Research progress on intelligent optimization techniques for energy-efficient design of ship hull forms

Zhu, Shuwei; Sun, Ning; Lv, Siying; Chen, Kaifeng; Fang, Wei; Cao, Leilei

doi:10.1007/s41965-024-00169-6

J Membr Comput

2024

DOI: 10.1007/s41965-024-00169-6

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Research progress on intelligent optimization techniques for energy-efficient design of ship hull forms

Shuwei Zhu,

Ning Sun,

Siying Lv

et al.

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2024

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Cited by 2 publications

References 131 publications

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Toward Optimal Design of a Factory Air Conditioning System Based on Energy Consumption Prediction

Zhu,

Lv,

Wang

et al. 2024

Processes

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The Make-up Air Unit (MAU) is an air conditioning system which plays an important role in serving semiconductor cleanrooms. It provides constant temperature and humidity for fresh air through various sections, including fresh air filtration, preheating, precooling, humidification, recooling, reheating, air supply, and high-efficiency filtration. However, the commonly used PID control method of the MAU indicates a deficiency in energy consumption. Hence, this research introduces a proactive energy-saving optimization control method based on machine learning and intelligent optimization algorithms. Firstly, the machine learning methods are used to train historical data of the MAU, resulting in a data-driven prediction model of energy consumption for the system. Subsequently, the customized genetic algorithm (GA) is used to optimize energy in cold and hot water systems. It facilitates the dynamic adjustment of the regulating valve opening for the cold and hot water coil in the fresh air unit, responding to real-time variations in outdoor air conditions. Meanwhile, it ensures that the supply air temperature and humidification adhere to specified requirements, thereby reducing the energy consumption associated with cold and hot water usage in the MAU. The experimental results indicate that the proposed algorithm can provide significant energy conservation in the MAU.

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Toward Optimal Design of a Factory Air Conditioning System Based on Energy Consumption Prediction

Zhu,

Lv,

Wang

et al. 2024

Processes

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Symmetrical Generalized Pareto Dominance and Adjusted Reference Vector Cooperative Evolutionary Algorithm for Many-Objective Optimization

Zhu,

Zeng,

Cui

2024

Symmetry

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In Pareto-based many-objective evolutionary algorithms, performance usually degrades drastically as the number of objectives increases due to the poor discriminability of Pareto optimality. Although some relaxed Pareto domination relations have been proposed to relieve the loss of selection pressure, it is hard to maintain good population diversity, especially in the late phase of evolution. To solve this problem, we propose a symmetrical Generalized Pareto Dominance and Adjusted Reference Vectors Cooperative (GPDARVC) evolutionary algorithm to deal with many-objective optimization problems. The symmetric version of generalized Pareto dominance (GPD), as an efficient framework, provides sufficient selection pressure without degrading diversity, no matter of the number of objectives. Then, reference vectors (RVs), initially generated evenly in the objective space, guide the selection with good diversity. The cooperation of GPD and RVs in environmental selection in part ensures a good balance of convergence and diversity. Also, to further enhance the effectiveness of RV-guided selection, we regenerate more RVs according to the proportion of valid RVs; thereafter, we select the most valid RVs for adjustment after the association operation. To validate the performance of GPDARVC, we compare it with seven representative algorithms on commonly used sets of problems. This comprehensive analysis results in 26 test problems with different objective numbers and 6 practical problems, which show that GPDARVC outperforms other algorithms in most cases, indicating its great potential to solve many-objective optimization problems.

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Research progress on intelligent optimization techniques for energy-efficient design of ship hull forms

Cited by 2 publications

References 131 publications

Toward Optimal Design of a Factory Air Conditioning System Based on Energy Consumption Prediction

Toward Optimal Design of a Factory Air Conditioning System Based on Energy Consumption Prediction

Symmetrical Generalized Pareto Dominance and Adjusted Reference Vector Cooperative Evolutionary Algorithm for Many-Objective Optimization

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