Wastewater Treatment Bioprocess Research Articles

Zonotopic state estimation and sensor fault detection for a wastewater treatment bioprocess

AbstractIn this paper, a fault diagnosis method for the basic process of microbial growth in wastewater treatment based on interval estimation technology is proposed. First, a nonlinear microbial growth model is converted into a linear time‐varying system. A time‐varying observer is then designed for the generated system, which takes into account an performance. Furthermore, a zonotope‐based set‐membership estimation algorithm is analyzed to synchronize interval estimation operations. In addition, interval residuals are computed from the generated interval state estimation and fault diagnosis is performed for a wastewater treatment bioprocess with sensor fault signals. Finally, the feasibility of the proposed fault diagnosis strategy for the wastewater treatment bioprocess is illustrated through numerical simulations.

Salt stimulates sulfide−driven autotrophic denitrification: Microbial network and metagenomics analyses

Sulfur autotrophic denitrification (SADN) is a promising biological wastewater treatment technology for nitrogen removal, and its performance highly relies on the collective activities of the microbial community. However, the effect of salt (a prevailing characteristic of some nitrogen−containing industrial wastewaters) on the microbial community of SADN is still unclear. In this study, the response of the sulfide−SADN process to different salinities (i.e., 1.5 % salinity, 0.5 % salinity, and without salinity) as well as the involved microbial mechanisms were investigated by molecular ecological network and metagenomics analyses. Results showed that the satisfactory nitrogen removal efficiency (>97 %) was achieved in the sulfide−SADN process (S/N molar ratio of 0.88) with 1.5 % salinity. In salinity scenarios, the genus Thiobacillus significantly proliferated and was detected as the dominant sulfur−oxidizing bacteria in the sulfide−SADN system, occupying a relative abundance of 29.4 %. Network analysis further elucidated that 1.5 % salinity had enabled the microbial community to form a more densely clustered network, which intensified the interactions between microorganisms and effectively improved the nitrogen removal performance of the sulfide−SADN. Metagenomics sequencing revealed that the abundance of functional genes encoding for key enzymes involved in SADN, dissimilatory nitrate reduction to ammonium, and nitrification was up−regulated in the 1.5 % salinity scenario compared to that without salinity, stimulating the occurrence of multiple nitrogen transformation pathways. These multi−paths contributed to a robust SADN process (i.e., nitrogen removal efficiency >97 %, effluent nitrogen <2.5 mg N/L). This study deepens our understanding of the effect of salt on the SADN system at the community and functional level, and favors to advance the application of this sustainable bioprocess in saline wastewater treatment.

Immobilized-laccase bioreactors for wastewater treatment.

Laccases have shown to be efficient biocatalysts for the removal of recalcitrant pollutants from wastewater. Thus, they catalyze the oxidation of a wide variety of organic compounds by reducing molecular oxygen to water. However, the use of free laccases holds several drawbacks such as poor reusability, high cost, low stability and sensitivity to different denaturing agents that may occur in wastewater. Such drawbacks can be circumvented by immobilizing laccase enzymes in/on solid carriers. Hence, during the last decades different approaches considering various techniques and solid carriers to immobilize laccase enzymes have been developed and tested for the removal of pollutants from wastewater. To scale up wastewater treatment bioprocesses, immobilized laccases are placed in different reactor configurations.

Insights into the microbial response mechanisms to ciprofloxacin during sulfur-mediated biological wastewater treatment using a metagenomics approach

The fate and removal of ciprofloxacin, a class of fluoroquinolone antibiotic, during sulfur-mediated biological wastewater treatment has been recently well documented. However, little is known regarding the genetic response of microorganisms to ciprofloxacin. Here, a lab-scale anaerobic sulfate-reducing bioreactor was continuously operated over a long term for ciprofloxacin-contaminated wastewater treatment to investigate the response of the microorganisms to ciprofloxacin by adopting a metagenomics approach. It was found that total organic carbon (TOC) removal and sulfate reduction were promoted by approximately 10% under ciprofloxacin stress, along with the enrichment of functional genera (e.g., Desulfobacter, Geobacter) involved in carbon and sulfur metabolism. The metagenomic analytical results demonstrated that ciprofloxacin triggered the microbial SOS response, as demonstrated by the up-regulation of the multidrug efflux pump genes (8–125-fold higher than that of the control) and ciprofloxacin-degrading genes (4–33-fold higher than that of the control). Moreover, the contents of ATP, NADH, and cytochrome C, as well as related functional genes (including genes involved in energy generation, electron transport, carbon metabolism, and sulfur metabolism) were markedly increased under ciprofloxacin stress. This demonstrated that the carbon and sulfur metabolisms were enhanced for energy (ATP) generation and electron transport in response to ciprofloxacin-induced stress. Interestingly, the microbes tended to cooperate while being subjected exposure to exogenous ciprofloxacin according to the reconstructed metabolic network using the NetSeed model. Particularly, the species with higher complementarity indices played more pivotal roles in strengthening microbial metabolism and the SOS response under long-term ciprofloxacin stress. This study characterized the response mechanisms of microorganisms to ciprofloxacin at the genetic level in sulfur-mediated biological wastewater treatment. These new understandings will contribute the scientific basis for improving and optimizing the sulfur-mediated bioprocess for antibiotics-laden wastewater treatment.

Combination of physicochemical operations and algal culture as a new bioprocess for olive mill wastewater treatment

This work presents a new bioprocess design which allows a substantial reduction of organic and inhibitory compounds and a better quality of the final treated water. The process involves a physicochemical (primary) and a biological (microalgae) treatment, which were tested separately with lab equipment, for olive oil mill wastewater (OMW). Primary treatment of OMW involved flocculation-sedimentation by Flocudex CS-51 and microfiltration using a 0.2 μm membrane. Secondary treatment consisted of Scenedesmus obliquus culture in different OMW dilutions in ultrapure water as culture media: 5, 10, 25, 50, 75 and 100%. Experiments were performed on a laboratory scale in stirred batch tank reactors. The common operating conditions were: pH = 7, temperature = 25 °C, agitation rate = 3.33 Hz, aeration rate = 0.5 min−1 and illumination intensity = 359 μE m−2 s−1. High global removal levels were achieved after primary treatment for chemical oxygen demand (92.6%), total phenolic compounds (98.9%), total organic carbon (75.9%), total nitrogen (63.5%) and inorganic carbon (55.3%). Similar results were obtained for the main OMW constituents after secondary treatment with final harvested biomass rich in energetic compounds, where the highest values of carbohydrates (72.5%) in culture with 5% OMW and lipids (44.9%) in 100% OMW culture were determined.

Viral composition and context in metagenomes from biofilm and suspended growth municipal wastewater treatment plants.

SummaryWastewater treatment plants (WWTPs) contain high density and diversity of viruses which can significantly impact microbial communities in aquatic systems. While previous studies have investigated viruses in WWTP samples that have been specifically concentrated for viruses and filtered to exclude bacteria, little is known about viral communities associated with bacterial communities throughout wastewater treatment systems. Additionally, differences in viral composition between attached and suspended growth wastewater treatment bioprocesses are not well characterized. Here, shotgun metagenomics was used to analyse wastewater and biomass from transects through two full‐scale WWTPs for viral composition and associations with bacterial hosts. One WWTP used a suspended growth activated sludge bioreactor and the other used a biofilm reactor (trickling filter). Myoviridae, Podoviridae and Siphoviridae were the dominant viral families throughout both WWTPs, which are all from the order Caudovirales. Beta diversity analysis of viral sequences showed that samples clustered significantly both by plant and by specific sampling location. For each WWTP, the overall bacterial community structure was significantly different than community structure of bacterial taxa associated with viral sequences. These findings highlight viral community composition in transects through different WWTPs and provide context for dsDNA viral sequences in bacterial communities from these systems.

Modeling and multi-objective optimization for ANAMMOX process under COD disturbance using hybrid intelligent algorithm.

Anaerobic ammonium oxidation (ANAMMOX) has been regarded as an efficient process to treat nitrogen-containing wastewater. However, the treatment process is not fully understood in terms of reaction mechanisms, process simulation, and control. In this paper, a multi-objective control strategy mixed soft-sensing model (MCSSM) is developed to systematically design the operating variations for multi-objective control by integrating the developed model, a least square support vector machine optimized with principal component analysis (PCA-LSSVM) and non-dominated sorting genetic algorithm-II (NSGA-II). The results revealed that the PCA-LSSVM model is a feasible and efficient tool for predicting the effluent ammonia nitrogen concentration ([Formula: see text]) and the total nitrogen removal concentration (CTN, rem) with determination coefficients (R2) were 0.997 for [Formula: see text] and 0.989 for CTN, rem, and gives us the reasonable solutions in influent by using NSGA-II. To achieve a better removal effect, the influent pH should be kept between 7.50 and 7.52, the COD/TN ratio is suggested to maintain at 0.15 and the NH4+-N/NO2--N ratio is suggested to maintain at 0.61. The developed MCSSM approach and its general modeling framework have a high potential of applicability and guidance to bioprocess in wastewater treatment, and numerical models can be structured for predicting and optimization and experiments can be conducted for data acquisition and model establishment.

Complete Nutrient Removal Coupled to Nitrous Oxide Production as a Bioenergy Source by Denitrifying Polyphosphate-Accumulating Organisms.

Coupled aerobic-anoxic nitrous decomposition operation (CANDO) is a promising emerging bioprocess for wastewater treatment that enables direct energy recovery from nitrogen (N) in three steps: (1) ammonium oxidation to nitrite; (2) denitrification of nitrite to nitrous oxide (N2O); and (3) N2O conversion to N2 with energy generation. However, CANDO does not currently target phosphorus (P) removal. Here, we demonstrate that denitrifying polyphosphate-accumulating organism (PAO) enrichment cultures are capable of catalyzing simultaneous biological N and P removal coupled to N2O generation in a second generation CANDO process, CANDO+P. Over 7 months (>300 cycles) of operation of a prototype lab-scale CANDO+P sequencing batch reactor treating synthetic municipal wastewater, we observed stable and near-complete N removal accompanied by sustained high-rate, high-yield N2O production with partial P removal. A substantial increase in abundance of the PAO Candidatus Accumulibacter phosphatis was observed, increasing from 5% of the total bacterial community in the inoculum to over 50% after 4 months. PAO enrichment was accompanied by a strong shift in the dominant Accumulibacter population from clade IIC to clade IA, based on qPCR monitoring of polyphosphate kinase 1 (ppk1) gene variants. Our work demonstrates the feasibility of combining high-rate, high-yield N2O production for bioenergy production with combined N and P removal from wastewater, and it further suggests a putative denitrifying PAO niche for Accumulibacter clade IA.

The short- and long-term effects of environmental conditions on anaerobic methane oxidation coupled to nitrite reduction

Anaerobic oxidation of methane coupled to nitrite reduction (n-damo) plays an important role in global carbon and nitrogen cycles and also is a potential bioprocess in wastewater treatment. In this work, the effects of environmental conditions – temperature, pH and salinity – on the metabolic activity and growth rate of n-damo bacteria were investigated by short-term batch test and long-term bacterial incubation. Quantitative PCR and 16S rRNA and pmoA gene sequencing were applied to detect the microbial community in the long-term incubation. The results indicated that all the three environmental factors significantly affected the metabolic activity and growth rate of n-damo bacteria and the optimum temperature, pH and salinity were 35 °C, 7.6 and 0 g NaCl L−1, respectively. Notably, salinity adaption of n-damo bacteria was first observed under salinity stress of 20 g NaCl L−1. It's predicted that n-damo process might occur in saline environments and future work could focus on this.

Robust‐Adaptive Control Strategies for a Time Delay Bioelectrochemical Process Using Interval Observers

AbstractThe paper addresses the design and the analysis of adaptive and robust‐adaptive control strategies for a complex recycled wastewater treatment bioprocess. The design procedures are developed under the realistic assumptions that the bacterial growth rates are unknown and the influent flow rates are time‐varying and uncertain, but some lower and upper bounds of these uncertainties are known. The proposed control structures are achieved by combining a linearizing control law with an appropriately (asymptotic or interval based) state observer and with a parameter estimator used for on‐line estimation of unknown kinetics. These approaches are applied to a complex time delay bioprocess resulting from the association of a recycling bioreactor with an electrochemical reactor. Numerical simulations are performed in order to validate the proposed algorithms.

Cultivation of nitrite-dependent anaerobic methane-oxidizing bacteria: impact of reactor configuration

Nitrite-dependent anaerobic methane oxidation (n-damo) is mediated by bacteria that anaerobically oxidize methane coupled with nitrite reduction and is a potential bioprocess for wastewater treatment. In this work, the effect of reactor configuration on n-damo bacterial cultivation was investigated. A magnetically stirred gas lift reactor (MSGLR), a sequencing batch reactor (SBR), and a continuously stirred tank reactor (CSTR) were selected to cultivate the bacteria. Microbial community was monitored by using quantitative PCR, 16S rRNA gene sequencing, pmoA gene sequencing, and fluorescence in situ hybridization (FISH). The effects of substrate inhibition, methane mass transfer, and biomass washout in the three reactors were focused on. The results indicated that the MSGLR had the best performance among the three reactor systems, with the highest total and specific n-damo activities. Its maximum volumetric nitrogen removal rate was up to 76.9mgNL(-1)day(-1), which was higher than previously reported values (5.1-37.8mgNL(-1) d(-1)).

Parameter Identification of Anaerobic Wastewater Treatment Bioprocesses Using Particle Swarm Optimization

This paper deals with the offline parameters identification for a class of wastewater treatment bioprocesses using particle swarm optimization (PSO) techniques. Particle swarm optimization is a relatively new heuristic method that has produced promising results for solving complex optimization problems. In this paper one uses some variants of the PSO algorithm for parameter estimation of an anaerobic wastewater treatment process that is a complex biotechnological system. The identification scheme is based on a multimodal numerical optimization problem with high dimension. The performances of the method are analyzed by numerical simulations.

A multivariable robust-adaptive control strategy for a recycled wastewater treatment bioprocess

This paper presents the design and analysis of a multivariable robust-adaptive control strategy for a continuous aerobic wastewater treatment bioprocess. The design procedure is developed under the realistic assumption that the bacterial growth rates are uncertain and the influent flow rates are disturbed, but some lower and upper bounds of these uncertainties (possibly time varying) are known. The control structure is achieved by combining a linearizing control law with an interval observer able to estimate lower and upper bounds of unmeasurable states. The performance of the control algorithms are illustrated by simulations performed for a recycled wastewater treatment bioprocess for which kinetic dynamics are highly nonlinear, time-varying and incompletely known.

Adaptive and robust-adaptive control strategies for anaerobic wastewater treatment bioprocesses

This paper presents the design and analysis of adaptive and robust-adaptive control strategies for a class of anaerobic wastewater treatment processes. The design procedures are developed under the realistic assumption that both bacterial growth rates and influent flow rates are time-varying and uncertain, but some lower and upper bounds of these uncertainties are known. To achieve these control strategies, firstly, a state asymptotic observer and a robust interval observer for a class of bioprocesses are presented. Secondly, by using these observers, adaptive, robust and robust-adaptive control schemes are developed and analyzed. The adaptive control structure is achieved by combining a linearizing control law with a state asymptotic observer and with a parameter estimator used for on-line estimation of unknown kinetics. The robust control structure is designed by combining a linearizing control law with an interval observer able to estimates a lower and an upper bound of unmeasurable states. Also, the uncertain process parameters are replaced by their lower and upper bounds assumed known. The robust-adaptive controller uses a linearizing control law coupled with an interval observer for the unmeasured states, and with a parameter estimator. These approaches are applied to a particular wastewater treatment bioprocess based on anaerobic fermentation. The effectiveness of the proposed algorithms is validated by several numerical simulations.

Nonlinear on-line Estimation and Adaptive Control of a Wastewater Treatment Bioprocess

Pateikti realaus laiko nuotekų valymo proceso, vykstancio bioreaktoriuje, vertinimas ir adaptyvusis valdymas. Sis procesas yra netiesinis, naudojami būsenų matavimai turi trūkumų ir reakcijos kinetika nėra tiksliai žinoma. Kinetiniai parametrai įvertinami naudojant didelio stiprinimo netiesinius stebiklius, o nesamos būsenos yra rekonstruojamos naudojant asimptotinį būsenų stebiklį. Valdymo tikslas – palaikyti žemą tersalų koncentracijos lygį. Adaptyviojo valdymo taisyklė yra sudaryta kombinuojant tiesinimo valdiklį su netiesiniais stebikliais. Siūlomų algoritmų veiksmingumui nustatyti buvo atliktos skaitmeninės imitacijos. Il. 8, bibl. 17 (anglų kalba; santraukos anglų ir lietuvių k.). DOI: http://dx.doi.org/10.5755/j01.eee.117.1.1047

Pseudo bond graph modeling of wastewater treatment bioprocesses

The modeling of biotechnological processes is a complex task, because of the high interaction degree of the phenomena, non-linearities, and uncertainties of reaction kinetics. A novel approach for bioprocess modeling, based on pseudo bond graphs, is proposed in this paper. Bond graph method is an appropriate tool for modeling non-linear processes, eventually along with their control systems. Firstly, the pseudo bond graph method is applied in order to model some prototype bioprocesses starting from the reactions schemes and taking into account the biochemical phenomena. Then the rules used for the design of these models are applied on two widespread wastewater treatment processes. The first is an activated sludge bioprocess taking place in a sequential batch reactor, and the second is a wastewater biodegradation with the production of methane gas, which is carried out inside a continuous stirred tank bioreactor. The obtained bond graph models and several simulations are conducted using the 20sim modeling and simulation environment. This modeling procedure represents a valuable illustration of the power of the bond graph technique, and can be used as a base for the development of models of bioprocesses with a high level of complexity.

Scale-up of a bioprocess for textile wastewater treatment using Bjerkandera adusta

Twelve basidiomycetes were investigated for their ability to degrade 13 industrial dyes and to treat four model wastewaters from textile and tannery industry, defined on the basis of discharged amounts, economic relevance and representativeness of chemical structures of the contained dyes. The best degradation yields were recorded for one strain of Bjerkandera adusta able to completely decolourise most of the dyes and to decolourise and detoxify three simulated wastewaters, showing a significant physiological versatility which is very useful for application purposes. The effects of different nutrient sources were investigated in order to optimize the yields of decolourisation and detoxification. Manganese-peroxidase and manganese-independent peroxidase were the only recorded enzymatic activities. In order to evaluate its true bioremediation potential, this strain was packed in a fixed-bed bioreactor, for treatment of large volumes of a real wastewater. The fungus resulted effective during 10 cycles of decolourisation, remaining active for a very long period, in non-sterile conditions.

A levenberg-marquardt learning applied for recurrent neural identification and control of a wastewater treatment bioprocess

The paper proposed a new recurrent neural network (RNN) model for systems identification and states estimation of nonlinear plants. The proposed RNN identifier is implemented in direct and indirect adaptive control schemes, incorporating a noise rejecting plant output filter and recurrent neural or linear-sliding mode controllers. For sake of comparison, the RNN model is learned both by the backpropagation and by the recursive Levenberg–Marquardt (L–M) learning algorithm. The estimated states and parameters of the RNN model are used for direct and indirect adaptive trajectory tracking control. The proposed direct and indirect schemes are applied for real-time control of wastewater treatment bioprocess, where a good, convergence, noise filtering, and low mean squared error of reference tracking is achieved for both learning algorithms, with priority of the L–M one. © 2009 Wiley Periodicals, Inc.

Neural networks-based adaptive control for a class of nonlinear bioprocesses

The paper studies the design and analysis of a neural adaptive control strategy for a class of square nonlinear bioprocesses with incompletely known and time-varying dynamics. In fact, an adaptive controller based on a dynamical neural network used as a model of the unknown plant is developed. The neural controller design is achieved by using an input–output feedback linearization technique. The adaptation laws of neural network weights are derived from a Lyapunov stability property of the closed-loop system. The convergence of the system tracking error to zero is guaranteed without the need of network weights convergence. The resulted control method is applied in a depollution control problem in the case of a wastewater treatment bioprocess, belonging to the square nonlinear class, for which kinetic dynamics are strongly nonlinear, time varying and not exactly known.

RECURRENT NEURAL CONTROL OF WASTEWATER TREATMENT BIOPROCESS VIA MARQUARDT LEARNING

The aim of this paper is to propose a new Recurrent Neural Network (RTNN) topology and a dynamic recursive Levenberg-Marquardt algorithm of its learning capable to estimate the states and parameters of a highly nonlinear wastewater treatment bioprocess. The proposed RTNN identifier is implemented in a direct adaptive control scheme incorporating feedback/feedforward recurrent neural controllers and a noise rejecting filter. The proposed control scheme is applied for continuous wastewater treatment bioprocess plant model, taken from the literature, where a good convergence, noise filtering and a low Mean Squared Error of reference tracking is achieved.