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Parametric analysis and optimization of 660 MW supercritical power plant
660 兆瓦超临界发电厂的参数分析与优化

Keval Chandrakant Nikam , Laxmikant Jathar , Sagar Dnyaneshwar Shelare ,
Keval Chandrakant Nikam , Laxmikant Jathar , Sagar Dnyaneshwar Shelare
Kiran Shahapurkar , Sunil Dambhare , Manzoore Elahi M. Soudagar ,
Kiran Shahapurkar , Sunil Dambhare , Manzoore Elahi M. Soudagar
Nabisab Mujawar Mubarak , Tansir Ahamad , M.A. Kalam Department of Mechanical Engineering, Dr. D.Y.Patil Institute of Engineering, Management and Research, Akurdi, Savitribai Phule Pune University, Pune, 411044,
机械工程系,D.Y.Patil 博士工程、管理和研究学院,阿库尔迪,萨维特里拜-普莱-普纳大学,普纳,411044、
India 印度 Department of Mechanical Engineering, Army Institute of Technology, Pune, 411015, India
陆军技术学院机械工程系,印度,浦那,411015
Department of Mechanical Engineering, Priyadarshini College of Engineering, RTM Nagpur University, Nagpur, Maharashtra, India
印度马哈拉施特拉邦那格浦尔,RTM 那格浦尔大学 Priyadarshini 工程学院机械工程系
d Department of Mechanical Engineering, School of Mechanical, Chemical and Materials Engineering, Adama Science and Technology University, Adama, 1888, Ethiopia
d 机械工程系,机械、化学和材料工程学院,阿达玛科技大学,埃塞俄比亚阿达玛,1888 年
Institute of Sustainable Energy, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, 43000, Kajang, Selangor, Malaysia
马来西亚雪兰莪州加影市IKRAM-UNITEN路43000号,Tenaga Nasional大学可持续能源研究所
Department of Mechanical Engineering and University Centre for Research & Development, Chandigarh University, Mohali, Punjab, 140413, India
昌迪加尔大学机械工程系和大学研究与发展中心,印度旁遮普省莫哈里, 邮编 140413
Petroleum and Chemical Engineering, Faculty of Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan, BE1410, Brunei Darussalam
文莱达鲁萨兰国斯里巴加湾市,Bandar Seri Begawan, BE1410,文莱技术大学工程学院,石油与化学工程系
Department of Chemistry, College of Science, King Saud University, Riyadh, Saudi Arabia
沙特阿拉伯利雅得沙特国王大学理学院化学系
School of Civil and Environmental Engineering, FEIT, University of Technology Sydney, NSW, 2007, Australia
澳大利亚新南威尔士州悉尼科技大学 FEIT 土木与环境工程学院,2007 年

A R T I C L E I N F O

Handling Editor: L Luo
处理编辑:L Luo

Keywords: 关键词:

Particle swarm optimization
粒子群优化
Fossil fuel-fired supercritical plant
化石燃料超临界发电厂
Exergy efficiency 能效
Plant efficiency 工厂效率
Cost of electricity 电费

Abstract 摘要

A B S T R A C T The newly set up power plant has been committed to fulfilling the power supply demand of the world. Therefore, optimizing operating variables within constraints of varying power demand becomes necessary. The research aims to identify and optimize several parameters influencing the performance and efficiency of a 660 MW supercritical power plant at different operating conditions, such as steam temperature, pressure, feedwater flow rate, and fuel consumption. Ultimately, the research aims to contribute to developing sustainable and environmentally friendly power generation technologies. The present study covers the multi-objective optimization of a 660 MW capacity fossil fuel-fired SUPP. The overall plant efficiency, cost of electricity, and exergetic efficiency are taken as objective functions. The Particle Swarm Optimization (PSO) technique and a semi-empirical model of energy, economic, and exergy analysis of fossil fuel-fired SUPP have been employed. The varying power outputs, coal calorific value, amount of coal consumption, inlet temperature, and pressure conditions of turbines set are decision variables taken for the study. The parametric study was carried out with the variation in plant load and mass of coal consumption concerning the variation of the objective function. The lower temperature at the inlet of the low-pressure turbine is preferred for lowing the cost of electricity. The maximum value of plant efficiency of and exergy efficiency of with a minimum cost of electricity of 3.1456 INR/Unit have been evaluated using multi-objective PSO. The outcome of the present study is that the optimized value of decision variables will reduce the dependency on high-grade coal from an energy, exergy, and economic point of view. The outcome of the present study will explore the scope for future researchers and engineers.
A B S T R A C T 新建立的发电厂致力于满足全球的电力供应需求。因此,有必要在电力需求不断变化的约束条件下优化运行变量。本研究旨在确定和优化影响 660 兆瓦超临界发电厂在不同运行条件下的性能和效率的几个参数,如蒸汽温度、压力、给水流量和燃料消耗。研究的最终目的是为开发可持续的环保发电技术做出贡献。本研究涉及 660 兆瓦化石燃料发电厂的多目标优化。目标函数包括电厂总体效率、发电成本和能效。采用了粒子群优化(PSO)技术和化石燃料燃烧 SUPP 能源、经济和放能分析的半经验模型。不同的功率输出、煤炭热值、煤炭消耗量、进气温度和涡轮机组的压力条件是研究的决策变量。在进行参数研究时,电厂负荷和耗煤量的变化与目标函数的变化有关。低压涡轮机入口处的温度越低,发电成本越低。使用多目标 PSO 评估了 的最高电厂效率值和 的最高能效值,以及 3.1456 印度卢比/单位的最低电力成本。本研究的结果是,从能源、放能和经济角度来看,决策变量的优化值将减少对高品位煤炭的依赖。本研究的成果将为未来的研究人员和工程师开拓新的领域。

1. Introduction 1.导言

The thermal power plant involves various complex simultaneous competing objectives. Multi-objective optimization follows an effective method for optimizing these objectives. The analysis of thermal power plants is not only limited to thermodynamic analysis. It involves analysis based on economic, combined economic, and thermodynamic analysis, environmental analysis, and combined environmental and thermodynamic analysis. More than one objective is parallel studied to find optimum values of the variables. Researchers worldwide are using different optimization techniques inspired by the behavior of animals, naturally occurring phenomena, to find the alternate tradeoff solution. Ameri et al. [1] performed multi-objective optimization on the natural gas-fired combined cycle power plant. The genetic algorithm technique maximizes exergy efficiency and minimizes electricity costs. The choice
火力发电厂同时涉及各种复杂的竞争目标。多目标优化是优化这些目标的有效方法。火力发电厂的分析不仅限于热力学分析。它包括基于经济、综合经济和热力学分析、环境分析以及综合环境和热力学分析的分析。为了找到变量的最佳值,需要同时研究多个目标。世界各地的研究人员从动物行为和自然现象中汲取灵感,使用不同的优化技术来寻找替代的折衷解决方案。Ameri 等人[1]对天然气联合循环发电厂进行了多目标优化。遗传算法技术最大限度地提高了放能效率,并最小化了电力成本。选择

Nomenclature 术语

HPTu Higher Pressure Turbine 高压涡轮机
IPTu Intermediate Pressure Turbine
中压涡轮机
LPTu Lower Pressure Turbine 低压涡轮机
G Electrical Generator 发电机
CON Water Condenser 水冷凝器
CEPP Condensate Extraction Pump
冷凝水抽取泵
BFPP Boiler Feed-water Pump 锅炉给水泵
LPH Lower Pressure Heater 低压加热器
DE Deareator 脱氧剂
PDTR Pump Drive Turbine 泵驱动涡轮
DC Drain Cooler 排水冷却器
SG Steam Generator 蒸汽发生器
RHE Re-heater 再加热器
HPH
PSO Higher Pressure Heater 高压加热器
Particle Swarm Optimization
粒子群优化
Genetic Algorithm 遗传算法
Exegetic Efficiency 注释效率
Overall Plant Efficiency
工厂整体效率
Cost of coal 煤炭成本
Thermodynamic properties at reference state
参考状态下的热力学性质
Exergy flow of the stream
流体的能流
The calorific value of coal
煤的热值
mcoal 木炭 Mass coal consumption 煤炭消耗量
mwe Power generated 发电量
Velocity of particle 粒子速度
Position of particle 粒子位置
The individual Particle fitness value
个体粒子的适应度值
The global Particle fitness value
全局粒子适应度值
Inlet pressure of HPTu
HPTu 的入口压力
Inlet pressure of IPTu
IPTu 的入口压力
Inlet pressure of LPTu
LPTu 的入口压力
Indian Rupees 印度卢比
of objective function plays a key role in comparing a Pareto curve obtained using natural gas and liquid fuel. Roque et al. [2] carried out multi-objective optimization by minimizing economic and environmental objectives. The biased random key-based GA technique was proposed and proven to be better than other approaches from the diversity performance point of view. The tradeoff curves and CPU time were compared. Rahat et al. [3] proposed a new multi-objective optimization technique based on the Gaussian process and minimized nitrogen and oxide, maximizing the efficiency of a coal-fired power plant. Ahmadi et al. [4] optimized an integrated plant by minimizing pollutants, maximizing exergy efficiency, and minimizing the total cost rate. The Genetic algorithm was utilized, and it found decrement in environmental impacts can be achieved by selecting proper components and a low fuel flow rate.
目标函数在比较使用天然气和液体燃料得到的帕累托曲线中起着关键作用。Roque 等人[2]通过最小化经济和环境目标进行了多目标优化。他们提出了基于偏置随机密钥的 GA 技术,并证明从多样性性能的角度来看,该技术优于其他方法。他们还比较了权衡曲线和 CPU 时间。Rahat 等人[3]提出了一种基于高斯过程的新的多目标优化技术,并最小化了氮氧化物,最大化了 燃煤电厂的效率。Ahmadi 等人[4]通过最小化 污染物、最大化能效和最小化总成本率来优化综合电厂。他们利用遗传算法,发现通过选择适当的组件和较低的燃料流量,可以减少对环境的影响。