Predictive Model for Design of Fixed-Bed Adsorbers: Parameter Estimation and Model Development

1978 ◽  
Vol 104 (2) ◽  
pp. 185-197
Author(s):  
John C. Crittenden ◽  
Walter J. Weber
Keyword(s):  
Parameter Estimation ◽  
Predictive Model ◽  
Model Development ◽  
Fixed Bed

scholarly journals Random forest predictive model development with uncertainty analysis capability for the estimation of evapotranspiration in an arid oasis region

2020 ◽  
Vol 51 (4) ◽  
pp. 648-665
Author(s):  
Min Wu ◽  
Qi Feng ◽  
Xiaohu Wen ◽  
Ravinesh C. Deo ◽  
Zhenliang Yin ◽  
...  
Keyword(s):  
Random Forest ◽  
Uncertainty Analysis ◽  
Predictive Model ◽  
Air Temperature ◽  
Model Development ◽  
Influential Factor ◽  
Weather Data ◽  
Data Sets ◽  
Oasis Area ◽  
Lower Uncertainty

Abstract The study evaluates the potential utility of the random forest (RF) predictive model used to simulate daily reference evapotranspiration (ET0) in two stations located in the arid oasis area of northwestern China. To construct an accurate RF-based predictive model, ET0 is estimated by an appropriate combination of model inputs comprising maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine durations (Sun), wind speed (U2), and relative humidity (Rh). The output of RF models are tested by ET0 calculated using Penman–Monteith FAO 56 (PMF-56) equation. Results showed that the RF model was considered as a better way to predict ET0 for the arid oasis area with limited data. Besides, Rh was the most influential factor on the behavior of ET0, except for air temperature in the proposed arid area. Moreover, the uncertainty analysis with a Monte Carlo method was carried out to verify the reliability of the results, and it was concluded that RF model had a lower uncertainty and can be used successfully in simulating ET0. The proposed study shows RF as a sound modeling approach for the prediction of ET0 in the arid areas where reliable weather data sets are available, but relatively limited.

Slow delayed rectifying potassium current (IKs) - analysis of thein vitroinhibition data and predictive model development

2012 ◽  
Vol 33 (8) ◽  
pp. 723-739 ◽  
Author(s):  
Sebastian Polak ◽  
Barbara Wiśniowska ◽  
Anna Glinka ◽  
Kamil Fijorek ◽  
Aleksander Mendyk
Keyword(s):  
Predictive Model ◽  
Potassium Current ◽  
Model Development

On-line measurement-based model parameter estimation for synchronous generators: model development and identification schemes

1994 ◽  
Vol 9 (2) ◽  
pp. 330-336 ◽  
Author(s):  
Chiang-Tsung Huang ◽  
Yung-Tien Chen ◽  
Chung-Liang Chang ◽  
Chiung-Yi Huang ◽  
Hsiao-Dong Chiang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Model Development ◽  
Synchronous Generators ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Identification Schemes ◽  
Line Measurement ◽  
On Line

scholarly journals Landfill Methane Oxidation: Predictive Model Development

2015 ◽  
Vol 15 (2) ◽  
pp. 283-288 ◽  
Author(s):  
M.F.M. Abushammal ◽  
N.E.A. Basri ◽  
M.K. Younes
Keyword(s):  
Predictive Model ◽  
Methane Oxidation ◽  
Model Development

scholarly journals Causal Models for the Result of Percutaneous Coronary Intervention in Coronary Chronic Total Occlusions

2021 ◽  
Vol 11 (19) ◽  
pp. 9258
Author(s):  
Maria Ganopoulou ◽  
Ioannis Kangelidis ◽  
Georgios Sianos ◽  
Lefteris Angelis
Keyword(s):  
Predictive Model ◽  
Causal Structure ◽  
Model Development ◽  
Coronary Intervention ◽  
Causal Models ◽  
Percutaneous Coronary Interventions ◽  
Graph Structure ◽  
Chronic Total Occlusions ◽  
Operator Experience ◽  
Percutaneous Coronary

Background: Patients undergoing coronary angiography very frequently exhibit coronary chronic total occlusions (CTOs). Over the last decade, there has been an increasing acceptance of the percutaneous coronary interventions (PCI) in CTOs due to, among else, rising operator experience and advances in technology. This study is an effort to address the problem of identifying important factors related to the success or failure of the PCI. Methods: The analysis is based on the EuroCTO Registry, which is the largest database available worldwide, consisting of 164 variables and 29,995 cases for the period 2008–2018. The aim is to assess the dynamics of causal models and causal discovery, using observational data, in predicting the result of the PCI. Causal models use graph structure to assess the cause–effect relationships between variables. In this study, the constrained-based algorithm PC was employed. The focus was to find the local causal structure around the PCI result and use it as a feature selection tool for building a predictive model. Results: The model developed was compared with other modeling approaches from the literature, and it was found to perform equally well or better. Conclusions: The analysis showcased the potential of employing local causal structure in predictive model development.

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