scholarly journals Performance tests on the ANN model prediction accuracy for cooling load of buildings during the setback period

KIEAE Journal ◽  
2017 ◽  
Vol 17 (4) ◽  
pp. 83-88 ◽  
Author(s):  
Bo Rang Park ◽  
Eunji Choi ◽  
Jin Woo Moon
Keyword(s):  
Model Prediction ◽  
Prediction Accuracy ◽  
Cooling Load ◽  
Performance Tests ◽  
Ann Model

Using Stage-Dependent Temperature Parameters to Improve Phenological Model Prediction Accuracy in Rice Models

Crop Science ◽  
2016 ◽  
Vol 57 (1) ◽  
pp. 444-453 ◽  
Author(s):  
Hussain Sharifi ◽  
Robert J. Hijmans ◽  
James E. Hill ◽  
Bruce A. Linquist
Keyword(s):  
Model Prediction ◽  
Prediction Accuracy ◽  
Phenological Model

On Improving Algorithm Efficiency of Gas-Kick Simulations toward Automated Influx Management: A Robertson Differential-Algebraic-Equation Problem Approach

2021 ◽  
pp. 1-24
Author(s):  
Chen Wei ◽  
Yuanhang Chen
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Model Prediction ◽  
Computational Efficiency ◽  
Prediction Accuracy ◽  
Numerical Scheme ◽  
Computational Time ◽  
Numerical Efficiency ◽  
Flow Models ◽  
Gas Kick

Summary Improved numerical efficiency in simulating wellbore gas-influx behaviors is essential for realizing real-time model-prediction-based gas-influx management in wells equipped with managed-pressure-drilling (MPD) systems. Currently, most solution algorithms for high-fidelitymultiphase-flow models are highly time consuming and are not suitable for real-time decision making and control. In the application of model-predictive controllers (MPCs), long calculation time can lead to large overshoots and low control efficiency. This paper presents a drift-flux-model (DFM)-based gas-influx simulator with a novel numerical scheme for improved computational efficiency. The solution algorithm to a Robertson problem as differential algebraic equations (DAEs) was used as the numerical scheme to solve the control equations of the DFM in this study. The numerical stability and computational efficiency of this numerical scheme and the widely used flux-splitting methods are compared and analyzed. Results show that the Robertson DAE problem approach significantly reduces the total number of arithmetic operations and the computational time compared with the hybrid advection-upstream-splitting method (AUSMV) while maintaining the same prediction accuracy. According to the “Big-O notation” analysis, the Robertson DAE approach shows a lower-order growth of computational complexity, proving its good potential in enhancing numerical efficiency, especially when handling simulations with larger scales. The validation of both the numerical schemes for the solution of the DFM was performed using measured data from a test well drilled with water-based mud (WBM). This study offers a novel numerical solution to the DFM that can significantly reduce the computational time required for gas-kick simulation while maintaining high prediction accuracy. This approach enables the application of high-fidelity two-phase-flow models in model-prediction-based decision making and automated influx management with MPD systems.

scholarly journals Using JABOWA-3 for forest growth and yield predictions under diverse forest conditions of Nova Scotia, Canada

2012 ◽  
Vol 88 (06) ◽  
pp. 708-721 ◽  
Author(s):  
M. Irfan Ashraf ◽  
Charles P.-A. Bourque ◽  
David A. MacLean ◽  
Thom Erdle ◽  
Fan-Rui Meng
Keyword(s):  
Climate Change ◽  
Nova Scotia ◽  
Model Prediction ◽  
Prediction Accuracy ◽  
Basal Area ◽  
Forest Growth ◽  
Growth And Yield ◽  
Individual Tree ◽  
Forest Conditions ◽  
Forest Growth And Yield

Empirical growth and yield models developed from historical data are commonly used in developing long-term strategic forest management plans. Use of these models rests on an assumption that there will be no future change in the tree growing environment. However, major impacts on forest growing conditions are expected to occur with climate change. As a result, there is a pressing need for tools capable of incorporating outcomes of climate change in their predictions of forest growth and yield. Process-based models have this capability and may, therefore, help to satisfy this requirement. In this paper, we evaluate the suitability of an ecological, individual-tree-based model (JABOWA-3) in generating forest growth and yield projections for diverse forest conditions across Nova Scotia, Canada. Model prediction accuracy was analyzed statistically by comparing modelled with observed basal area and merchantable volume changes for 35 permanent sample plots (PSPs) measured over periods of at least 25 years. Generally, modelled basal area and merchantable volume agreed fairly well with observed data, yielding coefficients of determination (r2) of 0.97 and 0.94 and model efficiencies (ME) of 0.96 and 0.93, respectively. A Chi-square test was performed to assess model accuracy with respect to changes in species composition. We found that 83% of species-growth trajectories based on measured basal area were adequately modelled with JABOWA-3 (P > 0.9). Model-prediction accuracy, however, was substantially reduced for those PSPs altered by some level of disturbance. In general, JABOWA-3 is much better at providing forest yield predictions, subject to the availability of suitable climatic and soil information.

scholarly journals Dataset and ANN model prediction of performance of graphene nanolubricant with R600a in domestic refrigerator system

Data in Brief ◽  
2020 ◽  
Vol 32 ◽  
pp. 106098
Author(s):  
T.O. Babarinde ◽  
S.A. Akinlabi ◽  
D.M. Madyira ◽  
F.M. Ekundayo ◽  
P.A. Adedeji
Keyword(s):  
Model Prediction ◽  
Ann Model ◽  
Domestic Refrigerator

Modeling of Phenol Degradation in Spouted Bed Contactor Using Artificial Neural Network (ANN)

2008 ◽  
Vol 3 (2) ◽  
Author(s):  
Madhukar A. Dabhade ◽  
M. B. Saidutta ◽  
D. V. R. Murthy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Model Prediction ◽  
Phenol Degradation ◽  
Mean Square ◽  
Ann Model ◽  
Spouted Bed ◽  
The Mean ◽  
Artificial Neural Network Ann

Presence of phenol and phenolic compounds in various wastewaters and its harmful effects has led to the use of different treatment methods. Work on biological methods shows the use of different microorganisms and different bioreactors so as to improve the removal efficiency economically. The present work deals with the use of N. hydrocarbonoxydans (NCIM 2386), an actinomycetes, for the degradation of phenol. N. hydrocarbonoxydans was immobilized on GAC and used in a spouted bed contactor for effective contact of microorganisms and the substrate. The contactor performance was studied by varying flow rates, influent concentrations and the solids loading in the contactor. The effect of these variables on phenol degradation was investigated and modeling study was carried out using the artificial neural network (ANN). A feed forward neural network with back propagation was used for the model development. The experiments were planned as per the face centered cube design (FCCD) and used for training of the model, whereas data from four other experimental runs were used for testing and validation of the model. The network was optimized for the number of neurons based on the mean square error. The ANN model with three layers with three input neurons, eight neurons in hidden layers and one output neuron was found to predict effectively the effluent concentration for the given operating conditions in the spouted bed contactor. The mean square error was found to be 9.318e-12 for this ANN model. Also the experimental data was used to develop second order nonlinear empirical model obtained using multiple regression (MR) and the results compared with ANN using correlation coefficient (R2), average absolute error (AAE) and root mean square error (RMSE). Results show that R2, AAE and RMSE values of MR model were 0.9363, 2.085 % and 2.338 % respectively, while in case of ANN model these values were 0.9995, 0.59 % and 1.263 % respectively. This shows that ANN model prediction is better than multiple regression model prediction.

scholarly journals A Neural Network Model for the Compressive Strength of a Hybrid LM6 Aluminium Alloy Composite

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1652-1654
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Aluminium Alloy ◽  
Prediction Accuracy ◽  
Good Prediction ◽  
Central Composite Rotatable Design ◽  
Alloy Composite ◽  
Ann Model ◽  
Good Prediction Accuracy ◽  
Central Composite

Adding more than one reinforcement increases the flexibility in composites. The objective of the work is to develop a model to predict the compressive strength in an LM6 aluminium alloy reinforced with SiC and flyash particles. Central composite rotatable design had been employed to carry out the experiments with size and composition of the reinforcements as the parameters. ANN model developed has good prediction accuracy with error being less than 5%.

scholarly journals Colorimetric detection on paper analytical device using machine learning

2021 ◽  
Author(s):  
Bidur Khanal ◽  
Pravin Pokhrel ◽  
Bishesh Khanal ◽  
Basant Giri
Keyword(s):  
Machine Learning ◽  
Enzyme Inhibition ◽  
Prediction Accuracy ◽  
Low Cost ◽  
Colorimetric Detection ◽  
Field Testing ◽  
Support Vector ◽  
Ann Model ◽  
Fine Grained ◽  
Imaging Conditions

Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. The PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary resulting in less accurate results. Recently, machine learning (ML) assisted models have been used in image analysis. We evaluated a combinations of four ML models - logistic regression, support vector machine, random forest, and artificial neural network, and three image color spaces - RGB, HSV, and LAB for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras, and users for food color and enzyme inhibition assays to create training and test datasets. Prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML model and colorspace combinations. All models better predicted coarse level classification than fine grained concentration labels. ML models using sample color along with a reference color increased the models’ ability in predicting the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration label prediction accuracy obtained for food color was 0.966 when using ANN model and LAB colorspace. The accuracy for enzyme inhibition assay was 0.908 when using SVM model and LAB colorspace. Appropriate model and colorspace combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool.

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