Wastewater treatment companies are facing several challenges related to the optimization of energy efficiency, meeting more restricted water quality standards, and resource recovery potential. Over the past decades, computational models have gained recognition as effective tools for addressing some of these challenges, contributing to the economic and operational efficiencies of wastewater treatment plants (WWTPs). To predict the performance of WWTPs, numerous deterministic, stochastic, and time series-based models have been developed. Mechanistic models, incorporating physical and empirical knowledge, are dominant as predictive models. However, these models represent a simplification of reality, resulting in model structure uncertainty and a constant need for calibration. With the increasing amount of available data, data-driven models are becoming more attractive. The implementation of predictive models can revolutionize the way companies manage WWTPs by permitting the development of digital twins for process simulation in (near) real-time. In data-driven models, the structure is not explicitly specified but is instead determined by searching for relationships in the available data. Thus, the main objective of the present review is to discuss the implementation of machine learning models for the prediction of WWTP effluent characteristics and wastewater inflows as well as anomaly detection studies and energy consumption optimization in WWTPs. Furthermore, an overview considering the merging of both mechanistic and machine learning models resulting in hybrid models is presented as a promising approach. A critical assessment of the main gaps and future directions on the implementation of mathematical modeling in wastewater treatment processes is also presented, focusing on topics such as the explainability of data-driven models and the use of Transfer Learning processes.

Per- and polyfluoroalkyl substances (PFAS) are recalcitrant pollutants which tend to persist in soils and aquatic environments and their remediation is among the most challenging with respect to organic pollutants. Anaerobic digestion (AD) supplemented with low amounts of carbon materials (CM), acting as electron drivers, has proved to be an efficient process for the removal of organic compounds from wastewater. This work explores the impact of PFAS on different trophic groups in anaerobic communities, and the effect of carbon nanotubes (CNT), activated carbon (AC), and oxidized AC (AC-HNO₃), as electron shuttles on the anaerobic bioremoval of these compounds, based on CH₄ production. The inhibition of the specific methanogenic activity (SMA) exerted by perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), at a concentration of 0.1 mg L⁻¹, was below 10% for acetoclastic and below 15%, for acetogenic communities. Hydrogenotrophic methanogens were not affected by the presence of PFAS. All CM reduced the negative impact of PFAS on the CH₄ production rate, but AC was the best. Moreover, the methanization percentage (MP) of sewage sludge (SS) increased 41% in the presence of PFOS (1.2 g L⁻¹) and AC. In addition, AC fostered an increase of 11% in the MP of SS+PFOS, relative to the condition without AC. AC promoted detoxification of PFOA- and PFOS-treated samples by 51% and 35%, respectively, as assessed by Vibrio fischeri assays, demonstrating the advantage of bringing AD and CM together for PFAS remediation.

Anaerobic digestion (AD) is a well-established method for waste/wastewater treatment and biogas production, but challenges remain in improving its performance, particularly for toxic/inhibitory organic compounds such as lipids, hydrocarbons, polyphenols, pharmaceuticals, and other pollutants. Micro-aeration, which involves the controlled introduction of small amounts of oxygen, has emerged as a promising strategy to enhance microbial activity, stimulate the degradation of challenging compounds, and improve methane yields. This review addresses the different strategies used for effective oxygen dosing, measurement, and control, while also delving into the bioenergetics of the coexisting anaerobic and micro-aerobic pathways. Studies demonstrating the potential of micro-aeration to enhance the anaerobic treatment of recalcitrant organic pollutants, such as BTEX (benzene, toluene, ethylbenzene, and xylene) and pharmaceutical compounds, are reviewed. Several works use micro-aeration as a pretreatment, while those implementing it directly within bioreactors typically apply it intermittently. Nevertheless, in most cases, the application of micro-aeration is guided by a “trial-and-error” approach, and a systematic understanding of optimal strategies and dosing for different classes of pollutants remains lacking. The review also explores the diverse roles of micro-aeration in the AD of lipids, highlighting key microorganisms and underlying mechanisms that drive these processes, for instance, the role of facultative anaerobes in converting oleate into palmitate and protecting methanogenic communities. Finally, this work highlights future directions and remaining challenges in applying micro-aeration for the anaerobic treatment of organic pollutants. Key points: AD of lipids, hydrocarbons, dyes, pharmaceuticals, and other pollutants is challenging. Micro-aeration reshapes microbial communities and enhances pollutant degradation. Effective micro-aeration depends on several factors and is not fully mastered. Graphical

The hydrocarbon-based economy is moving at a large pace to a decarbonized sustainable bioeconomy based on biorefining all types of secondary carbohydrate-based raw materials. In this work, 50 g L⁻¹ in COD of a mixture of food waste, brine and wastewater derived from a biodiesel production facility were used to produce organic acids, important building-blocks for a biobased industry. High salinity (12–18 g L⁻¹), different reactors configuration operated in batch mode, and different initial pH were tested. In experiment I, a batch stirred reactor (BSR) at atmospheric pressure and a granular sludge bed column (GSBC) were tested with an initial pH of 5. In the end of the experiment, the acidification yield (η_a) was similar in both reactors (22–24%, w/w); nevertheless, lactic acid was in lower concentrations in BSR (6.3 g L⁻¹ in COD), when compared to GSBC (8.0 g L⁻¹ in COD), and valeric was the dominant acid, reaching 17.3% (w/w) in the BSR. In experiment II, the BSR and a pressurized batch stirred reactor (PBSR, operated at 6 bar) were tested with initial pH 7. The η_a and the VFA concentration were higher in the BSR (46%, 22.8 g L⁻¹ in COD) than in the PBSR (41%, 20.3 g/L in COD), and longer chain acids were more predominant in BSR (24.4% butyric, 6.7% valeric, and 6.2% caproic acids) than in PBSR (23.2%, 6.2%, and 4.2%, respectively). The results show that initial pH of 7 allows achieving higher η_a, and the BSR presents the most suitable reactor among tested configurations to produce VFA from wastes/wastewaters with high salinity.