Maximizing Production with Real-Time Integrity Operating Windows

2021 ◽  
Author(s):  
Shintaro Honjo ◽  
Shunsaku Matsumoto ◽  
Takeshi Sano
Keyword(s):  
Risk Analysis ◽  
Real Time ◽  
Large Scale ◽  
Phase 1 ◽  
Operating Conditions ◽  
Individual Risk ◽  
Study Phase ◽  
Operation And Maintenance ◽  
Offshore Industry ◽  
Module Development

Abstract A conceptual design of digital Intelligent Production Integrity Operating Windows (IP-IOW) system, which is an unique and transformational solution to the oil and gas offshore industry focusing on maximizing production while optimizing equipment operation, was developed through a Nippon Foundation (NF) - DeepStar® partnership project. This connects the fluids system with the equipment system using IP-IOW architecture and specifications of a digital platform. The developed digital IP-IOW architecture contains five major evaluation modules, which are Component risk analysis (CRA), Failure Mode and Effect Analysis (FMEA), Failure Evaluation (FE), Maintenance Evaluation (ME), and Reliability Availability, and Maintainability Analysis (RAMA) focusing on critical components, including subsea choke, flowline and riser, topside choke, topside equipment, and crude export line. Each module has a function of monitoring, risk evaluation or analysis of each component based on various existing databases and/or industrial standards. Component risk analysis (CRA) module is designed as a key module to evaluate individual risk of each component based on the evaluation results of the other modules and to provide IP-IOW dashboards through operation and maintenance analysis methods. CRA module analyzes operation and maintenance based on the likelihood of failure (probability of failure) as a function of operation and maintenance conditions and impact of the damage. Calculated safety operating windows (SOWs) and reliability operating windows (ROWs) would be indicated on the IP-IOW dashboard. In this project, detailed gap analysis was also conducted to gain an understanding of what relevant industry standards and practices have been published and how these publications have gaps with respect to IP-IOW. A completed search was made of technical indices and reference sources to identify codes and standards that may or can be used for developing Integrity Operating Windows (IOWs) for topside fixed equipment. Current API, DNV, and EI applicable recommended practices (RP) cover damaging effect, in-service inspection, risk ranking, repair items prioritization, and alteration of fixed equipment systems. However, the RPs do not cover how to integrate industry best practices into a real-time digital operating environment that is integral to the next generation O&M system. A critical recommendation is to connect the systems digitally allowing for data analytics using the Digital Twin of the asset. Several case studies on module development were conducted to demonstrate an example of the module development process and the workflow of module. RAM analysis on one of selected offshore production facilities identified top 30 high risk components from around 1200 components. A pilot physical model was also developed to enable the embodiment of a new industry recommended practice for offshore large scale of FPSO asset. This conceptual pilot has used the topside choke sand erosion connecting the fluid characterization (multi-phase flow) with the selected equipment to develop the correlation addressing major technical challenges of fixed equipment while maximizing production. An example of validation results of erosion module showed good agreement between actual inspection results and CFD calculation results. Overall, IP-IOW is designed based on rigorous CFD and field data to predict critical section of system and/or equipment likelihood of failure (LOF), such as Erosion with solids, flow induced vibration (FIV), acoustic induced vibration (AIV), vortex induced vibration (VIV), and corrosion in offshore environment. Mitsubishi Heavy Industries (MHI) has been sponsored by Nippon Foundation (NF) and DeepStar (DS) Joint Ocean Innovation R&D Program to perform a research study (Phase 1) of project titled "19143 Fixed Equipment Integrity Operating Windows based on Facility Operating Conditions" started from May 2019.

Architecture and Key Technologies of Intelligent Dispatching Based on Situational Awareness

2013 ◽  
Vol 448-453 ◽  
pp. 2259-2265
Author(s):  
Sheng Chun Yang ◽  
Bi Qiang Tang ◽  
Jian Guo Yao ◽  
Feng Li ◽  
Yi Jun Yu ◽  
...  
Keyword(s):  
Decision Making ◽  
Risk Analysis ◽  
Human Computer Interaction ◽  
Early Warning ◽  
Large Scale ◽  
Situational Awareness ◽  
Power Grid ◽  
Operating Conditions ◽  
Knowledge Mining ◽  
Grid Operation

With the construction of UHV power grid, integration of large-scale renewable clean energy, and large-scale energy base putting into operation, the power grid dispatching faced with more and more complex challenges. On the basis of existing research results, architecture of intelligent dispatching based on situation awareness is proposed, so as to accurately achieve prevention and control of the power system. The shortcomings of traditional dispatching mode are analyzed firstly, and the concepts and characterization approaches of grid situational awareness and operation state trajectory of power grid are then introduced. The overall objective of intelligent dispatching is presented, including data processing and integrated knowledge mining, predictive perception of grid operation, risk analysis and comprehensive early warning, so as to achieve "automatic cruise under normal operating conditions, automatic navigation under abnormal operating conditions ". The functional framework of intelligent dispatching is also proposed in details, including four major aspects of the perception and forecasts, risk analysis, decision-making support, and automatic control, as well as three supporting functions such as post-assessment of dispatching, trajectory index calculation, and human-computer interaction (HCI).Technical innovations to support automatic intelligent dispatching are discussed and organised in three levels, i.e. perception, comprehension and projection. The breakthroughs are: construction of index system, trajectory recognition based on massive information and knowledge mining, trajectory projection taking into accounts the uncertainties, online risk assessment and early warning, power grid intelligent decision-making support, automatic coordination of grid operation control, online assessment, natural human-computer interaction mode, and etc... These are the future research areas of automatic intelligent dispatching.

scholarly journals MJO Initiation in the Real-Time Multivariate MJO Index

2013 ◽  
Vol 26 (4) ◽  
pp. 1130-1151 ◽  
Author(s):  
Katherine H. Straub
Keyword(s):  
Indian Ocean ◽  
Real Time ◽  
Case Studies ◽  
Large Scale ◽  
Phase 1 ◽  
Winter Season ◽  
Global Scale ◽  
The Real ◽  
Zonal Wavenumber ◽  
The Indian Ocean

Abstract Madden–Julian oscillation (MJO) initiation in the real-time multivariate MJO (RMM) index is explored through an analysis of observed case studies and composite events. Specific examples illustrate that both the dates of MJO initiation and the existence of the MJO itself can vary substantially among several well-known MJO indices, depending on whether the focus is on convection or circulation. Composites of “primary” MJO initiation events in which the RMM index rapidly increases in amplitude from a non-MJO state to an MJO state are presented and are supplemented by two case studies from the 1985/86 winter season. Results illustrate that, for primary MJO initiation events in the Indian Ocean (RMM phase 1), slowly eastward-propagating 850-hPa (200 hPa) easterly (westerly) anomalies over the Indian Ocean precede the amplification of the RMM index by at least 10 days, while suppressed convection over the western Pacific Ocean precedes the amplification by 5 days. These “local” Eastern Hemispheric predecessor signals are similar to those found in successive (well established) MJO events but are not captured by the global-scale RMM index because of their smaller zonal scale. The development of a primary MJO event is thus often transparent in the RMM index, since it occurs on scales smaller than zonal wavenumber 1, particularly in convection. Even when the RMM index is altered to respond to convection only, the same local precursor signals are found. Both composites and case studies suggest that, for primary MJO initiation events in the Indian Ocean, the development of global-scale circulation anomalies typically precedes the onset of large-scale deep convection.

Running State Assessment for Induced Draft Fans Using Auto Encoder Model Combined With Fuzzy Synthetic

2019 ◽  
Author(s):  
Jiawei Yang ◽  
Di Hu ◽  
Tao Yang ◽  
Wei Gao ◽  
Chunmei Li ◽  
...  
Keyword(s):  
Power Plant ◽  
Large Scale ◽  
Thermal Power ◽  
State Assessment ◽  
Operating Conditions ◽  
Assessment Model ◽  
Fuzzy Evaluation ◽  
Auxiliary Equipment ◽  
Operation And Maintenance ◽  
Induced Draft Fan

Abstract In order to explore the operation and maintenance characteristics of important auxiliary machines in large-scale power plant coal-fired boilers, a running state assessment model for auxiliary equipment is established. In this paper, taking the complex variability of the operating conditions of thermal power equipment into consider, auto encoder model combined with fuzzy synthetic is proposed. Based on the residual of the model results and the actual power plant operation data, combined with the fuzzy evaluation model to establish a state assessment model, and analyze the actual situation of the induced draft fan of the power plant, to make a real-time assessment of operation status. The evaluation results show the advantages of the state assessment strategy proposed in this paper, and it can reflect the deterioration of the induced draft fan status in time, providing guidance for the operation and maintenance of the equipment.

A Practical Real-Time Motion Cueing Algorithm for Crane Simulator

2014 ◽  
Vol 532 ◽  
pp. 280-284
Author(s):  
Ze Zhu ◽  
Han Bin Xiao ◽  
Guo Xian Wang ◽  
Kan Hu
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Computing Time ◽  
Reference Value ◽  
Simulation Models ◽  
Field Application ◽  
Operating Conditions ◽  
Motion Simulation ◽  
Time Motion ◽  
Motion Cueing

This paper addresses motion simulation issues involved in a crane simulator with a 6-Degree-Of-Freedom platform. The crane dynamical models in terms of a range of motion equations are built for the operator cab in various main operating conditions. The ways to make the motion to be reproduced are investigated and the corresponding real-time motion cueing algorithm is proposed based on a special curve-merging approach. The algorithm is of simplified form and the needed computing time is very short, which makes it very practicable. The crane simulation system with this algorithm being adopted has been successfully developed and put into practice. The field application showed that the algorithm is able to ensure achievement of satisfied motion cueing effects. The proposed motion simulation models and the corresponding algorithm will provide significant reference value for developing similar motion simulation systems for all types of large-scale cranes or construction machinery.

RBF-trained POD-accelerated CFD analysis of wind loads on PV systems

2017 ◽  
Vol 27 (3) ◽  
pp. 660-673 ◽  
Author(s):  
Victor Huayamave ◽  
Andres Ceballos ◽  
Carolina Barriento ◽  
Hubert Seigneur ◽  
Stephen Barkaszi ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Operating Conditions ◽  
Time Response ◽  
Cfd Analysis ◽  
Pv System ◽  
Data Set ◽  
Content Type ◽  
Pv Systems ◽  
Design Variables

Purpose Wind loading calculations are currently performed according to the ASCE 7 standard. Values in this standard were estimated from simplified models that do not necessarily take into account relevant flow characteristics. Thus, the standard does not have provisions to handle the majority of rooftop photovoltaic (PV) systems. Accurate solutions for this problem can be produced using a full-fledged three-dimensional computational fluid dynamics (CFD) analysis. Unfortunately, CFD requires enormous computation times, and its use would be unsuitable for this application which requires real-time solutions. To this end, a real-time response framework based on the proper orthogonal decomposition (POD) method is proposed. Design/methodology/approach A real-time response framework based on the POD method was used. This framework used beforehand and off-line CFD solutions from an extensive data set developed using a predefined design space. Solutions were organized to form the basis snapshots of a POD matrix. The interpolation network using a radial-basis function (RBF) was used to predict the solution from the POD method given a set of values of the design variables. The results presented assume varying design variables for wind speed and direction on typical PV roof installations. Findings The trained POD–RBF interpolation network was tested and validated by performing the fast-algebraic interpolation to obtain the pressure distribution on the PV system surface and they were compared to actual grid-converged fully turbulent 3D CFD solutions at the specified values of the design variables. The POD network was validated and proved that large-scale CFD problems can be parametrized and simplified by using this framework. Originality/value The solar power industry, engineering design firms and the society as a whole could realize significant savings with the availability of a real-time in situ wind-load calculator that can prove essential for plug-and-play installation of PV systems. Additionally, this technology allows for automated parametric design optimization to arrive at the best fit for a set of given operating conditions. All these tasks are currently prohibited because of the massive computational resources and time required to address large-scale CFD analysis problems, all made possible by a simple but robust technology that can yield massive savings for the solar industry.

scholarly journals Design of Optimal Real Time Emulators for Steam and Wind Turbines for Avoiding Risky Severe Conditions

2022 ◽  
Author(s):  
helmy El-Zoghby ◽  
Haitham S. Ramadan ◽  
Hassan Haes Alhelou
Keyword(s):  
Real Time ◽  
Wind Turbines ◽  
Large Scale ◽  
Short Circuit ◽  
Synchronous Generator ◽  
Fast Response ◽  
Operating Conditions ◽  
Control Approach ◽  
Three Phase ◽  
Experimental Validity

Abstract Modern energy infrastructures may face critical impacts on distributed generation and microgrids in presence of renewable and conventional energy sources. Fast restorations for these networks through proposing convenient proactive protection systems become mandatory for securing energy particularly after severe faults. This paper deals with presenting a descriptive modelling and comprehensive analysis of both steam and wind turbines using optimal real time emulators with unique testbench. Based on the dynamics of each turbine, both emulators are performed using 4kW, 180V, 1500r.p.m separately exited DC motor coupled to 2kW, 380V, 50Hz, 1500r.p.m three-phase synchronous generator. For real-time interface implementation, the mathematical models of steam and wind turbines are realized using LabVIEWTM software. The characterization and verification of both emulated steam and wind turbines are examined at different normal operating conditions in terms of steam valve position and wind speed, respectively. To regulate the current for both systems despite their diverse dynamics, a simple industrial proportional-integral (PI) controller is considered. Unlike other artificial intelligence-based controllers, the offline-controller gains are scheduled using genetic algorithm (GA) via MatlabTM software to ensure the due fast response to cope with unexpected faults. The experimental validity of both emulators is tested at the most severe abnormal operating conditions. The three-phase short circuit is considered at the generator terminals with different fault periods until reaching out-of-step conditions. From numerical analysis and experimental results, the characterization of both emulated steam and wind turbines explicitly mimics their real large-scale turbines in normal conditions. The emulators’ fast responses using the proposed GA-PI control approach are verified. Besides, the experimental dynamic behavior convergence and interoperability between the emulated and real systems for both steam and wind turbines are validated under severe conditions. The practical results confirm the fast-nature performance of the GA in avoid risky instability conditions.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

Methods for the Quantification of Salivary Cortisol and of a-amylase in Biosensors and Portable Devices

2018 ◽  
Vol 68 (12) ◽  
pp. 2857-2859
Author(s):  
Cristina Mihaela Ghiciuc ◽  
Andreea Silvana Szalontay ◽  
Luminita Radulescu ◽  
Sebastian Cozma ◽  
Catalina Elena Lupusoru ◽  
...  
Keyword(s):  
Real Time ◽  
Medical Practice ◽  
Salivary Cortisol ◽  
Clinical Studies ◽  
Large Scale ◽  
Psychological Research ◽  
Portable Devices ◽  
Salivary Amylase ◽  
Specificity And Sensitivity

There is an increasing interest in the analysis of salivary biomarkers for medical practice. The objective of this article was to identify the specificity and sensitivity of quantification methods used in biosensors or portable devices for the determination of salivary cortisol and salivary a-amylase. There are no biosensors and portable devices for salivary amylase and cortisol that are used on a large scale in clinical studies. These devices would be useful in assessing more real-time psychological research in the future.

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