Bioprocesses measured variables

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. 

Relationships among manipulated (controlled) variables, online measured variables, and product (uncontrolled) variables in most biosystems are nonlinear to some extent [95]. A forward model is when parameters, starting conditions, and relevant equations governing behavior are known, readily measurable inputs and the outputs are variables an inverse model is when the inputs are readily measurable variables and the outputs are difficult to measure parameters [69]. The forward model is most applicable to process validation, whereas the inverse model is most applicable to metabolic pathway analysis. Modeling systems such as neural networks have been used to describe the characteristics of extremely complex bioprocess systems [95]. 

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. 

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 ... 

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. 

These data are utilized for process control, improvement of product quality, the saving of raw material and energy, and an assurance of safety. In this section, we focus on the measurement of process variables as the basis of bioprocess control. 

Process variables measured in bioprocess instrumentation can be categorized into one ofthree groups physical, chemical, and biochemical variables. Table 13.1 summarizes these three types of variables. 

Table 13.1 Process variables measured and estimated in bioprocess instrumentation.

Furthermore, using the primary measurements to obtain the secondary process variables (so-called gateway sensor ) is a form of bioprocess control. For example, the measurement of optical density (primary measurement) can be used for the estimation of cell concentration, and, subsequently, the time course of cell concentration can be employed for the estimation of specific growth rate (secondary... 

A closed-loop system with feedback, which is illustrated in Figure 13.2, is the central feature of a control system in bioprocess control, as well as in other processing industries. First, a set-point is established for a process variable. Then, the process variable measured in a bioreactor is compared with the set-point value to determine a deviation e. Based on the deviation, a controller uses an algorithm to calculate an output signal O that determines a control action to manipulate a control variable. By repeating this cycle during operation, successful process control is performed. The controller can be the operator when manual control is being employed. 

Cellular activities such as those of enzymes, DNA, RNA and other components are the primary variables which determine the performance of microbial or cellular cultures. The development of specific analytical tools for measurement of these activities in vivo is therefore of essential importance in order to achieve direct analytical access to these primary variables. The focus needs to be the minimization of relevant disturbances of cultures by measurements, i. e. rapid, non-invasive concepts should be promoted in bioprocess engineering science [110,402]. What we can measure routinely today are the operating and secondary variables such as the concentrations of metabolites which fully depend on primary and operating variables. 

There are undoubtedly a few variables that are generally regarded as a must in bioprocess engineering. Among these are several physical, less chemical and even less biological variables. Figure 1 gives a summary of what is nowadays believed to be a minimum set of required measurements in a bioprocess. Such a piece of equipment is typical for standard production of material, see, e.g. 

Basics of Quantification Methods for Bioprocesses 19 Table 2.1. Conventional process variables and their measurements. 

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