scholarly journals Evolutionary Algorithm to Support Field Architecture Scenario Screening Automation and Optimization Using Decentralized Subsea Processing Modules

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 184
Author(s):  
Mariana J. C. Díaz Arias ◽  
Allyne M. dos Santos ◽  
Edmary Altamiranda
Keyword(s):  
Thermal Management ◽  
Concept Development ◽  
Test Cases ◽  
Production Network ◽  
Concept Selection ◽  
Genetic Operators ◽  
Number Of Solutions ◽  
Field Development ◽  
Variation Operators ◽  
Selection For

Manual generation of test cases and scenario screening processes, during field architecture concept development, may produce a limited number of solutions that do not necessarily lead to an optimal concept selection. For more complex subsea field architectures, which might include processing modules for enhancing pressure and thermal management for the production network, the number of configuration cases and scenarios to evaluate can be extremely large and time and resource-consuming to handle through conventional manual design processes. This paper explores the use of evolutionary algorithms (EA) to automate case generation, scenario screening, and optimization of decentralized subsea processing modules during field development. An evaluation of various genetic operators and evolution strategies was performed to compare their performance and suitability to the application. Based on the evaluation results, an EA using structural uniform crossover and a gradient plus boundary mutation as the main variation operators was developed. The methodology combines EA and an integrated modeling approach to automate and optimize the concept selection and field architecture design when considering decentralized subsea processing modules.

scholarly journals Concept Selection for Hydrocarbon Field Development Planning

Engineering ◽  
2012 ◽  
Vol 04 (11) ◽  
pp. 794-808 ◽  
Author(s):  
J. Efrain Rodriguez-Sanchez ◽  
J. Martin Godoy-Alcantar ◽  
Israel Ramirez-Antonio
Keyword(s):  
Development Planning ◽  
Concept Selection ◽  
Field Development ◽  
Selection For ◽  
Hydrocarbon Field

Riser Concept Selection for FPSO in Deepwater Norwegian Sea: A Case Study

2018 ◽  
Author(s):  
Arvind Keprate ◽  
R. M. Chandima Ratnayake
Keyword(s):  
Literature Review ◽  
Geographical Location ◽  
Weather Conditions ◽  
Large Vessel ◽  
Concept Selection ◽  
Flexible Riser ◽  
Norwegian Sea ◽  
Daunting Task ◽  
Selection For

Selecting a riser concept for FPSOs stationed in deep water has posed challenges, due to the high hydrostatic pressure and large vessel payload. One of the major factors governing the riser concept selection for deepwater FPSOs is the geographical location and weather conditions prevalent in the region. For example, the free hanging flexible riser has been mostly used in the moderate environments of offshore Brazil, while concepts like the SCR and Hybrid Riser Tower (HRT) are dominant in the calm weather conditions of the West of Africa (WoA). Selecting a riser concept for an FPSO stationed in harsh weather conditions like those of the Northern Norwegian Sea is a daunting task. This is due to the large vessel offsets and dynamics, which are directly transferred along the riser’s length to its base, thereby causing considerable fatigue damage to the riser. The main aim of this paper is to recommend a suitable riser concept, which may be hooked to an internal turret moored FPSO stationed in water of 1500m depth and in the harsh environmental conditions of the Northern Norwegian Sea. The recommendations are based on the literature review and the case study performed in the manuscript. On the basis of the literature review, a lazy wave configuration of flexible riser and Steel Lazy Wave Riser (SLWR) has been considered as a viable riser concept. Thereafter, a case study is performed to compare the two riser concepts, on the basis of vessel payload, fabrication cost and installation cost.

scholarly journals Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling

2020 ◽  
Author(s):  
Fangfang Zhang ◽  
Yi Mei ◽  
S Nguyen ◽  
Mengjie Zhang
Keyword(s):  
Genetic Programming ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Computational Time ◽  
Flexible Job Shop ◽  
Flexible Job Shop Scheduling ◽  
Genetic Operators ◽  
Operation Sequencing ◽  
Shop Scheduling ◽  
Selection For

© 2020, Springer Nature Switzerland AG. Dynamic flexible job shop scheduling (DFJSS) has been widely studied in both academia and industry. Both machine assignment and operation sequencing decisions need to be made simultaneously as an operation can be processed by a set of machines in DFJSS. Using scheduling heuristics to solve the DFJSS problems becomes an effective way due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, the subtrees of the selected parents are randomly chosen in traditional GP for crossover and mutation, which may not be sufficiently effective, especially in a huge search space. This paper proposes new strategies to guide the subtree selection rather than picking them randomly. To be specific, the occurrences of features are used to measure the importance of each subtree of the selected parents. The probability to select a subtree is based on its importance and the type of genetic operators. This paper examines the proposed algorithm on six DFJSS scenarios. The results show that the proposed GP algorithm with the guided subtree selection for crossover can converge faster and achieve significantly better performance than its counterpart in half of the scenarios while no worse in all other scenarios without increasing the computational time.

Mission concept selection for an asteroid mining mission

2016 ◽  
Vol 88 (3) ◽  
pp. 458-470 ◽  
Author(s):  
Alena Probst ◽  
Graciela González Peytaví ◽  
Bernd Eissfeller ◽  
Roger Förstner
Keyword(s):  
Concept Selection ◽  
Selection For

Concept development and candidate technologies selection for the DEMO-FNS fuel cycle systems

2021 ◽  
Author(s):  
Sergey S Ananyev ◽  
Boris Ivanov ◽  
Alexey Dnestrovskiy ◽  
Andrei S Kukushkin ◽  
Alexander Spitsyn ◽  
...  
Keyword(s):  
Fuel Cycle ◽  
Concept Development ◽  
Cycle Systems ◽  
Selection For

Test Pair Selection for Test Case Prioritization in Regression Testing for WS-BPEL Programs

2013 ◽  
Vol 10 (1) ◽  
pp. 73-102 ◽  
Author(s):  
Lijun Mei ◽  
Yan Cai ◽  
Changjiang Jia ◽  
Bo Jiang ◽  
W.K. Chan
Keyword(s):  
Web Services ◽  
Structural Similarity ◽  
Regression Testing ◽  
Test Pair ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Test Case Prioritization ◽  
Empirical Results ◽  
Selection For

Many web services not only communicate through XML-based messages, but also may dynamically modify their behaviors by applying different interpretations on XML messages through updating the associated XML Schemas or XML-based interface specifications. Such artifacts are usually complex, allowing XML-based messages conforming to these specifications structurally complex. Testing should cost-effectively cover all scenarios. Test case prioritization is a dimension of regression testing that assures a program from unintended modifications by reordering the test cases within a test suite. However, many existing test case prioritization techniques for regression testing treat test cases of different complexity generically. In this paper, the authors exploit the insights on the structural similarity of XML-based artifacts between test cases in both static and dynamic dimensions, and propose a family of test case prioritization techniques that selects pairs of test case without replacement in turn. To the best of their knowledge, it is the first test case prioritization proposal that selects test case pairs for prioritization. The authors validate their techniques by a suite of benchmarks. The empirical results show that when incorporating all dimensions, some members of our technique family can be more effective than conventional coverage-based techniques.

Multi-criteria test cases selection for model transformations

2020 ◽  
Vol 27 (1-2) ◽  
pp. 91-118
Author(s):  
Bader Alkhazi ◽  
Chaima Abid ◽  
Marouane Kessentini ◽  
Dorian Leroy ◽  
Manuel Wimmer
Keyword(s):  
Model Transformations ◽  
Test Cases ◽  
Selection For
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