Krist V. Gernaey, Jakob K. Huusom and Rafiqul Gani (Eds.), 12th International Symposium on Process Systems Engineering and 25th European Symposium on Computer Aided Process Engineering. 31 May – 4 June 2015, Copenhagen, Denmark © 2015 Elsevier B.V. All rights reserved.
A 3erspective on PSE in )ermentation 3rocess 'evelopment and 2peration Krist V. Gernaey* CAPEC-PROCESS, Department of Chemical and Biochemical Engineering, Technical University of Denmark (DTU), DK-2800 Lyngby, Denmark *
[email protected]
Abstract Compared to the chemical industry, the use of PSE methods and tools is not as widespread in industrial fermentation processes. This paper gives an overview of some of the main engineering challenges in industrial fermentation processes. Furthermore, a number of mathematical models are highlighted as examples of PSE methods and tools that are used in the context of industrial fermentation technology. Finally, it is discussed what could be done to increase the future use of PSE methods and tools within the industrial fermentation technology area. Keywords: control, fermentation, modelling, on-line sensor, optimisation
1. Introduction Industrial fermentation processes are increasingly popular for the production of bulk and fine chemicals, pharmaceuticals etc. It is indeed remarkable that the term ‘fermentation process’ covers a broad range of production hosts: (1) Filamentous fungi are used for production of organic acids, where citric acid production by Aspergillus niger (Shu and Johnson, 1948) is a well-known example; (2) Penicillin, the first antibiotic that was discovered, is produced at large scale by fermentation of Penicillium chrysogenum (Moyer, 1948); (3) Recombinant proteins such as insulin are produced by fermentation with Escherichia coli (Johnson, 1983) and the yeast Saccharomyces cerevisiae (Ostergaard et al., 2000). Fermentation also plays a prominent role in 2nd generation bioethanol production processes. As a consequence, industrial fermentation processes are considered to form an important technological asset for reducing our future 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 production processes, particularly when it comes to using engineering tools such as mathematical models, process control algorithms and optimization techniques to support the search for improved and more efficient processes. This perspective starts with a description of some of the most important engineering challenges within industrial fermentation technology, since a basic understanding of these challenges is an advantage when trying to understand the limitations in the current use of PSE methods and tools. Afterwards, the focus shifts towards PSE tools and their application in the fermentation area, with special focus on mathematical models. The paper ends with a number of future perspectives in this area: several potential solutions are proposed to facilitate the future use of PSE methods and tools in fermentation process development, operation and optimisation.
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2. Engineering challenges within fermentation technology An extended discussion of engineering challenges is given in Formenti et al. (2014). 2.1. Scaling up and scaling down Most fermentations are operated as batch or fed-batch systems. Experiments in laboratory (0.5-20 L) and pilot scale (20-2000 L) have traditionally been used to screen for conditions that yield maximal volumetric productivity, since it is too expensive – due to loss of valuable production time – to do such screening in a full-scale reactor. In production of pharmaceuticals, where Good Manufacturing Practice (GMP) applies, performing experiments at large scale is usually completely out of the question. Scaling up a fermentation process serves the purpose of transforming optimal operating conditions from laboratory/pilot scale to production scale bioreactors. The results from experiments at laboratory and pilot scale are, however, often difficult to compare to large industrial bioreactors, and scale-up is therefore still one of the major challenges in the fermentation industry. The main reason for this is that lab and pilot-scale fermenters can be considered well-mixed; in large scale, on the contrary, concentration gradients do exist, as for example documented in the study by Enfors et al. (2001), and such gradients are known to have a significant influence on the productivity of the host organism. In practice, the scale-up to a production scale reactor is done iteratively: as soon as pilot plant experiments demonstrate a feasible business case, pilot plant results are attempted to be transferred to industrial scale. If similar performance can be demonstrated at large scale, scale-up is considered successful. If not, additional pilot plant experiments might be performed, to investigate the effect of additional process parameters on process performance. Scaling down also poses a challenge in industrial fermentation technology. Most biotechnological processes are designed for existing equipment, since full scale bioreactors are very expensive and therefore used for several decades. As a consequence, the available process equipment ranges are known before a new process is designed, and it is important to find a setup of the small scale equipment that mimics the effect of full-scale process parameter changes on the process performance as closely as possible in order to develop new processes as efficiently as possible. During the past decades, fermentation experiments at very small scale have become popular for gaining initial knowledge on fermentation process performance – sometimes the term ‘ultra-scale-down’ is used. This has resulted in the creation of completely new devices (microfluidic devices, microbioreactors and milliliter-scale stirred systems) that are considered suitable scale-down versions of larger bioreactors. Experience has shown that each small scale system brings benefits as well as problems during process development across scales, and it is especially important that the user is aware of this when applying such ultra-scale-down systems to a practical case. Successful scaling was reported by Isett et al. (2007), who demonstrated scalability from a 24-well plate (4-6 mL) to a laboratory scale stirred tank (20 L) using S. cerevisiae, while Islam et al. (2008) showed predictive scale-up from micro well plate (2 mL) to laboratory (7.5 L) and pilot (75 L) scale using E. coli. However, even with such positive results in mind, it is important to realize that there is still a long way if the aim is to demonstrate successful scale-up to industrial scale reactors, which can have a volume of 100 m3 or more. 2.2. Mass transfer, morphology and rheology Mass transfer is crucial for a fermentation process, as O2 and nutrients have to be distributed, and possible toxic compounds, such as CO2, have to be removed. The mass
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transfer is strongly dependent on the viscosity of the fermentation broth, where the rheology of the broth is dependent on both the biomass concentration and the morphology of the production host cells (Cascaval et al., 2003). The complex relation between these variables is still not completely understood, which contributes to making scaling up/scaling down difficult. In general, stirring and aeration are a prerequisite for almost all types of cells, even for cell cultures with mammalian cells, in order to have a proper oxygen supply. The most challenging expression systems are the filamentous microorganisms in terms of mass transfer and rheology: their morphology, both at the microscopic and macroscopic level, will have a major influence on mass transfer. At the microscopic level, the optimal morphology for a given bioprocess varies and cannot be generalized, and relies on the desired product (Gibbs et al., 2000). Dependent on the fermentation and the employed strain, pellet formation can occur. In some processes, a pellet type morphology is preferred since it allows for simplified downstream processing and yields a Newtonian fluid behavior of the medium (macroscopic level), which results in low aeration and agitation power input. However, the pelleted morphology has the disadvantage that it results in nutrient concentration gradients within the pellet. The latter is not observed in freely dispersed mycelia, although gradients can occur in such a system dependent on the mixing time of the reactor system, due to the high broth viscosity (Riley et al., 2000). Thus, freely dispersed mycelia allow enhanced growth and production. This has been attributed to the influence of morphology on the production kinetics at the microscopic level, e.g. higher enzyme secretion was observed from a more densely branched mutant of Aspergillus oryzae (Spohr et al., 1998). It still remains challenging to estimate a reliable shear rate to evaluate the viscosity across scales for filamentous fungi, a problem that is usually not experienced in bacteria and yeast cultivation which exhibit a Newtonian behaviour which is attributed to the spherical shape of their morphology (Oniscu et al., 2003). This facilitates the work with these types of microorganisms considerably, and it also explains why many fermentation process optimization studies are focused on filamentous fungi. 2.3. Data handling, advanced sensors and control The sensors in industrial bioreactors are most often limited to pH, dissolved oxygen and temperature sensors which are placed at a single location in vessels with large volumes, often with concentration gradients (Larsson et al., 1996). As a consequence, at best such standard univariate sensors display an average value for the entire process which can be correlated to the processes in the vessel, but little or no information on the spatial heterogeneity in the vessel is available. A PID controller is the standard controller in fermentation processes to keep a controlled variable at or close to its set point or set point trajectory. However, the PID controller cannot guarantee that the fermentation is operated optimally. The set points (often time-varying trajectories in batch or fed-batch operation) only result in an optimal operation under certain nominal conditions. In most cases, a set point trajectory is the result of a time-consuming procedure where small adjustments are made to the ideal set point trajectory – the golden batch – whenever results of the last batch demonstrate an improvement. However, disturbances such as changes in substrate quality or composition take place, and ideally the set points should be modified accordingly. The latter optimization is difficult in industrial fermentations for a number of reasons: (1) Most common controlled variables, dissolved oxygen (DO) and pH, are only indirectly linked to the optimal operation of a fermenter, i.e. the operation that leads to the highest volumetric productivity. Important variables such as the biomass, substrate, product or
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by-product concentration are only sporadically monitored (and less frequently controlled); (2) Models can be used to synthesize controllers that operate close to optimal conditions and to determine optimal trajectories for (fed-)batch operations. In the fermentation industry, however, sufficiently accurate models that can be used for this purpose are often lacking, as for example pointed out by Smets et al. (2004) in a review on optimal feed rate strategies. Introduction of more advanced sensors in industrial scale fermentation could be beneficial and allow the introduction of more advanced or improved control strategies. However, this is not an easy development task. Cervera et al. (2009), in a literature review on the use of near infrared spectroscopy in fermentation and cell culture, concluded that most reported applications refer to at-line or off-line measurements. What industry really needs is a demonstration that such advanced sensors can be operated on-line to generate real-time data that can be used as input for controlling the fermentation process. Also worth mentioning is that a major limitation in the introduction of new sensors for process monitoring originates from GMP aspects, especially in the pharmaceutical biotechnology industry. Process knowledge extraction from historical data is another area which has not been explored that much. The fermentation industry possesses large databases with historical data, but in most cases these are not used for a number of reasons: (1) Retrieval of the data is often experienced as far too time-consuming; (2) Data compression algorithms sometimes remove the most interesting dynamics from the data that are stored; (3) In many cases, especially for smaller companies, the knowledge and experience to work with large historical data sets is not available within the company. Furthermore, it is often problematic, due to proprietary reasons, to involve external partners – academia or consultants – in such a historical data interpretation task.
3. Fermentation process modelling The main purpose of this section is to briefly illustrate the use of PSE methods and tools in the frame of fermentation projects, and this will be done by giving insight in the broad range of models that are available for studying fermentation processes. 3.1. Mechanistic models Mechanistic models are useful for representing available process knowledge, and can therefore support process development. Reviews on mathematical models can be found in Nielsen and Villadsen (1992), Henson (2003) and Gernaey et al. (2010), among othres. The opinion article by Bailey (1998) is still highly relevant as well, and highlights both the history and the future of mathematical modelling in biochemical engineering. Mechanistic fermentation process models are based on mass, heat and momentum balances, supplemented with an appropriate mathematical formulation of the key mechanisms (e.g. kinetic expressions to reflect process dynamics). The kinetic expressions are often empirical, thus providing a simplified and idealized view of a complex biological mechanism, with the Monod expression for microbial growth kinetics as the most well-known example. Assuming a homogeneous reactor environment, a generally accepted classification of mechanistic models of cell populations is presented in Figure 1 (Frederickson et al., 1970; Bailey, 1998). If the assumption of a homogeneous reactor environment does not hold, then a distributed model is needed (i.e. a model where not only time, but also space (1-, 2- or 3D), forms an independent variable): well-known examples are compartment models (Vrabel et al., 2000), as well as computational fluid dynamics (CFD). Unsegregated models are most
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common, and rely on an average cell description. Within this category, unstructured models are the simplest – and also the most popular) models that use a single variable to describe biomass. Unsegregated unstructured
Description ofthe biomass
Biomass asablack box
Heterogeneity in thepopulation Different biomass subpopulations (e.g.multiorganism cultivations Segregated unstructured
Description ofasingle cell
Unsegregated structured Intracellular models (e.g.metabolic networks) Heterogeneity in thesinglecell
1dimension(e.g.ageͲ or massͲ structured); 2or+dimensions(e.g. chemically structured) Segregated structured
Figure 1. Schematic classification of mechanistic models for cell cultivations (Frederickson et al., 1970; Bailey, 1998; Lencastre-Fernandes et al., 2011).
Unsegregated structured models describe the biomass as consisting of several variables (such as NADH, precursors, metabolites, ATP, biomass), and have been used for modeling complex processes such as yeast intracellular metabolism (Nielsen and Villadsen 1992). Morphologically structured models (Agger et al. 1998) were specifically developed to describe growth of important production organisms, filamentous fungi,–– and distinguish between different regions of the hyphal elements. Segregated models consider individual cells, in recognition of the fact that cells in a population – a pure culture – are different, and are most often formulated as a population balance model (PBM). Lencastre Fernandes et al. (2011) provide a concise review of the status of PBM in the fermentation area. An unstructured segregated model characterizes cells by one distributed property (i.e. cell size or age of individual cells; Zamamiri et al., 2002) without considering intracellular composition. Obviously, structured segregated models are more complex, since the distribution of one or more intracellular variables is also considered. Solving the resulting multi-dimensional PBM is difficult, unless the intracellular state can be captured with just a few variables (Henson, 2003). One alternative to PBMs is cell ensemble modeling (Domach and Shuler, 1984; Henson, 2003), where the parameters of a single cell model are randomized to simulate a cell population. Apart from the cells, the PBM framework also offers the possibility to make detailed studies of other phenomena of interest, for example the behaviour of air bubbles in an aeration tank, where the bubble size will change when the gas bubbles move through the reactor as a result of the action of the impeller on the one hand, and coalescence of gas bubbles on the other hand. 3.2. Computational fluid dynamics (CFD) Aeration and agitation design and scale-up have for a long time been performed based on empirical correlations and engineering experiences. Typical scale-up parameters that are used in practice are constant power to volume ratio, constant tip speed, constant Reynolds number and constant volumetric air flow rate. Scale up with constant power to volume ratio will result in an increased tip speed (i.e. shear) while the mixing time is decreased (Stanbury et al., 1995). However, none of these correlations includes vessel
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geometry, mixing intensities, operating conditions, feed location, physical properties etc. (Leng et al., 2008). In view of the general lack of sufficient understanding of aeration and agitation design/scale-up, there is general agreement that Computational Fluid Dynamics (CFD) is a valuable 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. Ideally, assuming that the kinetics of a process hold across scales, CFD can ideally be used to support transfer of research results across scales: from the first experiment in an ultra-scale-down reactor via well-mixed pilot scale experiments towards the full-scale reactor with its concentration gradients. One of the challenges in this area is the fact that many academic groups with an interest in CFD only have access to laboratory scale and pilot scale data. This is illustrated by the fact that one of the only published data sets in large scale (30 m3) is almost 20 years old (Larsson et al., 1996).
4. Future perspectives 4.1. Industry-academia collaboration Industrial scale data are proprietary, which forms a severe limitation. Data from process scale up studies can for example not be found as part of the scientific literature, and can thus not be used as a basis for extending the available knowledge about this important engineering task. Significant progress with respect to understanding the scaling up problem can thus only be made through a close collaboration between industry and academia: industry has equipment – large scale reactors which are not affordable for university – and knowhow about how to run a full-scale fermentation, whereas academia can contribute with expertise in for example CFD and detailed knowledge related to the cellular behavior at large scale to develop a better understanding of the major scaling up challenges. For the future, it should be considered to define a number of industrially relevant cases, using old production strains that are no longer in use or wild type strains with industrial value, which should be described in detail in order to allow people to compare results and make progress. Another area where industry-academia collaboration is important is specifically on the transformation of historical data to useful information. Academia often lacks industrially relevant data sets of significant size that can be used to demonstrate the value of data mining tools. The main hurdle to be taken there, again, is the fact that data are proprietary, but this can often be solved by appropriate scaling of the raw data before they are transferred from industry to academia. Here as well, it could be useful to establish collaboration between several industrial partners, in order to make a number of data sets publicly available such that academic groups can test their research work on industrially relevant data, instead of solely relying on idealized toy examples with academic value which cannot be transferred to industrial practice. 4.2. Soft sensors Introducing new, more advanced sensors is not straightforward. Therefore, one method to increase both data quality of on-line sensors and data quantity, even without violating GMP regulations, is by increased use of soft sensors. In a soft sensor, robust on-line measurements, which are not subject to time delay, are used to calculate the expected value of new variables of interest that could be useful to allow improved control of fermentation processes. A recent report by an expert group concluded that the use of soft sensors in the fermentation industry is very limited at this moment, despite the obvious potential (Luttmann et al., 2012). This is clearly a topic with a considerable unexplored potential that should be more in focus in the future.
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4.3. Hybrid models Hybrid models combine fundamental knowledge with data-driven techniques to model fundamentally unknown dependencies, and more attention should be paid to the future use of such hybrid models. It has indeed been demonstrated that the integration of knowledge from first-principles with multivariate data analysis methods, resulting into a hybrid semi-parametric modeling approach, has the potential to improve process understanding and can enable significantly increased prediction performance (von Stosch et al., 2012). The strength of hybrid models is that they can integrate different sources of knowledge in form of parametric and nonparametric structures, where the structure of the parametric models is a priori fixed on the basis of first-principles knowledge, whereas the structure of nonparametric models is identified from data. 4.4. Benchmarking of control strategies In the wastewater treatment field, an area that is closely related to fermentation, the development of models to support benchmarking of control strategies – i.e. the objective simulation-based comparison of control strategies – has been very successful, and has resulted in the recent publication of a benchmarking report to document an effort that has lasted almost 20 years (Gernaey et al., 2014). The result of the benchmarking work is a set of validated models, a number of reference control strategies and a set of standardized evaluation criteria to compare performance of different control strategies. Such an effort should be repeated in the fermentation area as well with the aim of promoting the use of modelling and more advanced control in the fermentation area as well. The key to success in the benchmarking developments in the wastewater treatment area is the fact that validated simulation models were made available for free, such that the model user could focus on development and testing of control strategies instead of getting lost in the tedious task of writing and validating the model code.
5. Conclusions The increased use of models and PSE tools in the fermentation area will result in improved understanding of reactor operation across scales, and can potentially support more efficient transfer of results across scales. However, for these efforts to be successful, close collaboration between industry and academia will be required.
Acknowledgements Financial support of the following organizations is acknowledged: The Danish Council for Strategic Research, project “Towards robust fermentation processes by targeting population heterogeneity at microscale” (project number 0603-00203B). The Novo Nordisk Foundation, project “Exploring biochemical process performance limits through topology optimization.” Region Zealand, the European Regional Development Fund (ERDF), CAPNOVA, CP Kelco, DONG Energy, Novo Nordisk, and Novozymes for funding the BIOPRO project
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