Multiobjective Optimisation of a Marine Dual Fuel Engine Equipped with Exhaust Gas Recirculation and Air Bypass Systems

Stoumpos, Sokratis; Theotokatos, Gerasimos

doi:10.3390/en13195021

Cited by 15 publications

(8 citation statements)

References 43 publications

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“…The genetic algorithm is a derivative-free optimisation method. Thanks to simplicity of implementation, it is very frequently used to solve numerous scientific and engineering tasks, such as: optimisation of gains of state observers [20], optimisation of controllers' parameters [21], optimisation of control policy in MPC [22], optimisation of settings for a fuel engine [23], optimisation of hybrid energy systems [24], integrated process planning, and scheduling [25].…”

Section: Genetic Algorithmmentioning

confidence: 99%

A New Solar Assisted Heat Pump System with Underground Energy Storage: Modelling and Optimisation

Ocłoń

Ławryńczuk

Czamara³

2021

Energies

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The objectives of this work are: (a) to present a new system for building heating which is based on underground energy storage, (b) to develop a mathematical model of the system, and (c) to optimise the energy performance of the system. The system includes Photovoltaic Thermal Hybrid Solar Panels (PVT) panels with cooling, an evacuated solar collector and a water-to-water heat pump. Additionally, storage tanks, placed underground, are used to store the waste heat from PVT panels cooling. The thermal energy produced by the solar collectors is used for both domestic hot water preparation and thermal energy storage. Both PVT panels and solar collectors are assembled with a sun-tracking system to achieve the highest possible solar energy gain. Optimisation of the proposed system is considered to achieve the highest Renewable Energy Sources (RES) share during the heating period. Because the resulting optimisation problem is nonlinear, the classical gradient-based optimisation algorithm gives solutions that are not satisfying. As alternatives, three heuristic global optimisation methods are considered: the Genetic Algorithm (GA), the Particle Swarm Optimisation (PSO) algorithm, and the Jaya algorithm. It is shown that the Jaya algorithm outperforms the GA and PSO methods. The most significant result is that 93% of thermal energy is covered by using the underground energy storage unit consisting of two tanks.

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Section: Genetic Algorithmmentioning

confidence: 99%

A New Solar Assisted Heat Pump System with Underground Energy Storage: Modelling and Optimisation

Ocłoń

Ławryńczuk

Czamara³

2021

Energies

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“…Finally, the engine torque, fuel consumption rate and emission parameters were optimized. Stoumpos et al [19] simulated and optimized the exhaust gas recirculation and air bypass system of a natural gas/diesel dual-fuel engine. The optimized dual-fuel engine was able to meet the International Maritime Organization's "Tier III" standard for NOx emissions in diesel-only operation.…”

Section: Introductionmentioning

confidence: 99%

Study on the Effect of Parameter Sensitivity on Engine Optimization Results

Jiang,

Zeng,

et al. 2023

Energies

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The effects of six control parameters, intake valve opening timing (IVO), exhaust valve opening timing (EVO), compression ratio (CR), engine speed, intake temperature, and intake pressure on engine output power, indicated specific fuel consumption (ISFC), and nitrogen oxides (NOx) emissions, are analyzed through engine simulation. The six parameters were categorized into two groups based on the degree of influence: high influence (EVO, speed and intake pressure) and low influence (CR, IVO and intake temperature). The relationship between these two groups of parameters and power, ISFC and NOx emissions was explored. Optimization was carried out for each of the two groups of parameters, and the optimization of the high impact parameters resulted in a higher diversity and wider distribution of the solution set. On the other hand, the optimization of the low-impact parameters resulted in a more concentrated distribution of the solution set, while better reflecting the trade-off between the optimization objectives. For the optimal solutions for both sets of parameters, the high-impact parameters provided significant optimization performance compared to the standard operating conditions. Although power and ISFC were optimized, the optimal solution for the low-impact parameter performed poorly with a significant increase in NOx emissions. Therefore, the parameters should be evaluated for optimization using high impact parameters to improve engine performance.

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“…Golzari et al studied the influence of water injection on the efficiency and emissions of a small single-cylinder gasoline direct injection engine, and the results showed that increasing water injection volume could improve the net efficiency of the engine while reducing emissions [19]. Stoumpos and Theotokatos used MOGA and design of experiment (DOE) parameters to optimize the engine and EGR/ABP (air bypass) system setting, and the results showed that the combination of EGR and ABP system enabled the engine emissions to meet more stringent regulatory requirements [20]. Cai et al optimized the EGR rate using the multiobjective gray situation decision method and Pareto frontier analysis method.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization of the Performance and Emissions of a Large Low-Speed Dual-Fuel Marine Engine Based on MNLR-MOPSO

Cong

Gan

Wang

et al. 2021

JMSE

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With increasingly strict emission regulations and growing environmental concerns, it is urgent to improve engine performance and reduce emissions. In this paper, multivariate nonlinear regression (MNLR) combined with multiobjective particle swarm optimization (MOPSO) was implemented to optimize the performance and emissions of a large low-speed two-stroke dual-fuel marine engine. First, a simulation model of a dual-fuel engine was established using AVL-BOOST software. Next, a single-factor scanning value method was applied to control a range of variables, including intake pressure, intake temperature, and natural gas mass fraction. Then, a nonlinear regression model was established using the statistical multivariate nonlinear regression equation. Finally, the multiobjective optimization algorithm implementing MOPSO was used to solve the trade-off between performance and emissions. It was found that when the intake pressure was 3.607 bar, the intake temperature was 297.15 K and the natural gas mass fraction was 0.962. The engine power increased by 0.34%, the brake specific fuel consumption (BSFC) reduced by 0.21%, and the NOx emissions reduced by 39.56%. The results show that the combination of multiple nonlinear regression and intelligent optimization algorithm is an effective method to optimize engine parameter settings.

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Multiobjective Optimisation of a Marine Dual Fuel Engine Equipped with Exhaust Gas Recirculation and Air Bypass Systems

Cited by 15 publications

References 43 publications

A New Solar Assisted Heat Pump System with Underground Energy Storage: Modelling and Optimisation

A New Solar Assisted Heat Pump System with Underground Energy Storage: Modelling and Optimisation

Study on the Effect of Parameter Sensitivity on Engine Optimization Results

Multiobjective Optimization of the Performance and Emissions of a Large Low-Speed Dual-Fuel Marine Engine Based on MNLR-MOPSO

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