Comprehensive Modeling in Predicting Biodiesel Density Using Gaussian Process Regression Approach

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.

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

Microwave-Assisted Catalytic Synthesis of Bio-Based Copolymers from Waste Cooking Oil

10.20944/preprints201703.0137.v1 ◽

2017 ◽

Author(s):

Mahrzadi Noureen Shahi ◽

Muhammad Arshad ◽

Aman Ullah

Keyword(s):

Molecular Weight ◽

Gel Permeation Chromatography ◽

Waste Cooking Oil ◽

Attenuated Total Reflection ◽

Fatty Acid Esters ◽

Proton Nuclear Magnetic Resonance ◽

Co Catalyst ◽

Cooking Oil ◽

First Time ◽

Acid Esters

Solvent free copolymerization of epoxides derived from fatty acid esters of waste cooking oil with phthalic anhydride using (salen)CrIII Cl as catalyst and n-Bu4NCl/DMAP as co-catalyst was carried out for the first time under microwave irradiation, where reaction time was reduced from number of hours to minutes. The polyesters were obtained with molecular weight (Mw = 3084-6740 g/mol) and dispersity values (D = 1.18-1.92), when (salen)CrIII Cl/n-Bu4NCl was used as catalysts. While in case of DMAP as a co-catalyst, polyesters with improved molecular weight (Mw = 5537-6925 g/mol) and narrow dispersity values (D = 1.07-1.28) were obtained even at reduced concentrations of (salen)CrIII Cl and DMAP. The obtained products were characterized and evaluated by attenuated total reflection-fourier transform infrared spectroscopy (ATR-FTIR), proton nuclear magnetic resonance (1H-NMR) spectroscopy, gel permeation chromatography (GPC) and thermogravimetric analysis (TGA) Techniques.

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

Scientific support for innovative development of wheat bread long-term storage production

E3S Web of Conferences ◽

10.1051/e3sconf/202017603021 ◽

2020 ◽

Vol 176 ◽

pp. 03021

Author(s):

Natalia Zavorokhina ◽

Natalia Pankratyeva ◽

Nadezhda Goncharova

Keyword(s):

Shelf Life ◽

Fatty Acid Esters ◽

Wheat Bread ◽

Innovative Development ◽

Long Term Storage ◽

High Degree ◽

First Time ◽

Term Storage ◽

Acid Esters

Nowadays in the context of innovative development the bread intended for long-term storage is necessary in hard-to-reach areas of Sverdlovsk region, participants of expeditions, workers of logging, geological parties, etc. Taking into account the constant demand for such products, it is necessary to develop new approaches to their creation.The aim of the study was the development of long-term storage bread from wheat flour, preserving consumer characteristics throughout the shelf life.The synergism of betulin,nisin and ascorbic acid was studied for the first time when they are used together to inhibit potato bread disease (hereinafter CBH). It is proved that the use of additives, including 0.015% nisin together with 0.005% betulin to the mass of flour increases the shelf life of wheat bread up to 7 days since betulin inhibits CBH, nisin inhibits the development of mold under pH conditions in the range of 5.5-6.5.For the first time, a technique is proposed for producing betulin nanosuspension with a high degree of dispersion. The effect of betulin nanosuspension with emulsifiers of calcium lactylate, polyglycerol fatty acid esters, lecithin on the inhibition of CBH and an increase in the shelf life of wheat bread was studied.

Download Full-text

Enzymatic Hydrolysis of Carotenoid Fatty Acid Esters of Red Pepper (Capsicum annuum L.) by a Lipase from Candida rugosa

Zeitschrift für Naturforschung C ◽

10.1515/znc-2000-11-1220 ◽

2000 ◽

Vol 55 (11-12) ◽

pp. 971-975 ◽

Cited By ~ 16

Author(s):

Dietmar Ernst Breithaupt

Keyword(s):

Enzymatic Hydrolysis ◽

Candida Rugosa ◽

Fatty Acid Esters ◽

Red Pepper ◽

Free Form ◽

Enzymatic Cleavage ◽

Reaction Conditions ◽

First Time ◽

Hydrolysis Of ◽

Acid Esters

Analyses of red pepper extracts which had been pretreated with lipase type VII (EC 3.1.1.3.) from Candida rugosa showed for the first time pepper carotenoid esters to be substrates of this enzyme. However, the extent of enzymatic hydrolysis depends on the respective carotenoid and was not quantitative compared to chemical saponification. After enzymatic cleavage, 67-89% of total capsanthin, 61-65% of total zeaxanthin, 70-81% of total β-cryptoxanthin and 70-86% of total violaxanthin were detected in free form. Nevertheless, the method described here offers the possibility to cleave in part several carotenoid esters originating from red pepper quickly and under comparatively mild reaction conditions. Replacement of the generally performed alkaline hydrolysis by enzymatic cleavage allows the resulting product to be used in food industry as “natural” coloring agent e.g. to colour cheese and jellies.

Download Full-text

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.

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

First Apocarotenoids Profiling of Four Microalgae Strains

Antioxidants ◽

10.3390/antiox8070209 ◽

2019 ◽

Vol 8 (7) ◽

pp. 209 ◽

Cited By ~ 5

Author(s):

Mariosimone Zoccali ◽

Daniele Giuffrida ◽

Fabio Salafia ◽

Carmen Socaciu ◽

Kari Skjånes ◽

...

Keyword(s):

Supercritical Fluid ◽

Fatty Acid Esters ◽

Nannochloropsis Gaditana ◽

Tetraselmis Chuii ◽

Chromatography Tandem Mass Spectrometry ◽

Wide Range ◽

First Time ◽

Chlamydomonas Sp ◽

Acid Esters

Both enzymatic or oxidative carotenoids cleavages can often occur in nature and produce a wide range of bioactive apocarotenoids. Considering that no detailed information is available in the literature regarding the occurrence of apocarotenoids in microalgae species, the aim of this study was to study the extraction and characterization of apocarotenoids in four different microalgae strains: Chlamydomonas sp. CCMP 2294, Tetraselmis chuii SAG 8-6, Nannochloropsis gaditana CCMP 526, and Chlorella sorokiniana NIVA-CHL 176. This was done for the first time using an online method coupling supercritical fluid extraction and supercritical fluid chromatography tandem mass spectrometry. A total of 29 different apocarotenoids, including various apocarotenoid fatty acid esters, were detected: apo-12’-zeaxanthinal, β-apo-12’-carotenal, apo-12-luteinal, and apo-12’-violaxanthal. These were detected in all the investigated strains together with the two apocarotenoid esters, apo-10’-zeaxanthinal-C4:0 and apo-8’-zeaxanthinal-C8:0. The overall extraction and detection time for the apocarotenoids was less than 10 min, including apocarotenoids esters, with an overall analysis time of less than 20 min. Moreover, preliminary quantitative data showed that the β-apo-8’-carotenal content was around 0.8% and 2.4% of the parent carotenoid, in the C. sorokiniana and T. chuii strains, respectively. This methodology could be applied as a selective and efficient method for the apocarotenoids detection.

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