Artificial neural network and techno-economic estimation with algae-based tertiary wastewater treatment. (April 2021)

• Record Type: Journal Article
• Authors: Ansari, Faiz Ahmad; Nasr, Mahmoud; Rawat, Ismail; Bux, Faizal
• Is Part Of: Journal of water process engineering. Volume 40(2021)
• Publication Date: 2021-04
• Subjects: Artificial intelligence -- Biochemical composition -- Microalgae -- Nutrient removal -- Payback period -- Secondary-treated wastewater; Water-supply engineering -- Periodicals; Saline water conversion -- Periodicals; Seawater -- Distillation -- Periodicals; Sanitary engineering -- Periodicals; Sewage -- Purification -- Periodicals; 627
• DOI: 10.1016/j.jwpe.2020.101761
• Languages: English
• ISSNs: 2214-7144

Abstract

Graphical abstract: Highlights: Microalgae were cultivated in pilot-scale pools under natural environmental conditions. Microalgae were used for nutrient recovery from secondary treated effluents. Lipid, protein, and carbohydrate contents (% DCW) were 18.3–25.6, 22.0–29.0, and 16.0–18.4. ANN (6–10 – 1) structure showed high prediction accuracy for biomass growth. Payback period of the wastewater-based algae cultivation project was economically feasible. Abstract: In this study, secondary-treated (ST) wastewater effluents supplemented with different nutrient sources were employed for microalgae cultivation in outdoor pilot-scale pools operated under natural environmental conditions. The BG11-supplemented wastewater showed a high algal biomass concentration = 0.79 ± 0.04 g/L, with NO3 −, NH4 +, and PO4 3− removal efficiencies = 83.20 ± 2.90 %, ≈ 100 %, and 93.50 ± 3.28 %, respectively. The corresponding lipid, protein, and carbohydrate contents of microalgae were 25.60 ± 0.80 %, 29.00 ± 0.88 %, and 18.40 ± 1.00 % w/w dry cell weight (DCW) basis, respectively. The ranges of protein and carbohydrate contents of lipid-extracted biomass were 25–40 % and 17–23 %, respectively. A three-layer feed-forward back-propagation artificial neural network (ANN) was used to predict the microalgae DCW, based on six inputs, i.e., temperature, pH, dissolved oxygen (DO), electrical conductivity (EC), NO3 −, and PO4 3- . The optimized ANN architecture (6–10 – 1), with the Levenberg-Marquardt training algorithm, achieved the highest predictive performance ( R 2 : 0.983). Based on the ANN sensitivity analysis, the environmental factor's relative importance arranged as NO3 − > PO4 3- > pH ≈ DO > temperature > EC. The nutrient removal ability and biochemical composition of microalgae were expressed regarding capital and operational costs, and profits. The payback period of the wastewater-based algal cultivation system was shorter than the project's lifetime, implying a sustainable and feasible application.