Statement of Problem: Environmental engineering confronts complex challenges characterized by significant uncertainties. Traditional modeling methods often fail to effectively address these uncertainties. As a promising direction, this study explores fuzzy machine learning (ML) as an underutilized alternative. Research Question: Although the potential of fuzzy logic is widely acknowledged, can its capabilities truly enhance environmental engineering applications? Purpose: This research aims to deepen the understanding of the role and significance of fuzzy logic in managing uncertainty within environmental engineering applications. The objective is to contribute to both theoretical insights and practical implementations in this domain. Method: This research performs a systematic review carried out in alignment with PRISMA guidelines, encompassing 27 earlier studies that compare fuzzy ML with other methods across a variety of applications within the field of environmental engineering. Results: The findings demonstrate how fuzzy-based models consistently outperform traditional methods in scenarios marked by uncertainty. The originality of this research lies in its systematic comparison and the identification of fuzzy logic’s transparent, interpretable nature as particularly suited for environmental engineering challenges. This approach provides a new perspective on integrating fuzzy logic into environmental engineering, emphasizing its capability to offer more adaptable and resilient solutions. Conclusions: The analysis reveals that fuzzy-based models significantly excel in managing uncertainty compared to other methods. However, the study advocates for a case-by-case evaluation rather than a blanket replacement of traditional methods with fuzzy models. This approach encourages optimal selection based on specific project needs. Practical Implications: Our findings offer actionable insights for researchers and engineers, highlighting the transparent and interpretable nature of fuzzy models, along with their superior ability to handle uncertainties. Such attributes position fuzzy logic as a promising alternative in environmental engineering applications. Moreover, policymakers can leverage the reliability of fuzzy logic in developing ML-aided sustainable policies, thereby enhancing decision-making processes in environmental management.

The influence of CO2 addition and hydraulic retention time (5 and 7 days) on removal of Pseudomonas aeruginosa, Clostridium perfringens, Staphylococcus sp., Enterococcus sp., and Escherichia coli was evaluated in a system with three parallel 21 L high rate algal ponds. Both the addition of CO2 and an increase in HRT had no significant influence on bacterial removal, but bacterial removal was higher than found in previous studies. The removal was 3.4–3.8, 2.5–3.7, 2.6–3.1, 2.2–2.6 and 1.3–1.7 units log for P. aeruginosa, E. coli, Enterococcus sp., C. perfringens, and for Staphylococcus sp., respectively. Although CO2 addition did not increase disinfection, it did significantly increase biomass productivity (by ≈60%) and settleability (by ≈350%). Additionally, even at the lower 5-day hydraulic retention time, CO2 addition improves removal of chemical oxygen demand (COD), total organic carbon (TOC), total organic nitrogen and phosphorus by 97, 91, 12 and 50%, respectively.

This paper shows the influence of turbidity (in Nephelometric Turbidity Units - NTU), chemical oxygen demand (COD) and aeration (CO2 supply) on the productivity and growth rate and lipid content of microalgae (a mixed culture predominantly composed of Chlorella vulgaris), using anaerobically digested vinasse as a culture medium. The microalgae can be cultivated in anaerobically digested vinasse, at turbidity and chemical oxygen demand of 690 NTU and 2.5 gCOD L -1, respectively, according to the modified Gompertz model, and removal of turbidity by filtration did not influence the microalgae productivity (≈ 77 mg L1 d1). Furthermore, aeration increased the productivity up to 139 mg L1 d1, with a biomass dry weight of 2.7 g L-1. Finally, a maximum lipid content of 265 mg L -1 was obtained, while a nitrogen removal of 98% was recorded for all conditions. Thus, the combination of anaerobic digestion followed by the use of the digestate for the cultivation of microalgae may be an efficient way to treat large quantities of this residue, in turn yielding large amounts of microalgae biomass, which can be transformed into fertilizer and biofuel.