scholarly journals Isotherm investigation for the sorption of fluoride onto Bio-F: comparison of linear and non-linear regression method

2017 ◽  
Vol 7 (8) ◽  
pp. 4793-4800 ◽  
Author(s):  
Manish Yadav ◽  
Nitin Kumar Singh
Keyword(s):  
Linear Regression ◽  
Regression Method ◽  
Linear Regression Method ◽  
Non Linear

A NEW PARAMETER ESTIMATION METHOD FOR ONLINE SOFT TISSUE CHARACTERIZATION

2016 ◽  
Vol 16 (08) ◽  
pp. 1640019 ◽  
Author(s):  
JAEHYUN SHIN ◽  
YONGMIN ZHONG ◽  
JULIAN SMITH ◽  
CHENGFAN GU
Keyword(s):  
Soft Tissue ◽  
Linear Regression ◽  
Tissue Characterization ◽  
Soft Tissues ◽  
Unscented Kalman Filter ◽  
Estimation Method ◽  
Regression Method ◽  
Linear Regression Method ◽  
Non Linear

Dynamic soft tissue characterization is of importance to robotic-assisted minimally invasive surgery. The traditional linear regression method is unsuited to handle the non-linear Hunt–Crossley (HC) model and its linearization process involves a linearization error. This paper presents a new non-linear estimation method for dynamic characterization of mechanical properties of soft tissues. In order to deal with non-linear and dynamic conditions involved in soft tissue characterization, this method improves the non-linearity and dynamics of the HC model by treating parameter [Formula: see text] as independent variable. Based on this, an unscented Kalman filter is developed for online estimation of soft tissue parameters. Simulations and comparison analysis demonstrate that the proposed method is able to estimate mechanical parameters for both homogeneous tissues and heterogeneous and multi-layer tissues, and the achieved performance is much better than that of the linear regression method.

Polynomial (Non Linear) Regression Method for Improved Estimation Based on Sampling

2008 ◽  
Vol 8 (8) ◽  
pp. 1597-1599 ◽  
Author(s):  
Emmanuel John Ekpen ◽  
Mfon Ime Okonna ◽  
Eno Donatus Jo
Keyword(s):  
Linear Regression ◽  
Regression Method ◽  
Linear Regression Method ◽  
Non Linear

scholarly journals Adsorption of anionic dyes onto natural, thermally and chemically modified smectite clays

2014 ◽  
Vol 16 (4) ◽  
pp. 33-40 ◽  
Author(s):  
Joanna Kyzioł-Komosińska ◽  
Czesława Rosik-Dulewska ◽  
Magdalena Pająk ◽  
Iwona Krzyżewska ◽  
Agnieszka Dzieniszewska
Keyword(s):  
Linear Regression ◽  
Adsorption Capacity ◽  
Regression Method ◽  
Linear Regression Method ◽  
Anionic Dyes ◽  
Charged Surfaces ◽  
Smectite Clays ◽  
Direct Blue ◽  
Non Linear ◽  
Better Than

Abstract The aim of this study was to determine the adsorption capacity of the smectite clays (from the overburden of the lignite deposit in Belchatow) for two anionic dyes, i.e. Reactive Blue 81 (RB-81) and Direct Blue 74 (DB-74). Additionally, the influence of the thermal and chemical (acid and alkali) clay modifications on the amount of bonded dyes was investigated. The adsorption capacity of the clay (natural and modified) was different for studied dyes and depended on the initial concentration and modification type. All the modified clays adsorbed the dyes at pH>pHPZC as the negatively charged surfaces of their particles (in accordance with the formula: AOH ↔ AO- + H+) prevented the formation of electrostatic bonds between the anionic dyes and the clay surface. The dyes were mainly bound with the hydrogen bonds forming between the donor groups in the dyes and the acceptor groups (-SiO and -Al2OH) in the clays. The coefficients in the adsorption isotherms were estimated with the linear and non-linear regression. The linear regression method was found that the Freundlich and Dubinin-Radushkevich isotherms described the dye sorption much better than the Langmuir model. On the other hand, all three models described well the experimental data in the non-linear regression method. Furthermore, the 1/n value (<1) obtained from the Freundlich equation for all the dye-sorbent systems indicated the favorable sorption.

Kinetic analysis of non-isothermal solid-state reactions: multi-stage modeling without assumptions in the reaction mechanism

2017 ◽  
Vol 19 (5) ◽  
pp. 3606-3615 ◽  
Author(s):  
Alexey L. Pomerantsev ◽  
Alla V. Kutsenova ◽  
Oxana Ye. Rodionova
Keyword(s):  
Reaction Mechanism ◽  
Solid State ◽  
Linear Regression ◽  
Kinetic Analysis ◽  
Regression Method ◽  
Solid State Reactions ◽  
Linear Regression Method ◽  
Multi Stage ◽  
Thermogravimetric Data ◽  
Non Linear

A novel non-linear regression method for modeling non-isothermal thermogravimetric data is proposed.

scholarly journals Home Thermal Modeling: Cooling Energy Consumption and Costs in Saudi Arabia

2016 ◽  
Vol 9 (4) ◽  
pp. 22
Author(s):  
Areej A. Malibari ◽  
Amjad H. Gamlo
Keyword(s):  
Saudi Arabia ◽  
Linear Regression ◽  
Thermal Model ◽  
Cooling System ◽  
Linear Regression Analysis ◽  
Grid Method ◽  
Data Fitting ◽  
Regression Method ◽  
Linear Regression Method ◽  
Non Linear

<p><strong>Objectives: </strong>The consumption of electricity and its costs are expected to be increased in Saudi Arabia due to its rapid growth in population. As the Kingdom is characterized by extreme hot climate, a massive amount of electricity consumed by the residential sector goes to power air conditioners. To control this huge amount of energyconsumedin homes, thermal models have been generated with two or more parameters. <strong>Methodology: </strong>The households’ surveys have been conducted in order to collect the data. The Non-linear regression analysis has been carried out to obtain the outcomes of study. Moreover, household surveys have been conducted for data collection. The grid algorithm and the non-linear regression have been used to learn the parameters in the model to simulate the weather in Saudi Arabia. The temperature loggers have been placed in the houses to observe the behavior of residents of using cooling system. The web forecast has been used to analyze the temperature of cities on hourly basis. <strong>Results: </strong>Simple thermal model has been built using two parameters by applying the grid and non-linear regression methods for data fitting. Then the thermal model with envelope has also been created using four parameters by applying non-linear regression method for data fitting. <strong>Conclusion: </strong>It has been evaluated through outcomes that thermal model with envelope is better as compared to simple thermal model. Moreover, the data fitting by non-linear regression method has also been observed to perform better than data fitting by grid method.</p>

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