Different Nonlinear Regression Techniques and Sensitivity Analysis as Tools to Optimize Oil Viscosity Modeling

Despite the drive for increased environmental protection and the achievement of the Sustainable Development Goals (SDGs), coal, oil, and natural gas use continues to dominate Japan’s energy mix. In light of this issue, this research assessed the position of natural gas, oil, and coal energy use in Japan’s environmental mitigation efforts from the perspective of sustainable development with respect to economic growth between 1965 and 2019. In this regard, the study employs Bayer and Hanck cointegration, fully modified Ordinary Least Square (FMOLS), and dynamic ordinary least square (DOLS) to investigate these interconnections. The empirical findings from this study revealed that the utilization of natural gas, oil, and coal energy reduces the sustainability of the environment with oil consumption having the most significant impact. Furthermore, the study validates the environmental Kuznets curve (EKC) hypothesis in Japan. The outcomes of the Gradual shift causality showed that CO2 emissions can predict economic growth, while oil, coal, and energy consumption can predict CO2 emissions in Japan. Given Japan’s ongoing energy crisis, this innovative analysis provides valuable policy insights to stakeholders and authorities in the nation’s energy sector.

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The use of Bayesian nonlinear regression techniques for the modelling of the retention behaviour of volatile components ofArtemisiaspecies

SAR and QSAR in Environmental Research ◽

10.1080/1062936x.2012.665083 ◽

2012 ◽

Vol 23 (5-6) ◽

pp. 461-483

Author(s):

M. Jalali-Heravi ◽

A. Mani-Varnosfaderani ◽

D. Taherinia ◽

M.M. Mahmoodi

Keyword(s):

Nonlinear Regression ◽

Volatile Components ◽

Retention Behaviour ◽

Regression Techniques

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Second-hand housing batch evaluation model of zhengzhou city based on big data and MGWR model

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-210917 ◽

2021 ◽

pp. 1-20

Author(s):

Chaojie Liu ◽

Jie Lu ◽

Wenjing Fu ◽

Zhuoyi Zhou

Keyword(s):

Real Estate ◽

Geographically Weighted Regression ◽

Euclidean Distance ◽

Evaluation Model ◽

Housing Prices ◽

Information Criterion ◽

Access Point ◽

Least Square ◽

Weighted Regression ◽

Spatial Error

How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.

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Use of the Distance Transform for Integration of Local Measurements: Principle and Application in Chemical Engineering

Microscopy and Microanalysis ◽

10.1017/s1431927616000015 ◽

2016 ◽

Vol 22 (2) ◽

pp. 422-431 ◽

Cited By ~ 5

Author(s):

Loïc Sorbier ◽

Frédéric Bazer-Bachi ◽

Yannick Blouët ◽

Maxime Moreaud ◽

Virginie Moizan-Basle

Keyword(s):

Chemical Engineering ◽

Least Square ◽

Infinite Cylinder ◽

Distance Transform ◽

High Symmetry ◽

Object A ◽

Local Measurements ◽

Symmetrical Object ◽

Relative Errors ◽

The Cost

AbstractWe propose an original methodology to integrate local measurement for nontrivial object shape. The method employs the distance transform of the object and least-square fitting of numerically computed weighting functions extracted from it. The method is exemplified in the field of chemical engineering by calculating the global metal concentration in catalyst grains from uneven metal distribution profiles. Applying the methodology on synthetic profiles with the help of a very simple deposition model allows us to evaluate the accuracy of the method. For high symmetry objects such as an infinite cylinder, relative errors on global concentration are lower than 1% for well-resolved profiles. For a less symmetrical object, a tetralobe, the best estimator gives a relative error below 5% at the cost of increased measurement time. Applicability on a real case is demonstrated on an aged hydrodemetallation catalyst. Sampling of catalyst grains at the inlet and outlet of the reactor allowed conclusions concerning different reactivity for the trapped metals.

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Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation

Advances in Environmental Engineering and Green Technologies - Smart Cities as a Solution for Reducing Urban Waste and Pollution ◽

10.4018/978-1-5225-0302-6.ch008 ◽

2016 ◽

pp. 224-251 ◽

Cited By ~ 1

Author(s):

J. Jagan ◽

Yıldırım Dalkiliç ◽

Pijush Samui

Keyword(s):

Support Vector Machine ◽

System Modeling ◽

Medical Waste ◽

Least Square ◽

Support Vector ◽

Waste Generation ◽

Nonlinear System Modeling ◽

Hospital Capacity ◽

Regression Techniques ◽

Rate Type

The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.

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Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation

Waste Management ◽

10.4018/978-1-7998-1210-4.ch045 ◽

2020 ◽

pp. 990-1012

Author(s):

J. Jagan ◽

Yıldırım Dalkiliç ◽

Pijush Samui

Keyword(s):

Support Vector Machine ◽

System Modeling ◽

Medical Waste ◽

Least Square ◽

Support Vector ◽

Waste Generation ◽

Nonlinear System Modeling ◽

Hospital Capacity ◽

Regression Techniques ◽

Rate Type

The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.

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Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation

Sustainable Infrastructure ◽

10.4018/978-1-7998-0948-7.ch038 ◽

2020 ◽

pp. 808-829

Author(s):

J. Jagan ◽

Yıldırım Dalkiliç ◽

Pijush Samui

Keyword(s):

Support Vector Machine ◽

System Modeling ◽

Medical Waste ◽

Least Square ◽

Support Vector ◽

Waste Generation ◽

Nonlinear System Modeling ◽

Hospital Capacity ◽

Regression Techniques ◽

Rate Type

The prediction of wastes generated in the hospital will help their management for several activities like storage, transport and disposing. This chapter adopts Support Vector Machine (SVM), Least Square Support Vector Machine (LSSVM) and Genetic Programming (GP) in order to estimate the rate of medical waste generation. In the event of predicting the rate, type of hospital, capacity and bed occupancy has been used as inputs of SVM, LSSVM and GP. SVM is based on statistical learning theory, which provides an elegant tool for nonlinear system modeling. LSSVM is the re-formulation to the general SVM. GP, a best part of evolutionary algorithm and also the specification of Genetic Algorithm (GA). These SVM, LSSVM and GP have been used as the regression techniques. The results show the performance of the developed SVM, LSSVM and GP models were elegant and outstanding.

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Prediction of Crude Oil Viscosity and Gas/Oil Ratio Curves Using Recent Advances to Neural Networks

10.3997/2214-4609-pdb.170.spe125360 ◽

2009 ◽

Author(s):

Munirudeen A. Oloso ◽

Amar Khoukhi ◽

Abdulazeez Abdulraheem ◽

Moustafa Elshafei

Keyword(s):

Neural Networks ◽

Crude Oil ◽

Gas Oil ◽

Oil Viscosity ◽

Recent Advances

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Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Water ◽

10.3390/w12010261 ◽

2020 ◽

Vol 12 (1) ◽

pp. 261 ◽

Cited By ~ 2

Author(s):

Maria Marques ◽

Ana Álvarez ◽

Pilar Carral ◽

Iris Esparza ◽

Blanca Sastre ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Near Infrared ◽

Diffuse Reflectance Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Prediction Ability ◽

Greenhouse Gas Inventories ◽

Starting Point

Contents of soil organic carbon (SOC), gypsum, CaCO3, and quartz, among others, were analyzed and related to reflectance features in visible and near-infrared (VIS/NIR) range, using partial least square regression (PLSR) in ParLes software. Soil samples come from a sloping olive grove managed by frequent tillage in a gypsiferous area of Central Spain. Samples were collected in three different layers, at 0–10, 10–20 and 20–30 cm depth (IPCC guidelines for Greenhouse Gas Inventories Programme in 2006). Analyses were performed by C Loss-On-Ignition, X-ray diffraction and water content by the Richards plates method. Significant differences for SOC, gypsum, and CaCO3 were found between layers; similarly, soil reflectance for 30 cm depth layers was higher. The resulting PLSR models (60 samples for calibration and 30 independent samples for validation) yielded good predictions for SOC (R2 = 0.74), moderate prediction ability for gypsum and were not accurate for the rest of rest of soil components. Importantly, SOC content was related to water available capacity. Soils with high reflectance features held c.a. 40% less water than soils with less reflectance. Therefore, higher reflectance can be related to degradation in gypsiferous soil. The starting point of soil degradation and further evolution could be established and mapped through remote sensing techniques for policy decision making.

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