Full-scale Membrane Bioreactor Plant Research Articles

Optimizing Air Scouring Energy for Sustainable Membrane Bioreactor Operation by Characterizing the Combination of Factors Leading to Threshold Limiting Conditions.

Key operating variables to predict the necessary scour air flowrate in full-scale Membrane Bioreactor (MBR) systems are identified, aiming to optimize energy consumption while avoiding the limiting condition (i.e., rapid increasing total resistance). The resulting metric, referred to here as the K value, was derived by balancing hydrodynamic conditions between the particle deposit rate imposed by permeate flux normalized by fouling condition and its removal by shear stress induced from air scouring. The metric includes air scouring flow, permeate flow, Mixed Liquor Suspended Solids (MLSS) concentration, Mixed Liquor (ML) viscosity, membrane packing density, and total resistance. Long-term (year-long) data from two full-scale MBR plants were analyzed. The value of K corresponding to limiting operational operation and referred to as the limiting K value, KLim, is estimated by detecting the occurrence of threshold limiting flux from the data stream and calculating the resulting value for K. Then, using KLim, the minimum required specific air demand per permeate (SADp,Crit) is calculated, indicating a potential reduction of over half the air scouring energy in typical operational conditions. The results from this data driven analysis suggest the feasibility of employing KLim to predict the adequate scour air flowrate in terms of dynamically varying operational conditions. This approach will lead to the development of energy-efficient algorithms, significantly reducing scour air energy consumption in the full-scale MBR system.

Wastewater toxicity removal: Integrated chemical and effect-based monitoring of full-scale conventional activated sludge and membrane bioreactor plants

The literature is currently lacking effect-based monitoring studies targeted at evaluating the performance of full-scale membrane bioreactor plants. In this research, a monitoring campaign was performed at a full-scale wastewater treatment facility with two parallel lines (traditional activated sludge and membrane bioreactor). Beside the standard parameters (COD, nitrogen, phosphorus, and metals), 6 polynuclear aromatic hydrocarbons, 29 insecticides, 2 herbicides, and 3 endocrine disrupting compounds were measured. A multi-tiered battery of bioassays complemented the investigation, targeting different toxic modes of action and employing various biological systems (uni/multicellular, prokaryotes/eukaryotes, trophic level occupation). A traffic light scoring approach was proposed to quickly visualize the impact of treatment on overall toxicity that occurred after the exposure to raw and concentrated wastewater. Analysis of the effluents of the CAS and MBR lines show very good performance of the two systems for removal of organic micropollutants and metals. The most noticeable differences between CAS and MBR occurred in the concentration of suspended solids; chemical analyses did not show major differences. On the other hand, bioassays demonstrated better performance for the MBR. Both treatment lines complied with the Italian law's “ecotoxicity standard for effluent discharge in surface water”. Yet, residual biological activity was still detected, demonstrating the adequacy and sensitivity of the toxicological tools, which, by their inherent nature, allow the overall effects of complex mixtures to be taken into account.

Digitally-transformed early-warning protocol for membrane cleaning based on a fouling-cumulative sum chart: Application to a full-scale MBR plant

In the water industry, a state-of-the-art membrane bioreactor (MBR) requires relatively high operation expenditures due to foulants continually accumulating and clogging the surface and pores of the membrane. Hence, with the advent of digital innovations, developing an innovative tool based on data-driven approaches for membrane maintenance is important and challenging issue to achieve sustainable MBR operations. We developed a digitally-transformed early-warning protocol (DT-EWP) for membrane cleaning by predicting, diagnosing, and producing a warning for biofouling phenomena in a full-scale MBR plant. Biofouling progress was recursively predicted utilizing Kalman filter method and then performed diagnosis to identify the dominant fouling mechanism, incorporating genetic algorithm. In addition, membrane cleaning warning rule based on fouling-cumulative sum (FCUSUM) control chart was proposed to precautionary and gradationally produce an alarm for operational failure in the targeted plant. The proposed DT-EWP method was evaluated using the experimental data of a full-scale MBR plant in South Korea over a year. The results showed that the DT-EWP can estimate the biofouling progress of the trans-membrane pressure with R2 of 97% as the highest degree of accuracy and extend operational lifespan of membranes by an average of 45.29%, while accomplishing qualitative operations by statistically controlling the operational failures.

Dual-objective optimization for energy-saving and fouling mitigation in MBR plants using AI-based influent prediction and an integrated biological-physical model

Optimal operational strategies for wastewater treatment plants have been employed widely to improve the economic and environmental operations. However, inadequate optimization systems have been applied to membrane bioreactor (MBR) plants, resulting in high energy consumption due to uncertain influent associated with complex biological and physical interactions associated with membrane fouling. Here, we develop a dual-objective optimization system based on harmony search algorithm via process simulation. A bidirectional gated recurrent unit produced the most accurate predictions of fluctuating variations in hourly influent among competing models. And the predicted influent information was used to suggest operational strategies. The optimization system searched predictive operational strategies, including aeration intensities and permeation-cleaning durations, using the integrated biological and physical model. The suggested aeration intensities were then used for scouring air on membrane surfaces when the permeation-cleaning duration was optimized. A pilot MBR plant equipped with the suggested optimization system improved energy efficiency by 4% and mitigated fouling by 39% compared with a manually operated system. Dual-objective optimization also demonstrated feasibility and reliability at a full-scale MBR plant by increasing energy efficiency by 12% and decreasing fouling by 26%.

Aggregate size-dependence of bacterial community separation in a full-scale membrane bioreactor plant.

The size of bacterial aggregates can determine both nutrient removal and sludge/water separation in activated sludge processes. In this study, the bacterial community structures and network associations of different sized aggregates obtained from a full-scale membrane bioreactor plant over a one-year period were investigated. Our results showed that biodiversity of larger sized aggregates was significantly higher than that of smaller ones and that the bacterial compositions of different sized aggregates differed significantly from each other. Bacteria related to nutrient removal (e.g. denitrification, hydrolysis and fermentation) were found to be significantly more abundant in larger aggregates than smaller ones. Network analysis revealed significant difference in species-species interactions, topological roles of individual OTU and keystone populations among different sized aggregates. Moreover, the occurrence of keystone OTUs affiliated with denitrifiers (Thermomonas) in networks of large and medium aggregates may suggest that denitrification influences bacterial interactions in large and medium aggregates. Overall, our results indicate the aggregates size-dependence of bacterial community separation in activated sludge. The current findings not only can provide guidance for process design and operation optimization, but also highlight the necessity for paying more attentions to the aggregate-based community, metabolic function and gene expression of activated sludge in future studies.

An autonomous operational trajectory searching system for an economic and environmental membrane bioreactor plant using deep reinforcement learning.

Optimal operation of membrane bioreactor (MBR) plants is crucial to save operational costs while satisfying legal effluent discharge requirements. The aeration process of MBR plants tends to use excessive energy for supplying air to micro-organisms. In the present study, a novel optimal aeration system is proposed for dynamic and robust optimization. Accordingly, a deep reinforcement learning (DRL)-based optimal operating system is proposed, so as to meet stringent discharge qualities while maximizing the system's energy efficiency. Additionally, it is compared with the manual system and conventional reinforcement learning (RL)-based systems. A deep Q-network (DQN) algorithm automatically learns how to operate the plant efficiently by finding an optimal trajectory to reduce the aeration energy without degrading the treated water quality. A full-scale MBR plant with the DQN-based autonomous aeration system can decrease the MBR's aeration energy consumption by 34% compared to other aeration systems while maintaining the treatment efficiency within effluent discharge limits.

Nitrification kinetics and microbial communities of activated sludge as a full-scale membrane bioreactor plant transitioned to low dissolved oxygen operation

Abstract With population growth, water scarcity, and strict regulations, there is an increasing need for the water industry to search for advanced treatment technologies to reuse effluent from wastewater treatment plants. To accomplish an energy efficient and environment friendly facility for producing reclaimed water, a Water Recycling Plant successfully transitioned its aeration tank dissolved oxygen from moderate (0.8 mg/L) to low (

Molecular-based detection of potentially pathogenic bacteria in membrane bioreactor (MBR) systems treating municipal wastewater: a case study

Although membrane bioreactor (MBR) systems provide better removal of pathogens compared to conventional activated sludge processes, they do not achieve total log removal. The present study examines two MBR systems treating municipal wastewater, one a full-scale MBR plant and the other a lab-scale anaerobic MBR. Both of these systems were operated using microfiltration (MF) polymeric membranes. High-throughput sequencing and digital PCR quantification were utilized to monitor the log removal values (LRVs) of associated pathogenic species and their abundance in the MBR effluents. Results showed that specific removal rates vary widely regardless of the system employed. Each of the two MBR effluents’ microbial communities contained genera associated with opportunistic pathogens (e.g., Pseudomonas, Acinetobacter) with a wide range of log reduction values (< 2 to >5.5). Digital PCR further confirmed that these bacterial groups included pathogenic species, in several instances at LRVs different than those for their respective genera. These results were used to evaluate the potential risks associated both with the reuse of the MBR effluents for irrigation purposes and with land application of the activated sludge from the full-scale MBR system.

Full-scale Membrane Bio-Reactor Plant Performance and Membrane maintenance for a Refinery Wastewater Treatment

Full-scale Membrane Bio-Reactor Plant Performance and Membrane maintenance for a Refinery Wastewater Treatment

Effect of simultaneous nitrification and denitrification on nitrogen removal performance and filamentous microorganism diversity of a full-scale MBR plant

The nutrient removal performance of a membrane bioreactor (MBR) plant treating the wastewater of 10,000 PE was investigated with dynamic simulations. The average process performance with respect to chemical oxygen demand and total nitrogen were reported to be 97 and 81%, respectively. The modeling study showed that low dissolved oxygen (DO) levels (0.2-0.3 mgO2/L) due to limited aeration capacity within aeration tank that provided additional total nitrogen removal of 15-20 mgN/L. Simultaneous nitrification and denitrification process was found to be the reason of performance increase. However, low DO levels <0.3 mgO2/L in the aeration tank triggered the proliferation of filamentous microorganisms within one month as a side effect. In this respect, the morphotypes of Type 0092 and Nocardia (Gordonia) amarae were found to be excessively abundant in the MBR system. Overflow of foam layer covering the tanks was frequently reported during bulking period. A hypochloride dosing of 4.5 gCL/kgMLSS/day was applied to get over filamentous bulking problem as a short term action.

Design and Performance of the First Full Scale Membrane Bioreactor Plant Treating Oil Refinery Effluent in Brazil

Design and Performance of the First Full Scale Membrane Bioreactor Plant Treating Oil Refinery Effluent in Brazil

Operational experience with a seasonally operated full-scale membrane bioreactor plant

A seasonally operated full-scale membrane bioreactor plant (flat sheet, 0.03μm) treating municipal wastewater from a recreation facility was monitored for 2years. In particular, membrane bioreactor operation characteristics and development and changes in extracellular polymeric substances and soluble microbial product concentrations were observed, which were both dependent on volume and quality of incoming wastewater. Microbiological effluent quality, nutrient removal efficiency and activated sludge characteristics were analysed on a regular basis. Correlations between activated sludge quality, extracellular polymeric substance and soluble microbial product concentrations were identified. Pathogen related changes in effluent quality during plant operation were also observed. Nutrient removal efficiency was very good, despite fluctuations in influent flow.

Monitoring the variations of the oxygen transfer rate in a full scale membrane bioreactor using daily mass balances

Oxygen transfer in biological wastewater treatment processes with high sludge concentration, such as membrane bioreactor (MBR), is an important issue. The variation of alpha-factor versus mixed liquor suspended solids (MLSS) concentration was investigated in a full scale MBR plant under process conditions, using mass balances. Exhaustive data from the Supervisory Control And Data Acquisition (SCADA) and from additional online sensors (COD, DO, MLSS) were used to calculate the daily oxygen consumption (OC) using a non-steady state mass balance for COD and total N on a 24-h basis. To close the oxygen balance, OC has to match the total oxygen transfer rate (OTRtot) of the system, which is provided by fine bubble (FB) diffusers in the aeration tank and coarse bubbles (CB) in separate membrane tanks. First assessing OTR(CB) then closing the balance OC = OTRtot allowed to calculate OTR(FB) and to fit an exponential relationship between OTR(FB) and MLSS. A comparison of the alpha-factor obtained by this balance method and by direct measurements with the off-gas method on the same plant is presented and discussed.

Review of the performance of full-scale MBR plants: design, operation and on-site experimental data

The membrane bioreactor (MBR) process has become an effective alternative wastewater treatment technology that produces effluent with excellent quality. Globally, a wide range of municipal and industrial MBR plants are in operation, varying both in size and complexity. The objective of this investigation was to develop a better understanding of the long term performance of MBR plants. To achieve this objective, eight full-scale municipal MBR plants were examined. The methodology included a review of plant design parameters, pre-treatment system, biological operation, membrane operation, disinfection system and nutrient removal system. In addition, on-site tests were done on permeate, final effluent and mixed liquor to understand MBR performance.