Article
Energy & Fuels
Aliyu Adebayo Sulaimon, Habineswaran A. L. Rajan, Ali Qasim, Nwankwo Princess Christiana, Pearl Isabellah Murungi
Summary: This study developed new correlations to estimate the stability of asphaltenes in crude oil and proposed new asphaltene stability plots based on oil density. The results showed that the density-based correlations were relatively more accurate than the viscosity-based correlations. The newly developed correlations can be used to predict the stability of asphaltenes based on the physical properties of crude oil.
GEOENERGY SCIENCE AND ENGINEERING
(2023)
Article
Energy & Fuels
Kheira Gharbi, Chahrazed Benamara, Khaled Benyounes, Malcolm A. Kelland
Summary: The study highlights the importance of separating the acidic and basic fractions of asphaltenes in order to understand their structure and influence on the selection and mechanism of asphaltene inhibitors. Results indicate that the acid and base fractions of asphaltenes contain aromatic and polar compounds with specific functional groups such as carboxylic acids, phenols, amines, and aliphatic chains.
Article
Spectroscopy
Fatemeh Ahmadinouri, Parviz Parvin, Ahmad Reza Rabbani
Summary: In this paper, a modified Beer-Lambert method of laser-induced fluorescence spectroscopy is proposed for rapid identification and differentiation of crude oil fractions, which is of great significance in marine pollution research.
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY
(2024)
Article
Chemistry, Multidisciplinary
Azadeh Karevan, Mohsen Zirrahi, Hassan Hassanzadeh
Summary: A fully automated standardized SARA analysis for bitumen and heavy crudes has been developed, which is more efficient, cost-effective, and consistent compared to the conventional method.
Article
Computer Science, Artificial Intelligence
Chien-Feng Kung, Pei-Yi Hao
Summary: This study proposes a novel formulation called fuzzy hyperplane based least squares support vector machine (FH-LS-SVM) by using fuzzy set theory for LS-SVM. The FH-LS-SVM assigns fuzzy membership degrees to data vectors based on their importance and fuzzifies the parameters for the hyperplane. The proposed method captures the ambiguity in real-world classification tasks and decreases the effect of noise.
NEURAL PROCESSING LETTERS
(2023)
Article
Energy & Fuels
Jarlene da C. Silva, Lindamara M. Souza, Victor R. Fonseca, Wanderson Romao, Watson Loh, Elizabete F. Lucas
Summary: The identification of petroleum molecules or classes that stabilize water-in-oil emulsions is crucial for efficient treatment methods. This study aimed to obtain fractions of interfacially active and non-interfacially active molecules from asphaltic petroleum samples and compare them to a fraction isolated from the same sample. Various spectroscopic and analytical techniques were used to characterize the samples, and the results indicated the presence of both maltenes and asphaltenes at the water-oil interface.
Article
Computer Science, Information Systems
Zichen Zhang, Shifei Ding, Yuting Sun
Summary: This paper introduces a new method called multiple birth support vector regression (MBSVR), which constructs the regressor from multiple hyperplanes obtained by solving small quadratic programming problems, aiming for faster computation and better fitting precision.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Santos Kumar Baliarsingh, Khan Muhammad, Sambit Bakshi
Summary: A hybrid algorithm combining simulated annealing and Rao algorithm is proposed in this paper for gene selection and cancer classification. Experimental results demonstrate that the approach achieves high classification accuracy in selecting discriminating genes.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Somaye Moslemnejad, Javad Hamidzadeh
Summary: This study proposed a novel weighted support vector machine to address the noisy sensitivity problem of standard support vector machine for multiclass data classification, by introducing entropy degree and using lower and upper approximation of membership function in fuzzy rough set theory.
Article
Computer Science, Information Systems
Xiaobo Chen, Yan Xiao
Summary: This paper introduces a novel binary classifier GPTSVM, which utilizes geometric interpretation and minimum Mahalanobis norm problems to effectively solve support vector machine classification problems.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Guoquan Li, Linxi Yang, Zhiyou Wu, Changzhi Wu
Summary: Proximal support vector machine (PSVM) is a variant of support vector machine (SVM) which aims to generate a pair of non-parallel hyperplanes for classification. Introducing l(0)-norm regularization in PSVM enables simultaneous selection of important features and removal of redundant features for classification. The proposed method utilizes a continuous nonconvex function and difference of convex functions algorithms (DCA) to solve the optimization problem efficiently.
INFORMATION SCIENCES
(2021)
Article
Mathematics
Jianli Shao, Xin Liu, Wenqing He
Summary: The article introduces the use of data-adaptive SVM for instance classification in multi-class classification problems and proposes a multi-class data-dependent kernel function to enhance classification accuracy. Through simulation studies and real dataset, the excellent performance of the method is demonstrated, especially in detecting rare class instances.
Article
Computer Science, Artificial Intelligence
Chun-Na Li, Yuan-Hai Shao, Huajun Wang, Yu-Ting Zhao, Naihua Xiu, Nai-Yang Deng
Summary: This paper investigates the general forms and characteristics of nonparallel support vector machines (NSVMs) and categorizes them into two types. It reveals the advantages and defects of different types and points out the inconsistency problems. Based on this observation, a novel max-min distance-based NSVM is proposed with desired consistency. The proposed NSVM has the consistency of training and test and the consistency of metric, and it assigns each sample an ascertained loss.
APPLIED SOFT COMPUTING
(2023)
Article
Environmental Sciences
Guangxin Liu, Liguo Wang, Danfeng Liu, Lei Fei, Jinghui Yang
Summary: This article proposes a non-parallel SVM model, which improves the classification effect and generalization performance for hyperspectral images by adding an additional empirical risk minimization term and bias constraint.
Article
Mining & Mineral Processing
Chuanqi Li, Jian Zhou, Kun Du, Daniel Dias
Summary: This paper aims to develop hybrid support vector machine (SVM) models improved by three metaheuristic algorithms known as grey wolf optimizer (GWO), whale optimization algorithm (WOA) and sparrow search algorithm (SSA) for predicting the hard rock pillar stability. The results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local indices. However, the performance of the SSA-SVM model for predicting the unstable pillar is not as good as those for stable and failed pillars.
INTERNATIONAL JOURNAL OF MINING SCIENCE AND TECHNOLOGY
(2023)