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Efficient Productivity-Aware Control Parameter Optimization in Cutter Suction Dredger Construction Using Machine Learning with Parallel Global Search

Year: 2024

Type: article

Abstract: This paper proposes an efficient productivity-aware optimization framework that utilizes hybrid machine learning with parallel global search to timely and appropriately adjust the critical control parameters (CCPs) of a cutter suction dredger (CSD) during construction. This optimization framework consists of three main parts. First, a hybrid Jaya–multilayer perceptron (MLP) algorithm was developed to rapidly construct a model that captures the interaction between construction parameters and slurry concentration. Next, the preliminary coarse results for the CCPs are determined through multi-parameter sensitivity analysis. Finally, the proposed resilient-zone parallel global search algorithm was employed to further optimize the CCPs, yielding more precise optimization results. To validate the proposed optimization framework and implement the in-situ service, it is applied to a real-world case study involving “Tianda” CSD construction. The results demonstrated that the average optimization duration is 6.7 s, which is shorter than the data acquisition interval of 8 s. Our approach improves the computational efficiency by 9.4 times compared with traditional optimization control methods. Additionally, there is a significant increase in the slurry concentration, with the maximum growth rate reaching 81.64%.

Source: Water

DOI: 10.3390/w16213067

Authors: Hao Liu, Ruizhe Liu, Shuo Bai, Yong Chen, Leping Liu

Institutions: Tianjin University, PowerChina (China), Wuhan University of Technology

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,955