Process control bioprocessing operations

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. 

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. 

Process control can be done manually or automatically. Needless to say, current refinery and petrochemical process plants are fully automated however, it should be noted that manual control by experienced operators still occupies an important portion of bioprocess control due to the difficulties, as previously described. 

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. 

The undelayed evaluation of state of a culture by using software sensors and computers, based on the quantitative analytical information provided by hardware sensors and intelligent analytical subsystems, constitutes an excellent basis for targeted process control. Experts - either human or computer - have the data and the deterministic knowledge to trace observed behavior back to the physical, chemical and physiological roots thereby gaining a qualitative improvement of bioprocess control, a quantum leap process control can act on the causes of effects rather than just cure symptoms. A simple standard operating procedure [398] has proven useful, namely ... 

Identifying an environment that avoids induction of undesired enzymes and repression of desired ones and implementing bioreactor control systems that maintain these desired conditions in a bioprocess are subjects of future importance. For example, accumulation of a product in the cell environment can often repress synthesis of some of the enzymes required for production of that compound. Product repression and inhibition phenomena have motivated special interest recently in combined bioprocessing operations which accomplish separation simultaneously with bioreaction. By continuously removing a product that inhibits its own synthesis, production of that material is improved. Development of new selective membranes and other process strategies for accomplishing these separations is an important area for future research. 

Mode of operation (fed)-batch versus continuous Parameters for decision kinetics, stability and form of the biocatalyst desired substrate conversion, product concentration (solubility) need of process control, inhibitory/toxic effect of substrate or product. Examples for fed-batch bioprocesses acrylamide process (Nitto Chemicals), L-carnitine process (Lonza). Examples for continuous operation.- production of L-tert. leucine and other amino acids (Degussa). 

Utility systems such as water for injection (WFl). clean steam, clean-in-placc (CIP) solutions and sterile process air must be similarly proven. Also the building system itself has to be validated. Many bioprocess operations which contain potentially hazardous materials are operated in closely-controlled negative pressure enclosures with filtration of exhaust ventilating air. Sterile and particularly parenteral products arc processed in clean rooms which are maintained at positive pressure with filtered incoming air. Validation of building control systems and of personnel changing facilities and systems of work are necessary to meet CMP requirements. Manuals for formal test procedures are required to validate these activities. 

This is particularly true in the case of bioprocesses where the state of the living part of the system must be closely monitored. Extensive surveys have been published and several international conferences have been held on this topic. Furthermore, the last two decades have seen an increasing interest to improve the operation of bioprocesses by applying advanced control schemes. In particular, biological Wastewater Treatment Processes (WWTP s), more efficient than the traditional physico-chemical methods but at the same time... 

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... 

As with refining and petrochemical processes, bioprocesses must be operated automatically so as to achieve a consistent production of various bioproducts in a cost-effective way. In particular, there is a strong demand to optimize bioprocesses by controlling them automatically to promote labor-saving operations. To achieve this, it is necessary to understand what is happening in a bioreactor (instrumentation) and to properly manipulate the control variables that affect the performance of a bioreactor operation (control). 

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. 

Figure 13.3 illustrates a typical control action (below) and the response of a process variable (above) in an on - off control system. As a result of on - off control, the response of a process variable inevitably becomes oscillatory around a set-point (overshoot and hunting). Thus, the precise control of a process variable is difficult to achieve using an on-off control system, and, therefore, the on-off control can be applicable only when the minimum and maximum values of the response are acceptable for successful operation of the bioprocess. To decrease the oscillation around a set-point, a differential gap (or a dead zone) is normally used to determine a threshold value. 

For the optimum operation and control, reliable on-line sensing devices need to be developed. On-line optimization algorithms need to be developed and used to enhance the operability of bioprocess and to ensure that these processes are operated at the most economical points. 

Lessons may also be learned from applications of control systems in the food processing industries. These applications must satisfy hygiene requirements (including periodic cleaning and sterilization), time constraints imposed by product perishability, and requirements for accurate records of sources and operation histories of materials.21 The industry also experiences slim profit margins, short production runs, and frequent product changeovers—characteristics shared with many industrial bioprocesses. 

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