to 1% of the observed maximum temperature. The algorithms can successfully restore the structure of the heat transfer equation in cylindrical coordinates and determine the thermal diffusivity coefficient with an error of 2... [more]
Response Surface Methodology (RSM) Optimization of Pulsed Electric Field (PEF) Pasteurization Process of Milk-Date Beverage
Mahmoud Younis, Khaled A. Ahmed, Isam A. Mohamed Ahmed, Hany M. Yehia, Diaeldin O. Abdelkarim, Abdulla Alhamdan, Ahmed Elfeky, Mansour N. Ibrahim
January 12, 2024 (v1)
Keywords: date powder, pulsed electric field, response surface methodology, total viable count
Milk beverage with added natural sweetener is well appreciated by consumers as a nutritious and healthy product with unique sensorial quality attributes. However, this product requires a suitable pasteurization method without significant impact on the sensorial and physicochemical quality characteristics of the product. This study optimizes the pulsed electric filed (PEF) conditions for the pasteurization of a milk-date beverage with conserved physicochemical quality properties. The effect of process variables, such as pulse off time (20, 30, and 40 μs), number of pulses (20, 50, and 80), powder ratio (10, 15, 20, and 25% w/w), storage time (2, 4, and 6 days), and storage temperature (5, 15, and 25 °C) on the responses of total viable count (TVC), color difference (∆E), pH, and total soluble solids (TSS) was evaluated using the RSM central composite design (CCD). Pulse off time, number of pulses, date powder/milk ratio (w/w), storage time, and storage temperature greatly impacted the m... [more]
Fast Prediction of the Temperature Field Surrounding a Hot Oil Pipe Using the POD-BP Model
Feng Yan, Kaituo Jiao, Chaofei Nie, Dongxu Han, Qifu Li, Yujie Chen
January 12, 2024 (v1)
Keywords: BP neural network, hot oil pipe, POD prediction, temperature field
The heat transfer assessment of a buried hot oil pipe is essential for the economical and safe transportation of the pipeline, where the basis is to determine the temperature field surrounding the pipe quickly. This work proposes a novel method to efficiently predict the temperature field surrounding a hot oil pipe, which combines the proper orthogonal decomposition (POD) method and the backpropagation (BP) neural network, named the POD-BP model. Specifically, the BP neural network is used to establish the mapping relationship between spectrum coefficients and the preset parameters of the sample. Compared with the classical POD reduced-order model, the POD-BP model avoids solving the system of reduced-order governing equations with spectrum coefficients as variables, thus improving the prediction speed. Another advantage is that it is easy to implement and does not require tremendous mathematical derivation of reduced-order governing equations. The POD-BP model is then used to predict... [more]
Optimizing the Control of the Hydraulic Driving System for the Power Shift Gearbox of a Cotton Picker Based on Dual Working Conditions
Yuangang Lin, Jingan Feng, Pengda Zhao, Xiangdong Ni, Huajun Chen, Haoyun Ye, Yongqiang Zhao, Wenlong Pan, Bao Song
January 12, 2024 (v1)
Keywords: automatic control, BP neural network control, cotton picker, power shifting, Statechart logic control
In response to the issues of slow dynamic response, uneven shifting, and strong jolting during the starting and shifting operations of the cotton picker, we established a model for automatic power shifting control. We proposed optimization strategies using the Statechart logic control method and BP neural network control method. Different control effects were analyzed concerning pressure, flow rate, motor speed, vehicle speed, impact degree, and slip-grinding work. The results showed that the Statechart logic control method increased the response time of the flow rate by 46.67% during the starting process, with a good linear characteristic during the variation. It reduced the impact during starting and shifting by 38.57% and 67%, and the sliding friction power during starting and shifting by 51.95% and 33.33% respectively. The BP neural network control method reduced the pressure overshoot during starting and shifting by 25% and 30.77%, respectively, demonstrating better robustness. Th... [more]
CLAP: Gas Saturation Prediction in Shale Gas Reservoir Using a Cascaded Convolutional Neural Network−Long Short-Term Memory Model with Attention Mechanism
Xuefeng Yang, Chenglin Zhang, Shengxian Zhao, Tianqi Zhou, Deliang Zhang, Zhensheng Shi, Shaojun Liu, Rui Jiang, Meixuan Yin, Gaoxiang Wang, Yan Zhang
January 12, 2024 (v1)
Keywords: attention mechanism, CNN, deep learning, gas saturation, LSTM
Gas saturation prediction is a crucial area of research regarding shale gas reservoirs, as it plays a vital role in optimizing development strategies and improving the efficiency of exploration efforts. Despite the advancements in deep learning techniques, accurately modeling the complex nonlinear relationships involved in gas saturation prediction remains a challenge. To address this issue, we propose a novel cascaded model, CLAP, combining convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) with an attention mechanism. It effectively captures and visualizes the intricate nonlinear relationships, enabling accurate gas saturation prediction in shale gas reservoirs. In this study, nine logging curves from 27 shale gas wells in the Changning area of the Sichuan Basin were used to train the CLAP model for predicting the gas saturation of the Wufeng-Longmaxi Formation shale. Compared to the Archie and random forest models, the CLAP model exhibited enhanced accuracy in pr... [more]
Prediction of Leakage Pressure during a Drilling Process Based on SSA-LSTM
Dong Chen, Baolun He, Yanshu Wang, Chao Han, Yucong Wang, Yuqiang Xu
January 12, 2024 (v1)
Keywords: during the drilling process, leakage pressure, mechanism model, SSA-LSTM
Drilling-fluid loss has always been one of the challenging issues in the field of drilling engineering. This article addresses the limitations of a single fluid-loss pressure mechanism model and the challenges in predicting positive drilling-fluid-loss pressure. By categorizing fluid losses of various types encountered during drilling, different geological formations associated with distinct mechanisms are considered. The actual drilling-fluid density in the wellbore at the time of fluid-loss occurrence is taken as a reference value for calculating the positive drilling-fluid-loss pressure of the already drilled well. Building upon this foundation, a combined model utilizing the Sparrow Search Algorithm (SSA) and Long Short-Term Memory (LSTM) neural network is constructed. This model effectively explores the intricate nonlinear relationship between well logging, logging engineering data, and fluid-loss pressure. By utilizing both data from the already drilled wells and upper formation... [more]
Prediction Model of Fouling Thickness of Heat Exchanger Based on TA-LSTM Structure
Jun Wang, Lun Sun, Heng Li, Ruoxi Ding, Ning Chen
November 30, 2023 (v1)
Keywords: attention mechanism, deep learning, heat exchanger fouling, neural network, two-layer LSTM
Heat exchangers in operation often experience scaling, which can lead to a decrease in heat exchange efficiency and even safety accidents when fouling accumulates to a certain thickness. To address this issue, manual intervention is currently employed to monitor fouling thickness in advance. In this study, we propose a two-layer LSTM neural network model with an attention mechanism to effectively learn fouling thickness data under different working conditions. The model accurately predicts the scaling thickness of the heat exchanger during operation, enabling timely human intervention and ensuring that the scaling remains within a safe range. The experimental results demonstrate that our proposed neural network model (TA-LSTM) outperforms both the traditional BP neural network model and the LSTM neural network model in terms of accuracy and stability. Our findings provide valuable technical support for future research on heat exchanger descaling and fouling growth detection.
Photocatalytic Degradation of Neonicotinoid Insecticides over Perlite-Supported TiO2
Vanja Kosar, Ana-Marija Križanac, Ivana Elizabeta Zelić, Stanislav Kurajica, Vesna Tomašić
November 30, 2023 (v1)
Keywords: acetamiprid, design of experiments (DoE), immobilization, perlite-based TiO2, photocatalytic degradation, response surface methodology (RSM)
The aim of this study was to investigate the photocatalytic degradation of the neonicotinoid insecticide acetamiprid in aqueous solution. Experiments were carried out in a 250 mL batch reactor with recirculation of the reaction mixture and using a UVA-LED radiation source with a heterogeneous UVC-modified perlite-based TiO2 photocatalyst. The photocatalytic degradation of acetamiprid was optimized using a Box−Behnken design (BBD) of the response surface methodology (RSM). The variables in the process optimization were catalyst type, volume of the reaction mixture, and light radiation intensity. From the experimental data obtained, the conversions of the photocatalytic reactions, the reaction rate constants, and the mean square deviations were calculated. The experimental results have shown that the conversion of the reaction is significantly affected by the type of catalyst, i.e., the method used to immobilise the photocatalytic layer on the perlite granules. The highest conversions of... [more]
A Convolutional Fuzzy Neural Network Active Noise Cancellation Approach without Error Sensors for Autonomous Rail Rapid Transit
Tao Li, Yuyao He, Minqi Wang, Kaihui Zhao, Ning Wang, Weihua Gui, Jianghua Feng, Jun Yang
November 30, 2023 (v1)
Keywords: active noise cancellation, autonomous rail rapid transit, convolutional fuzzy neural network, error sensors
Autonomous rail rapid transit (ART) is a new type of multiunit, articulated, rubber-wheeled urban transport system. The noise sources of ART have significant time-varying characteristics. It is unsuitable to track the error signal by installing too many error sensors, which poses a significant challenge in the active noise control of ART. Thus, this paper proposes a convolutional fuzzy neural network-based active noise cancellation approach without error sensors to solve this problem. The proposed approach utilizes convolutional neural network (CNN) to extract the noise signal characteristics of ART and trains multiple noise source signals using a CNN to estimate the virtual error signal in the target area. In addition, the proposed approach adopts fuzzy neural network (FNN) for adaptive adjustment of filter weight coefficients to achieve real-time noise tracking control with fast convergence and small error in the convergence process. The experimental results demonstrate that the prop... [more]
A Novel Hybrid Optimization Approach for Fault Detection in Photovoltaic Arrays and Inverters Using AI and Statistical Learning Techniques: A Focus on Sustainable Environment
Ahmad Abubakar, Mahmud M. Jibril, Carlos F. M. Almeida, Matheus Gemignani, Mukhtar N. Yahya, Sani I. Abba
November 30, 2023 (v1)
Keywords: Artificial Intelligence, boosted tree algorithms, Elman neural network, Fault Detection, Gaussian processes regression, multi-layer perceptron, sustainable development
Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman neural network (ENN), boosted tree algorithms (BTA), multi-layer perceptron (MLP), and Gaussian processes regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages its strengths for the accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction. The fault detection and diagnosis were assessed using several statistical criteria includi... [more]
Numerical Study on the Separation Performance of Hydrocyclones with Different Secondary Cylindrical Section Diameters
Duanxu Hou, Peikun Liu, Qiang Zhao, Lanyue Jiang, Baoyu Cui, Dezhou Wei
November 30, 2023 (v1)
Keywords: hydrocyclone, particle circulation flow, secondary-cylindrical section, separation performance
The particle motion behavior in hydrocyclones has received increasing attention, but the particle circulation flow has received relatively limited attention. In this paper, the particle circulation flow is regulated by changing the secondary-cylindrical section diameter to optimize the separation effect. The effects of secondary-cylindrical section diameters on flow field characteristics and separation performance are explored using the two-fluid model (TFM). The findings demonstrate that particle circulation flows are ubiquitous in the secondary-cylindrical hydrocyclone and are induced by the axial velocity wave zone. The increase in the secondary-cylindrical section diameter intensifies the coarse particle circulation and aggrandizes the coarse particle’s aggregation degree and aggregation region, leading to an increment in cut size. The circulation flow component can be regulated by adjusting the secondary-cylindrical section, thus improving the classification effect. An appropriate... [more]
A Fault Warning Approach Using an Enhanced Sand Cat Swarm Optimization Algorithm and a Generalized Neural Network
Youchun Pi, Yun Tan, Amir-Mohammad Golmohammadi, Yujing Guo, Yanfeng Xiao, Yan Chen
November 30, 2023 (v1)
Keywords: Fault Detection, generalized neural network, industrial systems, Machine Learning, sand cat swarm optimization
With the continuous development and complexity of industrial systems, various types of industrial equipment and systems face increasing risks of failure during operation. Important to these systems is fault warning technology, which can timely detect anomalies before failures and take corresponding preventive measures, thereby reducing production interruptions and maintenance costs, improving production efficiency, and enhancing equipment reliability. Machine learning techniques have proven highly effective for fault detection in modern production processes. Among numerous machine learning algorithms, the generalized neural network stands out due to its simplicity, effectiveness, and applicability to various fault warning scenarios. However, the increasing complexity of systems and equipment presents significant challenges to the generalized neural network. In real-world scenarios, it suffers from drawbacks such as difficulties in determining parameters and getting trapped in local opt... [more]
Innovative Method for Determining Young’s Modulus of Elasticity in Products with Irregular Shapes: Application on Peanuts
Joelle Nader, Jean Claude Assaf, Espérance Debs, Nicolas Louka
November 30, 2023 (v1)
Keywords: Hooke’s theory, peanuts, response surface methodology, stress-strain, Young’s modulus of elasticity
Accurate determination of Young’s modulus of elasticity in irregularly shaped products is quite challenging. This study introduces a novel method that can measure the elasticity in non-uniform products, such as peanuts. Variations of the contact surface between the peanut and a crosshead were precisely calculated using this technique based on kernels blueprints remaining on graph paper after compression. The elastic modulus was assessed by stress-strain tests using Hooke’s theory. The significance of the effects of water content and loading rate on the elastic modulus of peanuts was studied using the Response Surface Methodology (RSM). Results showed that the elasticity was mostly influenced by the kernel’s water content. It decreased from 3.75 to 0.10 MPa when the initial water content increased from 7 to 18% (dry basis). Water content had a significant effect on Young’s modulus (p < 0.05) at 95% confidence level with a correlation coefficient (R2) of 95.52%. Conversely, the effect... [more]
Extended Runge-Kutta Scheme and Neural Network Approach for SEIR Epidemic Model with Convex Incidence Rate
Ahmed A. Al Ghafli, Yasir Nawaz, Hassan J. Al Salman, Muavia Mansoor
November 30, 2023 (v1)
Keywords: consistency, neural network, SEIR model, stability, two-stage scheme
For solving first-order linear and nonlinear differential equations, a new two-stage implicit−explicit approach is given. The scheme’s first stage, or predictor stage, is implicit, while the scheme’s second stage is explicit. The first stage of the proposed scheme is an extended form of the existing Runge−Kutta scheme. The scheme’s stability and consistency are also offered. In two phases, the technique achieves third-order accuracy. The method is applied to the SEIR epidemic model with a convex incidence rate. The local stability is also examined. The technique is evaluated compared to existing Euler and nonstandard finite difference methods. In terms of accuracy, the produced plots show that the suggested scheme outperforms the existing Euler and nonstandard finite difference methods. Furthermore, a neural network technique is being considered to map the relationship between time and the amount of susceptible, exposed, and infected people.
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]
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