Fermentation process model

A Vasilache, B Dahhou, G Roux, and G Goma. Classification of fermentation process models using recurrent neural networks. Int. J. Systems Science, 32(9) 1139-1154, 2001. 

As mentioned earlier, the developed algorithm employs dynopt to solve the intermediate problems associated with the local interaction of the agents. Specifically, dynopt is a set of MATLAB functions that use the orthogonal collocation on finite elements method for the determination of optimal control trajectories. As inputs, this toolbox requires the dynamic process model, the objective function to be minimized, and the set of equality and inequality constraints. The dynamic model here is described by the set of ordinary differential equations and differential algebraic equations that represent the fermentation process model. For the purpose of optimization, the MATLAB Optimization Toolbox, particularly the constrained nonlinear rninimization routine fmincon [29], is employed. 

The authors gratefully acknowledge the financial support from West Virginia University and DOE through award DE-FE0012451. The authors also thank Matthew Steinheimer and Zachary Chow (WVU undergraduate students) for the useful discussions on the GREENSCOPE tool and the fermentation process model. 

Minimal representations are known to have no redundant elements, therefore are of great importance. Based on the notions of performance and quality indices and measures for process systems, the paper proposes conditions for a process model being minimal in a set of functionally equivalent models with respect to a quality norm. Existing procedures to obtain minimal process models for a given modelling goal are discussed and generalized. The notions and procedures are illustrated and compared on a simple case study, on the example of a simple nonlinear fermentation process model. 

In this paper, only the latter will be considered for the purpose of showing the influence of algebraic equations on open loop stability of process systems using illustrative examples of a continuous fermentation process model and a countercurrent heat exchanger. Special emphasis is put into the effect of different mechanisms, such as convection, transfer and reaction, occurring in lumped parameter process systems on local stability. 

Lor example, in a fermentation process, tlie modeler may be interested in various controlled parameters such as the feed rate, rate and mode... 

Figure 3.7 shows the growth of R. rubrum in a batch fermentation process using a gaseous carbon source (CO). The data shown follow the logistic model as fitted by (3.14.2.11) with the solid lines, which also represent an unstructured rate model without any lag phase. The software Sigma Plot was used to fit model (3.14.2.11) to the experimental data. An increase in concentration of acetate in the prepared culture media did not improve the cell dry weight at values of 2.5 and 3 gT-1 acetate, as shown in Figure 3.7. However, the exponential growth rates were clearly observed with acetate concentrations of 0.5-2 g-F1 hi the culture media.

There is an interior optimum. For this particular numerical example, it occurs when 40% of the reactor volume is in the initial CSTR and 60% is in the downstream PFR. The model reaction is chemically unrealistic but illustrates behavior that can arise with real reactions. An excellent process for the bulk polymerization of styrene consists of a CSTR followed by a tubular post-reactor. The model reaction also demonstrates a phenomenon known as washout which is important in continuous cell culture. If kt is too small, a steady-state reaction cannot be sustained even with initial spiking of component B. A continuous fermentation process will have a maximum flow rate beyond which the initial inoculum of cells will be washed out of the system. At lower flow rates, the cells reproduce fast enough to achieve and hold a steady state. 

Neither method will achieve a bumpless startup for complex kinetic schemes such as fermentations. There is a general method, known as constant RTD control, that can minimize the amount of off-specification material produced during the startup of a complex reaction (e.g., a fermentation or polymerization) in a CSTR. It does not require a process model or even a realtime analyzer. We first analyze shutdown strategies, to which it is also applicable. 

The manufacture of benzylpenicillin (penieillin G, originally just penicillin ) is chosen as a model for the antibiotic production process. It is the most renowned of antibioties and is the first to have been manufactured in bulk. It is still universally prescribed and is also in demand as input material for semisynthetic antibiotics (Chapter 5). Developments associated with the penicillin fermentation process have been a significant factor in the development of modem bioteehnology. It was a further 30 years, i.e. not until the 1970s, before there were signifieant new advances in industrial fermentations. 

In another fermentation process, Mosheky et al.18 reacted Saccharomyces cerevisiae with sugars and followed the progress of the fermentation with MIR-ATR. Two PLS models were used one for sucrose, fructose, and glucose and one for the ethanol. The authors did not specify SEPs for the experiment, but showed correlation coefficients of better than 0.998 for all analytes. 

S. TriadaphUlon, E. Martin, G. Montagne, A. Norden, P. Jeffkins and S. Stimpson, Fermentation process tracking through enhanced spectral modeling, Biotechnol Eng., 97(3), 554—567 (2007). 

An example of the use of soft sensors is given by the automation of a penicillin production dependent on strict adherence to certain hmits in the fermentation process since such biological systems are sensitive to changes in operational conditions. An important issue in the use of soft sensors is what to do if one or more of the input variables are not available due, for example, to sensor failure or maintenance needs. Under such circumstances, one must rely on multivariate models to reconstruct or infer the missing sensor variable. ... 

Computer] In fermentation processes sugar (AJ is converted to ethanol (C) as a byproduct of yeast (B) reproduction. In a simple model we can represent this process as... 

There are several barriers to the successful control of bioprocesses due to particular circumstances that are related to their characteristics the complexities of microbial metabolisms, the nonlinearity of microbial reactions, the frequent use of batch and fed-batch operations, and the limited availability of sterihzable online sensors for important process variables such as cell and product concentrations. Furthermore, it is difficult to construct mathematical models that can predict the entire range of batch or fed-batch operations that many fermentation processes require. 

Mathematical optimization always requires a deterministic process model to predict the future behavior of a process. However, as previously mentioned, it is difficult to construct mathematical models that can cover the entire range of fermentation due to the complexity of intracellular metabolic reactions. As an alternative to the deterministic mathematical models, Kishimoto et al. proposed a statistical procedirre that uses linear multiple regression models [7], as shown below, instead of a deterministic mathematical model such as a Monod equation. 

Leigh, J.R. (1987) Modelling and Control of Fermentation Processes, lEE Control Engineering Series, vol. 31, Peter Peregrious Ltd. 

In this fermentation process, sustained oscillations have been reported frequently in experimental fermentors and several mathematical models have been proposed. Our approach in this section shows the rich static and dynamic bifurcation behavior of fermentation systems by solving and analyzing the corresponding nonlinear mathematical models. The results of this section show that these oscillations can be complex leading to chaotic behavior and that the periodic and chaotic attractors of the system can be exploited for increasing the yield and productivity of ethanol. The readers are advised to investigate the system further. 

We use a two-compartment model in this section. One of the most widely used models for fermentation processes is the maintenance model in which the substrate S consumption rs is expressed in the form... 

S. Elnashaie, G. Ibrahim, Heterogeneous Modeling for the Alcoholic Fermentation Process, Applied Biochemistry and Biotechnology, 19(1), 71-101, 1988... 

The development of a commercially viable enzyme process for the production of ECB nucleus was achieved by improving the ECB and ECB deacylase fermentation processes and the bioconversion processes. Increased yields in the fermentation processes were achieved through linked programs for strain improvement and fermentation development. The bioconversion process was improved by the choice of substrate and enzyme conditions and subsequent optimization of operating conditions. An economic model was used to decide where development resources should be focused. 

A software sensor for on-line determination of substrate was developed based on a model for fed-batch alcoholic fermentation process and on-line measured signals of ethanol, biomass, and feed flow. The ethanol and biomass signals were obtained using a colorimetric biosensor and an optical sensor developed in previous works that permitted determination of ethanol at a concentration of 0-40 g/L and biomass of 0-60 g/L. The volume in the fermentor could be continuously calculated using the total measured signal of the feed flow. The results obtained show that the model used is adequate for the proposed software sensor and determines continuously the substrate concentration with efficiency and security during the fermentation process. 

The control of a fed-batch alcoholic fermentation process can be obtained by controlling the substrate concentration in the medium by manipulation of the feed flow. The fermentation process presents complicated kinetic mechanisms. In addition, there is the absence of accurate and reliable mathematical models as well as the difficulty of obtaining direct measurements of the process variables owing to a lack of appropriate on-line analyzers and sensors. Control systems are formed by a set of instruments and control mechanisms connected through electrical signals in the... 

This article presents the design and implementation of a software sensor for the continuous determination of substrate concentration based on a simple model of a fed-batch fermentation process and the available signals of two other sensors—one for on-line biomass determination (7) and the other for on-line ethanol determination (8)—developed in previous works. The software sensor proposed provides a continuous signal that can be used in a control loop to manipulate the substrate feed flow in order to maintain almost constant substrate concentration and obtain an excellent level of productivity and yield during all of the process, as shown in experimental control strategy studies in previous works (9). 

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