Artificial Intelligence for the Evaluation of Operational Parameters Influencing Nitrification and Nitrifiers in an Activated Sludge Process

Metagenomics studies in aquaculture systems: Big data analysis, bioinformatics, machine learning and quantum computing

The burgeoning field of aquaculture has become a pivotal contributor to global food security and economic growth, presently surpassing capture fisheries in aquatic animal production as evidenced by recent statistics. However, the dense fish populations inherent in aquaculture systems exacerbate abiotic stressors and promote pathogenic spread, posing a risk to sustainability and yield. This study delves into the transformative potential of metagenomics, a method that directly retrieves genetic material from environmental samples, in elucidating microbial dynamics within aquaculture ecosystems. Our findings affirm that metagenomics, bolstered by tools in big data analytics, bioinformatics, and machine learning, can significantly enhance the precision of microbial assessment and pathogen detection. Furthermore, we explore quantum computing's emergent role, which promises unparalleled efficiency in data processing and model construction, poised to address the limitations of conventional computational techniques. Distinct from metabarcoding, metagenomics offers an expansive, unbiased profile of microbial biodiversity, revolutionizing our capacity to monitor, predict, and manage aquaculture systems with high accuracy and adaptability. Despite the challenges of computational demands and variability in data standardization, this study advocates for continued technological integration, thereby fostering resilient and sustainable aquaculture practices in a climate of escalating global food requirements.

Artificial intelligence-enabled optimization of Fe/Zn@biochar photocatalyst for 2,6-dichlorophenol removal from petrochemical wastewater: A techno-economic perspective

While numerous studies have addressed the photocatalytic degradation of 2,6-dichlorophenol (2,6-DCP) in wastewater, an existing research gap pertains to operational factors' optimization by non-linear prediction models to ensure a cost-effective and sustainable process. Herein, we focus on optimizing the photocatalytic degradation of 2,6-DCP using artificial intelligence modeling, aiming at minimizing initial capital outlay and ongoing operational expenses. Hence, Fe/Zn@biochar, a novel material, was synthesized, characterized, and applied to harness the dual capabilities of 2,6-DCP adsorption and degradation. Fe/Zn@biochar exhibited an adsorption energy of -21.858 kJ/mol, effectively capturing the 2,6-DCP molecules. This catalyst accumulated photo-excited electrons, which, upon interaction with adsorbed oxygen and/or dissolved oxygen generated •O2-. The •OH radicals could also be produced from h+ in the Fe/Zn@biochar valence band, cleaving the C-Cl bonds to Cl- ions, dechlorinated byproducts, and phenols. An artificial neural network (ANN) model, with a 4-10-1 topology, "trainlm" training function, and feed-forward back-propagation algorithm, was developed to predict the 2,6-DCP removal efficiency. The ANN prediction accuracy was expressed as R2 = 0.967 and mean squared error = 5.56e-22. The ANN-based optimized condition depicted that over 90% of 2,6-DCP could be eliminated under C0 = 130 mg/L, pH = 2.74, and catalyst dosage = 168 mg/L within ∼4 h. This optimum condition corresponded to a total cost of $7.70/m3, which was cheaper than the price estimated from the unoptimized photocatalytic system by 16%. Hence, the proposed ANN could be employed to enhance the 2,6-DCP photocatalytic degradation process with reduced operational expenses, providing practical and cost-effective solutions for petrochemical wastewater treatment.

Impact of starvation conditions on the nitrifying performance and sludge properties in SBR system with a limited filamentous bulking state

Limited filamentous bulking (LFB) induced by low dissolved oxygen in activated sludge system is an effective energy saving process. However, starvation environment is liable to result in the unbalance between filaments and flocs, affecting the LFB system performance. The variations in nitrifying performance and properties of LFB sludge during 14 days of four starvation conditions (aerobic, alternating anaerobic/aerobic, anaerobic and anoxic) and their subsequent recovery were investigated in sequencing batch reactor (SBR) system. The results showed that the highest activity decay rates of ammonia- and nitrite-oxidizing bacteria (AOB and NOB) were observed under aerobic starvation condition, followed by anoxic, anaerobic, and alternating anaerobic/aerobic starvation conditions. In the reactivation period, the faster recovery of AOB activity and cell number, relative to NOB, particularly in aerobic case, led to temporary nitrite accumulation. Besides, the sludge settleability rapidly improved (SVI of ~30 mL/g) due to filamentous bacteria suppression under aerobic starvation, while the filaments (e.g. Type 0092) overgrew (SVI of ~250 mL/g) under anoxic starvation, triggering unexpected biomass loss and going against the nitrifying performance recovery of the system. In contrast, alternating anaerobic/aerobic and anaerobic starvations avoid pure aerobic or anoxic starvation condition, effectively maintaining the nitrifying performance and LFB state, and therefore are the best storage strategies for LFB sludge.

Trimethylamine abatement in algal-bacterial photobioreactors

Trimethylamine (TMA) is an odorous volatile organic compound emitted by industries. Algal-based biotechnologies have been proven as a feasible alternative for wastewater treatment, although their application to abate polluted air emissions is still scarce. This work comparatively assessed the removal of TMA in a conventional bacterial bubble column bioreactor (BC) and a novel algal-bacterial bubble column photobioreactor (PBC). The PBC exhibited a superior TMA abatement performance compared to the conventional BC. In this sense, the BC reached a removal efficiency (RE) and an elimination capacity (EC) of 78% and 12.1 g TMA m^-3 h^-1, respectively, while the PBC achieved a RE of 97% and a EC of 16.0 g TMA m^-3·h^-1 at an empty bed residence time (EBRT) of 2 min and a TMA concentration ~500 mg m^-3. The outstanding performance of the PBC allowed to reduce the operating EBRT to 1.5 and 1 min while maintaining high REs of 98 and 94% and ECs of 21.2 and 28.1 g m^-3·h^-1, respectively. Moreover, the PBC improved the quality of the gas and liquid effluents discharged, showing a net CO₂ consumption and decreasing by ~ 30% the total nitrogen concentration in the liquid effluent via biomass assimilation. A high specialization of the bacterial community was observed in the PBC, Mumia and Aquamicrobium sp. being the most abundant genus within the main phyla identified. GraphicalAbstract.

Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques

Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. The analytical method considers the protein as the only source of ammonia production in AD after degradation. Total ammonia nitrogen (TAN), total solids (TS), chemical oxygen demand (COD), and total volatile solids (TVS) were measured in the influent and effluent of the process. The TAN concentration in the effluent was predicted, this being the most inhibiting and polluting compound in AD. Despite the limited data available, the SVM-based model outperformed the analytical method for the TAN prediction, achieving a relative average error of 15.2% against 43% for the analytical method. Moreover, SVM showed higher prediction accuracy in comparison with Artificial Neural Networks. This result reveals the future promise of SVM for prediction in non-linear and dynamic AD processes. Graphical abstract ᅟ.

Characteristics of DO, organic matter, and ammonium profile for practical-scale DHS reactor under various organic load and temperature conditions

Profile analysis of the down-flow hanging sponge (DHS) reactor was conducted under various temperature and organic load conditions to understand the organic removal and nitrification process for sewage treatment. Under high organic load conditions (3.21-7.89 kg-COD m^-3day^-1), dissolved oxygen (DO) on the upper layer of the reactor was affected by organic matter concentration and water temperature, and sometimes reaches around zero. Almost half of the COD_Cr was removed by the first layer, which could be attributed to the adsorption of organic matter on sponge media. After the first layer, organic removal proceeded along the first-order reaction equation from the second to the fourth layers. The ammoniacal nitrogen removal ratio decreased under high organic matter concentration (above 100 mg L^-1) and low DO (less than 1 mg L^-1) condition. Ammoniacal nitrogen removal proceeded via a zero-order reaction equation along the reactor height. In addition, the profile results of DO, COD_Cr, and NH₃-N were different in the horizontal direction. Thus, it is thought the concentration of these items and microbial activities were not in a uniform state even in the same sponge layer of the DHS reactor.

Low temperature effects on nitrification and nitrifier community structure in V-ASP for decentralized wastewater treatment and its improvement by bio-augmentation

The vegetation-activated sludge process (V-ASP) has been proved to be an environment-friendly decentralized wastewater treatment system with extra esthetic function and less footprint. However, the effects of low temperature on the treatment performance of V-ASP and related improvement methods are rarely investigated, up to now. In this work, the effect of low temperature on nitrification in V-ASP was comprehensively investigated from overall nitrification performance, substrate utilization kinetics, functional enzymatic activities, and microbial community structure shift by comparison with conventional ASP. Bio-augmentation methods in terms of single-time nitrifier-enriched biomass dosage were employed to improve nitrification efficiency in bench- and full-scale systems. The experiment results demonstrated that the NH₄⁺-N removal efficiency in V-ASP system decreased when the operational temperature decreased from 30 to 15 °C, and the decreasing extent was rather smaller compared to ASP, as well as ammonium and nitrite oxidation rates and enzymatic activities, which indicated the V-ASP system possesses high resistance to low temperature. With direct dosage of 1.6 mg nitrifier/gSS sludge, the nitrification efficiency in V-ASP was enhanced dramatically from below 50% to above 90%, implying that bio-augmentation was effective for V-ASP whose enzymatic activities and microbial communities were both also improved. The feasibility and effectiveness of bio-augmentation was further confirmed in a full-scale V-ASP system after a long-term experiment which is instructive for the practical application.