Heat consumption forecasting using partial least squares, artificial neural network and support vector regression techniques in district heating systems

Abstract With the development of Fused Deposition Modeling (FDM) technology, the quality of fabricated parts is getting more attention. The present study highlights the predictive model for dimensional accuracy in the FDM process. Three process parameters, namely extruder temperature, layer thickness, and infill density, are considered in the model. To achieve better prediction accuracy, three models are studied, namely multivariate linear regression, Artificial Neural Network (ANN), and Support Vector Regression (SVR). The models are used to characterize the complex relationship between the input variables and dimensions of fabricated parts. Based on the experimental data set, it is found that the ANN model performs better than the multivariate linear regression and SVR models. The ANN model is able to study more quality characteristics of fabricated parts with more process parameters of FDM.

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Modeling the viscosity of nanofluids using artificial neural network and Bayesian support vector regression

Journal of Applied Physics ◽

10.1063/5.0008977 ◽

2020 ◽

Vol 128 (8) ◽

pp. 085306

Author(s):

Ibrahim Olanrewaju Alade ◽

Mohd Amiruddin Abd Rahman ◽

Amjed Hassan ◽

Tawfik A. Saleh

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Regression ◽

Support Vector ◽

Artificial Neural ◽

Bayesian Support

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Determination of Cefoperazone Sodium in Presence of Related Impurities by Linear Support Vector Regression and Partial Least Squares Chemometric Models

Journal of Analytical Methods in Chemistry ◽

10.1155/2015/593892 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Ibrahim A. Naguib ◽

Eglal A. Abdelaleem ◽

Hala E. Zaazaa ◽

Essraa A. Hussein

Keyword(s):

Least Squares ◽

Support Vector Regression ◽

Partial Least Squares ◽

Multivariate Calibration ◽

Drug Product ◽

Support Vector ◽

Control Analysis ◽

Least Squares Regression ◽

Significant Difference ◽

Cefoperazone Sodium

A comparison between partial least squares regression and support vector regression chemometric models is introduced in this study. The two models are implemented to analyze cefoperazone sodium in presence of its reported impurities, 7-aminocephalosporanic acid and 5-mercapto-1-methyl-tetrazole, in pure powders and in pharmaceutical formulations through processing UV spectroscopic data. For best results, a 3-factor 4-level experimental design was used, resulting in a training set of 16 mixtures containing different ratios of interfering moieties. For method validation, an independent test set consisting of 9 mixtures was used to test predictive ability of established models. The introduced results show the capability of the two proposed models to analyze cefoperazone in presence of its impurities 7-aminocephalosporanic acid and 5-mercapto-1-methyl-tetrazole with high trueness and selectivity (101.87 ± 0.708 and 101.43 ± 0.536 for PLSR and linear SVR, resp.). Analysis results of drug products were statistically compared to a reported HPLC method showing no significant difference in trueness and precision, indicating the capability of the suggested multivariate calibration models to be reliable and adequate for routine quality control analysis of drug product. SVR offers more accurate results with lower prediction error compared to PLSR model; however, PLSR is easy to handle and fast to optimize.

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A comparison between performance of support vector regression and artificial neural network in prediction of pipe burst rate in water distribution networks

KSCE Journal of Civil Engineering ◽

10.1007/s12205-014-0537-8 ◽

2014 ◽

Vol 18 (4) ◽

pp. 941-948 ◽

Cited By ~ 40

Author(s):

Akbar Shirzad ◽

Massoud Tabesh ◽

Raziyeh Farmani

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Support Vector Regression ◽

Water Distribution ◽

Distribution Networks ◽

Water Distribution Networks ◽

Support Vector ◽

Pipe Burst ◽

Artificial Neural

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Anwendung spektroskopischer Messverfahren und Multivariater Datenanalyse zur Bewertung und Beobachtung von Bioprozessen

10.51202/9783186293176 ◽

2017 ◽

Author(s):

Jan-Patrick Voß

Keyword(s):

Pichia Pastoris ◽

Least Squares ◽

Support Vector Regression ◽

Partial Least Squares ◽

Partial Least Squares Regression ◽

Process Analytical Technology ◽

Support Vector ◽

Least Squares Regression ◽

Analytical Technology

Die vorliegende Arbeit befasst sich mit dem Bioprozessmonitoring unter Verwendung spektroskopischer Messverfahren und multivariater Datenanalyse nach den Grundsätzen von PAT – Process Analytical Technology. Mit NIR-Spektroskopie und dem Verfahren Soft Independent Modelling of Class Analogy (SIMCA) wurde eine Quali¬tätsbewertung von Hefeextrakten realisiert. Im Vordergrund stand jedoch die Quanti¬fizierung nicht direkt messbarer Größen aus NIR-, Raman- und 2D-Fluoreszenzspektren in pharmazeutischen Produktionsprozessen mit Pichia pastoris. Eine entsprechende Online-Bestimmung mit der Methode Partial Least Squares Regression (PLSR) kam weiterführend zur Regelung der Glycerolkonzentration zum Einsatz. Darüber hinaus wurde die Verwendung nichtspektraler Online-Daten zur Prozessbeobachtung erprobt. Dabei gelang mit Hilfe des nichtlinearen Verfahrens Support Vector Regression (SVR) unter anderem die Bestimmung zellspezifischer Reaktionsraten. ...

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