Effective multi-objective optimization of Stirling engine systems

Punnathanam, Varun; Kotecha, Prakash

doi:10.1016/j.applthermaleng.2016.07.029

Cited by 30 publications

(7 citation statements)

References 28 publications

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“…For the complex thermochemical process, most scholars use third-party software (Aspen plus or Fluent et al) to simulate the sensitivity and optimization of a single variable, and it is difficult to achieve multiobjective optimization. Among the multi-objective optimization algorithms, the genetic algorithm is a way to search for the optimal solution by simulating the natural evolution process, which is widely used in various optimization studies [9,10] . This paper uses BP neural network to connect simulation results and the genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Modeling, prediction and multi-objective optimization of the coal gasification system

Yang

Duan

2021

E3S Web Conf.

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As global energy demand continues to increase, coal as basic energy still accounts for a significant proportion. Under the pressure of environmental protection, clean and efficient coal utilization technologies are in great demand. Coal gasification technology has the potential to realize near-zero-emissions for coal utilization. This paper establishes the coal gasification system model and analyzes the effect of oxygen/coal ratio and water/coal ratio on the system performance index of cold syngas efficiency, effective component ratio, carbon conversion ratio, and production ratio of hydrogen. The results show that when the oxygen/coal ratio increases, the efficiency of cold syngas and effective components ratio increase first and then decrease, carbon conversion ratio first increases and then remains unchanged, hydrogen production ratio gradually decreases; When the steam/coal ratio increases, the cold syngas efficiency, and carbon conversion ratio first increase and then decrease, effective component ratio ingredients gradually decreases, and the hydrogen production ratio increases. Using BP neural network to realize the prediction of the gasification system, and the mean square error reaches the magnitude of 10e-7. Multi-objective optimization results show that the oxygen/coal ratio and steam/coal ratio corresponding to the highest production ratio of hydrogen is 0.52 and 0.05. The highest carbon conversion ratio corresponds to the oxygen/coal ratio of 0.95 and the steam/coal ratio of 0.05.

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Section: Introductionmentioning

confidence: 99%

Modeling, prediction and multi-objective optimization of the coal gasification system

Yang

Duan

2021

E3S Web Conf.

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“…Further, Punnathanam and Kotecha 26 have examined the effect of NSGA‐II with the polynomial mutation operators and the replicated binary crossover for the Stirling engine optimization. Three models were studied by the authors, namely the thermal model of Stirling heat engine with internal irreversibility consideration, FTT model, and polytropic finite speed model.…”

Section: Introductionmentioning

confidence: 99%

Thermodynamic optimization of Stirling heat engine with methane gas using finite speed thermodynamic model

et al. 2021

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With the daily rise in environmental issues due to the use of conventional fuels, researchers are motivated to use renewable energy sources. One of such waste heat and low‐temperature differential driven energy sources is the Stirling engine. The performance of the Stirling engine can be improved by finding out the optimum operating and geometrical parameters with suitable working gas and thermal model. Based on this motivation, the current work focuses on the multiobjective optimization of the Stirling engine using the finite speed thermodynamic model and methane gas as the working fluid. Considering output power and pressure drop as two objective functions, the system is optimized using 11 geometrical and thermal design parameters. The optimization results are obtained in the form of the Pareto frontier. A sensitivity assessment is carried out to observe the decision variables, which are having a more sensitive effect on the optimization objectives. Optimization results reveal that 99.83% change in power output and 78% change in total pressure drop can take place in the two‐dimensional optimization space. The optimal solution closest to the ideal solution has output power and pressure drop values as 12.31 kW and 22.76 kPa, respectively.

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“…Even though photovoltaic panels are a great development, the solar concentrators using a Stirling engine accomplish a higher conversion efficiency of solar energy into electricity-29.4% [31]. By comparison, solar collectors that have a parabolic trough or Fresnel-type system can achieve a solar-to-electricity conversion efficiency of only 18-22% [32].…”

Section: Introductionmentioning

confidence: 99%

PLC Automation and Control Strategy in a Stirling Solar Power System

et al. 2020

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The Stirling engine together with a solar concentrator represents a solution for increasing energy efficiency. Thus, within the National Research and Development Institute for Cryogenic and Isotopic Technologies, an automation system was designed and implemented in order to control the processes inside the solar conversion unit using a programmable logic controller from Schneider Electric. The acquired parameters from the installed sensors were monitored using Unity Pro L software. The main objective of this paper is to solve the starting, operating, and shut-down sequences in safe conditions, as well as monitor the working parameters.

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Effective multi-objective optimization of Stirling engine systems

Cited by 30 publications

References 28 publications

Modeling, prediction and multi-objective optimization of the coal gasification system

Modeling, prediction and multi-objective optimization of the coal gasification system

Thermodynamic optimization of Stirling heat engine with methane gas using finite speed thermodynamic model

PLC Automation and Control Strategy in a Stirling Solar Power System

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