Developing a Robust Model Based on the Gaussian Process Regression Approach to Predict Biodiesel Properties

Biodiesel is assumed a renewable and environmentally friendly fuel that possesses the potential to substitute petroleum diesel. The basic purpose of the present study is to design a precise algorithm based on Gaussian Process Regression (GPR) model with several kernel functions, i.e., Rational Quadratic, Squared Exponential, Matern, and Exponential, to estimate biodiesel properties. These properties include kinematic viscosity (KV), pour point (PP), iodine value (IV), and cloud point (CP) as a function of fatty acid composition. In order to develop this model, some variables are assumed, such as molecular weight, carbon number, double bond numbers, monounsaturated fatty acids, polyunsaturated fatty acid, weight percent of saturated acid, and temperature. The performance and efficiency of the GPR model are measured through several statistical criteria and the results are summarized in root mean square error (RMSE) and coefficients of determination ( R 2 ). R 2 and RMSE are sorted as 0.992 & 0.15697, 0.998 & 0.96580, 0.966 & 1.38659, and 0.968 & 1.56068 for four properties such as KV, IV, CP, and PP, respectively. It is worth to mention this point that the kernel function Squared Exponential shows a great performance for IV and PP and kernel functions Exponential and Matern indicate appropriate efficiency for CP and KV properties, respectively. On the other hand, the results of the offered GPR models are compared with those of the previous models, LSSVM-PSO and ANFIS. The outcomes proved the superiority of this model over two former models in point of estimating the biodiesel properties.

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Evaluation of Gaussian process regression kernel functions for improving groundwater prediction

Journal of Hydrology ◽

10.1016/j.jhydrol.2021.126960 ◽

2021 ◽

pp. 126960

Author(s):

Yue Pan ◽

Xiankui Zeng ◽

Hongxia Xu ◽

Yuanyuan Sun ◽

Dong Wang ◽

...

Keyword(s):

Gaussian Process ◽

Gaussian Process Regression ◽

Kernel Functions

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Topographic Kernels for Gaussian Process Regression in Digital Soil Mapping

10.5194/egusphere-egu21-2452 ◽

2021 ◽

Author(s):

Thomas Gläßle ◽

Kerstin Rau ◽

Thomas Scholten ◽

Philipp Hennig

Keyword(s):

Gaussian Process ◽

Domain Knowledge ◽

Gaussian Process Regression ◽

Kernel Functions ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Base Case ◽

Training Samples ◽

Additive Regression Model ◽

Additive Regression

<p>Gaussian Processes provide a theoretically well-understood regression framework that is widely used in the context of Digital Soil Mapping. Among the reasons to use Gaussian Process Regression (GPR) are its interpretability, its builtin support for uncertainty quantification, and its ability to handle unevenly spaced and correlated training samples through a user-specified covariance kernel. The base case of GPR is performed with covariance models that are specified functions of Euclidean distance. In order to incorporate information other than the relative positions, regression-kriging extends GPR by an additive regression model of choice, and co-kriging considers a covariance model between covariates and the target variable. In this work, we use the alternative approach of incorporating topographic information directly into the kernel function by use of a non-Euclidean, non-stationary distance function. In particular, we devise kernels based on a path of least effort, where <em>effort</em> is locally specified as a function constructed from prior knowledge. It can e.g. be derived from local topographic variables. We demonstrate that our candidate models improve prediction accuracy over the base model. This shows that domain knowledge can be integrated into the model by means of handcrafted kernel functions. The approach is not per se restricted to topographic variables, but could be used for any covariate quantity that is available at output resolution.</p>

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Towards estimation of CO2 adsorption on highly porous MOF-based adsorbents using gaussian process regression approach

Scientific Reports ◽

10.1038/s41598-021-95246-6 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Majedeh Gheytanzadeh ◽

Alireza Baghban ◽

Sajjad Habibzadeh ◽

Amin Esmaeili ◽

Otman Abida ◽

...

Keyword(s):

Gaussian Process ◽

Fossil Fuels ◽

Global Climate ◽

Gaussian Process Regression ◽

Accurate Estimate ◽

Kernel Functions ◽

Predictive Tool ◽

Input Variables ◽

Capture Techniques ◽

The Impact

AbstractIn recent years, new developments in controlling greenhouse gas emissions have been implemented to address the global climate conservation concern. Indeed, the earth's average temperature is being increased mainly due to burning fossil fuels, explicitly releasing high amounts of CO2 into the atmosphere. Therefore, effective capture techniques are needed to reduce the concentration of CO2. In this regard, metal organic frameworks (MOFs) have been known as the promising materials for CO2 adsorption. Hence, study on the impact of the adsorption conditions along with the MOFs structural properties on their ability in the CO2 adsorption will open new doors for their further application in CO2 separation technologies as well. However, the high cost of the corresponding experimental study together with the instrument's error, render the use of computational methods quite beneficial. Therefore, the present study proposes a Gaussian process regression model with four kernel functions to estimate the CO2 adsorption in terms of pressure, temperature, pore volume, and surface area of MOFs. In doing so, 506 CO2 uptake values in the literature have been collected and assessed. The proposed GPR models performed very well in which the exponential kernel function, was shown as the best predictive tool with R2 value of 1. Also, the sensitivity analysis was employed to investigate the effectiveness of input variables on the CO2 adsorption, through which it was determined that pressure is the most determining parameter. As the main result, the accurate estimate of CO2 adsorption by different MOFs is obtained by briefly employing the artificial intelligence concept tools.

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Gaussian Process Prediction Model to Estimate Excess Adsorption Capacity of Supercritical CO2

10.20944/preprints202002.0069.v1 ◽

2020 ◽

Author(s):

Narjes Nabipour ◽

Sultan Noman Qasem ◽

Amir Mosavi ◽

Shahab Shamshirband

Keyword(s):

Carbon Dioxide ◽

Gaussian Process ◽

Adsorption Capacity ◽

Gaussian Process Regression ◽

Kernel Functions ◽

Excess Adsorption ◽

Effective Parameters ◽

Methane Recovery ◽

Coal Beds ◽

Process Prediction

Deep coal beds have been suggested as possible usable underground geological locations for carbon dioxide storage. Furthermore, injecting carbon dioxide into coal beds can improve the methane recovery. Due to importance of this issue, a novel investigation has been done on adsorption of carbon dioxide on various types of coal seam. This study has proposed four types of Gaussian Process Regression (GPR) approaches with different kernel functions to estimate excess adsorption of carbon dioxide in terms of temperature, pressure and composition of coal seams. The comparison of GPR outputs and actual excess adsorption expresses that proposed models have interesting accuracy and also the Exponential GPR approach has better performance than other ones. For this structure, R2=1, MRE=0.01542, MSE=0, RMSE=0.00019 and STD=0.00014 have been determined. Additionally, the impacts of effective parameters on excess adsorption capacity have been studied for the first time in literature. According to these results, the present work has valuable and useful tools for petroleum and chemical engineers who dealing with enhancement of recovery and environment protection.

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Wireless Indoor Localization Using Convolutional Neural Network and Gaussian Process Regression

Sensors ◽

10.3390/s19112508 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2508 ◽

Cited By ~ 8

Author(s):

Guolong Zhang ◽

Ping Wang ◽

Haibing Chen ◽

Lan Zhang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Gaussian Process ◽

Hybrid Model ◽

Nearest Neighbor ◽

Gaussian Process Regression ◽

Kernel Functions ◽

Training Dataset ◽

K Nearest Neighbor ◽

Positive Effects

This paper presents a localization model employing convolutional neural network (CNN) and Gaussian process regression (GPR) based on Wi-Fi received signal strength indication (RSSI) fingerprinting data. In the proposed scheme, the CNN model is trained by a training dataset. The trained model adapts to complex scenes with multipath effects or many access points (APs). More specifically, the pre-processing algorithm makes the RSSI vector which is formed by considerable RSSI values from different APs readable by the CNN algorithm. The trained CNN model improves the positioning performance by taking a series of RSSI vectors into account and extracting local features. In this design, however, the performance is to be further improved by applying the GPR algorithm to adjust the coordinates of target points and offset the over-fitting problem of CNN. After implementing the hybrid model, the model is experimented with a public database that was collected from a library of Jaume I University in Spain. The results show that the hybrid model has outperformed the model using k-nearest neighbor (KNN) by 61.8%. While the CNN model improves the performance by 45.8%, the GPR algorithm further enhances the localization accuracy. In addition, the paper has also experimented with the three kernel functions, all of which have been demonstrated to have positive effects on GPR.

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Estimation of municipal waste generation of Turkey using socio-economic indicators by Bayesian optimization tuned Gaussian process regression

Waste Management & Research ◽

10.1177/0734242x20906877 ◽

2020 ◽

Vol 38 (8) ◽

pp. 840-850 ◽

Cited By ~ 5

Author(s):

Zeynep Ceylan

Keyword(s):

Gaussian Process ◽

Gaussian Process Regression ◽

Kernel Functions ◽

Machine Learning Algorithms ◽

Economic Indicators ◽

Bayesian Optimization ◽

Superior Performance ◽

Coefficient Of Determination ◽

Accurate Estimation ◽

Support Vector

Accurate estimation of municipal solid waste (MSW) generation has become a crucial task in decision-making processes for the MSW planning and management systems. In this study, the Gaussian process regression (GPR) model tuned by Bayesian optimization was used to forecast the MSW generation of Turkey. The Bayesian optimization method, which can efficiently optimize the hyperparameters of kernel functions in the machine learning algorithms, was applied to reduce the computation redundancy and enhance the estimation performance of the models. Four socio-economic indicators such as population, gross domestic product per capita, inflation rate, and the unemployment rate were used as input variables. The performance of the Bayesian GPR (BGPR) model was compared with the multiple linear regression (MLR) and Bayesian support vector regression (BSVR) models. Different performance measures such as mean absolute deviation (MAD), root mean square error (RMSE), and coefficient of determination (R2) values were used to evaluate the performance of the models. The exponential-GPR model tuned by Bayesian optimization showed superior performance with minimum MAD (0.0182), RMSE (0.0203), and high R2 (0.9914) values in the training phase and minimum MAD (0.0342), RMSE (0.0463), and high R2 (0.9841) values in the testing phase. The results of this study can help decision-makers to be aware of social-economic factors associated with waste management and ensure optimal usage of their resources in future planning.

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A comparative study of Gaussian process regression with other three machine learning approaches in the performance prediction of centrifugal pump

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/09544062211050542 ◽

2021 ◽

pp. 095440622110505

Author(s):

Xutao Zhao ◽

Desheng Zhang ◽

Renhui Zhang ◽

Bin Xu

Keyword(s):

Machine Learning ◽

Gaussian Process ◽

Centrifugal Pump ◽

Kernel Function ◽

Performance Prediction ◽

Optimization Design ◽

Gaussian Process Regression ◽

Kernel Functions ◽

Performance Indices ◽

Pump Performance

Accurate prediction of performance indices using impeller parameters is of great importance for the initial and optimal design of centrifugal pump. In this study, a kernel-based non-parametric machine learning method named with Gaussian process regression (GPR) was proposed, with the purpose of predicting the performance of centrifugal pump with less effort based on available impeller parameters. Nine impeller parameters were defined as model inputs, and the pump performance indices, that is, the head and efficiency, were determined as model outputs. The applicability of three widely used nonlinear kernel functions of GPR including squared exponential (SE), rational quadratic (RQ) and Matern5/2 was investigated, and it was found by comparing with the experimental data that the SE kernel function is more suitable to capture the relationship between impeller parameters and performance indices because of the highest R square and the lowest values of max absolute relative error (MARE), mean absolute proportional error (MAPE), and root mean square error (RMSE). In addition, the results predicted by GPR with SE kernel function were compared with the results given by other three machine learning models. The comparison shows that the GPR with SE kernel function is more accurate and robust than other models in centrifugal pump performance prediction, and its prediction errors and uncertainties are both acceptable in terms of engineering applications. The GPR method is less costly in the performance prediction of centrifugal pump with sufficient accuracy, which can be further used to effectively assist the design and manufacture of centrifugal pump and to speed up the optimization design process of impeller coupled with stochastic optimization methods.

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Investigating the Predictive Performance of Gaussian Process Regression in Evaluating Reservoir Porosity and Permeability

Energies ◽

10.3390/en11123261 ◽

2018 ◽

Vol 11 (12) ◽

pp. 3261 ◽

Cited By ~ 8

Author(s):

Solomon Asante-Okyere ◽

Chuanbo Shen ◽

Yao Yevenyo Ziggah ◽

Mercy Moses Rulegeya ◽

Xiangfeng Zhu

Keyword(s):

Neural Network ◽

Gaussian Process ◽

Covariance Function ◽

Back Propagation ◽

Gaussian Process Regression ◽

Kernel Functions ◽

Generalized Regression Neural Network ◽

Model Parameters ◽

Porosity And Permeability ◽

Reservoir Porosity

In this paper, a new predictive model based on Gaussian process regression (GPR) that does not require iterative tuning of user-defined model parameters has been proposed to determine reservoir porosity and permeability. For this purpose, the capability of GPR was appraised statistically for predicting porosity and permeability of the southern basin of the South Yellow Sea using petrophysical well log data. Generally, the performance of GPR is deeply reliant on the type covariance function utilized. Therefore, to obtain the optimal GPR model, five different kernel functions were tested. The resulting optimal GPR model consisted of the exponential covariance function, which produced the highest correlation coefficient (R) of 0.85 and the least root mean square error (RMSE) of 0.037 and 6.47 for porosity and permeability, respectively. Comparison was further made with benchmark methods involving a back propagation neural network (BPNN), generalized regression neural network (GRNN), and radial basis function neural network (RBFNN). The statistical findings revealed that the proposed GPR is a powerful technique and can be used as a supplement to the widely used artificial neural network methods. In terms of computational speed, the GPR technique was computationally faster than the BPNN, GRNN, and RBFNN methods in estimating reservoir porosity and permeability.

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Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels

Scientific Reports ◽

10.1038/s41598-020-74394-1 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Marcus M. Noack ◽

Gregory S. Doerk ◽

Ruipeng Li ◽

Jason K. Streit ◽

Richard A. Vaia ◽

...

Keyword(s):

Decision Making ◽

Gaussian Process ◽

Materials Science ◽

Positive Impact ◽

Gaussian Process Regression ◽

Experimental Tests ◽

Kernel Functions ◽

Measurement Noise ◽

Dimensional Parameter ◽

Parameter Spaces

Abstract A majority of experimental disciplines face the challenge of exploring large and high-dimensional parameter spaces in search of new scientific discoveries. Materials science is no exception; the wide variety of synthesis, processing, and environmental conditions that influence material properties gives rise to particularly vast parameter spaces. Recent advances have led to an increase in the efficiency of materials discovery by increasingly automating the exploration processes. Methods for autonomous experimentation have become more sophisticated recently, allowing for multi-dimensional parameter spaces to be explored efficiently and with minimal human intervention, thereby liberating the scientists to focus on interpretations and big-picture decisions. Gaussian process regression (GPR) techniques have emerged as the method of choice for steering many classes of experiments. We have recently demonstrated the positive impact of GPR-driven decision-making algorithms on autonomously-steered experiments at a synchrotron beamline. However, due to the complexity of the experiments, GPR often cannot be used in its most basic form, but rather has to be tuned to account for the special requirements of the experiments. Two requirements seem to be of particular importance, namely inhomogeneous measurement noise (input-dependent or non-i.i.d.) and anisotropic kernel functions, which are the two concepts that we tackle in this paper. Our synthetic and experimental tests demonstrate the importance of both concepts for experiments in materials science and the benefits that result from including them in the autonomous decision-making process.

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Comprehensive Modeling in Predicting Biodiesel Density Using Gaussian Process Regression Approach

BioMed Research International ◽

10.1155/2021/6069010 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Bingxian Wang ◽

Issam Alruyemi

Keyword(s):

Gaussian Process ◽

Gaussian Process Regression ◽

Kernel Functions ◽

Fatty Acid Esters ◽

Effective Parameters ◽

Target Values ◽

Comprehensive Modeling ◽

First Time ◽

Acid Esters ◽

Better Than

In this study, four Gaussian process regression (GPR) approaches by various kernel functions have been proposed for the estimation of biodiesel density as the functions of pressure, temperature, molecular weight, and the normal melting point of fatty acid esters. Comparing the actual values with GPR outputs shows that these approaches have good accuracy, but the performance of the rational quadratic GPR model is better than others. In this GPR model, RMSE = 0.47 , MSE = 0.22 , MRE = 0.04 , R 2 = 1 , and STD is equal to 0.3. In addition, for the first time, this study shows that the effective parameters affect the biodiesel density. According to this analysis, it was shown that among the input parameters, pressure has the greatest effect on the target values with a relevancy factor of 0.59. This study can be used as a suitable and valuable work/tool for chemical and petroleum engineers who attempt environment protection and recovery improvement.

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