In food industry, bioprocesses like fermentation often are a crucial part of the manufacturing process and decisive for the final product quality. In general, they are characterized by highly nonlinear dynamics and uncertainties that make it difficult to control these processes by the use of traditional control techniques. In this context, fuzzy logic controllers offer quite a straightforward way to control processes that are affected by nonlinear behavior and uncertain process knowledge. However, in order to maintain process safety and product quality it is necessary to specify the controller performance and to tune the controller parameters. In this work, an approach is presented to establish an intelligent control system for oxidoreductive yeast propagation as a representative process biased by the aforementioned uncertainties. The presented approach is based on statistical process control and fuzzy logic feedback control. As the cognitive uncertainty among different experts about the limits that define the control performance as still acceptable may differ a lot, a data-driven design method is performed. Based upon a historic data pool statistical process corridors are derived for the controller inputs control error and change in control error. This approach follows the hypothesis that if the control performance criteria stay within predefined statistical boundaries, the final process state meets the required quality definition. In order to keep the process on its optimal growth trajectory (model based reference trajectory) a fuzzy logic controller is used that alternates the process temperature. Additionally, in order to stay within the process corridors, a genetic algorithm was applied to tune the input and output fuzzy sets of a preliminarily parameterized fuzzy controller. The presented experimental results show that the genetic tuned fuzzy controller is able to keep the process within its allowed limits. The average absolute error to the reference growth trajectory is 5.2 × 106 cells/mL. The controller proves its robustness to keep the process on the desired growth profile.
The monitoring and control of bioprocesses is a challenging task. This applies particularly if the actions to the process have to be carried out in real-time. This work presents a system for on-line monitoring and control of batch yeast propagation under limiting conditions based on a virtual plant operator, which uses the concept of intelligent control algorithms by means of fuzzy logic theory. Process information is provided on-line using a sensor array comprising the measurement of OD, operating temperature, pressure, density, dissolved oxygen, and pH value. In this context practical problems arising through on-line sensing and signal processing are addressed. The preprocessed sensor data are fed to a neural network for on-line biomass estimation. The root mean squared error of prediction is 4 × 10 6 cells/mL. The proposed system then triggers temperature and aeration by usage of a temperature dependent metabolic growth model and sensor data. The deviation of the predicted biomass from that of the reference trajectory as modeled by the metabolic growth model and its temporal derivative are used as inputs for the fuzzy temperature controller. The inputs used by the fuzzy aeration controller are the deviation of measured extract from that of the reference trajectory, the predicted cell count, and the dissolved oxygen concentration. The fuzzy-based expert system allows to provide the desired yeast cell concentration of 100-120 × 10 6 cells/mL at a minimum residual extract limit of 6.0 g/100 g at the required point of time. Thus, a dynamic adjustment of the propagation process to the overall production schedule is possible in order to produce the required amount of biomass at the right time.
The control of bioprocesses can be very challenging due to the fact that these kinds of processes are highly affected by various sources of uncertainty like the intrinsic behavior of the used microorganisms. Due to the reason that these kinds of process uncertainties are not directly measureable in most cases, the overall control is either done manually because of the experience of the operator or intelligent expert systems are applied, e.g., on the basis of fuzzy logic theory. In the latter case, however, the control concept is mainly represented by using merely positive rules, e.g., "If A then do B". As this is not straightforward with respect to the semantics of the human decision-making process that also includes negative experience in form of constraints or prohibitions, the incorporation of negative rules for process control based on fuzzy logic is emphasized. In this work, an approach of fuzzy logic control of the yeast propagation process based on a combination of positive and negative rules is presented. The process is guided along a reference trajectory for yeast cell concentration by alternating the process temperature. The incorporation of negative rules leads to a much more stable and accurate control of the process as the root mean squared error of reference trajectory and system response could be reduced by an average of 62.8 % compared to the controller using only positive rules.
To ensure optimal product quality of bioprocesses, it is necessary to develop intelligent control systems with integrated monitoring of key parameters. Having optimal yeast propagation in brewing technology is important to increase the efficiency of subsequent processes. Major drawbacks are: lacks in online detection of yeast attributes and temporal control schemes. One solution is to accurately detect essential process parameters combined with expert knowledge of linguistic control mechanisms. Those needs can be fulfilled by fuzzy logic or state observers including process dynamics associated with accurate multivariate calibration of sensing devices. Ultrasonic‐based devices could monitor key parameter but their inline implementation is limited due to influences of the temperature and gas bubbles. Thus, incipient stages for calibration of the device including temperature dependencies using time and frequency properties of ultrasonic waves are presented. A multivariate model using offline measurements with a maximum prediction error of 0.48 g/100 g is reported in this study. Additionally, we show preliminary results of a mechanistic model for the temperature dependency of yeast growth adapted from the literature (biomass and ethanol production, substrate consumption). The results will lead to flexible control of temperature and aeration resulting in vital yeast and enhanced transparency of propagation progress according to the demands.
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