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Estimation of photolysis half-lives of dyes in a continuous-flow system with the aid of quantitative structure-property relationship

Davoud BEIKNEJAD,Mohammad Javad CHAICHI

Frontiers of Environmental Science & Engineering 2014, Volume 8, Issue 5,   Pages 683-692 doi: 10.1007/s11783-014-0680-y

Abstract: relationship (QSPR) study was carried out using 21 descriptors based on different chemometric tools including stepwisemultiple linear regression (MLR) and partial least squares (PLS) for the prediction of the photolysis

Keywords： dye     photolysis half-life     quantitative structure-property relationship     continuous-flow     stepwise multiplelinear regression     partial least squares    

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Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 6,   Pages 474-485 doi: 10.1631/FITEE.1400295

Abstract: In this work, principal component analysis (PCA) is fused with forward stepwise regression (SWR), artificial

Keywords： Blood pressure (BP)     Principal component analysis (PCA)     Forward stepwise regression     Artificial neural    

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A stepwise process for carbon dioxide sequestration using magnesium silicates

Johan FAGERLUND, Experience NDUAGU, Ron ZEVENHOVEN, Inês ROMÃO,

Frontiers of Chemical Science and Engineering 2010, Volume 4, Issue 2,   Pages 133-141 doi: 10.1007/s11705-009-0259-5

Abstract: This work involves the production of magnesium in the form of Mg(OH) from serpentinite rock (nickel mine tailing) material followed by conversion into MgCO using a pressurised fluidised bed (PFB) reactor operating at 400°C–600°C and pressures up to 2.85MPa. Our approach is rooted in the thermodynamic fact that the reaction between Mg(OH) and gaseous CO forming MgCO and water releases significant amounts of heat. The main problem is, however, the chemical kinetics; the reaction is slow and has to be accelerated in order to be used in an economically viable process for large-scale (~1Mt/a) CO sequestration. We have constructed a lab-scale PFB reactor test-setup for optimising the carbonation reaction. At high enough temperatures and conversion levels the reaction should provide the heat for the proceeding Mg(OH) production step, making the overall process energy neutral. So far we have been able to achieve a conversion degree of 26% at 500°C and 2.85MPa after 30min (particle size 125–212μm). In this paper the test facility and our latest results and progress on CO mineral carbonation are summarised. Also, the possible integration of the iron as a feedstock for iron and steel production will be briefly addressed. An interesting side-effect of this carbon dioxide capture and storage (CCS) route is that significant amounts of iron are obtained from the serpentinite rock material. This is released during the Mg(OH) production and can be of great interest to the iron- and steel producing sector, which at the same time is Finland’s largest CO producer.

Keywords： carbonation reaction     reactor     producer     large-scale     process    

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Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

Frontiers in Energy 2013, Volume 7, Issue 1,   Pages 103-110 doi: 10.1007/s11708-012-0220-z

Abstract: Then on the basis of the simulated results, the multiple regression models were developed respectivelyAccording to the analysis of regression coefficients, the appropriate building envelope design schemesAt last, the regression model evaluations consisting of the simulation evaluations and the actual caseevaluations were performed to verify the feasibility and accuracy of the regression models.It is believed that the regression models developed in this paper can be used to estimate the energy

Keywords： regression model     energy consumption     building envelope     office building     different climates    

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Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 221-236 doi: 10.1007/s11705-021-2061-y

Abstract: Two approaches can be adopted in the estimation of the model parameters: stepwise regression, used inseveral of previous publications, and Lasso regression, which is newly incorporated in this paper forTherefore, DRSM with Lasso regression can provide faster and more accurate data-driven models for a variety

Keywords： data-driven modeling     pharmaceutical organic synthesis     Lasso regression     dynamic response surface methodology    

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Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

Frontiers of Structural and Civil Engineering 2021, Volume 15, Issue 5,   Pages 1181-1198 doi: 10.1007/s11709-021-0744-6

Abstract: The obtained results are then checked statistically with nonlinear multiple regression (NMR) and artificial

Keywords： interaction     load sharing ratio     piled raft     nonlinear regression     artificial neural network    

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Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 1,   Pages 103-109 doi: 10.1007/s11709-018-0474-6

Abstract: Therefore, this paper presents a non-linear regression model for the analysis of effect of embedment

Keywords： settlement     embedment     Fox depth correction factor     regression     multivariable    

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of driver-response relationships: identifying factors using a novel framework integrating quantile regression

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1676-2

Abstract:

● A novel framework integrating quantile regression with machine learning

Keywords： Driver-response     Upper boundary of relationship     Interpretable machine learning     Quantile regression    

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compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression

Ali Reza GHANIZADEH, Morteza RAHROVAN

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 4,   Pages 787-799 doi: 10.1007/s11709-019-0516-8

Abstract: compressive strength (UCS) of soil-RAP blend stabilized with Portland cement based on multivariate adaptive regression

Keywords： soil-reclaimed asphalt pavement blend     Portland cement     unconfined compressive strength     multivariate adaptive regression    

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Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 2,   Pages 133-136 doi: 10.1007/s11709-013-0202-1

Abstract: This article examines the capability of Gaussian process regression (GPR) for prediction of effective

Keywords： unsaturated soil     effective stress parameter     Gaussian process regression (GPR)     artificial neural network    

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Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

Frontiers of Structural and Civil Engineering 2017, Volume 11, Issue 1,   Pages 90-99 doi: 10.1007/s11709-016-0363-9

Abstract: In this paper, considering the experimental results, three different models of multiple linear regressionevaluate the compressive strength of concrete with different mix designs, however, multiple linear regression

Keywords： concrete     28 days compressive strength     multiple linear regression     artificial neural network     ANFIS     sensitivity    

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SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 1,   Pages 185-198 doi: 10.1007/s11709-019-0591-x

Abstract: With this study, along with the conventional methods of simple and multiple linear regression models,

Keywords： undrained shear strength     linear regression     random forest     gradient boosting     machine learning     standard    

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Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various

Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 2,   Pages 250-265 doi: 10.1007/s11709-021-0785-x

Abstract: Regression analysis was then applied to determine the relationship between SMS, flow rate, andBy the use of regression analysis, the mathematical relationships between variables in all facilities

Keywords： pedestrian density     regression analysis     GP model     GMDH model    

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Identifying factors that influence soil heavy metals by using categorical regression analysis: A case

Jun Yang, Jingyun Wang, Pengwei Qiao, Yuanming Zheng, Junxing Yang, Tongbin Chen, Mei Lei, Xiaoming Wan, Xiaoyong Zhou

Frontiers of Environmental Science & Engineering 2020, Volume 14, Issue 3, doi: 10.1007/s11783-019-1216-2

Abstract: In this study, a categorical regression was used to identify the factors that influence soil heavy metalsinfluence of different factors on the soil heavy metal contents in Beijing was analyzed using a categorical regressionA categorical regression represents a suitable method for identifying the factors that influence soil

Keywords： Soil     Heavy metal     Influencing factor     Categorical regression     Identification method    

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presence of different random noises and uncertainty: Implementation of generalized Gaussian process regression

Nasser L. AZAD,Ahmad MOZAFFARI

Frontiers of Mechanical Engineering 2015, Volume 10, Issue 4,   Pages 405-412 doi: 10.1007/s11465-015-0354-x

Abstract: the modeling and analysis of automotive engines’ behavior during coldstart operations by using regressionThis encourages automotive engineers to take advantage of knowledge-based modeling tools and regressionThen, by using a Gaussian process regression machine (GPRM), a reliable model is used for the sake of

Keywords： automotive engine     calibration     coldstart operation     Gaussian process regression machine (GPRM)     uncertainty    

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Title Author Date Type Operation

Estimation of photolysis half-lives of dyes in a continuous-flow system with the aid of quantitative structure-property relationship

Davoud BEIKNEJAD,Mohammad Javad CHAICHI

Journal Article

Using hybrid models to predict blood pressure reactivity to unsupported back based on anthropometric characteristics

Gurmanik KAUR,Ajat Shatru ARORA,Vijender Kumar JAIN

Journal Article

A stepwise process for carbon dioxide sequestration using magnesium silicates

Johan FAGERLUND, Experience NDUAGU, Ron ZEVENHOVEN, Inês ROMÃO,

Journal Article

Multiple regression models for energy consumption of office buildings in different climates in China

Siyu ZHOU, Neng ZHU

Journal Article

Dynamic response surface methodology using Lasso regression for organic pharmaceutical synthesis

Journal Article

Interaction behavior and load sharing pattern of piled raft using nonlinear regression and LM algorithm-based

Journal Article

Multivariable regression model for Fox depth correction factor

Ravi Kant MITTAL, Sanket RAWAT, Piyush BANSAL

Journal Article

of driver-response relationships: identifying factors using a novel framework integrating quantile regression

Journal Article

compressive strength of soil-RAP blend stabilized with Portland cement using multivariate adaptive regression

Ali Reza GHANIZADEH, Morteza RAHROVAN

Journal Article

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Journal Article

Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive

Faezehossadat KHADEMI,Mahmoud AKBARI,Sayed Mohammadmehdi JAMAL,Mehdi NIKOO

Journal Article

SPT based determination of undrained shear strength: Regression models and machine learning

Walid Khalid MBARAK, Esma Nur CINICIOGLU, Ozer CINICIOGLU

Journal Article

Presentation of regression analysis, GP and GMDH models to predict the pedestrian density in various

Iraj BARGEGOL; Seyed Mohsen HOSSEINIAN; Vahid NAJAFI MOGHADDAM GILANI; Mohammad NIKOOKAR; Alireza OROUEI

Journal Article

Identifying factors that influence soil heavy metals by using categorical regression analysis: A case

Jun Yang, Jingyun Wang, Pengwei Qiao, Yuanming Zheng, Junxing Yang, Tongbin Chen, Mei Lei, Xiaoming Wan, Xiaoyong Zhou

Journal Article

presence of different random noises and uncertainty: Implementation of generalized Gaussian process regression

Nasser L. AZAD,Ahmad MOZAFFARI

Journal Article