Recording and analysing measurements from an RTG crane

Harrison, I.; Pietrosanti, Stefano; Ĺuque, A.; Mayer, Rayner M.; Holderbaum, William

doi:10.1016/j.measurement.2018.04.089

Cited by 9 publications

(2 citation statements)

References 20 publications

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“…For example, in order to move more than 4 million TEUs each year, Port of Felixstowe, UK, operates 85 RTG cranes. These cranes work daily up to 18 hours for 350 days a year [16,17]. According to the Port's administration, the annual electricity energy costs for two RTG cranes is around £21,000.…”

Section: Resultsmentioning

confidence: 99%

A Comparative Study of Energy Storage Systems and Active Front Ends for Networks of Two Electrified RTG Cranes

Alasali

Ĺuque

Mayer³

et al. 2019

Energies

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The global consumerism trend and the increase in worldwide population is increasing the need to improve the efficiency of marine container transportation. The high operating costs, pollution and noise of the diesel yard equipment is leading sea ports to move towards replacing diesel RTG cranes with electric Rubber Tyre Gantry (RTG) cranes which offer reduced environmental impact and higher energy efficiency. However, ports will require smarter solutions to meet the increased demand on the electrical distribution network due to the electrification of RTGs. This paper aims to highlight the peak demand problem in the two electrical cranes network and attempts to increase the energy saving at ports by using two different technologies: Energy Storage System (ESS) and Active Front End (AFE). This article introduces one of the first extensive investigations into different networks of RTG crane models and compares the benefits of using either AFE or ESS. The proposed RTG crane models and network parameters are validated using data collected at the Port of Felixstowe, UK. The results of the proposed RTG cranes network show a significant peak demand reduction and energy cost saving.

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Section: Resultsmentioning

confidence: 99%

A Comparative Study of Energy Storage Systems and Active Front Ends for Networks of Two Electrified RTG Cranes

Alasali

Ĺuque

Mayer³

et al. 2019

Energies

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“…To acquire the necessary statistical data to train the algorithm, we developed a sensory system and applied it to real trans-shipment operations using quay cranes, spreader systems, AGVs, and trucks [54]. The system's design and experimental background were modeled according to the methods presented by Harrison et al [55].…”

Section: A Description Of the Equipment Used To Collect The Training Datamentioning

confidence: 99%

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

2021

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Smart control systems are mostly applied in industry to control the movements of heavy machinery while optimizing overall operational efficiency. Major shipping companies use large quay cranes to load and unload containers from ships and still rely on the experience of on-site operators to perform transportation control procedures using joysticks and visual contact methods. This paper presents the research results of an EU-funded project for the Klaipeda container terminal to develop a novel container transportation security and cargo safety assurance method and system. It was concluded that many risks arise during the container handling procedures performed by the quay cranes and operators. To minimize these risks, the authors proposed controlling the sway of the spreader using a model predictive control method which applies a multi-layer perceptron (MLP) neural network (NN). The paper analyzes current neural network architectures and case studies and provides the engineering community with a unique case study which applies real operation statistical data. Several key training algorithms were tested, and the initial results suggest that the Levenberg-Marquardt (LM) algorithm and variable learning rate backpropagation perform better than methods which use the multi-layer perceptron neural network structure.

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Energy supply for onshore and offshore drilling rigs with utilizing energy wind and recycled from drawworks

Gholami,

Namdari,

Shakarami

et al. 2024

Electr Eng

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Recording and analysing measurements from an RTG crane

Cited by 9 publications

References 20 publications

A Comparative Study of Energy Storage Systems and Active Front Ends for Networks of Two Electrified RTG Cranes

A Comparative Study of Energy Storage Systems and Active Front Ends for Networks of Two Electrified RTG Cranes

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

Energy supply for onshore and offshore drilling rigs with utilizing energy wind and recycled from drawworks

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