scholarly journals Simple and accurate calculation model of viscosity for supercritical CO2

2021 ◽  
Vol 2076 (1) ◽  
pp. 012030
Author(s):  
Jia Deng ◽  
Guangjie Zhao ◽  
Lan Zhang ◽  
Huizhong Ma ◽  
Fuquan Song ◽  
...  
Keyword(s):  
Empirical Formula ◽  
Supercritical Co2 ◽  
Pressure Range ◽  
Absolute Error ◽  
Engineering Calculation ◽  
High Accuracy ◽  
Calculation Model ◽  
Average Absolute Error ◽  
Accurate Calculation ◽  
Utilization Of Technology

Abstract The high-precision calculation method suitable for the viscosity of supercritical CO2 is reported. The modified LBC model, modified DS model and Ehsan empirical formula are selected to calculate viscosity. The error analysis result shows that the average absolute error of the modified DS model is the lowest, which can be applied to the engineering calculation. The LBC model is suitable at 7.5–25MPa and the Ehsan empirical formula has high accuracy at 25–60MPa. Then the viscosity plate is drawn at temperature range of 310–800K and pressure range of 7.5–60MPa, which can be applied in carbon dioxide resources recovery and utilization of technology.

Simulation on Torpedo Unsteady Hydrodynamic with the Movement in the Longitudinal Plane

2013 ◽  
Vol 791-793 ◽  
pp. 1073-1076
Author(s):  
Ming Yang ◽  
Shi Ping Zhao ◽  
Han Ping Wang ◽  
Lin Peng Wang ◽  
Shao Zhu Wang
Keyword(s):  
Three Dimensional ◽  
Engineering Application ◽  
Calculation Model ◽  
Trajectory Design ◽  
Design Calculation ◽  
Variable Speed ◽  
Accurate Calculation ◽  
Longitudinal Plane ◽  
Submerged Body ◽  
Mesh Method

The unsteady hydrodynamic accurate calculation is the premise of submerged body trajectory design and maneuverability design. Calculation model of submerged body unsteady hydrodynamic with the movement in the longitudinal plane was established, which based on unsteady three-dimensional incompressible fluid dynamics theory. Variable speed translational and variable angular velocity of the pitching motion in the longitudinal plane of submerged body was achieved by dynamic mesh method. The unsteady hydrodynamic could be obtained by model under the premise of good quality grid by the results. Modeling methods can learn from other similar problems, which has engineering application value.

scholarly journals Hidden Semi-Markov Models for Predictive Maintenance

2015 ◽  
Vol 2015 ◽  
pp. 1-23 ◽  
Author(s):  
Francesco Cartella ◽  
Jan Lemeire ◽  
Luca Dimiccoli ◽  
Hichem Sahli
Keyword(s):  
Markov Models ◽  
Real Data ◽  
Information Criterion ◽  
Absolute Error ◽  
Predictive Maintenance ◽  
Average Absolute Error ◽  
Current State ◽  
Automatic Model Selection ◽  
State Duration ◽  
Useful Lifetime

Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs) with (i) no constraints on the state duration density function and (ii) being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL) of the machine is calculated.

scholarly journals Artificial Neural Network (FFBP-ANN) Based Grey Relational Analysis for Modeling Dyestuff Solubility in Supercritical CO2 with Ethanol as the Co-Solvent

2021 ◽  
Author(s):  
FNU SRINIDHI
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Mean Square Error ◽  
Supercritical Co2 ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Mean Square ◽  
Relational Analysis ◽  
Grey Relational ◽  
Supercritical Carbon

The research on dye solubility modeling in supercritical carbon dioxide is gaining prominence over the past few decades. A simple and ubiquitous model that is capable of accurately predicting the solubility in supercritical carbon dioxide would be invaluable for industrial and research applications. In this study, we present such a model for predicting dye solubility in supercritical carbon dioxide with ethanol as the co-solvent for a qualitatively diverse sample of eight dyes. A feed forward back propagation - artificial neural network model based on Levenberg-Marquardt algorithm was constructed with seven input parameters for solubility prediction, the network architecture was optimized to be [7-7-1] with mean absolute error, mean square error, root mean square error and Nash-Sutcliffe coefficient to be 0.026, 0.0016, 0.04 and 0.9588 respectively. Further, Pearson-product moment correlation analysis was performed to assess the relative importance of the parameters considered in the ANN model. A total of twelve prevalent semiempirical equations were also studied to analyze their efficiency in correlating to the solubility of the prepared sample. Mendez-Teja model was found to be relatively efficient with root mean square error and mean absolute error to be 0.094 and 0.0088 respectively. Furthermore, Grey relational analysis was performed and the optimum regime of temperature and pressure were identified with dye solubility as the higher the better performance characteristic. Finally, the dye specific crossover ranges were identified by analysis of isotherms and a strategy for class specific selective dye extraction using supercritical CO2 extraction process is proposed.

scholarly journals Study of a Privacy Preserving Logistic Regression Algorithm (PPLRA) For Data Privacy in the Context of Big Data

2021 ◽  
Vol 2083 (3) ◽  
pp. 032059
Author(s):  
Qiang Chen ◽  
Meiling Deng
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Privacy Protection ◽  
Data Privacy ◽  
Absolute Error ◽  
Average Absolute Error ◽  
Regression Algorithms ◽  
Hadoop Platform ◽  
Logistic Regression Algorithm ◽  
Computing Speed

Abstract Regression algorithms are commonly used in machine learning. Based on encryption and privacy protection methods, the current key hot technology regression algorithm and the same encryption technology are studied. This paper proposes a PPLAR based algorithm. The correlation between data items is obtained by logistic regression formula. The algorithm is distributed and parallelized on Hadoop platform to improve the computing speed of the cluster while ensuring the average absolute error of the algorithm.

scholarly journals Short-Term Traffic Flow Forecasting Model Based on GA-TCN

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rongji Zhang ◽  
Feng Sun ◽  
Ziwen Song ◽  
Xiaolin Wang ◽  
Yingcui Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Traffic Flow ◽  
Absolute Error ◽  
Forecasting Model ◽  
Average Absolute Error ◽  
Short Term ◽  
Traffic Flow Forecasting ◽  
Fitness Value

Traffic flow forecasting is the key to an intelligent transportation system (ITS). Currently, the short-term traffic flow forecasting methods based on deep learning need to be further improved in terms of accuracy and computational efficiency. Therefore, a short-term traffic flow forecasting model GA-TCN based on genetic algorithm (GA) optimized time convolutional neural network (TCN) is proposed in this paper. The prediction error was considered as the fitness value and the genetic algorithm was used to optimize the filters, kernel size, batch size, and dilations hyperparameters of the temporal convolutional neural network to determine the optimal fitness prediction model. Finally, the model was tested using the public dataset PEMS. The results showed that the average absolute error of the proposed GA-TCN decreased by 34.09%, 22.42%, and 26.33% compared with LSTM, GRU, and TCN in working days, while the average absolute error of the GA-TCN decreased by 24.42%, 2.33%, and 3.92% in weekend days, respectively. The results indicate that the model proposed in this paper has a better adaptability and higher prediction accuracy in short-term traffic flow forecasting compared with the existing models. The proposed model can provide important support for the formulation of a dynamic traffic control scheme.

Design and Simulation of Portable Data Terminal for Agriculture Equipment

2013 ◽  
Vol 336-338 ◽  
pp. 383-387
Author(s):  
Yan Xin Yin ◽  
Yu Tan ◽  
Shu Mao Wang
Keyword(s):  
Communication Protocol ◽  
Absolute Error ◽  
Wireless Sensor ◽  
Error Measurement ◽  
Average Absolute Error ◽  
Wireless Communication Protocol ◽  
Data Acquisition Software ◽  
Terminal Design ◽  
Data Terminal ◽  
Reliability And Availability

A portable data terminal design based on wireless sensor network was came up for agriculture equipment working status monitor, a JN5139 module was used as the hardware core of the terminal and Zigbee as the wireless communication protocol. Effect caused by time-delay and pocket loss was simulated and analyzed with Truetime1.5 under matlab, data acquisition software was developed according to the simulation that effectively reduced the influence. Error measurement test showed the analog average absolute error was 6.33mv and frequency average absolute error was 0.56Hz, that indicated the reliability and availability in agriculture application.

Calculation model of unsteady temperature–pressure fields in wellbores and fractures of supercritical CO2 fracturing

Fuel ◽  
2019 ◽  
Vol 253 ◽  
pp. 1168-1183 ◽  
Author(s):  
Jintang Wang ◽  
Baojiang Sun ◽  
Weiqing Chen ◽  
Jianchun Xu ◽  
Zhiyuan Wang
Keyword(s):  
Supercritical Co2 ◽  
Calculation Model ◽  
Unsteady Temperature ◽  
Pressure Fields

scholarly journals Review of existing and development of a novel mathematical model for supercritical CO2 cycles working fluid

2018 ◽  
Vol 240 ◽  
pp. 01036
Author(s):  
Marcin Wołowicz ◽  
Jarosław Milewski ◽  
Piotr Lis
Keyword(s):  
Experimental Data ◽  
Carbon Dioxide ◽  
Mathematical Model ◽  
Heat Capacity ◽  
Supercritical Co2 ◽  
Pressure Range ◽  
Critical Conditions ◽  
Working Fluid ◽  
Critical Properties ◽  
Area Of Interest

The paper aims to compare the models of working fluids against experimental data for carbon dioxide close to its critical conditions. Fortunately, most of the work is already done and published where the authors compared the models based on the equation of the state (EoS). There are a few other models which were not investigated, thus we would like to add a few new results here and focus only on near-critical properties where the biggest deviation between experimental and calculated properties can be observed. The area of interest was pressure range of 7.39 – 20 MPa and temperature range of 304-340 K just above fluid critical point (7.39 MPa, 304.25 K). Model validation was performed for density and heat capacity as one of the most important parameters in preliminary cycle analysis.

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