Evaluation of membrane cake fouling mechanism to estimate design parameters of a submerged AnMBR treating high strength industrial wastewater

Biological performance and membrane fouling of a microalgal-bacterial membrane photobioreactor for wastewater treatment without external aeration and carbonation

Biological nutrient removal processes involving the use of activated sludge (AS) to treat municipal wastewater normally result in high aeration energy consumption and significant greenhouse gas (GHG) emissions. Therefore, developing cost-efficient and environmentally friendly processes for wastewater treatment is vital. In this work, a novel non-aerated microalgal-bacterial membrane photobioreactor (MB-MPBR) was proposed, and its feasibility for organic contaminant and nutrient removals was evaluated, for the first time. The effects of inoculation ratio (microalgae to bacteria (M/B)) on the biological performance and membrane fouling were systematically investigated. The results showed that 95.9% of the chemical oxygen demand (COD), 74.5% of total nitrogen (TN), 98.5% of NH4+-N and 42.0% of total phosphorus (TP) were removed at an inoculation M/B ratio of 3:2 at steady state, representing a significant improvement compared to the M/B inoculation ratio of 1:3. Additionally, the higher inoculation M/B ratio (3:2) significantly promoted the biomass production owing to the favorable mutual exchange of oxygen and carbon dioxide between microalgae and bacteria. Cake layer formation was the primary fouling mechanism owing to the absence of aeration scouring on the membrane surface. The membrane fouling rate was slightly higher at the higher inoculation ratio (M/B = 3:2) owing to the increased biomass and extracellular polymeric substances (EPS) productions, despite the larger particle size. These results demonstrated that the non-aerated MB-MPBR could achieve superior biological performance, of which the inoculation M/B ratio was of critical importance for the initiation and maintenance of microalgal-bacterial symbiotic system, yet possibly caused severer membrane fouling in the absence of external aeration and carbonation. This study provides a new perspective for further optimizing and applying non-aerated MB-MPBR to enhance municipal wastewater treatment.

A Mechanistic Model for Hydrogen Production in an AnMBR Treating High Strength Wastewater

In the global race to produce green hydrogen, wastewater-to-H2 is a sustainable alternative that remains unexploited. Efficient technologies for wastewater-to-H2 are still in their developmental stages, and urgent process intensification is required. In our study, a mechanistic model was developed to characterize hydrogen production in an AnMBR treating high-strength wastewater (COD > 1000 mg/L). Two aspects differentiate our model from existing literature: First, the model input is a multi-substrate wastewater that includes fractions of proteins, carbohydrates, and lipids. Second, the model integrates the ADM1 model with physical/biochemical processes that affect membrane performance (e.g., membrane fouling). The model includes mass balances of 27 variables in a transient state, where metabolites, extracellular polymeric substances, soluble microbial products, and surface membrane density were included. Model results showed the hydrogen production rate was higher when treating amino acids and sugar-rich influents, which is strongly related to higher EPS generation during the digestion of these metabolites. The highest H2 production rate for amino acid-rich influents was 6.1 LH2/L-d; for sugar-rich influents was 5.9 LH2/L-d; and for lipid-rich influents was 0.7 LH2/L-d. Modeled membrane fouling and backwashing cycles showed extreme behaviors for amino- and fatty-acid-rich substrates. Our model helps to identify operational constraints for H2 production in AnMBRs, providing a valuable tool for the design of fermentative/anaerobic MBR systems toward energy recovery.

Development of a dynamic model for effective mitigation of membrane fouling through biogas sparging in submerged anaerobic membrane bioreactors (SAnMBRs)

The deterministic mechanistic model derived from the fundamental of the dynamical fouling system was investigated to estimate fouling parameters, with theoretical biogas sparging performance evaluated of a Submerged Anaerobic Membrane Bioreactor treating trade wastewater. The result showed that the sparging effectiveness of EPSc removal was average, 35% higher than the sparging effectiveness of EPSp, with the coefficient of fouling removal characterizing the dynamic time behaviour increasing with the organic loading rate. The dynamic system analysis predicted that the process gain for SAnMBR-1 was more than 30% compared with SAnMBR-2, which supported a widely known theory of fouling dependence of organic loading rate.

Predicting membrane fouling in a high solid AnMBR treating OFMSW leachate through a genetic algorithm and the optimization of a BP neural network model

Anaerobic membrane bioreactors are a promising technology in the treatment of high-strength wastewater; however, unpredictable membrane fouling largely limits their scale-up application. This study, therefore, adopted a backpropagation neural network model to predict the membrane filtration performance in a submerged system, which treats leachate from the organic fraction of municipal solid waste. Duration time, water yield flow, influent COD, pH, bulk sludge concentration, and the ratio of ΔTMP to filtration time were selected as input variables to simulate membrane permeability. The membrane pressure slightly increased by 1.1 kPa within 62 days of operation. The results showed that the AnMBR membrane filtration performance was acceptable when treating OFMSW leachate under a flux of 6 L/(m²·h). The model results indicated that the sludge concentration largely determined the membrane fouling with a contribution of 33.8%. Given the local minimization problem in the BP neural network process, a genetic algorithm was introduced to optimize the simulation process, and the relative error of the results was reduced from 5.57% to 3.57%. Conclusively, the artificial neural network could be a useful tool for the prediction of an AnMBR that is so far under development.