An Approach for Predicting the Specific Fuel Consumption of Dual-Fuel Two-Stroke Marine Engines

Marques, Crístofer Hood; Caprace, Jean‐David; Belchior, Carlos Rodrigues Pereira; Martini, Alberto

doi:10.3390/jmse7020020

Cited by 13 publications

(8 citation statements)

References 21 publications

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“…The basic elements of such propulsion systems are steam turbines -main [17] and auxiliary [18] along with all of its components necessary for operation [19]. However, the volume of diesel engines utilization in this part of shipping transport industry is rapidly increasing, due to utilization of dual fuel diesel engines [20].…”

Section: Introductionmentioning

confidence: 99%

Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components

et al. 2019

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In this paper multilayer perceptron (MLP) approach to condition-based maintenance of combined diesel-electric and gas (CODLAG) marine propulsion system is presented. By using data available in UCI, online machine learning repository, MLPs for prediction of gas turbine (GT) and GT compressor decay state coefficients are designed. Aforementioned MLPs are trained and tested by using 11 934 samples, of which 9 548 samples are used for training and 2 386 samples are used testing. In the case of GT decay state coefficient prediction, the lowest mean relative error of 0.622 % is achieved if MLP with one hidden layer of 50 artificial neurons (AN) designed with Tanh activation function is utilized. This configuration achieves the best results if it is trained by using L-BFGS solver. In the case of GT compressor decay state coefficient, the best results are achieved if MLP is designed with four hidden layers of 100, 50, 50 and 20 ANs, respectively. This configuration is designed by using Logistic sigmoid activation function. The lowest mean relative error of 1.094 % is achieved if MLP is trained by using L-BFGS solver.

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Section: Introductionmentioning

confidence: 99%

Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components

et al. 2019

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“…However, before performing any In most of these models, the combustion process and the injection system require careful attention in order to achieve the required power, to ensure the reliability and the safety of the engine and to reduce the brake specific fuel consumption (BSFC) of the engine. Huge work can be performed to find the best values of the different parameters using normal computations to validate the numerical model with real data [15][16][17][18][19].…”

Section: Figurementioning

confidence: 99%

Data Driven In-Cylinder Pressure Diagram Based Optimization Procedure

Tadros

Ventura

Soares

2020

JMSE

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An engine optimization model is developed to fit the calculated in-cylinder pressure diagram to the experimental data by finding the optimal values of the start angle of injection and the amount of injected fuel for different engine loads. Firstly, the engine model is built in Ricardo Wave software and some parts are calibrated using data collected from the manufacturer. Then, an optimization process is performed based on the fitness function that includes the objective of the study and the penalty functions to express constraints. This optimization environment simulates the performance of a marine generator system for three different loads by minimizing the mean absolute percentage error (MAPE) between the in-cylinder pressure simulated data and the measured data along 40 degrees of the combustion process and by verifying the firing pressure and the engine brake power. The percentage of error between the calculated and the real thermodynamic data does not exceed 3.4% and the MAPE between the calculated and the real in-cylinder pressure diagram along the combustion process does not exceed 5.7% for the different loads. The proposed method can be further used to find the optimal value of different input parameters during the calibration process of different engine numerical models.used and the required details in thermodynamics, combustion, heat transfer, fluid and chemical parts. Figure 2 presents a general overview of the model's structure to simulate the performance of different ICEs.

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“…Approaches could be model-based, which is usual in references or signal-based, which is considered in this paper. Model-based approaches tend to estimate the exact fuel consumption in the exact engine operating mode, examples of this are [1,2]. Such models are useful in, e.g., dual-fuel engines, when it is possible to calculate how much of fuels are being spent [1].…”

Section: Introductionmentioning

confidence: 99%

“…Model-based approaches tend to estimate the exact fuel consumption in the exact engine operating mode, examples of this are [1,2]. Such models are useful in, e.g., dual-fuel engines, when it is possible to calculate how much of fuels are being spent [1]. ANNs (Artificial Neural Network) in [2] need to be trained and re-trained for specific ships over time.…”

Section: Introductionmentioning

confidence: 99%

Predicting External Influences to Ship’s Average Fuel Consumption Based on Non-Uniform Time Set

Vujović¹,

Šoda²,

Kuzmanić³

et al. 2020

JMSE

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Nowadays, the impact of the ships on the World economy is enormous, considering that every ship needs fuel to sail from source to destination. It requires a lot of fuel, and therefore, there is a need to monitor and predict a ship’s average fuel consumption. However, although there are much models available to predict a ship’s consumption, most of them rely on a uniform time set. Here we show the model of predicting external influences to ship’s average fuel consumption based on a non-uniform time set. The model is based on the numeric fitting of recorded data. The first set of recorded data was used to develop the model, while the second set was used for validation. Statistical quality measures have been used to choose the optimal fitting function for the model. According to statistical measures, the Gaussian 7, Fourier 8, and smoothing spline fitting functions were chosen as optimal algorithms for model development. In addition to extensive data analysis, there is an algorithm for filter length determination for the preprocessing of raw data. This research is of interest to corporate logistics departments in charge of ensuring adequate fuel for fleets when and where required.

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An Approach for Predicting the Specific Fuel Consumption of Dual-Fuel Two-Stroke Marine Engines

Cited by 13 publications

References 21 publications

Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components

Multilayer Perceptron approach to Condition-Based Maintenance of Marine CODLAG Propulsion System Components

Data Driven In-Cylinder Pressure Diagram Based Optimization Procedure

Predicting External Influences to Ship’s Average Fuel Consumption Based on Non-Uniform Time Set

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