Hybrid support vector regression and crow search algorithm for modeling and multiobjective optimization of microalgae-based wastewater treatment. (1st January 2022)

• Record Type: Journal Article
• Authors: Hossain, S.M. Zakir; Sultana, Nahid; Mohammed, M. Ezzudin; Razzak, Shaikh A.; Hossain, Mohammad M.
• Is Part Of: Journal of environmental management. Volume 301(2022)
• Publication Date: 2022-01-01
• Subjects: Microalgae -- Wastewater treatment -- Modeling and optimization -- Support vector regression (SVR) -- Crow search algorithm (CSA); Environmental policy -- Periodicals; Environmental management -- Periodicals; Environment -- Periodicals; Ecology -- Periodicals; 363.705
• DOI: 10.1016/j.jenvman.2021.113783
• Languages: English
• ISSNs: 0301-4797

Abstract

Abstract: Microalgae-based wastewater treatment (and biomass production) is an environmentally benign and energetically efficient technique as compared to traditional practices. The present study is focused on optimization of the major treatment variables such as temperature, light-dark cycle (LD), and nitrogen (N)-to-phosphate (P) ratio (N/P) for the elimination of N and P from tertiary municipal wastewater utilizing Chlorella kessleri microalgae species. In this regard, a hybrid support vector regression (SVR) technique integrated with the crow search algorithm has been applied as a novel modeling/optimization tool. The SVR models were formulated using the experimental data, which were furnished according to the response surface methodology with Box-Behnken Design. Various statistical indicators, including mean absolute percentage error, Taylor diagram, and fractional bias, confirmed the superior performance of SVR models as compared to the response surface methodology (RSM) and generalized linear model (GLM). Finally, the best SVR model was hybridized with the crow search algorithm for single/multi-objective optimizations to acquire the global optimal treatment conditions for maximum N and P removal efficiencies. The best-operating conditions were found to be 29.3 °C, 24/0 h/h of LD, and 6:1 of N/P, with N and P elimination efficiencies of 99.97 and 93.48%, respectively. The optimized values were further confirmed by new experimental data. Highlights: Chlorella kessleri microalgae have the potential for wastewater treatment. Removal of N and P efficiencies are affected by temperature, photoperiod and N/P ratio. Hybrid BOA-SVR models are developed to predict the removal efficiencies of N and P. Crow search algorithm (CSA) is integrated with BOA-SVR for multi-objective optimization. A platform is developed that may facilitate research in wastewater treatment.