A PROBABILISTIC APPROACH FOR COMPRESSOR SIZING AND PLANT DESIGN

LectureEquipment sizing decisions in the Oil and Gas Industry often have to be made based on incomplete data. Often, the exact process conditions are based on numerous assumptions about well performance, market conditions, environmental conditions and others. Since the ultimate goal is to meet production commitments, the traditional way of addressing this is, to use worst case conditions, and often adding margins onto these. This will invariably lead to plants that are oversized, in some instances by large margins. In reality, the operating conditions are very rarely the assumed worst case conditions, but they are usually more benign most of the time. Plants designed based on worst case conditions, once in operation, will therefore usually not operate under optimum conditions, have reduced flexibility, and therefore cause both higher capital expenses and operating expenses. The authors outline a new probabilistic methodology that provides a framework for more intelligent process-machine designs . A standardized framework using Monte Carlo simulation and risk analysis is presented that more accurately defines process uncertainty and its impact on machine performance . This paper describes a new method for the design of efficient plants. The use of statistical and probabilistic tools allows to better account for the unpredictability of component performance, as well as for ambient conditions and demand. Using the methodology allows to design plants that perform best under the most likely scenarios, as opposed to traditional designs that tend to work best under unlikely worst case scenarios. A study was performed for a relatively simple scenario, but the method is not limited, and can easily be adapted to scenarios involving entire pipeline systems, complete plants, or platform operations. Based on these considerations, significant cost reductions are possible in many cases

A PROBABILISTIC APPROACH FOR COMPRESSOR SIZING AND PLANT DESIGN

LectureEquipment sizing decisions in the Oil and Gas Industry often have to be made based on incomplete data. Often, the exact process conditions are based on numerous assumptions about well performance, market conditions, environmental conditions and others. Since the ultimate goal is to meet production commitments, the traditional way of addressing this is, to use worst case conditions, and often adding margins onto these. This will invariably lead to plants that are oversized, in some instances by large margins. In reality, the operating conditions are very rarely the assumed worst case conditions, but they are usually more benign most of the time. Plants designed based on worst case conditions, once in operation, will therefore usually not operate under optimum conditions, have reduced flexibility, and therefore cause both higher capital expenses and operating expenses. The authors outline a new probabilistic methodology that provides a framework for more intelligent process-machine designs . A standardized framework using Monte Carlo simulation and risk analysis is presented that more accurately defines process uncertainty and its impact on machine performance . This paper describes a new method for the design of efficient plants. The use of statistical and probabilistic tools allows to better account for the unpredictability of component performance, as well as for ambient conditions and demand. Using the methodology allows to design plants that perform best under the most likely scenarios, as opposed to traditional designs that tend to work best under unlikely worst case scenarios. A study was performed for a relatively simple scenario, but the method is not limited, and can easily be adapted to scenarios involving entire pipeline systems, complete plants, or platform operations. Based on these considerations, significant cost reductions are possible in many cases

Assessment of a Neural-Network-Based Optimization Tool: a Low Specific-Speed Impeller Application

This work provides a detailed description of the fluid dynamic design of a low specific-speed industrial pump centrifugal impeller. The main goal is to guarantee a certain value of the specific-speed number at the design flow rate, while satisfying geometrical constraints and industrial feasibility. The design procedure relies on a modern optimization technique such as an Artificial-Neural-Network-based approach (ANN). The impeller geometry is parameterized in order to allow geometrical variations over a large design space. The computational framework suitable for pump optimization is based on a fully viscous three-dimensional numerical solver, used for the impeller analysis. The performance prediction of the pump has been obtained by coupling the CFD analysis with a 1D correlation tool, which accounts for the losses due to the other components not included in the CFD domain. Due to both manufacturing and geometrical constraints, two different optimized impellers with 3 and 5 blades have been developed, with the performance required in terms of efficiency and suction capability. The predicted performance of both configurations were compared with the measured head and efficiency characteristics

Aeronautical engineering: A continuing bibliography with indexes (supplement 271)

This bibliography lists 666 reports, articles, and other documents introduced into the NASA scientific and technical information system in October, 1991. Subject coverage includes design, construction and testing of aircraft and aircraft engines; aircraft components, equipment and systems; ground support systems; and theoretical and applied aspects of aerodynamics and general fluid dynamics

Potential and Challenges of ORC driven Heat Pumps Based on Gas Bearing Supported Turbomachinery

Electrically Driven Heat Pumps (EDHPs) have been identified as a key technology to reduce the energy consumption in the domestic space heating sector. However, since EDHPs require electrical power, they face issues related to network overload at peak-time, high operating costs, and increased carbon footprint. A promising alternative to address these shortcomings is the use of Thermally Driven Heat Pumps (TDHPs), which are powered by a heat source instead of electricity. TDHPs offer the possibility of running with numerous types of heat sources, even renewable ones. A promising TDHP technology is the ORC driven Heat Pump (HP-ORC). It consists in the combination of an Organic Rankine Cycle (ORC) and a Heat Pump (HP). This technology provides high flexibility in the heat source selection while offering the possibility of producing electricity. Furthermore, when combined with gas bearing supported turbomachinery, the HP-ORC technology offers an oil-free heating solution. The goal of this thesis is to identify the potential and challenges of the HP-ORC technology. Since HP-ORCs are complex systems, an integrated design and optimization procedure has been applied, aiming at objectives of performance, investment cost, and feasibility. While such integrated design procedures are attractive, they are complex and time consuming. Accurate reduced order models for the various system components are, therefore, highly beneficial for improving the design process. These models are, however, currently missing for small-scale turbomachinery, and hence are developed in a first step. These pre-design models are three orders of magnitude faster than mean-line analysis models while predicting isentropic efficiencies within a 4% error band. Moreover, the presented models provide updated design guidelines for radial turbomachinery and offer insights into the underlying phenomena that shape the efficiency contours. In a second step, the improved turbomachinery models have been used for the integrated optimization of the Compressor Turbine Unit (CTU). The results suggest that the performance trade-off is governed by the turbomachinery components. Further, the design robustness of the CTU is investigated, showing the importance of mitigating bearing manufacturing errors while having fluid leakage and turbomachinery tip clearances as small as possible. In a third step, the thermo-economic optimization of the HP-ORC is developed. For domestic heat pump applications, the optimum working fluid and heat exchanger design are retrieved. Using a hot source at 180°C, exergetic efficiencies over 50% and COPs above 1.8 are achieved, showing a 30% increase compared to the proof of concept. In addition, two configurations are compared, whether the ORC expander and HP compressor are mechanically coupled or not. Although the uncoupled HP-ORC offers more flexibility, it presents inferior thermo-economic trade-offs compared to the coupled configuration. The HP-ORC is compared to typical absorption systems, suggesting that single effect absorption heat pumps are competitive at low heat source temperatures (<120°C), whereas HP-ORCs outperform when the heat source temperature increases above 150°C. In a final step, the optimization tools developed in this thesis are applied to three case studies in which the HP-ORC may deploy its potential: domestic heating, greenhouse, and air conditioning in helicopters using the engine exhaust heat