Adaptive control of the E. coli-specific growth rate in fed-batch cultivation based on oxygen uptake rate.

In this study, an automatic control system is developed for the setpoint control of the cell biomass specific growth rate (SGR) in fed-batch cultivation processes. The feedback signal in the control system is obtained from the oxygen uptake rate (OUR) measurement-based SGR estimator. The OUR online measurements adapt the system controller to time-varying operating conditions. The developed approach of the PI controller adaptation is presented and discussed. The feasibility of the control system for tracking a desired biomass growth time profile is demonstrated with numerical simulations and fed-batch culture E.coli control experiments in a laboratory-scale bioreactor. The procedure was cross-validated with the open-loop digital twin SGR estimator, as well as with the adaptive control of the SGR, by tracking a desired setpoint time profile. The digital twin behavior statistically showed less of a bias when compared to SGR estimator performance. However, the adaptation-when using first principles-was outperformed 30 times by the model predictive controller in a robustness check scenario.

An oxygen-uptake-rate-based estimator of the specific growth rate in Escherichia coli BL21 strains cultivation processes.

The cell cultivation process in a bioreactor is a high-value manufacturing process that requires excessive monitoring and control compatibility. The specific cell growth rate is a crucial parameter that describes the online quality of the cultivation process. Most methods and algorithms developed for online estimations of the specific growth rate controls in batch and fed-batch microbial cultivation processes rely on biomass growth models. In this paper, we present a soft sensor - a specific growth rate estimator that does not require a particular bioprocess model. The approach for online estimation of the specific growth rate is based on an online measurement of the oxygen uptake rate. The feasibility of the estimator developed in this study was determined in two ways. First, we used numerical simulations on a virtual platform, where the cell culture processes were theoretically modeled. Next, we performed experimental validation based on laboratory-scale (7, 12, 15 L) bioreactor experiments with three different Escherichia coli BL21 cell strains.

Mathematical Model Coupled to Neural Networks Calculates the Extraction Recovery of Polycyclic Aromatic Hydrocarbons in Problematic Matrix.

Unknown extraction recovery from solid matrix samples leads to meaningless chemical analysis results. It cannot always be determined, and it depends on the complexity of the matrix and properties of the extracted substances. This paper combines a mathematical model with the machine learning method-neural networks that predict liquid extraction recovery from solid matrices. The prediction of the three-stage extraction recovery of polycyclic aromatic hydrocarbons from a wooden railway sleeper matrix is demonstrated. Calculation of the extraction recovery requires the extract's volume to be measured and the polycyclic aromatic hydrocarbons' concentration to be determined for each stage. These data are used to calculate the input values for a neural network model. Lowest mean-squared error (0.014) and smallest retraining relative standard deviation (20.7%) were achieved with the neural network setup 6:5:5:4:1 (six inputs, three hidden layers with five, five, and four neurons in a layer, and one output). To train such a neural network, it took less than 8000 steps-less than a second--using an average-performance laptop. The relative standard deviation of the extraction recovery predictions ranged between 1.13 and 5.15%. The three-stage recovery of the extracted dry sample showed 104% of three different polycyclic aromatic hydrocarbons. The extracted wet sample recovery was 71, 98, and 55% for phenanthrene, anthracene, and pyrene, respectively. This method is applicable in the environmental, food processing, pharmaceutical, biochemical, biotechnology, and space research areas where extraction should be performed autonomously without human interference.

From Physics to Bioengineering: Microbial Cultivation Process Design and Feeding Rate Control Based on Relative Entropy Using Nuisance Time.

For historic reasons, industrial knowledge of reproducibility and restrictions imposed by regulations, open-loop feeding control approaches dominate in industrial fed-batch cultivation processes. In this study, a generic gray box biomass modeling procedure uses relative entropy as a key to approach the posterior similarly to how prior distribution approaches the posterior distribution by the multivariate path of Lagrange multipliers, for which a description of a nuisance time is introduced. The ultimate purpose of this study was to develop a numerical semi-global convex optimization procedure that is dedicated to the calculation of feeding rate time profiles during the fed-batch cultivation processes. The proposed numerical semi-global convex optimization of relative entropy is neither restricted to the gray box model nor to the bioengineering application. From the bioengineering application perspective, the proposed bioprocess design technique has benefits for both the regular feed-forward control and the advanced adaptive control systems, in which the model for biomass growth prediction is compulsory. After identification of the gray box model parameters, the options and alternatives in controllable industrial biotechnological processes are described. The main aim of this work is to achieve high reproducibility, controllability, and desired process performance. Glucose concentration measurements, which were used for the development of the model, become unnecessary for the development of the desired microbial cultivation process.

Downscaling the in vitro test of fungal bioremediation of polycyclic aromatic hydrocarbons: methodological approach.

The miniaturization and optimization of a white rot fungal bioremediation experiment is described in this paper. The optimized procedure allows determination of the degradation kinetics of anthracene. The miniaturized procedure requires only 2.5 ml of culture medium. The experiment is more precise, robust, and better controlled comparing it to classical tests in flasks. Using this technique, different parts, i.e., the culture medium, the fungi, and the cotton seal, can be analyzed. A simple sample preparation speeds up the analytical process. Experiments performed show degradation of anthracene up to approximately 60% by Irpex lacteus and up to approximately 40% by Pleurotus ostreatus in 25 days. Bioremediation of anthracene by the consortium of I. lacteus and P. ostreatus shows the biodegradation of anthracene up to approximately 56% in 23 days. At the end of the experiment, the surface tension of culture medium decreased comparing it to the blank, indicating generation of surfactant compounds.

Data-based optimization of protein production processes.

While data-based modeling is possible in various ways, data-based optimization has not been previously described. Here we present such an optimization technique. It is based on dynamic programming principles and uses data directly from exploratory experiments where the influence of the adjustable variables u were tested at various values. Instead of formulating the performance index J as a function of time t within a cultivation process it is formulated as a function of the biomass x. The advantage of this representation is that in most biochemical production processes J(x) only depends of the vector u of the adjustable variables. This given, mathematical programming techniques allow determining the desired optimal paths u(opt)(x) from the x-derivatives of J(x). The resulting u(opt)(x) can easily be transformed back to the u(t) profiles that can then be used in an improved fermentation run. The optimization technique can easily be explained graphically. With numerical experiments the feasibility of the method is demonstrated. Then, two optimization runs for recombinant protein formations in E. coli are discussed and experimental validation results are presented.