Krist V. Gernaey

Activated sludge wastewater treatment plant modelling and simulation: state of the art

This review paper focuses on modelling of wastewater treatment plants (WWTP). White-box modelling is widely applied in this field, with learning, design and process optimisation as the main applications. The introduction of the ASM model family by the IWA task group was of great importance, providing researchers and practitioners with a standardised set of basis models. This paper introduces the nowadays most frequently used white-box models for description of biological nitrogen and phosphorus removal activated sludge processes. These models are mainly applicable to municipal wastewater systems, but can be adapted easily to specific situations such as the presence of industrial wastewater. Some of the main model assumptions are highlighted, and their implications for practical model application are discussed. A step-wise procedure leads from the model purpose definition to a calibrated WWTP model. Important steps in the procedure are: model purpose definition, model selection, data collection, data reconciliation, calibration of the model parameters and model unfalsification. The model purpose, defined at the beginning of the procedure, influences the model selection, the data collection and the model calibration. In the model calibration a process engineering approach, i.e. based on understanding of the process and the model structure, is needed. A calibrated WWTP model, the result of an iterative procedure, can usually be obtained by only modifying few model parameters, using the default parameter sets as a starting point. Black-box, stochastic grey-box and hybrid models are useful in WWTP applications for prediction of the influent load, for estimation of biomass activities and effluent quality parameters. These modelling methodologies thus complement the process knowledge included in white-box models with predictions based on data in areas where the white-box model assumptions are not valid or where white-box models do not provide accurate predictions. Artificial intelligence (AI) covers a large spectrum of methods, and many of them have been applied in applications related to WWTPs. AI methodologies and white-box models can interact in many ways; supervisory control systems for WWTPs are one evident application. Modular agent-based systems combining several AI and modelling methods provide a great potential. In these systems, AI methods on one hand can maximise the knowledge extracted from data and operator experience, and subsequently apply this knowledge to improve WWTP control. White-box models on the other hand allow evaluating scenarios based on the available process knowledge about the WWTP. A white-box model calibration tool, an AI based WWTP design tool and a knowledge representation tool in the WWTP domain are other potential applications where fruitful interactions between AI methods and white-box models could be developed.

Nitrification monitoring in activated sludge by oxygen uptake rate (OUR) measurements

Abstract A simple measuring system was developed that yields information about the presence of NH + 4 and NO − 2 nitrogen in mixed liquor samples. In addition, it allows monitoring of the rate of both NH + 4 and NO − 2 oxidation simultaneously with the carbon substrate oxidation using OUR measurements. The method is based on the subsequent addition of NaClO 3 and allylthiourea, selective inhibitors of Nitrobacter and Nitrosomonas respectively, to the mixed liquor sample in a closed batch respirometer. The presented method is valuable for detailed monitoring of the nitrification process in a reactor: it is simple, inexpensive, robust and generally applicable for nitrifying reactor systems. The measurement of NH + 4 and NO − 2 oxidation rates enables the operator to detect the presence of NH + 4 and NO − 2 species. This allows action to be taken to improve the performance of the system. The method is validated on a SBR. It is indicated that optimal SBR phase scheduling can be based on such OUR measurements.

Good Modeling Practice for PAT Applications: Propagation of Input Uncertainty and Sensitivity Analysis

The uncertainty and sensitivity analysis are evaluated for their usefulness as part of the model‐building within Process Analytical Technology applications. A mechanistic model describing a batch cultivation of Streptomyces coelicolor for antibiotic production was used as case study. The input uncertainty resulting from assumptions of the model was propagated using the Monte Carlo procedure to estimate the output uncertainty. The results showed that significant uncertainty exists in the model outputs. Moreover the uncertainty in the biomass, glucose, ammonium and base‐consumption were found low compared to the large uncertainty observed in the antibiotic and off‐gas CO2 predictions. The output uncertainty was observed to be lower during the exponential growth phase, while higher in the stationary and death phases ‐ meaning the model describes some periods better than others. To understand which input parameters are responsible for the output uncertainty, three sensitivity methods (Standardized Regression Coefficients, Morris and differential analysis) were evaluated and compared. The results from these methods were mostly in agreement with each other and revealed that only few parameters (about 10) out of a total 56 were mainly responsible for the output uncertainty. Among these significant parameters, one finds parameters related to fermentation characteristics such as biomass metabolism, chemical equilibria and mass‐transfer. Overall the uncertainty and sensitivity analysis are found promising for helping to build reliable mechanistic models and to interpret the model outputs properly. These tools make part of good modeling practice, which can contribute to successful PAT applications for increased process understanding, operation and control purposes.

Application of microbioreactors in fermentation process development: A review

Biotechnology process development involves strain testing and improvement steps aimed at increasing yields and productivity. This necessitates the high-throughput screening of many potential strain candidates, a task currently mainly performed in shake flasks or microtiter plates. However, these methods have some drawbacks, such as the low data density (usually only end-point measurements) and the lack of control over cultivation conditions in standard shake flasks. Microbioreactors can offer the flexibility and controllability of bench-scale reactors and thus deliver results that are more comparable to large-scale fermentations, but with the additional advantages of small size, availability of online cultivation data and the potential for automation. Current microbioreactor technology is analyzed in this review paper, focusing on its industrial applicability, and directions for future research are presented.

Process analytical technology (PAT) for biopharmaceuticals

Process analytical technology (PAT), the regulatory initiative for building in quality to pharmaceutical manufacturing, has a great potential for improving biopharmaceutical production. The recommended analytical tools for building in quality, multivariate data analysis, mechanistic modeling, novel models for interpretation of systems biology data and new sensor technologies for cellular states, are instrumental in exploiting this potential. Industrial biopharmaceutical production has gradually become dependent on large-scale processes using sensitive mammalian cell cultures. This further emphasizes the need for improved PAT solutions. We summarize recent progress in this area based on an expert workshop held at the 8(th) European Symposium on Biochemical Engineering Sciences (Bologna, 2010), and highlight new opportunities for exploiting PAT when applied in biopharmaceutical production. We conclude with recommendations for advancing PAT applications in the biopharmaceutical industry.

Application of near‐infrared spectroscopy for monitoring and control of cell culture and fermentation

Near‐infrared (NIR) spectroscopy can potentially provide on‐line information on substrate, biomass, product, and metabolite concentrations in fermentation processes, which could be useful for improved monitoring or control. However, several factors can negatively influence the quality of chemometric models built for interpretation of the spectra, thus impairing the analyte concentration predictions. The aim of this review was to provide an overview of necessary conditions and challenges that one has to face when developing a NIR application for monitoring of cell culture or fermentation processes. Important practical aspects are introduced, such as sampling, modeling of biomass concentration, influence of microorganism morphology on the spectra, effects of the hydrodynamic conditions in the fermenter, temperature influence, instrument settings, and signal optimization. Several examples from the literature are provided, which will hopefully guide the reader interested in the topic. Furthermore, the general procedure used for the development of calibration models is presented, and the influence of microorganism metabolism—potential source of correlation between analytes—is commented. Other important issues such as wavelength selection and evaluation of robustness are shortly introduced. Finally, some examples of potential applications of NIR monitoring are provided, including the implementation of control strategies, the combination with other monitoring tools (the so‐called sensor fusion), and the description of process trajectories. On the basis of the review, we conclude that acceptance of NIR spectroscopy as a standard monitoring tool by the fermentation industry will necessitate considerably more on‐line studies using industrially relevant—and highly challenging—fermentation conditions (high aeration intensity, high biomass concentration and viscosity, and filamentous production strain).

Uncertainty analysis in WWTP model applications: a critical discussion using an example from design.

This study focuses on uncertainty analysis of WWTP models and analyzes the issue of framing and how it affects the interpretation of uncertainty analysis results. As a case study, the prediction of uncertainty involved in model-based design of a wastewater treatment plant is studied. The Monte Carlo procedure is used for uncertainty estimation, for which the input uncertainty is quantified through expert elicitation and the sampling is performed using the Latin hypercube method. Three scenarios from engineering practice are selected to examine the issue of framing: (1) uncertainty due to stoichiometric, biokinetic and influent parameters; (2) uncertainty due to hydraulic behaviour of the plant and mass transfer parameters; (3) uncertainty due to the combination of (1) and (2). The results demonstrate that depending on the way the uncertainty analysis is framed, the estimated uncertainty of design performance criteria differs significantly. The implication for the practical applications of uncertainty analysis in the wastewater industry is profound: (i) as the uncertainty analysis results are specific to the framing used, the results must be interpreted within the context of that framing; and (ii) the framing must be crafted according to the particular purpose of uncertainty analysis/model application. Finally, it needs to be emphasised that uncertainty analysis is no doubt a powerful tool for model-based design among others, however clear guidelines for good uncertainty analysis in wastewater engineering practice are needed.

Soft sensors in bioprocessing: A status report and recommendations

The following report with recommendations is the result of an expert panel meeting on soft sensor applications in bioprocess engineering that was organized by the Measurement, Monitoring, Modelling and Control (M3C) Working Group of the European Federation of Biotechnology - Section of Biochemical Engineering Science (ESBES). The aim of the panel was to provide an update on the present status of the subject and to identify critical needs and issues for the furthering of the successful development of soft sensor methods in bioprocess engineering research and for industrial applications, in particular with focus on biopharmaceutical applications. It concludes with a set of recommendations, which highlight current prospects for the extended use of soft sensors and those areas requiring development.

Modelling nitrite in wastewater treatment systems: a discussion of different modelling concepts

Originally presented at the 1st IWA/WEF Wastewater Treatment Modelling Seminar (WWTmod 2008), this contribution has been updated to also include the valuable feedback that was received during the Modelling Seminar. This paper addresses a number of basic issues concerning the modelling of nitrite in key processes involved in biological wastewater water treatment. To this end, we review different model concepts (together with model structures and corresponding parameter sets) proposed for processes such as two-step nitrification/denitrification, anaerobic ammonium oxidation and phosphorus uptake processes. After critically discussing these models with respect to their assumptions and parameter sets, common points of agreement as well as disagreement were elucidated. From this discussion a general picture of the state-of-the-art in the modelling of nitrite is provided. Taking this into account, a number of recommendations are provided to focus further research and development on nitrite modelling in biological wastewater treatment.

Benchmarking of Control Strategies for Wastewater Treatment Plants

Wastewater treatment plants are large non-linear systems subject to large perturbations in wastewater flow rate, load and composition. Nevertheless these plants have to be operated continuously, meeting stricter and stricter regulations. Many control strategies have been proposed in the literature for improved and more efficient operation of wastewater treatment plants. Unfortunately, their evaluation and comparison – either practical or based on simulation – is difficult. This is partly due to the variability of the influent, to the complexity of the biological and biochemical phenomena and to the large range of time constants (from a few minutes to several days). The lack of standard evaluation criteria is also a tremendous disadvantage. To really enhance the acceptance of innovative control strategies, such an evaluation needs to be based on a rigorous methodology including a simulation model, plant layout, controllers, sensors, performance criteria and test procedures, i.e. a complete benchmarking protocol. This book is a Scientific and Technical Report produced by the IWA Task Group on Benchmarking of Control Strategies for Wastewater Treatment Plants . The goal of the Task Group includes developing models and simulation tools that encompass the most typical unit processes within a wastewater treatment system (primary treatment, activated sludge, sludge treatment, etc.), as well as tools that will enable the evaluation of long-term control strategies and monitoring tasks (i.e. automatic detection of sensor and process faults). Work on these extensions has been carried out by the Task Group during the past five years, and the main results are summarized in Benchmarking of Control Strategies for Wastewater Treatment Plants . Besides a description of the final version of the already well-known Benchmark Simulation Model no. 1 (BSM1), the book includes the Benchmark Simulation Model no. 1 Long-Term (BSM1_LT) – with focus on benchmarking of process monitoring tasks – and the plant-wide Benchmark Simulation Model no. 2 (BSM2). This title belongs to Scientific and Technical Report Series ISBN: 9781780401171 (eBook) ISBN: 9781843391463 (Print)