Design and Simulation-Based Validation of an AI Model for Predicting Grab-Type Ship Unloader Operation Data

Author:

Jung Ga-Eun1ORCID,Jeong Woo-Hee1,Lee Seok-Ju2ORCID,Lee Jae-In2ORCID,Kim Tae-Won3,Sung Hae-Jin3

Affiliation:

1. Department of Electrical Engineering, Changwon National University, Changwon 51140, Republic of Korea

2. Institute of Mechatronics, Changwon National University, Changwon 51140, Republic of Korea

3. Engineering Solution Research Group, Research Institute of Industrial Science & Technology, Pohang 37673, Republic of Korea

Abstract

Along with seaports automation, there is growing interest in the automation of Grab-Type Ship Unloader (GTSU) that unloads coal and iron ore from bulk carriers. Autonomous unloading operations of GTSU offer the potential for significant productivity improvement and cost savings. In this paper, an AI model trained with manual operation data was designed for GTSU automation operation, and the AI model was verified through the equation-of-motion-based GTSU operation simulator. The operation data of hoist, grab, and trolley were predicted by training the designed AI model with the manual operation data of GTSU. Before applying the predicted data to the actual equipment, the predicted driving data was verified using the equation-of-motion-based GTSU operation simulator. The AI prediction model was designed using the Multi-Layer Perception network, a type of artificial neural network. The AI prediction model was evaluated with the Mean-Squared Error indicator, and the validation loss was found to be less than 0.02. In addition, verification of the prediction data was performed using the GTSU dynamics-based simulator. The Mean Relative Error was up to 0.50, and the R2 score value exceeded 0.92, indicating that the model is effective in predicting operation data. The proposed AI prediction model will be effectively utilized to implement a fully automated unloading system.

Funder

Ministry of SMEs and Startups

Publisher

MDPI AG

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