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

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.

Download Full-text

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

Download Full-text

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>

Download Full-text

The uncertainty of the atmospheric integrated water vapour estimated from GNSS observations

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-8-8817-2015 ◽

2015 ◽

Vol 8 (8) ◽

pp. 8817-8857 ◽

Cited By ~ 1

Author(s):

T. Ning ◽

J. Wang ◽

G. Elgered ◽

G. Dick ◽

J. Wickert ◽

...

Keyword(s):

Water Vapour ◽

Global Climate ◽

Measurement Techniques ◽

Weather Conditions ◽

Total Delay ◽

Error Sources ◽

Integrated Water Vapour ◽

Input Variables ◽

The Impact ◽

Gnss Observations

Abstract. Within the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) there is a need for an assessment of the uncertainty in the Integrated Water Vapour (IWV) in the atmosphere estimated from ground-based GNSS observations. All relevant error sources in GNSS-derived IWV is therefore essential to be investigated. We present two approaches, a statistical and a theoretical analysis, for the assessment of the uncertainty of the IWV. It will be implemented to the GNSS IWV data stream for GRUAN in order to obtain a specific uncertainty for each data point. In addition, specific recommendations are made to GRUAN on hardware, software, and data processing practices to minimize the IWV uncertainty. By combining the uncertainties associated with the input variables in the estimations of the IWV, we calculated the IWV uncertainties for several GRUAN sites with different weather conditions. The results show a similar relative importance of all uncertainty contributions where the uncertainties in the Zenith Total Delay (ZTD) dominate the error budget of the IWV contributing with over 75 % to the total IWV uncertainty. The impact of the uncertainty associated with the conversion factor between the IWV and the Zenith Wet Delay (ZWD) is proportional to the amount of water vapour and increases slightly for moist weather conditions. The GRUAN GNSS IWV uncertainty data will provide a quantified confidence to be used for the validation of other measurement techniques, taking the uncertainty into account from diurnal to decadal time scales.

Download Full-text

Yield Optimization using Hybrid Gaussian Process Regression and a Genetic Multi-Objective Approach

Advances in Radio Science ◽

10.5194/ars-19-41-2021 ◽

2021 ◽

Vol 19 ◽

pp. 41-48

Author(s):

Mona Fuhrländer ◽

Sebastian Schöps

Keyword(s):

Gaussian Process ◽

Hybrid Approach ◽

Gaussian Process Regression ◽

Monte Carlo Analysis ◽

Computational Effort ◽

Benchmark Problem ◽

Yield Optimization ◽

Multi Objective ◽

Electromagnetic Devices ◽

The Impact

Abstract. Quantification and minimization of uncertainty is an important task in the design of electromagnetic devices, which comes with high computational effort. We propose a hybrid approach combining the reliability and accuracy of a Monte Carlo analysis with the efficiency of a surrogate model based on Gaussian Process Regression. We present two optimization approaches. An adaptive Newton-MC to reduce the impact of uncertainty and a genetic multi-objective approach to optimize performance and robustness at the same time. For a dielectrical waveguide, used as a benchmark problem, the proposed methods outperform classic approaches.

Download Full-text

The Nexus of World Electricity and Global Sustainable Development

Energies ◽

10.3390/en14185843 ◽

2021 ◽

Vol 14 (18) ◽

pp. 5843

Author(s):

Veronika Wittmann ◽

Elif Arici ◽

Dieter Meissner

Keyword(s):

Sustainable Development ◽

Fossil Fuels ◽

Energy Transport ◽

Global Climate ◽

The Sustainable Development ◽

Technical Developments ◽

Road Map ◽

Electricity Grids ◽

Development Goals ◽

The Impact

The main part of mankind’s ecological footprint is the carbon footprint, a measure of the environmental impact of humanity’s energy release from fossil fuels. The use of fossil fuels will have to change in the forthcoming decades to a largely climate-neutral use of solar energy enabled by dramatic cost reductions for PV and wind energy systems. The impact of this trend on world society has been discussed in a previous paper. In connection with these important technical developments, the role of electricity, its transport and storage will alter in the coming decades, allowing the design and use of larger and larger electricity grids and a parallel use of hydrogen for both storage and energy transport. This will further change the energy landscape of the world. All these developments and their relationship to global sustainable development are elaborated in this cross-disciplinary paper by specifically analyzing whether the Sustainable Development Goals by the United Nations are an effective road map for humanity to handle global climate change risks.

Download Full-text

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.

Download Full-text

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.

Download Full-text

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.

Download Full-text

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.

Download Full-text

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.

Download Full-text