Availability optimization of biological and chemical processing unit using genetic algorithm and particle swarm optimization

被引:8
|
作者
Saini, Monika [1 ]
Goyal, Drishty [1 ]
Kumar, Ashish [2 ]
Patil, Rajkumar Bhimgonda [3 ]
机构
[1] Manial Univ Jaipur, Jaipur, Rajasthan, India
[2] Manipal Univ Jaipur, Dept Math & Stat, Jaipur, Rajasthan, India
[3] Pimpri Chinchwad Coll Engn, Mech Engn, Pune, Maharashtra, India
关键词
Genetic algorithm; Availability; Particle swarm optimization; Biological and chemical unit; Sewage treatment plant; WASTE-WATER TREATMENT; RELIABILITY-ANALYSIS; TREATMENT-PLANT; CONSTRUCTED WETLAND; NEURAL-NETWORKS; EFFICIENCY;
D O I
10.1108/IJQRM-08-2021-0283
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
Purpose The demand of sewage treatment plants is increasing day by day, especially in the countries like India. Biological and chemical unit of such sewage treatment plants are critical and needs to be designed and developed to achieve desired level of reliability, maintainability and availability. Design/methodology/approach This paper investigates and optimizes the availability of biological and chemical unit of a sewage treatment plant. A novel mathematical model for this unit is developed using the Markovian birth-death process. A set of Chapman-Kolmogorov differential equations are derived for the model and a generalized solution is discovered using soft computing techniques namely genetic algorithm (GA) and particle swarm optimization (PSO). Findings Nature-inspired optimization techniques results of availability function depicted that PSO outperforms GA. The optimum value of the availability of biological and chemical processing unit is 0.9324 corresponding to population size 100, the number of evolutions 300, mutation 0.6 and crossover 0.85 achieved using GA while PSO results reflect that optimum achieved availability is 0.936240 after 45 iterations. Finally, it is revealed that PSO outperforms than GA. Research limitations/implications This paper investigates and optimizes the availability of biological and chemical units of a sewage treatment plant. A novel mathematical model for this unit is developed using the Markovian birth-death process. Originality/value Availability model of biological and chemical units of a sewage treatment is developed using field failure data and judgments collected from the experts. Furthermore, availability of the system has been optimized to achieve desired level of reliability and maintainability.
引用
收藏
页码:1704 / 1724
页数:21
相关论文
共 50 条
  • [1] Availability and performance optimization of physical processing unit in sewage treatment plant using genetic algorithm and particle swarm optimization
    Sinwar, Deepak
    Saini, Monika
    Singh, Dilbag
    Goyal, Drishty
    Kumar, Ashish
    INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT, 2021, 12 (06) : 1235 - 1246
  • [2] Availability and performance optimization of physical processing unit in sewage treatment plant using genetic algorithm and particle swarm optimization
    Deepak Sinwar
    Monika Saini
    Dilbag Singh
    Drishty Goyal
    Ashish Kumar
    International Journal of System Assurance Engineering and Management, 2021, 12 : 1235 - 1246
  • [3] Availability and performance optimization of urea decomposition system using genetic algorithm and particle swarm optimization
    Monika Saini
    Yashpal Singh Raghav
    Ashish Kumar
    Divya Chandnani
    Life Cycle Reliability and Safety Engineering, 2021, 10 (3) : 285 - 293
  • [4] Portfolio Optimization using Particle Swarm Optimization and Genetic Algorithm
    Kamali, Samira
    JOURNAL OF MATHEMATICS AND COMPUTER SCIENCE-JMCS, 2014, 10 (02): : 85 - 90
  • [5] Unit commitment optimization based on genetic algorithm and particle swarm optimization hybrid algorithm
    Zhang, Jiong
    Liu, Tian-Qi
    Su, Peng
    Zhang, Xin
    Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2009, 37 (09): : 25 - 29
  • [6] Thermal Unit Commitment Using hybrid Binary Particle Swarm Optimization and Genetic Algorithm
    Hosseini, S. M. Hassan
    Siahkali, H.
    Ghalandaran, Y.
    2012 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC), 2012,
  • [7] A new memetic algorithm using particle swarm optimization and genetic algorithm
    Soak, Sang-Moon
    Lee, Sang-Wook
    Mahalik, N. P.
    Ahn, Byung-Ha
    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 1, PROCEEDINGS, 2006, 4251 : 122 - 129
  • [8] Particle swarm optimization algorithm and comparison with genetic algorithm
    Shen, Yan
    Guo, Bing
    Gu, Tian-Xiang
    Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China, 2005, 34 (05): : 696 - 699
  • [9] The Particle Swarm Optimization based on the Genetic Algorithm
    Li, Li
    Chen, Kun
    Hu, Haibo
    2010 INTERNATIONAL CONFERENCE ON INFORMATION, ELECTRONIC AND COMPUTER SCIENCE, VOLS 1-3, 2010, : 305 - 308
  • [10] A Hybrid Particle Swarm Optimization Employing Genetic Algorithm for Unit Commitment Problem
    Singh, R. Lal Raja
    Rajan, C. Christober Asir
    INTERNATIONAL REVIEW OF ELECTRICAL ENGINEERING-IREE, 2011, 6 (07): : 3211 - 3217