Determining the linear correlation between dielectric spectroscopy and viable biomass concentration in filamentous fungal fermentations.

OBJECTIVES: Dielectric spectroscopy is commonly used for online monitoring of biomass growth. It is however not utilized for biomass concentration measurements due to poor correlation with Cell Dry Weight (CDW). A calibration methodology is developed that can directly measure viable biomass concentration in a commercial filamentous process using dielectric values, without recourse to independent and challenging viability determinations. RESULTS: The methodology is applied to samples from the industrial scale fermentation of a filamentous fungus, Acremonium fusidioides. By mixing fresh and heat-killed samples, linear responses were verified and sample viability could be fitted with the dielectric [Formula: see text] values and total solids concentration. The study included a total of 26 samples across 21 different cultivations, with a legacy at-line viable cell analyzer requiring 2 ml samples, and a modern on-line probe operated at-line with 2 different sample presentation volumes, one compatible with the legacy analyzer, a larger sample volume of 100 ml being compatible with calibration for on-line operation. The linear model provided an [Formula: see text] value of 0.99 between [Formula: see text] and viable biomass across the sample set using either instrument. The difference in ∆C when analyzing 100 mL and 2 mL samples with an in-line probe can be adjusted by a scalar factor of 1.33 within the microbial system used in this study, preserving the linear relation with [Formula: see text] of 0.97. CONCLUSIONS: It is possible to directly estimate viable biomass concentrations utilizing dielectric spectroscopy without recourse to extensive and difficult to execute independent viability studies. The same method can be applied to calibrate different instruments to measure viable biomass concentration. Small sample volumes are appropriate as long as the sample volumes are kept consistent.

Combining Group-Contribution Concept and Graph Neural Networks Toward Interpretable Molecular Property Models.

Quantitative structure-property relationships (QSPRs) are important tools to facilitate and accelerate the discovery of compounds with desired properties. While many QSPRs have been developed, they are associated with various shortcomings such as a lack of generalizability and modest accuracy. Albeit various machine-learning and deep-learning techniques have been integrated into such models, another shortcoming has emerged in the form of a lack of transparency and interpretability of such models. In this work, two interpretable graph neural network (GNN) models (attentive group-contribution (AGC) and group-contribution-based graph attention (GroupGAT)) are developed by integrating fundamentals using the concept of group contributions (GC). The interpretability consists of highlighting the substructure with the highest attention weights in the latent representation of the molecules using the attention mechanism. The proposed models showcased better performance compared to classical group-contribution models, as well as against various other GNN models describing the aqueous solubility, melting point, and enthalpies of formation, combustion, and fusion of organic compounds. The insights provided are consistent with insights obtained from the semiempirical GC models confirming that the proposed framework allows highlighting the important substructures of the molecules for a specific property.

Modelling of Harvesting Techniques for the Evaluation of the Density of Microalgae.

A better estimation of the density of cells has great relevance in the design of harvesting units. In the case of microalgae, the density is a function of the internal composition, which in turn is affected by external environmental conditions. The density of microalgae is often regarded as a constant or a generic value is retrieved from literature. This study proposes a procedure to evaluate the density of Chlorococcum sp. with simple sedimentation and centrifugation experiments coupled with the population balance equation (PBE), which is solved numerically. The density of cells is not constant; instead, it is a function of the size of particles, which in turn changes with the cells' phase of their life cycle. The calculated cellular density ranged between 1000 and 1100 kg m-3 in function of the cell size in both the sedimentation and centrifugation tests. The method can be extended to other microalgae species as well as to other types of cells.

Plant-wide assessment of alternative activated sludge configurations for biological nutrient removal under uncertain influent characteristics.

This study presents an extensive plant-wide model-based assessment of four alternative activated sludge (AS) configurations for biological nitrogen (N) and phosphorus (P) removal under uncertain influent loads and characteristics. Zeekoegat wastewater treatment plant (WWTP) in South Africa was chosen as case study due to its flexible design that enables operation in four different AS configurations: 3-stage Bardenpho (A2O), University of Cape Town (UCT), UCT modified (UCTM), and Johannesburg (JHB). A metamodeling based global sensitivity analysis was performed on a steady-state plant-wide simulation model using Activated Sludge Model No. 2d with the latest extension of physico-chemical processes describing the plant-wide P transformations. The simulation results showed that the predictions of effluent chemical oxygen demand (COD), N and P using the proposed approach fall within the interquartile range of measured data. The study also revealed that process configuration can affect: 1) how influent uncertainty is reflected in model predictions for effluent quality and cost related performances, and 2) the parameter rankings based on variance decomposition, particularly for effluent phosphate, sludge disposal and methane production. The results identified UCT and UCTM as more robust configurations for P removal (less propagated uncertainty and less sensitivity to N load) in the expense of incomplete denitrification. Moreover, based on the results of Monte-Carlo based scenario analysis, the balanced SRT for N and P removal is more sensitive to influent load variation/uncertainty for the A2O and JHB configurations. This gives a more operational flexibility to UCT and UCTM, where a narrow SRT range can ensure both N and P removal.

Integrated Model for Understanding N2O Emissions from Wastewater Treatment Plants: A Deep Learning Approach.

This study aims to demonstrate the application of deep learning to quantitatively describe long-term full-scale data observed from wastewater treatment plants (WWTPs) from the perspectives of process modeling, process analysis, and forecasting modeling. Approximately, 750,000 measurements including the influent flow rate, air flow rate, temperature, ammonium, nitrate, dissolved oxygen, and nitrous oxide (N2O) collected for more than a year from the Avedøre WWTP located in Denmark are utilized to develop a deep neural network (DNN) through supervised learning for process modeling, and the optimal DNN (R2 > 0.90) is selected for further evaluation. For process analysis, global sensitivity analysis based on variance decomposition is considered to identify the key parameters contributing to high N2O emission characteristics. For N2O forecasting, the proposed DNN-based model is compared with long short-term memory (LSTM), showing that the LSTM-based forecasting model performs significantly better than the DNN-based model (R2 > 0.94 and the root-mean-squared error is reduced by 64%). The results account for the feasibility of data-driven methods based on deep learning for quantitatively describing and understanding the rather complex N2O dynamics in WWTPs. Research into hybrid modeling concepts integrating mechanistic models of WWTPs (e.g., ASMs) with deep learning would be suggested as a future direction for monitoring N2O emissions from WWTPs.

Unravelling the environmental and economic impacts of innovative technologies for the enhancement of biogas production and sludge management in wastewater systems.

The retrofitting of wastewater treatment plants (WWTPs) should be addressed under sustainability criteria. It is well known that there are two elements that most penalize wastewater treatment: (i) energy requirements and (ii) sludge management. New technologies should reduce both of these drawbacks to address technical efficiency, carbon neutrality and reduced economic costs. In this context, the main objective of this work was to evaluate two real plants of different size in which major modifications were considered: enhanced recovery of organic matter (OM) in the primary treatment and partial-anammox nitrification process in the secondary treatment. Plant-wide modelling provided an estimate of the input and output flows of each process unit as well as the diagnosis of the main performance indicators, which served as a basis for the calculation of environmental and economic indicators using the LCA methodology. The combination of high-rate activated sludge (HRAS) + partial nitrification Anammox can decrease the environmental impacts by about 70% in the climate change (CC) category and 50% in the eutrophication potential (EP) category. Moreover, costs can be reduced by 35-45% depending on the size of the plant. In addition, the enhanced rotating belt filter (ERBF) can also improve the environmental profile, but to a lesser extent than the previous scenario, only up to 10% for CC and 15% for EP. These positive results are only possible considering the production of energy through biogas valorization according to the waste-to-energy scheme.

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.

Assessment of Full-Scale N2O Emission Characteristics and Testing of Control Concepts in an Activated Sludge Wastewater Treatment Plant with Alternating Aerobic and Anoxic Phases.

This work aims to obtain full-scale N2O emission characteristics translatable into viable N2O control strategies and conduct full-scale testing of the proposed N2O control concepts. Data of a long-term monitoring campaign was first used to quantify full-scale N2O emission and probe into the seasonal pattern. Then trends between N2O production/emission and process variables/conditions during typical operating cycles were revealed to explore the dynamic N2O emission behavior. A multivariate statistical analysis was performed to find the dependency of N2O emission on relevant process variables. The results show for the first time that relatively low/high N2O emission took place in seasons with a decreasing/increasing trend of water temperature, respectively. Aerobic phase contributed to N2O production/emission probably mainly through the hydroxylamine pathway. Comparatively, heterotrophic bacteria had a dual role in the anoxic phase and could be responsible for both net N2O production and consumption. Incomplete denitrification might contribute mainly to the N2O production/emission in the anoxic phase and the accumulation of N2O to be significantly emitted in the following cycle due to the competition between different denitrification steps for electron donors. Therefore, properly extending the length of anoxic phase could serve as a potential control means to regulate N2O accumulation in the anoxic phase. The full-scale testing not only verified the efficacy of reduced dissolved oxygen set-point in reducing N2O emission by 60%, but also confirmed the proposed concepts of control over the aerobic and anoxic phases collectively.

Impact of granule size distribution on nitrous oxide production in autotrophic nitrogen removal granular reactor.

This work applied an approach with reactor compartmentation and artificial diffusion to study the impact of granule size distribution on the autotrophic granular reactor performing partial nitritation and anaerobic ammonium oxidation with focus on the nitrous oxide (N2O) production. The results show that the microbial community and the associated N2O production rates in the granular structure are significantly influenced by the granule size distribution. Heterotrophic bacteria growing on microbial decay products tend to be retained and contribute to N2O consumption in relatively small granules. Ammonium-oxidizing bacteria are mainly responsible for N2O production via two pathways in granules of different sizes. Under the conditions studied, such heterogeneity in the granular structure disappears when the number of granule size classes considered reaches >4, where heterotrophic bacteria are completely outcompeted in the granules. In general, larger granules account for a higher portion of the net N2O production, while the trend regarding the volumetric contribution of each granule size class changes with a varied number of granule size classes, due to the different contributions of relevant N2O production pathways (with the heterotrophic denitrification pathway being the most decisive). Overall, with the increasing extent of granule size distribution, the nitrogen removal efficiency decreases slightly but consistently, whereas the N2O production factor increases until the number of granule size classes reaches 4 or above. Practical implications of this work include: i) granules should be controlled as well-distributed as possible in order to obtain high nitrogen removal while minimizing N2O production; ii) granule size distribution should be considered carefully and specifically when modelling N2O production/emission from the autotrophic nitrogen removal granular reactor.

Nitrous oxide production in autotrophic nitrogen removal granular sludge: A modeling study.

The sustainability of autotrophic granular system performing partial nitritation and anaerobic ammonium oxidation (anammox) for complete nitrogen removal is impaired by the production of nitrous oxide (N2 O). A systematic analysis of the pathways and affecting parameters is, therefore, required for developing N 2 O mitigation strategies. To this end, a mathematical model capable of describing different N 2 O production pathways was defined in this study by synthesizing relevant mechanisms of ammonium-oxidizing bacteria (AOB), nitrite-oxidizing bacteria, heterotrophic bacteria (HB), and anammox bacteria. With the model validity reliably tested and verified using two independent sets of experimental data from two different autotrophic nitrogen removal biofilm/granular systems, the defined model was applied to reveal the underlying mechanisms of N 2 O production in the granular structure as well as the impacts of operating conditions on N 2 O production. The results show that: (a) in the aerobic zone close to the granule surface where AOB contribute to N 2 O production through both the AOB denitrification pathway and the NH 2 OH pathway, the co-occurring HB consume N 2 O produced by AOB but indirectly enhance the N 2 O production by providing NO from NO 2- reduction for the NH 2 OH pathway, (b) the inner anoxic zone of granules with the dominance of anammox bacteria acts as a sink for NO 2- diffusing from the outer aerobic zone and, therefore, reduces N 2 O production from the AOB denitrification pathway, (c) operating parameters including bulk DO, influent NH 4+ , and granule size affect the N 2 O production in the granules mainly by regulating the NH 2 OH pathway of AOB, accounting for 34-58% of N 2 O turnover, and (d) the competition between the NH 2 OH pathway and heterotrophic denitrification for nitric oxide leads to the positive role of HB in reducing N 2 O production in the autotrophic nitrogen removal granules, which could be further enhanced in the presence of a proper level of influent organics.

CFD predicted pH gradients in lactic acid bacteria cultivations.

The formation of pH gradients in a 700 L batch fermentation of Streptococcus thermophilus was studied using multi-position pH measurements and computational fluid dynamics (CFD) modeling. To this end, a dynamic, kinetic model of S. thermophilus and a pH correlation were integrated into a validated one-phase CFD model, and a dynamic CFD simulation was performed. First, the fluid dynamics of the CFD model were validated with NaOH tracer pulse mixing experiments. Mixing experiments and simulations were performed whereas multiple pH sensors, which were placed vertically at different locations in the bioreactor, captured the response. A mixing time of about 46 s to reach 95% homogeneity was measured and predicted at an impeller speed of 242 rpm. The CFD simulation of the S. thermophilus fermentation captured the experimentally observed pH gradients between a pH of 5.9 and 6.3, which occurred during the exponential growth phase. A pH higher than 7 was predicted in the vicinity of the base solution inlet. Biomass growth, lactic acid production, and substrate consumption matched the experimental observations. Moreover, the biokinetic results obtained from the CFD simulation were similar to a single-compartment simulation, for which a homogeneous distribution of the pH was assumed. This indicates no influence of pH gradients on growth in the studied bioreactor. This study verified that the pH gradients during a fermentation in the pilot-scale bioreactor could be accurately predicted using a coupled simulation of a biokinetic and a CFD model. To support the understanding and optimization of industrial-scale processes, future biokinetic CFD studies need to assess multiple types of environmental gradients, like pH, substrate, and dissolved oxygen, especially at industrial scale.

Calibration of the comprehensive NDHA-N2O dynamics model for nitrifier-enriched biomass using targeted respirometric assays.

The NDHA model comprehensively describes nitrous oxide (N2O) producing pathways by both autotrophic ammonium oxidizing and heterotrophic bacteria. The model was calibrated via a set of targeted extant respirometric assays using enriched nitrifying biomass from a lab-scale reactor. Biomass response to ammonium, hydroxylamine, nitrite and N2O additions under aerobic and anaerobic conditions were tracked with continuous measurement of dissolved oxygen (DO) and N2O. The sequential addition of substrate pulses allowed the isolation of oxygen-consuming processes. The parameters to be estimated were determined by the information content of the datasets using identifiability analysis. Dynamic DO profiles were used to calibrate five parameters corresponding to endogenous, nitrite oxidation and ammonium oxidation processes. The subsequent N2O calibration was not significantly affected by the uncertainty propagated from the DO calibration because of the high accuracy of the estimates. Five parameters describing the individual contribution of three biological N2O pathways were estimated accurately (variance/mean < 10% for all estimated parameters). The NDHA model response was evaluated with statistical metrics (F-test, autocorrelation function). The 95% confidence intervals of DO and N2O predictions based on the uncertainty obtained during calibration are studied for the first time. The measured data fall within the 95% confidence interval of the predictions, indicating a good model description. Overall, accurate parameter estimation and identifiability analysis of ammonium removal significantly decreases the uncertainty propagated to N2O production, which is expected to benefit N2O model discrimination studies and reliable full scale applications.

Mechanistic Fermentation Models for Process Design, Monitoring, and Control.

Mechanistic models require a significant investment of time and resources, but their application to multiple stages of fermentation process development and operation can make this investment highly valuable. This Opinion article discusses how an established fermentation model may be adapted for application to different stages of fermentation process development: planning, process design, monitoring, and control. Although a longer development time is required for such modeling methods in comparison to purely data-based model techniques, the wide range of applications makes them a highly valuable tool for fermentation research and development. In addition, in a research environment, where collaboration is important, developing mechanistic models provides a platform for knowledge sharing and consolidation of existing process understanding.

A novel fuzzy-logic control strategy minimizing N2O emissions.

A novel control strategy for achieving low N2O emissions and low effluent NH4+ concentration is here proposed. The control strategy uses the measurements of ammonium and nitrate concentrations in inlet and outlet of the aerobic zone of a wastewater treatment plant to calculate a ratio indicating the balance among the microbial groups. More specifically, the ratio will indicate if there is a complete nitrification. In case nitrification is not complete, the controller will adjust the aeration level of the plant in order to inhibit the production of N2O from AOB and HB denitrification. The controller was implemented using the fuzzy logic approach. It was comprehensively tested for different model structures and different sets of model parameters with regards to its ability of mitigating N2O emissions for future applications in real wastewater treatment plants. It is concluded that the control strategy is useful for those plants having AOB denitrification as the main N2O producing process. However, in treatment plants having incomplete NH2OH oxidation as the main N2O producing pathway, a cascade controller configuration adapting the oxygen supply to respect only the effluent ammonium concentration limits was found to be more effective to ensure low N2O emissions.

A review of control strategies for manipulating the feed rate in fed-batch fermentation processes.

A majority of industrial fermentation processes are operated in fed-batch mode. In this case, the rate of feed addition to the system is a focus for optimising the process operation, as it directly impacts metabolic activity, as well as directly affecting the volume dynamics in the system. This review covers a range of strategies which have been employed to use the feed rate as a manipulated variable in a control strategy. The feed rate is chosen as the focus for this review, as it is seen that this variable may be used towards many different objectives depending on the process of interest, the characteristics of the strain, or the product being produced, which leads to different drivers for process optimisation. This review summarises the methods, as well as focusing on the different objectives for the controllers, and the choice of measured variables involved in the strategy. The discussion includes a summary of considerations for control strategy development.

A novel model-based control strategy for aerobic filamentous fungal fed-batch fermentation processes.

A novel model-based control strategy has been developed for filamentous fungal fed-batch fermentation processes. The system of interest is a pilot scale (550 L) filamentous fungus process operating at Novozymes A/S. In such processes, it is desirable to maximize the total product achieved in a batch in a defined process time. In order to achieve this goal, it is important to maximize both the product concentration, and also the total final mass in the fed-batch system. To this end, we describe the development of a control strategy which aims to achieve maximum tank fill, while avoiding oxygen limited conditions. This requires a two stage approach: (i) calculation of the tank start fill; and (ii) on-line control in order to maximize fill subject to oxygen transfer limitations. First, a mechanistic model was applied off-line in order to determine the appropriate start fill for processes with four different sets of process operating conditions for the stirrer speed, headspace pressure, and aeration rate. The start fills were tested with eight pilot scale experiments using a reference process operation. An on-line control strategy was then developed, utilizing the mechanistic model which is recursively updated using on-line measurements. The model was applied in order to predict the current system states, including the biomass concentration, and to simulate the expected future trajectory of the system until a specified end time. In this way, the desired feed rate is updated along the progress of the batch taking into account the oxygen mass transfer conditions and the expected future trajectory of the mass. The final results show that the target fill was achieved to within 5% under the maximum fill when tested using eight pilot scale batches, and over filling was avoided. The results were reproducible, unlike the reference experiments which show over 10% variation in the final tank fill, and this also includes over filling. The variance of the final tank fill is reduced by over 74%, meaning that it is possible to target the final maximum fill reproducibly. The product concentration achieved at a given set of process conditions was unaffected by the control strategy. Biotechnol. Bioeng. 2017;114: 1459-1468. © 2017 Wiley Periodicals, Inc.

Application of a mechanistic model as a tool for on-line monitoring of pilot scale filamentous fungal fermentation processes-The importance of evaporation effects.

A mechanistic model-based soft sensor is developed and validated for 550L filamentous fungus fermentations operated at Novozymes A/S. The soft sensor is comprised of a parameter estimation block based on a stoichiometric balance, coupled to a dynamic process model. The on-line parameter estimation block models the changing rates of formation of product, biomass, and water, and the rate of consumption of feed using standard, available on-line measurements. This parameter estimation block, is coupled to a mechanistic process model, which solves the current states of biomass, product, substrate, dissolved oxygen and mass, as well as other process parameters including kL a, viscosity and partial pressure of CO2 . State estimation at this scale requires a robust mass model including evaporation, which is a factor not often considered at smaller scales of operation. The model is developed using a historical data set of 11 batches from the fermentation pilot plant (550L) at Novozymes A/S. The model is then implemented on-line in 550L fermentation processes operated at Novozymes A/S in order to validate the state estimator model on 14 new batches utilizing a new strain. The product concentration in the validation batches was predicted with an average root mean sum of squared error (RMSSE) of 16.6%. In addition, calculation of the Janus coefficient for the validation batches shows a suitably calibrated model. The robustness of the model prediction is assessed with respect to the accuracy of the input data. Parameter estimation uncertainty is also carried out. The application of this on-line state estimator allows for on-line monitoring of pilot scale batches, including real-time estimates of multiple parameters which are not able to be monitored on-line. With successful application of a soft sensor at this scale, this allows for improved process monitoring, as well as opening up further possibilities for on-line control algorithms, utilizing these on-line model outputs. Biotechnol. Bioeng. 2017;114: 589-599. © 2016 Wiley Periodicals, Inc.

Systematic design of membership functions for fuzzy-logic control: A case study on one-stage partial nitritation/anammox treatment systems.

A methodology is developed to systematically design the membership functions of fuzzy-logic controllers for multivariable systems. The methodology consists of a systematic derivation of the critical points of the membership functions as a function of predefined control objectives. Several constrained optimization problems corresponding to different qualitative operation states of the system are defined and solved to identify, in a consistent manner, the critical points of the membership functions for the input variables. The consistently identified critical points, together with the linguistic rules, determine the long term reachability of the control objectives by the fuzzy logic controller. The methodology is highlighted using a single-stage side-stream partial nitritation/Anammox reactor as a case study. As a result, a new fuzzy-logic controller for high and stable total nitrogen removal efficiency is designed. Rigorous simulations are carried out to evaluate and benchmark the performance of the controller. The results demonstrate that the novel control strategy is capable of rejecting the long-term influent disturbances, and can achieve a stable and high TN removal efficiency. Additionally, the controller was tested, and showed robustness, against measurement noise levels typical for wastewater sensors. A feedforward-feedback configuration using the present controller would give even better performance. In comparison, a previously developed fuzzy-logic controller using merely expert and intuitive knowledge performed worse. This proved the importance of using a systematic methodology for the derivation of the membership functions for multivariable systems. These results are promising for future applications of the controller in real full-scale plants. Furthermore, the methodology can be used as a tool to help systematically design fuzzy logic control applications for other biological processes.

Group-contribution based property estimation and uncertainty analysis for flammability-related properties.

This study presents new group contribution (GC) models for the prediction of Lower and Upper Flammability Limits (LFL and UFL), Flash Point (FP) and Auto Ignition Temperature (AIT) of organic chemicals applying the Marrero/Gani (MG) method. Advanced methods for parameter estimation using robust regression and outlier treatment have been applied to achieve high accuracy. Furthermore, linear error propagation based on covariance matrix of estimated parameters was performed. Therefore, every estimated property value of the flammability-related properties is reported together with its corresponding 95%-confidence interval of the prediction. Compared to existing models the developed ones have a higher accuracy, are simple to apply and provide uncertainty information on the calculated prediction. The average relative error and correlation coefficient are 11.5% and 0.99 for LFL, 15.9% and 0.91 for UFL, 2.0% and 0.99 for FP as well as 6.4% and 0.76 for AIT. Moreover, the temperature-dependence of LFL property was studied. A compound specific proportionality constant (K(LFL)) between LFL and temperature is introduced and an MG GC model to estimate K(LFL) is developed. Overall the ability to predict flammability-related properties including the corresponding uncertainty of the prediction can provide important information for a qualitative and quantitative safety-related risk assessment studies.

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.