Ship Emission Mitigation Strategies Choice Under Uncertainty

Yuan, Jun; Wang, Haowei; Ng, Szu Hui; Nian, Victor

doi:10.3390/en13092213

Cited by 4 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of the BPNN and BNN with different numbers of neurons could be compared by MSE values to find the best model structure. Also, the performance of the BPNN and BNN with the best structure was further compared with the GP model, which made it easy to account for various uncertainties [16]. The GP model proposed by [39] was applied here.…”

Section: Mitigation Potential Evaluation Using Bpnn and Bnnmentioning

confidence: 99%

“…GP models are generally flexible and can account for various uncertainties associated with input data and variables used in the model [16]. However, the GP's performance deteriorates when the input data dimension or the data size is increased [15,16]. In comparison, ANN models are more suitable for handling high dimensional problems and a large amount of data [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…However, the GP's performance deteriorates when the input data dimension or the data size is increased [15,16]. In comparison, ANN models are more suitable for handling high dimensional problems and a large amount of data [15,16]. Due to the high volume of shipping data from various sources, an ANN model is proposed in this paper to evaluate mitigation measures.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures

2020

Self Cite

View full text Add to dashboard Cite

Climate change caused by greenhouse gas emissions is of critical concern to international shipping. A large portfolio of mitigation measures has been developed to mitigate ship gas emissions by reducing ship energy consumption but is constrained by practical considerations, especially cost. There are difficulties in ranking the priority of mitigation measures, due to the uncertainty of ship information and data gathered from onboard instruments and other sources. In response, a neural network model is proposed to evaluate the cost-effectiveness of mitigation measures based on decarbonization. The neural network is further enhanced with a Bayesian method to consider the uncertainties of model parameters. Three of the key advantages of the proposed approach are (i) its ability to simultaneously consider a wide range of sources of information and data that can help improve the robustness of the modeling results; (ii) the ability to take into account the input uncertainties in ranking and selection; (iii) the ability to include marginal costs in evaluating the cost-effectiveness of mitigation measures to facilitate decision making. In brief, a negative “marginal cost-effectiveness” would indicate a priority consideration for a given mitigation measure. In the case study, it was found that weather routing and draft optimization could have negative marginal cost-effectiveness, signaling the importance of prioritizing these measures.

show abstract

Section: Mitigation Potential Evaluation Using Bpnn and Bnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…D-G generating sets emit toxic compounds into the atmosphere. When analyzing various sources of air pollution in the port, it was stated that seagoing vessels are the main source [ 1 , 2 , 3 , 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Energy Optimization of the ‘Shore to Ship’ System—A Universal Power System for Ships at Berth in a Port

German-Galkin

Tarnapowicz

2020

Sensors

View full text Add to dashboard Cite

One of the most effective methods of limiting air pollution emissions by ships at a berth in a port is the power connection of ships to the on-shore system. “Shore to Ship” (STS)—A universal system for the connection of the ship’s electrical power network with the on-shore network—ensures the adoption of the voltage and frequency of the on-shore network for the exploitation of various types of ships in the port. The realization of such a system is possible due to the use of semiconductor technologies during the construction of mechatronic systems (i.e., systems that ensure the maintenance of electricity parameters). The STS system ensures energy efficiency for high-power ship systems through the use of an active semiconductor converter. This article presents an analysis of steady state electromagnetic and energy processes, allowing the determination of the active and reactive power and losses in the STS system. The presented analytical research enables the development of a control algorithm that optimizes the system energy efficiency. In the article, the control methods allowing the optimization of the energy characteristics of the system are considered and investigated. On the basis of theoretical studies, a model was developed in the Matlab-Simulink environment, which allowed us to study steady and transient processes in the STS system in order to reduce losses in power lines and semiconductor converters.

show abstract

A real-time green construction optimization strategy for engineering vessels considering fuel consumption and productivity: A case study on a cutter suction dredger

Shang¹,

Xu²,

Tian³

et al. 2023

Energy

View full text Add to dashboard Cite

Ship Emission Mitigation Strategies Choice Under Uncertainty

Cited by 4 publications

References 33 publications

Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures

Neural Network Modeling Based on the Bayesian Method for Evaluating Shipping Mitigation Measures

Energy Optimization of the ‘Shore to Ship’ System—A Universal Power System for Ships at Berth in a Port

A real-time green construction optimization strategy for engineering vessels considering fuel consumption and productivity: A case study on a cutter suction dredger

Contact Info

Product

Resources

About