A method for aggregating external operating conditions in multi-generation system optimization models

Abstract A network modeling campaign for 15 surface gathering centers involving more than 1800 completion strings has helped to lay out different risks on the existing surface pipeline network facility and improved the screening of different business and action plans for the South East Kuwait (SEK) asset of Kuwait Oil Company. Well and network hydraulic models were created and calibrated to support engineers from field development, planning, and operations teams in evaluating the hydraulics of the production system for the identification of flow assurance problems and system optimization opportunities. Steady-state hydraulic models allowed the analysis of the integrated wells and surface network under multiple operational scenarios, providing an important input to improve the planning and decision-making process. The focus of this study was not only in obtaining an accurate representation of the physical dimension of well and surface network elements, but also in creating a tool that includes standard analytical workflows able to evaluate wells and surface network behavior, thus useful to provide insightful predictive capability and answering the business needs on maintaining oil production and controlling unwanted fluids such as water and gas. For this reason, the model needs to be flexible enough in covering different network operating conditions. With the hydraulic models, the evaluation and diagnosis of the asset for operational problems at well and network level will be faster and more effective, providing reliable solutions in the short- and long-terms. The hydraulic models enable engineers to investigate multiple scenarios to identify constraints and improve the operations performance and the planning process in SEK, with a focus on optimal operational parameters to establish effective wells drawdown, evaluation of artificial lifting requirements, optimal well segregation on gathering centers headers, identification of flow assurance problems and supporting production forecasts to ensure effective production management.

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Multi-generation System Optimization Compromising Water-Energy-Environment Nexus

Green Energy and Technology - Synergy Development in Renewables Assisted Multi-carrier Systems ◽

10.1007/978-3-030-90720-4_7 ◽

2022 ◽

pp. 171-200

Author(s):

Mohammad Tajik Mansouri ◽

Zahra Ghaffarpour ◽

Majid Amidpour ◽

José María Ponce-Ortega

Keyword(s):

System Optimization ◽

Generation System ◽

Energy Environment

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High performance computing for energy system optimization models: Enhancing the energy policy tool kit

Energy Policy ◽

10.1016/j.enpol.2018.12.055 ◽

2019 ◽

Vol 128 ◽

pp. 66-74 ◽

Cited By ~ 5

Author(s):

Tarun Sharma ◽

James Glynn ◽

Evangelos Panos ◽

Paul Deane ◽

Maurizio Gargiulo ◽

...

Keyword(s):

High Performance Computing ◽

Energy Policy ◽

High Performance ◽

System Optimization ◽

Energy System ◽

Optimization Models ◽

Policy Tool ◽

Performance Computing

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CFD Study on Vermiculite Injection System Optimization in a Cyclone-Fired Boiler

ASME 2008 Power Conference ◽

10.1115/power2008-60055 ◽

2008 ◽

Author(s):

Guanghui Yu ◽

Larry Swanson ◽

Wei Zhou ◽

David Moyeda ◽

Joshua Rossow

Keyword(s):

Softening Temperature ◽

System Optimization ◽

Operating Conditions ◽

Low Rank ◽

Injection System ◽

Combustion Model ◽

Rotational Flow ◽

Low Rank Coal ◽

Boiler Furnace ◽

Areas Of Interest

Boilers firing low-rank coal generally experience high levels of slagging and fouling. To help manage convective pass fouling, various additives or conditioners can be injected into the boiler furnace high temperature region as physical disruptors of slag deposits, exhibiting a varying density or gas evolution, which physically breaks up slag or as chemical modifiers of the ash to increase its softening temperature. Vermiculite is one additive that has been applied with success; however, it is important to ensure that the injected material is placed in the most heavily slagged and fouled areas. The purpose of this study was to evaluate the effectiveness of a vermiculite injection system installed on a large cyclone-fired boiler and to identify improvements in the injection system that would permit more effective treatment of the areas of interest. During the study, a million-cell full boiler combustion model was developed. Typical features of the furnace flow and temperature field were obtained. Considering the particular operating conditions and features of the upper furnace flow field (biased gas velocities and rotational flow), optimized injection schemes were proposed. This study shows the usefulness of applying CFD to solve slagging and fouling issues for coal-fired boilers.

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System optimization of innovative tri-generation system for distributed power application

E3S Web of Conferences ◽

10.1051/e3sconf/201911106018 ◽

2019 ◽

Vol 111 ◽

pp. 06018

Author(s):

T.T. Chow ◽

Guangya Zhu ◽

C.K. Lee

Keyword(s):

System Optimization ◽

Primary Energy ◽

Pollutant Emission ◽

University Campus ◽

Generation System ◽

Distributed Power ◽

Mixed Use ◽

Prime Movers ◽

Power Application ◽

Air Conditioning Systems

The building sector is one major primary energy consumer and pollutant emission source. In recent years, the building-related research studies on the potential use of Maisotsenko-cycle in energy systems have been increasing in recent years. The growing interest lies in its expanded applications beyond the air-conditioning systems (the main “energy consumers” in buildings) into the prime movers (the key players in power generation). In order to evaluate its application merits in the practical tri-generation system of the urban districts, an extensive computer simulation platform has been developed. Based on a case study, this paper describes the techniques in the mixed use of numerical tools in performing system optimization studies for distributed power application on a university campus site. The practicality of the methodology is demonstrated through a hypothetical tri-generation system primed with Maisotsenko combustion turbine cycle. The findings are very much interesting.

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Auxiliary Bearing System Optimization for AMB Supported Rotors Based on Rotor Drop Analysis: Part I — Rotor Drop Analysis Method

Volume 7A: Structures and Dynamics ◽

10.1115/gt2016-56323 ◽

2016 ◽

Author(s):

Jianming Cao ◽

Paul Allaire ◽

Timothy Dimond ◽

Saeid Dousti

Keyword(s):

Ball Bearing ◽

System Optimization ◽

American Petroleum Institute ◽

Operating Conditions ◽

Rolling Element Bearing ◽

Hertzian Contact ◽

Analysis Method ◽

Contact Loads ◽

Auxiliary Bearing ◽

Bearing System

For rotors supported with active magnetic bearings (AMBs), the auxiliary bearing system or backup bearing system is needed to avoid serious potential internal damaging in the event of AMB loss of power or overload. The evolution of auxiliary systems has been made a priority by the American Petroleum Institute using analytical or experimental methods. In part I of this paper, a detailed rotor drop nonlinear transient analysis method including flexible shaft, rolling element bearing components including inner/outer races and balls, as well as flexible/damped supporting structures is given. A finite element based 6-DOF flexible rotor model is used to indicate shaft motion before the drop (operating conditions) and during the rotor drop event. Un-lubricated Hertzian contact models are used between the shaft and inner/outer races, between balls and races. To avoid heavy calculating time, two different methods to calculate ball bearing contact loads are discussed and the simulation results are compared. These models are applied to predict shaft-race-ball displacements and angular speeds, contact loads and ball bearing stresses during the drop for angular contact auxiliary bearings. This method also can be used to design and optimize the auxiliary bearing system as presented in the 2nd part of this paper.

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Adequacy aware long-term energy-system optimization models considering stochastic peak demand

Advances in Applied Energy ◽

10.1016/j.adapen.2021.100072 ◽

2021 ◽

pp. 100072

Author(s):

Tim Mertens ◽

Kenneth Bruninx ◽

Jan Duerinck ◽

Erik Delarue

Keyword(s):

System Optimization ◽

Energy System ◽

Optimization Models ◽

Peak Demand ◽

Term Energy

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Engine Combustion System Optimization Using Computational Fluid Dynamics and Machine Learning: A Methodological Approach

Journal of Energy Resources Technology ◽

10.1115/1.4047978 ◽

2020 ◽

Vol 143 (2) ◽

Cited By ~ 1

Author(s):

Jihad A. Badra ◽

Fethi Khaled ◽

Meng Tang ◽

Yuanjiang Pei ◽

Janardhan Kodavasal ◽

...

Keyword(s):

Machine Learning ◽

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Internal Combustion Engines ◽

Optimization Problems ◽

Combustion Engine ◽

Methodological Approach ◽

System Optimization ◽

Operating Conditions ◽

Attractive Alternative

Abstract Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. In this work, a Machine Learning-Grid Gradient Ascent (ML-GGA) approach was developed to optimize the performance of internal combustion engines. ML offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine Learning-Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variable limit extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools, and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a research octane number (RON) of 80. The ML-GGA approach yielded >2% improvements in the merit function, compared with the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared with traditional approaches.

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