Modeling the anaerobic treatment of sulfate-rich urban wastewater: Application to AnMBR technology

Use of nitrate, sulphate, and iron (III) as electron acceptors to improve the anaerobic degradation of linear alkylbenzene sulfonate: effects on removal potential and microbiota diversification

Linear alkylbenzene sulfonate (LAS) is a synthetic anionic surfactant that is found in certain amounts in wastewaters and even in water bodies, despite its known biodegradability. This study aimed to assess the influence of nitrate, sulphate, and iron (III) on LAS anaerobic degradation and biomass microbial diversity. Batch reactors were inoculated with anaerobic biomass, nutrients, LAS (20 mg L-1), one of the three electron acceptors, and ethanol (40 mg L-1) as a co-substrate. The control treatments, with and without co-substrate, showed limited LAS biodegradation efficiencies of 10 ± 2% and 0%, respectively. However, when nitrate and iron (III) were present without co-substrate, biodegradation efficiencies of 53 ± 4% and 75 ± 3% were achieved, respectively, which were the highest levels observed. Clostridium spp. was prominent in all treatments, while Alkaliphilus spp. and Bacillus spp. thrived in the presence of iron, which had the most significant effect on LAS biodegradation. Those microorganisms were identified as crucial in affecting the LAS anaerobic degradation. The experiments revealed that the presence of electron acceptors fostered the development of a more specialised microbiota, especially those involved in the LAS biodegradation. A mutual interaction between the processes of degradation and adsorption was also shown.

Influence of Solid Retention Time on Membrane Fouling and Biogas Recovery in Anerobic Membrane Bioreactor Treating Sugarcane Industry Wastewater in Sahelian Climate

Sugarcane industries produce wastewater loaded with various pollutants. For reuse of treated wastewater and valorization of biogas in a Sahelian climatic context, the performance of an anaerobic membrane bioreactor was studied for two solid retention times (40 days and infinity). The pilot was fed with real wastewater from a sugarcane operation with an organic load ranging from 15 to 22 gCOD/L/d for 353 days. The temperature in the reactor was maintained at 35 °C. Acclimatization was the first stage during which suspended solids (SS) and volatile suspended solids (VSS) evolved from 9 to 13 g/L and from 5 to 10 g/L respectively, with a VSS/SS ratio of about 80%. While operating the pilot at a solid retention time (SRT) of 40 days, the chemical oxygen demand (COD) removal efficiency reached 85%, and the (VSS)/(TSS) ratio was 94% in the reactor. At infinity solid retention time, these values were 96% and 80%, respectively. The 40-day solid retention time resulted in a change in transmembrane pressure (TMP) from 0.0812 to 2.18 bar, with a maximum methane production of 0.21 L/gCOD removed. These values are lower than those observed at an infinite solid retention time, at which the maximum methane production of 0.29 L/gCOD was achieved, with a corresponding transmembrane pressure variation of up to 3.1 bar. At a shorter solid retention time, the fouling seemed to decrease with biogas production. However, we note interesting retention rates of over 95% for turbidity.

Expanding mechanistic models to represent purple phototrophic bacteria enriched cultures growing outdoors

The economic feasibility of purple phototrophic bacteria (PPB) for resource recovery relies on using enriched-mixed cultures and sunlight. This work presents an extended Photo-Anaerobic Model (ePAnM), considering: (i) the diverse metabolic capabilities of PPB, (ii) microbial clades interacting with PPB, and (iii) varying environmental conditions. Key kinetic and stoichiometric parameters were either determined experimentally (with dedicated tests), calculated, or gathered from literature. The model was calibrated and validated using different datasets from an outdoors demonstration-scale reactor, as well as results from aerobic and anaerobic batch tests. The ePAnM was able to predict the concentrations of key compounds/components (e.g., COD, volatile fatty acids, and nutrients), as well as microbial communities (with anaerobic systems dominated by fermenters and PPB). The results underlined the importance of considering other microbial clades and varying environmental conditions. The model predicted a minimum hydraulic retention time of 0.5 d^-1. A maximum width of 10 cm in flat plate reactors should not be exceeded. Simulations showed the potential of a combined day-anaerobic/night-aerobic operational strategy to allow efficient continuous operation.

Multi-Agent Evolutionary Game in the Recycling Utilization of Sulfate-Rich Wastewater

Current industrial development has led to an increase in sulfate-rich industrial sewage, threatening industrial ecology and the environment. Incorrectly treating high-concentration sulfate wastewater can cause serious environmental problems and even harm human health. Water with high sulfate levels can be treated as a resource and treated harmlessly to meet the needs of the circular economy. Today, governments worldwide are working hard to encourage the safe disposal and reuse of industrial salt-rich wastewater by recycling sulfate-rich wastewater (SRW) resources. However, the conflict of interests between the SRW production department, the SRW recycling department, and the governments often make it challenging to effectively manage sulfate-rich wastewater resources. This study aims to use the mechanism of evolutionary game theory (EGT) to conduct theoretical modelling and simulation analysis on the interaction of the behaviour of the above three participants. This paper focuses on the impact of government intervention and the ecological behaviour of wastewater producers on the behavioural decisions of recyclers. The results suggest that the government should play a leading role in developing the SRW resource recovery industry. SRW producers protect the environment in the mature stage, and recyclers actively collect and recover compliant sulfate wastewater resources. Governments should gradually deregulate and eventually withdraw from the market. Qualified recyclers and environmentally friendly wastewater producers can benefit from a mature SRW resources recovery industry.

Advanced HRT-Controller Aimed at Optimising Nitrogen Recovery by Microalgae: Application in an Outdoor Flat-Panel Membrane Photobioreactor

A fuzzy knowledge-based controller of hydraulic retention time (HRT) was designed and tested in an outdoor membrane photobioreactor (MPBR) to improve nitrogen recovery from a microalgae cultivation system, maintaining the algae as photosynthetically active as possible and limiting their competition with other microorganisms. The hourly flow of the MPBR system was optimised by adjusting the influent flow rate to the outdoor environmental conditions which microalgae were exposed to at any moment and to the nitrogen uptake capacity of the culture. A semi-empirical photosynthetically active radiation (PAR) prediction model was calibrated using total cloud cover (TCC) forecast. Dissolved oxygen, standardised to 25 °C (DO25), was used as an on-line indicator of microalgae photosynthetic activity. Different indexes, based on suspended solids (SS), DO25, and predicted and real PAR, were used as input variables, while the initial HRT of each operating day (HRT0) and the variation of HRT (ΔHRT) served as output variables. The nitrogen recovery efficiency, measured as nitrogen recovery rate (NRR) per nitrogen loading rate (NLR) in pseudo-steady state conditions, was improved by 45% when the HRT-controller was set in comparison to fixed 1.25-d HRT. Consequently, the average effluent total soluble nitrogen (TSN) concentration in the MPBR was reduced by 47%, accomplishing the discharge requirements of the EU Directive 91/271/EEC.

Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process

Dark fermentation process for simultaneous wastewater treatment and H₂ production is gaining attention. This study aimed to use machine learning (ML) procedures to model and analyze H₂ production from wastewater during dark fermentation. Different ML procedures were assessed based on the mean squared error (MSE) and determination coefficient (R²) to select the most robust models for modeling the process. The research showed that gradient boosting machine (GBM), support vector machine (SVM), random forest (RF) and AdaBoost were the most appropriate models, which were optimized by grid search and deeply analyzed by permutation variable importance (PVI) to identify the relative importance of process variables. All four models demonstrated promising performances in predicting H₂ production with high R² values (0.893, 0.885, 0.902 and 0.889) and small MSE values (0.015, 0.015, 0.016 and 0.015). Moreover, RF-PVI demonstrated that acetate, butyrate, acetate/butyrate, ethanol, Fe and Ni were of high importance in decreasing order.

Considering syntrophic acetate oxidation and ionic strength improves the performance of models for food waste anaerobic digestion

Current mechanistic anaerobic digestion (AD) models cannot accurately represent the underlying processes occurring during food waste (FW) AD. This work presents an update of the Anaerobic Digestion Model no. 1 (ADM1) to provide accurate estimations of free ammonia concentrations and related inhibition thresholds, and model syntrophic acetate oxidation as acetate-consuming pathway. A modified Davies equation predicted NH₃ concentrations and pH more accurately, and better estimated associated inhibitory limits. Sensitivity analysis results showed the importance of accurate disintegration kinetics and volumetric mass transfer coefficients, as well as volatile fatty acids (VFAs) and hydrogen uptake rates. In contrast to the default ADM1, the modified ADM1 could represent methane production and VFA profiles simultaneously (particularly relevant for propionate uptake). The modified ADM1 was also able to predict the predominant acetate-consuming and methane-producing microbial clades. Modelling results using data from reactors dosed with granular activated carbon showed that this additive improves hydrogen uptake.

Importance of thermodynamics dependent kinetic parameters in nitrate-based souring mitigation studies

Souring is the unwanted formation of hydrogen sulfide (H₂S) by sulfate-reducing microorganisms (SRM) in sewer systems and seawater flooded oil reservoirs. Nitrate treatment (NT) is one of the major methods to alleviate souring: The mechanism of souring remediation by NT is stimulation of nitrate reducing microorganisms (NRM) that depending on the nitrate reduction pathway can outcompete SRM for common electron donors, or oxidize sulfide to sulfate. However, some nitrate reduction pathways may challenge the efficacy of NT. Therefore, a precise understanding of souring rate, nitrate reduction rate and pathways is crucial for efficient souring management. Here, we investigate the necessity of incorporating two thermodynamic dependent kinetic parameters, namely, the growth yield (Y), and F_T, a parameter related to the minimum catabolic energy production required by cells to utilize a given catabolic reaction. We first show that depending on physiochemical conditions, Y and F_T for SRM change significantly in the range of [0-0.4] mole biomass per mole electron donor and [0.0006-0.5], respectively, suggesting that these parameters should not be considered constant and that it is important to couple souring models with thermodynamic models. Then, we highlight this further by showing an experimental dataset that can be modeled very well by considering variable F_T. Next, we show that nitrate based lithotrophic sulfide oxidation to sulfate (lNRM₃) is the dominant nitrate reduction pathway. Then, arguing that thermodynamics would suggest that S° consumption should proceed faster than S⁰ production, we infer that the reason for frequently observed S⁰ accumulation is its low solubility. Last, we suggest that nitrate based souring treatment will suffer less from S⁰ accumulation if we (i) act early, (ii) increase temperature and (iii) supplement stoichiometrically sufficient nitrate.

On-line monitoring of photosynthetic activity based on pH data to assess microalgae cultivation

Microalgae performance of outdoor cultivation systems is influenced by environmental and operating dynamics. Monitoring and control systems are needed to maximise biomass productivity and nutrient recovery. The goal of this work was to corroborate that pH data could be used to monitor microalgae performance by means of data from an outdoor membrane photobioreactor (MPBR) plant. In this system, microalgae photosynthetic activity was favoured over other physical and biological processes, so that the pH data dynamics was theoretically related to the microalgae carbon uptake rate (CUR). Short- and long-term continuous operations were tested to corroborate the relationship between the first derivate of pH data dynamics (pH') and microalgae photosynthetic activity. Short-term operations showed a good correlation between gross pH' values and MPBR performance. An indicator of the maximum daily average microalgae activity was assessed by a combination of on-line pH' measurements obtained in the long-term and a microalgae growth kinetic model. Both indicators contributed to the development of advanced real-time monitoring and control systems to optimise microalgae cultivation technology.