Energy efficiency in wastewater treatment plants: A framework for benchmarking method selection and application.

Utilities produce and store vast amount of data related to urban wastewater management. Not yet fully exploited, proper data analysis would provide relevant process information and represents a great opportunity to improve the process performance. In the last years, several statistical tools and benchmarking methods that can extract useful information from data have been described to analyse wastewater treatment plant (WWTP) energy efficiency. Improving energy efficiency at WWTPs is however a complex task which involves several actors (both internal and external to the water utility), requires an exchange of different types of information which can be analysed by a broad selection of methods. Benchmarking method therefore must not only be selected based on whether they provide a clear identification of inefficient processes; it must also match the available data and the skills of those performing the assessment and objectives of stakeholders interpreting the results. Here, we have identified the requirements of the most common benchmarking methods in terms of data, resources, complexity of use, and information provided. To do that, inefficiency is decomposed so that the analyst, considering the objective of the study and the available data, can link each element to the appropriate method for quantification and benchmarking, and relate inefficiency components with root-causes in wastewater treatment. Finally, a framework for selecting the most suitable benchmarking method to improve energy efficiency in WWTPs is proposed to assist water sector stakeholders. By offering guidelines on how integrates and links data, methods and actors in the water sector, the outcomes of this article are expected to move WWTPs towards increasing energy efficiency.

Resource allocation explains lactic acid production in mixed-culture anaerobic fermentations.

Lactate production in anaerobic carbohydrate fermentations with mixed cultures of microorganisms is generally observed only in very specific conditions: the reactor should be run discontinuously and peptides and B vitamins must be present in the culture medium as lactic acid bacteria (LAB) are typically auxotrophic for amino acids. State-of-the-art anaerobic fermentation models assume that microorganisms optimise the adenosine triphosphate (ATP) yield on substrate and therefore they do not predict the less ATP efficient lactate production, which limits their application for designing lactate production in mixed-culture fermentations. In this study, a metabolic model taking into account cellular resource allocation and limitation is proposed to predict and analyse under which conditions lactate production from glucose can be beneficial for microorganisms. The model uses a flux balances analysis approach incorporating additional constraints from the resource allocation theory and simulates glucose fermentation in a continuous reactor. This approach predicts lactate production is predicted at high dilution rates, provided that amino acids are in the culture medium. In minimal medium and lower dilution rates, mostly butyrate and no lactate is predicted. Auxotrophy for amino acids of LAB is identified to provide a competitive advantage in rich media because less resources need to be allocated for anabolic machinery and higher specific growth rates can be achieved. The Matlab™ codes required for performing the simulations presented in this study are available at https://doi.org/10.5281/zenodo.4031144.

Metabolic modeling for predicting VFA production from protein-rich substrates by mixed-culture fermentation.

Proteinaceous organic wastes are suitable substrates to produce high added-value products in anaerobic mixed-culture fermentations. In these processes, the stoichiometry of the biotransformation depends highly on operational conditions such as pH or feeding characteristics and there are still no tools that allow the process to be directed toward those products of interest. Indeed, the lack of product selectivity strongly limits the potential industrial development of these bioprocesses. In this work, we developed a mathematical metabolic model for the production of volatile fatty acids from protein-rich wastes. In particular, the effect of pH on the product yields is analyzed and, for the first time, the observed changes are mechanistically explained. The model reproduces experimental results at both neutral and acidic pH and it is also capable of predicting the tendencies in product yields observed with a pH drop. It also offers mechanistic insights into the interaction among the different amino acids (AAs) of a particular protein and how an AA might yield different products depending on the relative abundance of other AAs. Particular emphasis is placed on the utility of this mathematical model as a process design tool and different examples are given on how to use the model for this purpose.

A generic methodology for the optimisation of sewer systems using stochastic programming and self-optimizing control.

The design of sewer system control is a complex task given the large size of the sewer networks, the transient dynamics of the water flow and the stochastic nature of rainfall. This contribution presents a generic methodology for the design of a self-optimising controller in sewer systems. Such controller is aimed at keeping the system close to the optimal performance, thanks to an optimal selection of controlled variables. The definition of an optimal performance was carried out by a two-stage optimisation (stochastic and deterministic) to take into account both the overflow during the current rain event as well as the expected overflow given the probability of a future rain event. The methodology is successfully applied to design an optimising control strategy for a subcatchment area in Copenhagen. The results are promising and expected to contribute to the advance of the operation and control problem of sewer systems.

Biobased short chain fatty acid production - Exploring microbial community dynamics and metabolic networks through kinetic and microbial modeling approaches.

In recent years, there has been growing interest in harnessing anaerobic digestion technology for resource recovery from waste streams. This approach has evolved beyond its traditional role in energy generation to encompass the production of valuable carboxylic acids, especially volatile fatty acids (VFAs) like acetic acid, propionic acid, and butyric acid. VFAs hold great potential for various industries and biobased applications due to their versatile properties. Despite increasing global demand, over 90% of VFAs are currently produced synthetically from petrochemicals. Realizing the potential of large-scale biobased VFA production from waste streams offers significant eco-friendly opportunities but comes with several key challenges. These include low VFA production yields, unstable acid compositions, complex and expensive purification methods, and post-processing needs. Among these, production yield and acid composition stand out as the most critical obstacles impacting economic viability and competitiveness. This paper seeks to offer a comprehensive view of combining complementary modeling approaches, including kinetic and microbial modeling, to understand the workings of microbial communities and metabolic pathways in VFA production, enhance production efficiency, and regulate acid profiles through the integration of omics and bioreactor data.

pH variation and influence in an autotrophic nitrogen removing biofilm system using an efficient numerical solution strategy.

A pH simulator consisting of an efficient numerical solver of a system of nine nonlinear equations was constructed and implemented in the modeling software MATLAB. The pH simulator was integrated in a granular biofilm model and used to simulate the pH profiles within granules performing the nitritation-anammox process for a range of operating points. The simulation results showed that pH profiles were consistently increasing with increasing depth into the granule, since the proton-producing aerobic ammonium-oxidizing bacteria (AOB) were located close to the granule surface. Despite this pH profile, more NH3 was available for AOB than for anaerobic ammonium oxidizers, located in the center of the granules. However, operating at a higher oxygen loading resulted in steeper changes in pH over the depth of the granule and caused the NH3 concentration profile to increase from the granule surface towards the center. The initial value of the background charge and influent bicarbonate concentration were found to greatly influence the simulation result and should be accurately measured. Since the change in pH over the depth of the biofilm was relatively small, the activity potential of the microbial groups affected by the pH did not change more than 5% over the depth of the granules.

Fostering the valorization of organic wastes into carboxylates by a computer-aided design tool.

The carboxylate platform has the potential to constitute an outstanding opportunity for converting organic wastes into chemicals and other value-added products within a circular economy framework. However, its development is still hampered by technological and financial constraints due to difficulties at forecasting the carboxylates yields by different wastes. This work provides a framework that can be the key to foster circular economy and bridge the development risks, allowing early-stage evaluation of process performance. This framework, which is implemented as a computer-aided design tool, is comprised by: (i) a library of substrates including their characterization and appropriate kinetic parameter selection, (ii) an integral kinetic and stoichiometric model which solves both identified gaps regarding the disintegration mechanisms and the acidogenic stoichiometry variability in the anaerobic mono and cofermentation of complex organic wastes, and (iii) a set of indicators to interpret simulation results and assist the decision making; and presents a showcase of applications supported by two case studies. These case studies show that the optimal conditions to steer VFA spectrum towards odd-chain VFA in MCF of regrind pasta are neutral pH (6.5-7) and a relatively low HRT (3-4 days), while cofermentation of tuna canning wastewater and regrind pasta follows interactive mechanisms that cannot be captured by a "naïve approach", i.e. by adding up the individual contributions. Finally, it is discussed how value chain actors with different interests can benefit from the proposed tool: identifying technical, economic, and environmental bottlenecks, and proposing innovative solutions prior to costly lab research and piloting.

Quantifying current and future raw milk losses due to bovine mastitis on European dairy farms under climate change scenarios.

Bovine mastitis is an infectious disease that causes udder inflammation and is responsible for raw milk losses across European dairy farms. It is associated with reduced cow milk yield and contributes to elevated Somatic Cell Count (SCC) in raw milk. Staphylococcus aureus is one of the most prevalent mastitis pathogens that cause subclinical and clinical mastitis and can be present as a coloniser bacterium in cows. Climate change and geographical variability may influence the prevalence of this pathogen. Thus, this research aimed to predict the raw milk losses in three major dairy-producing regions across Europe (i.e. Mediterranean, Atlantic and Continental) under climate change scenarios. An exposure assessment model and a stepwise probabilistic model were developed to predict potential cow milk yield reduction, S. aureus and SCC concentrations in the bulk tank milk at dairy farms. Baseline (i.e. present) and future climate change scenarios were defined, and the resultant concentrations of SCC and S. aureus were compared to the actual European regulatory limits. Across the three regions, raw milk losses ranged from 1.06% to 2.15% in the baseline. However, they increased up to 3.21% in the climate change scenarios when no on-farm improvements were considered. Regarding geographical variation, the highest potential milk losses were reported for the Mediterranean and the lowest for the Continental region. Concerning the fulfilment of the regulatory limits, the mean of S. aureus and SCC levels in milk did not exceed them either in any region or scenario. Nevertheless, when looking at percentiles, the 10th percentile remained above the limits of S. aureus in Atlantic and Mediterranean, but not in the Continental region. The findings provide a snapshot of climate change impacts on raw milk losses due to mastitis. They will allow farmers to detect weaknesses and prepare them to develop adaptation plans to climate change.

Microbial inefficient substrate use through the perspective of resource allocation models.

Microorganisms extract energy from substrates following strategies that may seem suboptimal at first glance. Beyond the so-called yield-rate trade-off, resource allocation models, which focus on assigning different functional roles to the limited number of enzymes that a cell can support, offer a framework to interpret the inefficient substrate use by microorganisms. We review here relevant examples of substrate conversions where a significant part of the available energy is not utilised and how resource allocation models offer a mechanistic interpretation thereof, notably for open mixed cultures. Future developments are identified, in particular, the challenge of considering metabolic flexibility towards uncertain environmental changes instead of strict fixed optimality objectives, with the final goal of increasing the prediction capabilities of resource allocation models. Finally, we highlight the relevance of resource allocation to understand and enable a promising biorefinery platform revolving around lactate, which would increase the flexibility of waste-to-chemical biorefinery schemes.

The fate of SARS-COV-2 in WWTPS points out the sludge line as a suitable spot for detection of COVID-19.

SARS-CoV-2 genetic material is detectable in the faeces of a considerable part of COVID-19 cases and hence, in municipal wastewater. This fact was confirmed early during the spread of the COVID-19 pandemic and prompted several studies that proposed monitoring its incidence by wastewater. This paper studies the fate of SARS-CoV-2 genetic material in wastewater treatment plants using RT-qPCR with a two-fold goal: i) to check its presence in the water effluent and in the produced sludge and ii) based on the understanding of the virus particles fate, to identify the most suitable spots for detecting the incidence of COVID-19 and monitor its evolution. On the grounds of the affinity of enveloped virus towards biosolids, we hypothesized that the sludge line acts as a concentrator of SARS-CoV-2 genetic material. Sampling several spots in primary, secondary and sludge treatment at the Ourense (Spain) WWTP in 5 different days showed that, in effect, most of SARS-CoV-2 particles cannot be detected in the water effluent as they are retained by the sludge line. We identified the sludge thickener as a suitable spot for detecting SARS-CoV-2 particles thanks to its higher solids concentration (more virus particles) and longer residence time (less sensitive to dilution caused by precipitation). These findings could be useful to develop a suitable strategy for early warning of COVID-19 incidence based on WWTP monitoring.

Elimination of SARS-CoV-2 along wastewater and sludge treatment processes.

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is shed in the feces of infected people. As a consequence, genomic RNA of the virus can be detected in wastewater. Although the presence of viral RNA does not inform on the infectivity of the virus, this presence of genetic material raised the question of the effectiveness of treatment processes in reducing the virus in wastewater and sludge. In this work, treatment lines of 16 wastewater treatment plants were monitored to evaluate the removal of SARS-CoV-2 RNA in raw, processed waters and sludge, from March to May 2020. Viral RNA copies were enumerated using reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) in 5 different laboratories. These laboratories participated in proficiency testing scheme and their results demonstrated the reliability and comparability of the results obtained for each one. SARS-CoV-2 RNA was found in 50.5% of the 101 influent wastewater samples characterized. Positive results were detected more frequently in those regions with a COVID-19 incidence higher than 100 cases per 100,000 inhabitants. Wastewater treatment plants (WWTPs) significantly reduced the occurrence of virus RNA along the water treatment lines. Secondary treatment effluents showed an occurrence of SARS-CoV-2 RNA in 23.3% of the samples and no positive results were found after MBR and chlorination. Non-treated sludge (from primary and secondary treatments) presented a higher occurrence of SARS-CoV-2 RNA than the corresponding water samples, demonstrating the affinity of virus particles for solids. Furthermore, SARS-CoV-2 RNA was detected in treated sludge after thickening and anaerobic digestion, whereas viral RNA was completely eliminated from sludge only when thermal hydrolysis was applied. Finally, co-analysis of SARS-CoV-2 and F-specific RNA bacteriophages was done in the same water and sludge samples in order to investigate the potential use of these bacteriophages as indicators of SARS-CoV-2 fate and reduction along the wastewater treatment.

Designing a robust index for WWTP energy efficiency: The ENERWATER water treatment energy index.

The development and use of composite indexes has exploded in the last 15 years as a tool to summarise the large amount of information available nowadays. To ensure that composite indicators reflect faithfully the purpose of evaluation and are widely accepted and used, the users must understand the relationship between individual sub-indicators and the result (transparency) and the evaluation should not depend on weights, lack of information, etc. (robustness). It is proposed here for the first time a composite index to measure energy efficiency in a wastewater treatment plant, from the definition of the individual sub-indicators to the assignation of an energy label, easy to communicate to a broad public. Using as a demonstration real data from 44 wastewater treatment plants, the index robustness is tested and improved by uncertainty and sensitivity analysis results, finally achieving a robust algorithm which can be used by the large majority of wastewater treatment plants.

Protein composition determines the preferential consumption of amino acids during anaerobic mixed-culture fermentation.

The valorisation of protein-rich residual streams by anaerobic mixed-culture fermentation (MCF) has been barely studied in contrast to carbohydrate-rich wastes. The aim of this work was, therefore, to investigate how protein composition, i.e. the amino acid (AA) profile, affects the individual consumption of amino acids and, consequently, the outcome of the process. Mixed-culture fermentations were performed with two model proteins (casein and gelatin) using continuous and batch reactors at neutral pH values and 25 °C. The acidification was incomplete for both proteins, with casein achieving a higher value than gelatin. Albeit dominated by acetic acid, product spectra were different as well, with n-butyric acid as the second major product for casein and propionic acid for gelatin. The preferential consumption of amino acids was demonstrated, which interestingly depends on protein composition. The previously accepted stoichiometry accurately describes iso and n-butyric acid production, but it fails for propionic, iso and n-valeric acid generation. Overall, this study offers a better understanding of protein fermentation mechanisms, which will help to improve degradation models and to design fermentation processes, based on optimal substrate selection.

A metabolic model for targeted volatile fatty acids production by cofermentation of carbohydrates and proteins.

Anaerobic mixed-culture fermentations are interesting processes to valorise organic wastes by converting them to volatile fatty acids. One of the main issues is that certain operational conditions (e.g. pH or different substrate concentrations) can vary significantly the product spectrum. So far, there are no tools that take into the account the characteristic features of cofermentation processes, which hinders the possibility of designing processes that use real wastes as substrates. In this work a mathematical model was developed for the production of volatile fatty acids from organic wastes with a high concentration of carbohydrates and proteins. The model reproduces satisfactorily experimental results and is also able of giving mechanistic insight into the interactions between carbohydrates and proteins that explain the observed changes in the product spectrum. We envision this model as the core of an early-stage design tool for anaerobic cofermentation processes, as shown in this work with different examples.

Assessment of the fate of organic micropollutants in novel wastewater treatment plant configurations through an empirical mechanistic model.

Novel wastewater treatment plants (WWTPs) are expected to be less energetically demanding than conventional ones. However, scarce information is available about the fate of organic micropollutants (OMPs) in these novel configurations. Therefore, the objective of this work is to assess the fate of OMPs in three novel WWTP configurations by using a plant-wide simulation that integrates multiple units. The difference among the three configurations is the organic carbon preconcentration technology: chemically enhanced primary treatment (CEPT), high-rate activated sludge (HRAS) combined or not with a rotating belt filter (RBF); followed by a partial-nitritation (PN-AMX) unit. The simulation results show that the three selected novel configurations lead mainly to comparable OMPs removal efficiencies from wastewater, which were similar or lower, depending on the OMP, than those obtained in conventional WWTPs. However, the presence of hydrophobic OMPs in the digested sludge noticeably differs among the three configurations. Whereas the configuration based on sole HRAS to recover organic carbon leads to a lower presence of OMPs in digested sludge than the conventional WWTP, in the other two novel configurations this presence is noticeable higher. In conclusion, novel WWTP configurations do not improve the OMPs elimination from wastewater achieved in conventional ones, but the HRAS-based WWTP configuration leads to the lowest presence in digested sludge so it becomes the most efficient alternative.

Role of methanogenesis on the biotransformation of organic micropollutants during anaerobic digestion.

Several studies showed that some organic micropollutants (OMPs) are biotransformed during anaerobic digestion (AD). Yet, most of them aim at reporting removal efficiencies instead of understanding the biotransformation process. Indeed, how each of the main AD stages (i.e., hydrolysis, acidogenesis, and methanogenesis) contribute to OMP biotransformation remains unknown. This study focuses on investigating the role of methanogenesis, the most characteristic step of AD, to OMP removal. More specifically, the sorption and the biotransformation of 20 OMPs by methanogenic biomass were analyzed determining their concentrations in both liquid and solid phases. Sorption onto methanogenic biomass displayed a similar behavior as reported for digested sludge. Most of the OMPs were biotransformed to a medium extent (35-70%) and only sulfamethoxazole was completely removed. Comparing these results with those reported for the complete AD process, methanogenesis was proven to play a key role, accounting for more than 50% of the OMP biotransformation (except for roxithromycin) during AD. An increase in the organic loading rate from 1 to 2gCOD/Ld, typical loads employed in sewage sludge anaerobic digesters, did not exert a clear cometabolic effect on the OMPs biotransformation. It is hypothesized that biotransformation occurs in both liquid and solid phases because no link between the partition coefficient (Kd) and the overall biotransformation efficiency was found. These findings allow a better understanding of the OMPs fate under anaerobic conditions, which is necessary to design efficient biological mitigation strategies.

Why are organic micropollutants not fully biotransformed? A mechanistic modelling approach to anaerobic systems.

Biotransformation of most organic micropollutants (OMPs) during wastewater treatment is not complete and an unexplained steady decrease of the biotransformation rate with time is reported for many OMPs in different biological processes. To minimize and accurately predict the emission of OMPs into the environment, the mechanisms and limitations behind their biotransformations should be clarified. Aiming to achieve this objective, the present study follows a mechanistic modelling approach, based on the formulation of four models according to different biotransformation hypotheses: Michaelis-Menten kinetics, chemical equilibrium between the parent compound and the transformation product (TP), enzymatic inhibition by the TP, and a limited compound bioavailability due to its sequestration in the solid phase. These models were calibrated and validated with kinetic experiments performed in two different anaerobic systems: continuous reactors enriched with methanogenic biomass and batch assays with anaerobic sludge. Model selection was conducted according to model suitability criteria (goodness of fitting the experimental data, confidence of the estimated parameters, and model parsimony) but also considering mechanistic evidences. The findings suggest that reversibility of the biological reactions and/or sequestration of compounds are likely the causes preventing the complete biotransformation of OMPs, and biotransformation is probably limited by thermodynamics rather than by kinetics. Taking into account its simplicity and broader applicability spectrum, the reversible biotransformation is the proposed model to explain the incomplete biotransformation of OMPs.

Challenges in industrial fermentation technology research.

Industrial fermentation processes are increasingly popular, and are considered an important technological asset for reducing our dependence on chemicals and products produced from fossil fuels. However, despite their increasing popularity, fermentation processes have not yet reached the same maturity as traditional chemical processes, particularly when it comes to using engineering tools such as mathematical models and optimization techniques. This perspective starts with a brief overview of these engineering tools. However, the main focus is on a description of some of the most important engineering challenges: scaling up and scaling down fermentation processes, the influence of morphology on broth rheology and mass transfer, and establishing novel sensors to measure and control insightful process parameters. The greatest emphasis is on the challenges posed by filamentous fungi, because of their wide applications as cell factories and therefore their relevance in a White Biotechnology context. Computational fluid dynamics (CFD) is introduced as a promising tool that can be used to support the scaling up and scaling down of bioreactors, and for studying mixing and the potential occurrence of gradients in a tank.

Sensitivity analysis of autotrophic N removal by a granule based bioreactor: Influence of mass transfer versus microbial kinetics.

A comprehensive and global sensitivity analysis was conducted under a range of operating conditions. The relative importance of mass transfer resistance versus kinetic parameters was studied and found to depend on the operating regime as follows: Operating under the optimal loading ratio of 1.90(gO(2)/m(3)/d)/(gN/m(3)/d), the system was influenced by mass transfer (10% impact on nitrogen removal) and performance was limited by AOB activity (75% impact on nitrogen removal), while operating above, AnAOB activity was limiting (68% impact on nitrogen removal). The negative effect of oxygen mass transfer had an impact of 15% on nitrogen removal. Summarizing such quantitative analyses led to formulation of an optimal operation window, which serves a valuable tool for diagnosis of performance problems and identification of optimal solutions in nitritation/anammox applications.