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Exergy analysis and optimization of the Rankine cycle in steam power plants using the firefly algorithm

机译：萤火虫算法在蒸汽发电厂中的朗肯循环的放射性分析与优化

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The analysis of the exergy efficiency has always been considered as a fundamental criterion to study the behavior of the thermodynamic cycles. In this research, the exergy analysis of a steam power plant for generating electricity with Rankine thermodynamic cycle is carried out. Zarand steam power plant, which is located in the Kerman province, is considered as a case study. In order to optimize these thermodynamic processes and to achieve the highest exergy efficiency value, some primary parameters were considered as the decision variables. By changing the values of these parameters, an attempt was made to enhance the exergy efficiency by using a novel approach. The six decision variables, which are, output temperature and pressure values of the boiler, as well as the output pressure values of the four stages of the turbine, were chosen on the basis of probability of variations in a certain range of electricity generation parameters for the studied power plant. The exergy efficiency was considered as the objective function. Afterwards, optimization of the power plant by employing the firefly algorithm, which is one of the relatively latest invented algorithms for solving the optimization problems, was carried out. The firefly model performs the optimization process inspired by the behavior and action of fireflies to attract mates and reject enemies. For the purpose of analysis of the exergy efficiency, at the first stage, the optimization of exergy efficiency function was performed for the studied steam power plant, and then the results were compared with the solutions obtained using the genetic and particle swarm optimization algorithms. Final results are indicative of the fact that by appropriate changes in the decision variables and employing the firefly algorithm, the exergy efficiency of the thermal power plant increased from 30.1 to 30.7037 percent. This increase was equivalent to 0.6037 for the cycle, and compared to the results obtained from the genetic and swarm particle optimization algorithms, it was 0.04% and 0.0398% higher, respectively.
机译：对Deerveny效率的分析一直被认为是研究热力学循环行为的基本标准。在这项研究中，进行了用朗肯热力学周期发电的蒸汽发电厂的蒸汽发电厂的漏洞分析。位于克尔曼省的Zarand Steam Power厂被认为是案例研究。为了优化这些热力学过程并实现最高的效率值，将某些主要参数视为决策变量。通过改变这些参数的值，通过使用新方法来提高电气效率的尝试。选择六个判定变量，即锅炉的输出温度和压力值以及涡轮机的四个级的输出压力值，基于一定范围的发电参数的变化概率来选择学习的电厂。高级效率被认为是目标职能。然后，进行了通过采用萤火虫算法优化电厂，这是用于解决优化问题的相对最新的发明算法之一。萤火虫模型通过Fireflies的行为和行动来吸引配偶和拒绝敌人的优化过程。出于分析出电效率的目的，在第一阶段，对研究的蒸汽发电厂进行了优化效率功能，然后将结果与使用遗传和粒子群优化算法获得的溶液进行了比较。最终结果表明，通过采用决策变量和采用萤火虫算法的适当变化，火电厂的低通电效率从30.1增加到30.7037％。该循环的增加率相当于0.6037，与遗传和群粒子优化算法中获得的结果相比，其分别为0.04％和0.0398％。

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