Equilibrium Position Analysis for Offloading Operations With Turret-Moored FPSO

2018 ◽  
Author(s):  
Alex S. Huang ◽  
Felipe M. Moreno ◽  
Eduardo A. Tannuri ◽  
Joselito G. A. Câmara
Keyword(s):  
Machine Learning ◽  
Equilibrium Points ◽  
Environmental Data ◽  
Machine Learning Classification ◽  
Environmental Disasters ◽  
Deep Waters ◽  
Campos Basin ◽  
The Stability ◽  
Time Of Operation ◽  
The Given

With the expansion of oil exploration in deep waters, assessing the risks associated with offloading operations becomes essential in preventing accidents that may cause huge environmental disasters. In this paper, it will be studied the system composed of a turret-moored FPSO connected to a shuttle tanker which is assisted by a tug boat to maintain its position during an offloading operation. Using environmental data collected over a period of 6 years, from 2004 to 2009, from the Campos Basin in Brazil, it was calculated the equilibrium positions of the system considering its constraints (vessels have to keep a minimum alignment), and verified the stability of those equilibrium points for all the given conditions. The results obtained were simplified and grouped into operational rules using machine learning classification techniques. Based on this static analysis, it was calculated the FPSO headings probabilities during offloading operation and the expected down time of operation in the Campos Basin.

Equilibrium Position Analysis for Offloading Operations With Turret-Moored FPSO

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Alex S. Huang ◽  
Felipe M. Moreno ◽  
Eduardo Aoun Tannuri ◽  
Joselito G. A. Câmara
Keyword(s):  
Wind Wave ◽  
Equilibrium Points ◽  
Environmental Data ◽  
Risk Level ◽  
Environmental Disasters ◽  
Deep Waters ◽  
Campos Basin ◽  
Safety Margins ◽  
Operational Criteria ◽  
The Stability

With the expansion of oil exploration in deep waters, assessing the risks associated with offloading operations becomes essential in preventing accidents that may cause huge environmental disasters. In this paper, the system that composed of a turret-moored floating production storage and offloading (FPSO) connected to a conventional shuttle tanker, which is assisted by a tug boat to maintain its position during an offloading operation, will be studied. Using environmental data collected over a period of 6 years, from 2004 to 2009, from the Campos Basin in Brazil, the equilibrium positions of the system were calculated, considering its constraints (operational criteria defined by Petrobras) and verifying the stability of those equilibrium points. The hydrodynamic and aerodynamic static forces were calculated using models validated in the literature. Dynamic effects and oscillations are taken into account by adding safety margins to the operational sectors. With this analysis, we calculated the FPSO heading probabilities during an offloading operation and the expected downtime of operation in Campos Basin. We concluded that the downtime of the offloading operation with a conventional shuttle tanker is close to that with a dynamic positioned (DP) shuttle tanker (10% downtime). Furthermore, the results from the stability analysis were used to generate a simplified set of rules to classify the environmental conditions into four classes of operational risk by applying an unbiased decision tree. This method obtains practical rules based on measurements of wind, wave, and current, allowing the operator to quickly evaluate the risk level before starting the operation.

Slope movement hazards of the mining-dumps in the Dorog Basin, Hungary

2021 ◽  
Author(s):  
Gáspár Albert ◽  
Dávid Gerzsenyi ◽  
Réka Pogácsás
Keyword(s):  
Machine Learning ◽  
Waste Treatment ◽  
Slope Angle ◽  
Mining Area ◽  
Mining Waste ◽  
Cement Industry ◽  
Machine Learning Classification ◽  
Geomorphological Mapping ◽  
The Stability ◽  
Natural Slopes

<p>The Dorog Basin was a mining area in northern central Hungary for more than two centuries. Tunnel mining and quarrying of Eocene coal was the main industrial activity in the basin from the mid-19<sup>th</sup> century until the late 1990s. Extensive quarrying of the Cretaceous marl and Triassic limestone for the cement industry is also present in the area, along with pits of sand and fire clay and travertine quarries. Though the waste treatment is controlled by law and strict directives, the morphology and the material characteristics of the waste heaps are often enough to increase the chance of slope failures. As the mining waste heaps and tailings are often adjacent to residential and agricultural areas, they are considered as hazard sources. The combined use of remote sensing and machine learning methods can help to evaluate the stability of the waste heaps and select the sites where further hazard assessment is needed on the field.</p><p>The slopes of the area were sorted into six stability categories (scarps, transitional slopes, debris, low-lying accumulation areas, hilltops, stabile slopes) with random forest machine learning classification. The sample areas for the analysis were selected based on geomorphological mapping in the area and the re-evaluation of the recorded landslides from the landslide inventory. The classifier (Rstudio) analysed one lithological and two to six morphometric predictor variables. We tested several sets of different variables and selected the best performing set, which included the slope angle, profile curvature, TWI, mean upslope area, and the normalized height morphometric indices.</p><p>After the classification, the distribution of the stability categories was computed for three different areas: the mining waste heaps, the remediated quarries, and the natural slopes. The mining waste sites and the quarries were delineated using the national mining waste inventory, satellite images and topographic maps. Then a likelihood ratio analysis was done to calculate the relative frequencies of the stability categories in the different area types. It was expected that the stability category representing the slope debris at rest will be the most frequent in the waste heap areas. The statistical analysis reinforced this hypothesis by resulting a 54% larger likelihood compared to the natural slopes. It was also revealed that the most dangerous category, the scarps, are less likely on the waste heaps than on the natural slopes, which is a reassuring result. However, the transitional types (slopes that are still in movement) are more likely by 25% on the waste heaps. Even this slightly increased likelihood makes the local villages more prone to hazardous events, so an increased concern is also justified.</p><p>From the part of G.A. financial support was provided from the NRDI Fund of Hungary, Thematic Excellence Programme no. TKP2020-NKA-06 (National Challenges Subprogramme) funding scheme. D. G.: The study was supported by the ÚNKP-19-3 New National Excellence Program of the Ministry for Innovation and Technology, Hungary.</p>

scholarly journals Supervised Machine Learning Classification Algorithms for Detection of Fracture Location in Dissimilar Friction Stir Welded Joints

2021 ◽  
Vol 15 (58) ◽  
pp. 242-253
Author(s):  
Akshansh Mishra ◽  
Apoorv Vats
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Classification Algorithms ◽  
Accuracy Score ◽  
Friction Stir ◽  
Machine Learning Classification ◽  
Fracture Location ◽  
Svm Algorithm ◽  
The Given

Machine Learning focuses on the study of algorithms that are mathematical or statistical in nature in order to extract the required information pattern from the available data. Supervised Machine Learning algorithms are further sub-divided into two types i.e. regression algorithms and classification algorithms. In the present study, four supervised machine learning-based classification models i.e. Decision Trees algorithm, K- Nearest Neighbors (KNN) algorithm, Support Vector Machines (SVM) algorithm, and Ada Boost algorithm were subjected to the given dataset for the determination of fracture location in dissimilar Friction Stir Welded AA6061-T651 and AA7075-T651 alloy. In the given dataset, rotational speed (RPM), welding speed (mm/min), pin profile, and axial force (kN) were the input parameters while Fracture location is the output parameter. The obtained results showed that the Support Vector Machine (SVM) algorithm classified the fracture location with a good accuracy score of 0.889 in comparison to the other algorithms.

scholarly journals EEG Channel Selection Using A Modified Grey Wolf Optimizer

2021 ◽  
Vol 5 (1) ◽  
pp. 17-24
Author(s):  
Hussien Rezk Hussien ◽  
El-Sayed M. El-Kenawy ◽  
Ali I. El-Desouky
Keyword(s):  
Machine Learning ◽  
Search Space ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Eeg Signals ◽  
Data Set ◽  
Machine Learning Classification ◽  
The Stability ◽  
Exponential Change ◽  
The Brain

Consider an increasingly growing field of research, Brain-Computer Interface (BCI) is to form a direct channel of communication between a computer and the brain. However, extracting features of random time-varying EEG signals and their classification is a major challenge that faces current BCI. This paper proposes a modified grey wolf optimizer (MGWO) that can select optimal EEG channels to be used in (BCIs), the way that identifies main features and the immaterial ones from that dataset and the complexity to be removed. This allows (MGWO) to opt for optimal EEG channels as well as helping machine learning classification in its tasks when doing training to the classifier with the dataset. (MGWO), which imitates the grey wolves leadership and hunting manner nature and which consider metaheuristics swarm intelligence algorithms, is an integration with two modification to achieve the balance between exploration and exploitation the first modification applies exponential change for the number of iterations to increase search space accordingly exploitation, the second modification is the crossover operation that is used to increase the diversity of the population and enhance exploitation capability. Experimental results use four different EEG datasets BCI Competition IV- dataset 2a, BCI Competition IV- data set III, BCI Competition II data set III, and EEG Eye State from UCI Machine Learning Repository to evaluate the quality and effectiveness of the (MGWO). A cross-validation method is used to measure the stability of the (MGWO).

scholarly journals A Dynamic Duopoly Cournot Model with R&D Efforts and Its Dynamic Behavior Analysis

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Wei Zhou ◽  
Jie Zhou ◽  
Tong Chu ◽  
Hui Li
Keyword(s):  
Dynamic Behavior ◽  
Equilibrium Points ◽  
Flip Bifurcation ◽  
Nash Equilibrium Point ◽  
Dynamical Behaviors ◽  
Coexistence Of Attractors ◽  
Route To Chaos ◽  
The Stability ◽  
The Given ◽  
Jury Criterion

In this paper, a dynamic two-stage Cournot duopoly game with R&D efforts is built. Then, the local stability of the equilibrium points are discussed, and the stability condition of the Nash equilibrium point is also deduced through Jury criterion. The complex dynamical behaviors of the built model are investigated by numerical simulations. We found that the unique route to chaos is flip bifurcation, and the increase of adjusting speed will cause the system to lose stability and produce more complex dynamic behavior. In addition, we also found the phenomenon of multistability in the given model. Several kinds of coexistence of attractors are shown. In particular, we found that boundary attractors can coexist with internal attractors, which also aggravates the complexity of the system. At last, the chaotic state in the built system has been successfully controlled.

Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm

2019 ◽  
Author(s):  
Andrew Medford ◽  
Shengchun Yang ◽  
Fuzhu Liu
Keyword(s):  
Machine Learning ◽  
Chemical Potential ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Low Energy ◽  
Training Data ◽  
High Coverage ◽  
Metal Compounds ◽  
Adsorption Energies ◽  
The Stability

Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CH<sub>x</sub>, NH<sub>x</sub> and OH<sub>x</sub> species on the oxygen vacancy and pristine rutile TiO<sub>2</sub>(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N<sup>1.12</sup>) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions.

Intelligent Techniques Analysis for Glycosylation Site Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Alhassan Alkuhlani ◽  
Walaa Gad ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Cell Interaction ◽  
Glycosylation Site ◽  
Machine Learning Classification ◽  
Site Prediction ◽  
Glycosylation Sites ◽  
Wide Range ◽  
Feature Extraction And Selection ◽  
Computational Intelligent

Background: Glycosylation is one of the most common post-translation modifications (PTMs) in organism cells. It plays important roles in several biological processes including cell-cell interaction, protein folding, antigen’s recognition, and immune response. In addition, glycosylation is associated with many human diseases such as cancer, diabetes and coronaviruses. The experimental techniques for identifying glycosylation sites are time-consuming, extensive laboratory work, and expensive. Therefore, computational intelligence techniques are becoming very important for glycosylation site prediction. Objective: This paper is a theoretical discussion of the technical aspects of the biotechnological (e.g., using artificial intelligence and machine learning) to digital bioinformatics research and intelligent biocomputing. The computational intelligent techniques have shown efficient results for predicting N-linked, O-linked and C-linked glycosylation sites. In the last two decades, many studies have been conducted for glycosylation site prediction using these techniques. In this paper, we analyze and compare a wide range of intelligent techniques of these studies from multiple aspects. The current challenges and difficulties facing the software developers and knowledge engineers for predicting glycosylation sites are also included. Method: The comparison between these different studies is introduced including many criteria such as databases, feature extraction and selection, machine learning classification methods, evaluation measures and the performance results. Results and conclusions: Many challenges and problems are presented. Consequently, more efforts are needed to get more accurate prediction models for the three basic types of glycosylation sites.

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