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

Arias, Mariana J. C. Díaz; Santos, Allyne M. dos; Altamiranda, Edmary

doi:10.3390/pr9010184

Cited by 3 publications

(4 citation statements)

References 50 publications

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“…Fig. 7 EA-based ML framework [193] It is common practice for the procedure to include the following steps [194]- [196]:…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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Renewable energy research has become significant in the modern period owing to escalating prices of fossil fuels and the pressing need to reduce greenhouse gas emissions. Solar energy stands out among these sources due to its abundance and global accessibility. However, its weather-dependent and cyclical nature add inherent risks, making effective planning and management difficult. Soft computing technologies provide attractive solutions for modeling such systems, while machine learning and optimization techniques are gaining popularity in the solar energy industry. The current literature highlights the growing use of soft computing technologies, emphasizing their potential to address difficult challenges in solar energy systems. To effectively reap the benefits, these strategies must be seamlessly connected with emerging technologies like the Internet of Things (IoT), big data analytics, and cloud computing. This integration provides a unique opportunity to improve the scalability, flexibility, and efficiency of solar energy systems. Researchers can use these synergies to create intelligent, linked solar energy ecosystems capable of real-time optimization of energy production, delivery, and consumption. These technologies have the potential to transform the renewable energy environment, allowing for more resilient and sustainable energy infrastructures. Furthermore, as these technologies improve, there is a growing demand for trained experts to address associated cybersecurity problems, assuring the integrity and security of these sophisticated systems. Researchers may pave the road for a more sustainable and energy-efficient future by working collaboratively and using interdisciplinary methodologies.

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“…Fig. 7 EA-based ML framework [193] It is common practice for the procedure to include the following steps [194]- [196]:…”

Section: Evolutionary Algorithmsmentioning

confidence: 99%

Artificial intelligence applications in solar energy

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…Díaz Arias et al (2021) avoids the use of a superstructure approach by combining evolutionary algorithms (EA) and commercial simulation software to automate and optimize concept selection and field architecture design when considering decentralized subsea processing modules. The algorithm solves the field layout design problem with a great deal of precision, but it does require high computational effort.…”

Section: Previous Workmentioning

confidence: 99%

“…One major point lacking in previous studies is that the subsea design does not include the presence of subsea processing equipment. To overcome this, Krogstad (2018) and Díaz Arias et al (2021) dealt with the optimization of subsea processing systems. Krogstad (2018) proposed an MINLP model using the concept of superstructures to maximize the NPV of the planning and development of offshore oil field structures, considering a wide range of subsea equipment.…”

Section: Previous Workmentioning

confidence: 99%

Superstructure optimization of subsea processing layouts

Sales

Stanko

Jäschke

2023

J Petrol Explor Prod Technol

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As the oil and gas industry expands the use of subsea processing, the complexity of subsea layouts increases, making manual design processes cumbersome and suboptimal. Here we propose a method to support subsea field design where optimization is performed on a model of the subsea system, to maximize the net present value of the project. The proposed mixed-integer nonlinear (MINLP) model is solved to compute a global optimum design considering constraints in production, equipment duties and cost, and reliability and maintenance aspects. The subsea layout, equipment capacity, oil and gas production rates, and system pressures are optimized. The method was applied on a synthetic field based on the Goliat field in the Barents Sea. The method successfully finds the best designs, while the second and third-best layouts give general insights for subsea processing layout optimization.

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“…The initial population is randomly generated to ensure the diversity of the initial population and allow the initial population to cover the solution space [34]. Then, the initial population is encoded by a four-segment coding method in Section 4.3.…”

Section: Population Initialization and Fitness Valuementioning

confidence: 99%

The Low-Carbon Scheduling Optimization of Integrated Multispeed Flexible Manufacturing and Multi-AGV Transportation

et al. 2022

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As low-carbon and sustainable manufacturing becomes the mainstream development direction of the current manufacturing industry, the traditional heavy industry manufacturing enterprises in China urgently need to transform. For the heavy cement equipment manufacturing enterprise investigated here, there is a large amount of energy waste during the manufacturing operation due to scheduling confusion. In particular, the multispeed, multi-function machining and the transportation of multiple automated guided vehicles (multi-AGV) are the main influencing factors. Therefore, this paper addresses a novel low-carbon scheduling optimization problem that integrated multispeed flexible manufacturing and multi-AGV transportation (LCSP-MSFM & MAGVT). First, a mixed-integer programming (MIP) model is established to minimize the comprehensive energy consumption and makespan in this problem. In the MIP model, a time-node model is built to describe the completion time per workpiece, and a comprehensive energy consumption model based on the operation process of the machine and the AGV is established. Then, a distribution algorithm with a low-carbon scheduling heuristic strategy (EDA-LSHS) is estimated to solve the proposed MIP model. In EDA-LSHS, the EDA with a novel probability model is used as the main algorithm, and the LSHS is presented to guide the search direction of the EDA. Finally, the optimization effect and actual performance of the proposed method are verified in a case study. The experimental results show that the application of the proposed method in actual production can save an average of 43.52% comprehensive energy consumption and 64.43% makespan, which effectively expands the low-carbon manufacturing capacity of the investigated enterprise.

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

Cited by 3 publications

References 50 publications

Artificial intelligence applications in solar energy

Artificial intelligence applications in solar energy

Superstructure optimization of subsea processing layouts

The Low-Carbon Scheduling Optimization of Integrated Multispeed Flexible Manufacturing and Multi-AGV Transportation

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