Presenting a novel approach for designing chlorine contact reactors by combination of genetic algorithm with nonlinear condition functions, simulated annealing algorithm, pattern search algorithm and experimental efforts

Mohammad Gheibi*, Hossein Pouresmaeil, Mehran Akrami, Zahra Kian, Amir Takhtravan and Maryam Mohammadi

Nowadays, water supplies face critical conditions in terms of quality and quantity. Furthermore, growth in population along with their needs require an increasing level of water-related resources. Consequently, the potential application of purified wastewater supplies can be considered in agriculture, industry, and irrigation of green spaces. Hence the necessity of disinfection and reduction of microbial load in the outlet sewage of water treatment plants are so clear for all designers and operators. Chlorine contact reactors are one of the major pillars of any wastewater treatment plant, whether urban or industrial. A new method is presented in this study based on the optimization of the dispersion amount in a Chlorine Contact Plug Flow Reactor (CCPFR) using single-objective Genetic Algorithm (GA) and nonlinear condition functions, Simulated Annealing Algorithm (SAA) and Pattern Search Algorithm (PSA). Then, it is attempted to assess the hydraulic behavior of the reactor and the microbial load removal performance using statistical, probabilistic and experimental practices. This research was done in a case study of Mashhad city’s wastewater treatment plant. The results of presented study illustrate that GA model has the best outcomes for designing CCPFR and the desired reactor with a depth of 2.45m, width of 1.23m, length of 24.8m, a number of 15 channels, and a retention time of 87 minutes is able to reduce a population of 300000 microorganisms (MPN/100 ml) at the entry to 274 (MPN/100 ml) at the exit. As per this method, investment cost of CCPFR is reduced around 30 percentages in comparison of traditional computation system.

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Published on: Apr 7, 2021 Pages: 12-17

Full Text PDF Full Text HTML DOI: 10.17352/aest.000030
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