Novel Micro-scale Analytical Devices for On-line Bioprocess Monitoring: A Review

This review examines the potential of novel micro-scale microfluidic analytical devices – lab-on-a-chip (LOC), micro total analysis systems (µ-TAS) – for on-line monitoring and control of industrial bioprocesses. First, motivation for the current study is presented and potential benefits from the use of micro-scale analytical devices in bioprocess control and monitoring are outlined. This is followed by a review of the state of the art in the relevant application domain (cell analysis) for novel microfluidic analytical devices. Finally, the conclusion provides a summarizing comparison of the main features of the reported micro-scale analytical devices evaluating their potential applicability for on-line bioprocess monitoring, with the most promising concepts identified

Automated Classification of Bioprocess Based on Optimum Compromise Whitening and Clustering

The proposed methodology of technological state classification is based on data smoothing, dimensionality reduction, compromise whitening, and optimum clustering. The novelty of our approach is in the stabile state hypothesis which improves initialization of c-mean algorithm and enables interleaved cross-validation strategy. We also employ the Akaike information criterion to obtain the optimum number of technological states that minimize it, but using as many as possible clusters and components. The general approach is applied to state classification of Pseudomonas putida fed-batch cultivation on octanoic acid

Strategies for Automated Control of the Bioproduction of Mcl-PHA Biopolymers

Medium-chain-length polyhydroxyalkanoates (mcl-PHAs) are polyesters synthesized by numerous bacteria as storage material. Despite being promising candidates for biodegradable materials of industrial interest and environmental value, their usage is still rather limited because of high production costs. One of the areas with considerable potential for further improvements is control of the production process. This paper deals with the experimental work related to the design of control strategies for mcl-PHA biopolymer production process (Pseudomonas putida KT2442 fed-batch cultivations). For this bioprocess, a set of five control strategies (two main and three auxiliary strategies) have been proposed, together with the proper sequence of their switching during the fedbatch part of the production process. The application of these strategies with octanoic acid as a sole carbon source resulted in intracellular PHA content (max. mass fraction 65 % of mcl-PHA in cell dry mass (g g–1) and PHA productivity (max. 0.89 g L–1 h–1) comparable to the best results reported in the literature for this type of strain and carbon substrate. This work is licensed under a Creative Commons Attribution 4.0 International License

Adaptive Control of Saccharomyces cerevisiae Yeasts Fed-Batch Cultivations

In this paper, the application of an adaptive algorithm for control of fed-batch bioprocess capable of coping with time-variant process properties in the presence of uncertainties is introduced. The proposed adaptive controller uses Maršík’s heuristic algorithm for adaptation based on control error oscillation rate criterion without the need of a mathematical model of the controlled process or any special test signals. The intended application of the resulting controller was off-gas CO2 concentration control in fed-batch yeast cultivations where the set point has the form of a time-varying concentration profile. The controller has been tested in a series of experimental fed-batch cultivations with D7 Saccharomyces cerevisiae strain, a UV mutant suitable for ergosterol production, in 7‑litre laboratory bioreactor. Obtained results demonstrate good properties of this adaptive controller that can be used without the need for a tedious parameter identification of the complex bioprocess

Adaptive Control of Saccharomyces cerevisiae Yeasts Fed-Batch Cultivations

In this paper, the application of an adaptive algorithm for control of fed-batch bioprocess capable of coping with time-variant process properties in the presence of uncertainties is introduced. The proposed adaptive controller uses Maršík’s heuristic algorithm for adaptation based on control error oscillation rate criterion without the need of a mathematical model of the controlled process or any special test signals. The intended application of the resulting controller was off-gas CO2 concentration control in fed-batch yeast cultivations where the set point has the form of a time-varying concentration profile. The controller has been tested in a series of experimental fed-batch cultivations with D7 Saccharomyces cerevisiae strain, a UV mutant suitable for ergosterol production, in 7‑litre laboratory bioreactor. Obtained results demonstrate good properties of this adaptive controller that can be used without the need for a tedious parameter identification of the complex bioprocess

A structured mathematical model of PHA biopolymer production process

The paper describes a mathematical model of PHA biopolymer production process by Pseudomonas putida KT2442 where the octanoic acid is used as a substrate. The process is modeled using mass balances for fed-batch cultivation. Proper fitting to experimental data is obtained by identification of the model parameters. The model exhibits good agreement with experiments and its possible application for control is considered in the paper