Predicting the performance of a sequentially turbocharged marine diesel engine using ANFIS

Tadros, Mina; Ventura, Manuel; Soares, C. Guedes; Lampreia, Suzana

doi:10.1201/9780367810085-39

Cited by 2 publications

(4 citation statements)

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“…Hence, different optimization and machine learning (ML) methods are taking place and are coupled with the numerical engine models to easily find the optimal values of different parameters and to fulfill the objective and the purpose of the studies [20,21]. 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.…”

Section: Figurementioning

confidence: 99%

“…Hence, different optimization and machine learning (ML) methods are taking place and are coupled with the numerical engine models to easily find the optimal values of different parameters and to fulfill the objective and the purpose of the studies [20,21]. However, before performing any simulations, the engine models must be calibrated using real data at different operating points such as the compression ratio of the turbocharger, pressure before and after the intercooler, firing pressure, exhaust temperature before and after the turbine, amount of mass flow rate and BSFC to ensure the accuracy of the computed results [22,23].…”

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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Section: Figurementioning

confidence: 99%

“…Hence, different optimization and machine learning (ML) methods are taking place and are coupled with the numerical engine models to easily find the optimal values of different parameters and to fulfill the objective and the purpose of the studies [20,21]. However, before performing any simulations, the engine models must be calibrated using real data at different operating points such as the compression ratio of the turbocharger, pressure before and after the intercooler, firing pressure, exhaust temperature before and after the turbine, amount of mass flow rate and BSFC to ensure the accuracy of the computed results [22,23].…”

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Driven In-Cylinder Pressure Diagram Based Optimization Procedure

Tadros

Ventura

Soares

2020

JMSE

Self Cite

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“…The amount of SO x emissions in g/kW•h is directly proportional to the BSFC; once the BSFC increases, the SO x emissions increases and vice versa. This is why the new technologies (Woodyard 2004) and artificial intelligence (Mohd Noor et al 2015;Tadros et al 2020a) must be used to minimize the fuel consumed for the same operating point.…”

Section: Establishment Processmentioning

confidence: 99%

Optimization of the Performance of Marine Diesel Engines to Minimize the Formation of SOx Emissions

Tadros

Ventura

Soares

2020

J. Marine. Sci. Appl.

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Optimization procedures are required to minimize the amount of fuel consumption and exhaust emissions from marine engines. This study discusses the procedures to optimize the performance of any marine engine implemented in a 0D/1D numerical model in order to achieve lower values of exhaust emissions. From that point, an extension of previous simulation researches is presented to calculate the amount of SOx emissions from two marine diesel engines along their load diagrams based on the percentage of sulfur in the marine fuel used. The variations of SOx emissions are computed in g/kW·h and in parts per million (ppm) as functions of the optimized parameters: brake specific fuel consumption and the amount of air-fuel ratio respectively. Then, a surrogate model-based response surface methodology is used to generate polynomial equations to estimate the amount of SOx emissions as functions of engine speed and load. These developed non-dimensional equations can be further used directly to assess the value of SOx emissions for different percentages of sulfur of the selected or similar engines to be used in different marine applications.

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Predicting the performance of a sequentially turbocharged marine diesel engine using ANFIS

Cited by 2 publications

References 1 publication

Data Driven In-Cylinder Pressure Diagram Based Optimization Procedure

Data Driven In-Cylinder Pressure Diagram Based Optimization Procedure

Optimization of the Performance of Marine Diesel Engines to Minimize the Formation of SOx Emissions

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