A radial basis function neural network based multi-objective optimization for simultaneously enhanced nitrogen and phosphorus removal in a full-scale integrated surface flow treatment wetland–pond system

Li, Yiping; Nuamah, Linda A.; Pu, Yashuai; Zhang, Haikuo; Norgbey, Eyram; Nwankwegu, Amechi S.; Banahene, Patrick; Bofah, Robert

doi:10.1016/j.biortech.2021.126336

Cited by 7 publications

(3 citation statements)

References 38 publications

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“…The next sets of pareto solutions have very similar HRT values with even lower S i (12.7 mg L −1 ), without significantly affecting the removal efficiency. As HRT is very crucial for the biological removal of both selenite and COD, all pareto solutions projected a retention time long enough to offer sufficient contact time 55 . The S Re and C Re can be mainly attributed to the attached biomass on the kaldnes‐k1 media (bio‐support material) 9 .…”

Section: Resultsmentioning

confidence: 99%

“…As HRT is very crucial for the biological removal of both selenite and COD, all pareto solutions projected a retention time long enough to offer sufficient contact time. 55 The S Re and C Re can be mainly attributed to the attached biomass on the kaldnes-k1 media (bio-support material). 9 Though the predictor space for MOO can be extended outside the experimental data set, it may not be reasonable to accept ANN predictions beyond the training constellations.…”

Section: Predictive Capability Of the Modelmentioning

confidence: 99%

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Modelling selenite and chemical oxygen demand removal in an inverse fluidized bed bioreactor using genetic algorithm optimized artificial neural network

Sinharoy,

Mahanty,

Behera

et al. 2023

J of Chemical Tech & Biotech

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BACKGROUNDA genetic algorithm (GA) optimized artificial neural network (ANN) model was developed to simulate selenite removal efficiency (SRe) and chemical oxygen demand (COD) removal efficiency (CRe) in a single‐stage inverse fluidized bed reactor (IFBR) operated under different hydraulic retention time (HRT) (12–48 h), and inlet selenite concentration (12.7–635 mg L−1). Multi‐objective optimization for SRe and CRe with the ANN model was adopted to estimate the pareto optimal solutions in the bioreactor system.RESULTSGA optimized ANN topology suggested five neurons in the hidden layer, and the optimal data partitioning between training, test, and validation subsets (in 70:15:15 ratio) using cross‐validation. The average fitness for SRe and CRe of the resulting ANN model over training (R2 = 0.885), test (R2 = 0.967) and validation (R2 = 0.972) subsets were reasonably good. Sensitivity analysis suggested an increase in SRe and CRe with HRT, while an increase in inlet selenite concentration had a negative effect on SRe. Local interpretable model‐agnostic explanations and SHapley Additive exPlanations for the developed model suggested a positive coefficient of the predictors on CRe. Pareto optimal solution for SRe (92.87%–95.26%), and CRe (91.33%–99.35%) were suggested for HRT and inlet selenite concentration varying between 35.67 h and 36 h, and 0.1 g L−1 and 0.884 g L−1, respectively.CONCLUSIONSThe GA optimized ANN model could be exploited for the prediction of contaminant removal efficiencies in the bioreactor system with a fair degree of complexity (i.e., IFBR). Multiple objective pareto optimal solutions can identify the best operating conditions relevant for wastewater treatment. © 2023 Society of Chemical Industry (SCI).

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Section: Resultsmentioning

confidence: 99%

Section: Predictive Capability Of the Modelmentioning

confidence: 99%

Modelling selenite and chemical oxygen demand removal in an inverse fluidized bed bioreactor using genetic algorithm optimized artificial neural network

Sinharoy,

Mahanty,

Behera

et al. 2023

J of Chemical Tech & Biotech

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“…This causes a complex analysis of the sizing processes, as well as uncertainty in the behavior that the CW system will exhibit over time. The arguments presented above encourage the study of numerical modeling and simulation techniques to assess the impact of the system, and thus optimize processes [21].…”

Section: Introductionmentioning

confidence: 99%

Technological Innovations in the Application of Constructed Wetlands: A Review

Fernández Ramírez,

Zamora-Castro,

Sandoval-Herazo

et al. 2023

Processes

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Constructed wetlands (CWs) are highly effective in wastewater treatment and have generated lines of research with a focus on technological development and implemented innovations. This work concentrates on the most recent technical and scientific advances that have obtained optimal results in the construction of CWs using sustainable materials and the use of ornamental plants and other aquatic plants. Efficiency is also documented through models and simulation with neural networks, the use of the random forest method, and the use of software such as MODFLOW, MODPATH, and COMSOL Multiphysics. The information shown is structured by geographical area and addresses regions of Africa, Asia, Europe, North America, South America, and Oceania. It is important to consider that the optimization and innovation of CW for pollutant removal may benefit developing countries that do not have sufficient infrastructure to meet the demand for municipal and industrial wastewater.

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Artificial intelligence technologies in bioprocess: Opportunities and challenges

Cheng

et al. 2023

Bioresource Technology

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A radial basis function neural network based multi-objective optimization for simultaneously enhanced nitrogen and phosphorus removal in a full-scale integrated surface flow treatment wetland–pond system

Cited by 7 publications

References 38 publications

Modelling selenite and chemical oxygen demand removal in an inverse fluidized bed bioreactor using genetic algorithm optimized artificial neural network

Modelling selenite and chemical oxygen demand removal in an inverse fluidized bed bioreactor using genetic algorithm optimized artificial neural network

Technological Innovations in the Application of Constructed Wetlands: A Review

Artificial intelligence technologies in bioprocess: Opportunities and challenges

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