Measurement bioprocess

Bioprocess Measurement and Control in Large Scale Culture. 

Institute for Bioscience and Biotechnology Research, BioProcess Measurements Group, Biomolecular Measurement Division, National Institute of Standards and Technology (NIST), Rockville, MD, USA Bioanalytical and Discovery Analytical Sciences, Research and Development, Bristol-Myers Squibb Company, Princeton, NJ, USA... 

Institute for Bioscience and Biotechnology Research, BioProcess Measurements Group, Biomolecular Measurement Division... 

Bioprocess Control An industrial fermenter is a fairly sophisticated device with control of temperature, aeration rate, and perhaps pH, concentration of dissolved oxygen, or some nutrient concentration. There has been a strong trend to automated data collection and analysis. Analog control is stiU very common, but when a computer is available for on-line data collec tion, it makes sense to use it for control as well. More elaborate measurements are performed with research bioreactors, but each new electrode or assay adds more work, additional costs, and potential headaches. Most of the functional relationships in biotechnology are nonlinear, but this may not hinder control when bioprocess operate over a narrow range of conditions. Furthermore, process control is far advanced beyond the days when the main tools for designing control systems were intended for linear systems. 

Measurements and control of the fermentation conditions are very important for bioprocess control as they provide knowledge and hence a better understanding of the operation. 

Continuous and detailed knowledge of process conditions is necessary for the control and optimization of bioprocessing operations. Because of containment and contamination problems, this knowledge must often be obtained without sampling the process stream. At present, conditions such as temperatme, pressure, and acidity (pH) can be measured rapidly and accurately. It is more difficult to monitor the concentrations of the chemical species in the reaction medium, to say nothing of monitoring the cell density and intracellular concentrations of hundreds of compounds. 

It is likely that most biomaterials possess non-linear elastic properties. However, in the absence of detailed measurements of the relevant properties it is not necessary to resort to complicated non-linear theories of viscoelasticity. A simple dashpot-and-spring Maxwell model of viscoelasticity will provide a good basis to consider the main features of the behaviour of the soft-solid walls of most biomaterials in the flow field of a typical bioprocess equipment. 

As seen previously for some specific applications such as wastewater treatment plants, software sensors can be envisaged to provide on-line estimation of non-measurable variables, model parameters or to overcome measurement delays [81-83]. Software sensors have been developed mainly for monitoring bioprocesses because the control system design of bioreactors is not straightforward due to [84] significant model uncertainty, lack of reliable on-line sensors, the non-linear and time-varying nature of the system or slow response of the process. 

Fig. 6.13. Comparison of rates of sampling for a bioprocess (fermentation) the ability of NIR to measure in real time is compared with discrete sampling techniques.

A trivial yet important application is following ethanol production via a bioprocess. Sivakesava et al.1 simultaneously measured glucose, ethanol, and the optical cell density of Saccharomyces cerevisiae during ethanol fermentation, using an off-line approach. Samples were brought to an instrument located near the fermentation tanks and the measurements made in short order. While they eventually used MIR due to the interfering scatter of the media, they proved that Raman could be used for this application. 

Some of the earliest work on NIR of bioprocesses was performed on the nutrients and metabolites in a fermentation broth. A classic paper (if 1996 is antiquity) was written by Hall et al.30 on the determination of acetate, ammonia, biomass, and glycerol in E. coli fermentations. This early paper used NIR to simultaneously monitor all the above-mentioned parameters. The correlation coefficients were all better than 0.985 with variable SEPs acetate, 0.7 g/1 ammonia, 7 mM glycerol, 0.7 g/1 and biomass, 1.4 g/1. While later work with more modem equipment has attained better results, this remains as one of the first. The work was performed at line in a cuvette, but rapidly enough to be considered a process measurement. 

The deconvolution of spectra is the topic of a paper by Vaidyanathan et al.58 The authors use the somewhat complex matrix of mycelial bioprocesses for a model. Throughout the reactions of five different unicellular microorganisms, biomass, external proteins, penicillin, T-sugars, and ammonium were measured vs. time. Each analyte was justified from spectral interpretation. The spectral range used was from 700 to 2500 nm, with specific regions used for each experiment. 

In many practical applications, the measurement devices cover a specific range of operation, which may be used in the input or in the output of the bioprocess but not in both places. Input concentrations are usually higher than the output concentrations and thus they require two different sensors with different sensitivities which may cause an additional cost of measuring devices. This remark prevents the use of a single sensor that must be placed at the influent stream to measure a certain variable and the placed it at the output to record some ather variable or magnitud ... 

It has been shown that radio frequency impedance (RFI) is an effective tool for moifitoring cell density and cell growth of bioprocesses. The fermentation process, quite complex, is oftentimes difficult to sample and monitor. The RFI measurement could detect cell viability of Escherichia coli during the fermentation, serving as a qualitative measure of the metabolic load of the cell, and thus provide an in situ indicator of the optimal harvesting times. 

Due to the complexity of bioprocesses, and the lack of direct in-process measurements of critical process variables, much work is being done on development of soft sensors and model predictive control of such systems. Soft sensors have long been used to estimate biomass concentration in fed-batch cultivations. The soft sensors can be integrated into automated control structures to control the biomass growth in the fermentation. 

The BioView sensor (DELTA Light Optics, Denmark) was developed especially for industrial applications. It is capable of completely automatic optical measurement for monitoring and control of different bioprocesses. The instrument is conceived to withstand harsh industrial environments (e.g., high temperature, moisture) and electromagnetic interference. For data transfer a single-fiber asynchronous modem is used, which allows a distance between the computer and spectrometer of up to several hundred meters. 

High cell densities are not only a prerequisite for high productivity additionally an effective on-line control and modeling of the bioprocesses is necessary. For industrial applications, optical measurement methods are more attractive because they are non-invasive and more robust. The potential of the BioView sensor for on-line bioprocess monitoring and control was tested. For high-cell-density cultivation of Escherichia coli, maintaining aerobic conditions and removal of inhibitory by-products are essential. Acetic acid is known to be one of the critical metabolites. Information about changes in the cell metabolism and the time of important process operations is accessible on-line for optimization... 

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