Bioprocess

About: Bioprocess is a research topic. Over the lifetime, 2219 publications have been published within this topic receiving 50972 citations.

Dark fermentation biorefinery in the present and future (bio)chemical industry.

Abstract: Dark fermentation, also known as acidogenesis, involves the transformation of a wide range of organic substrates into a mixture of products, e.g. acetic acid, butyric acid and hydrogen. This bioprocess occurs in the absence of oxygen and light. The ability to synthesize hydrogen, by dark fermentation, has raised its scientific attention. Hydrogen is a non-polluting energy carrier molecule. However, for energy generation, there is a variety of other sustainable alternatives to hydrogen energy, e.g. solar, wind, tide, hydroelectric, biomass incineration, or nuclear fission. Nevertheless, dark fermentation appears as an important sustainable process in another area: the synthesis of valuable chemicals, i.e. an alternative to petrochemical refinery. Currently, acetic acid, butyric acid and hydrogen are mostly produced by petrochemical reforming, and they serve as precursors of ubiquitous petrochemical derived products. Hence, the future of dark fermentation relies as a core bioprocess in the biorefinery concept. The present article aims to present and discuss the current and future status of dark fermentation in the biorefinery concept. The first half of the article presents the metabolic pathways, product yields and its technological importance, microorganisms responsible for mixed dark fermentation, and operational parameters, e.g. substrates, pH, temperature and head-space composition, which affect dark fermentation. The minimal selling price of dark fermentation products is also presented in this section. The second half discusses the perspectives and future of dark fermentation as a core bioprocess. The relationship of dark fermentation with other (bio)processes, e.g. liquid fuels and fine chemicals, algae cultivation, biomethane–biohythane–biosyngas production, and syngas fermentation, is then explored.

Automatic control of bioprocesses.

Abstract: In this chapter, different approaches for open-loop and closed-loop control applied in bioprocess automation are discussed. Although in recent years many contributions dealing with closed-loop control have been published, only a minority were actually applied in real bioprocesses, the majority being simulations. As a result of the diversity of bioprocess requirements, a single control algorithm cannot be applied in all cases; rather, different approaches are necessary. Most publications combine different closed-loop control techniques to construct hybrid systems. These systems are supposed to combine the advantages of each approach into a well-performing control strategy. The majority of applications are soft sensors in combination with a proportional-integral-derivative (PID) controller. The fact that soft sensors have become this importance for control purposes demonstrates the lack of direct measurements or their large additional expense for robust and reliable online measurement systems. The importance of model predictive control is increasing; however, reliable and robust process models are required, as well as very powerful computers to address the computational needs. The lack of theoretical bioprocess models is compensated by hybrid systems combining theoretical models, fuzzy logic, and/or artificial neural network methodology. Although many authors suggest a possible transfer of their presented control application to other bioprocesses, the algorithms are mostly specialized to certain organisms or certain cultivation conditions as well as to a specific measurement system.

An aqueous–organic two-phase bioprocess for efficient production of the natural aroma chemicals 2-phenylethanol and 2-phenylethylacetate with yeast

Abstract: The natural aroma chemicals 2-phenylethanol (2-PE) and 2-phenylethylacetate (2-PEAc) are of high industrial relevance and can be produced from L-phenylalanine in a yeast-based process with growth-associated product formation. Due to product inhibition, in situ product removal is mandatory to obtain economically interesting concentrations. A fed-batch approach using polypropylene glycol 1200 as in situ extractant and the precursor in a saturated concentration led to the highest 2-PE productivity reported for a bioprocess so far. With Kluyveromyces marxianus CBS 600, 26.5 g/l 2-PE and 6.1 g/l 2-PEAc in the organic phase were obtained, corresponding to space-time yields of 0.33 and 0.08 g/l h, respectively.

Improvement of bioprocess monitoring: development of novel concepts

Abstract: The advancement of bioprocess monitoring will play a crucial role to meet the future requirements of bioprocess technology. Major issues are the acceleration of process development to reduce the time to the market and to ensure optimal exploitation of the cell factory and further to cope with the requirements of the Process Analytical Technology initiative. Due to the enormous complexity of cellular systems and lack of appropriate sensor systems microbial production processes are still poorly understood. This holds generally true for the most microbial production processes, in particular for the recombinant protein production due to strong interaction between recombinant gene expression and host cell metabolism. Therefore, it is necessary to scrutinise the role of the different cellular compartments in the biosynthesis process in order to develop comprehensive process monitoring concepts by involving the most significant process variables and their interconnections. Although research for the development of novel sensor systems is progressing their applicability in bioprocessing is very limited with respect to on-line and in-situ measurement due to specific requirements of aseptic conditions, high number of analytes, drift, and often rather low physiological relevance. A comprehensive survey of the state of the art of bioprocess monitoring reveals that only a limited number of metabolic variables show a close correlation to the currently explored chemical/physical principles. In order to circumvent this unsatisfying situation mathematical methods are applied to uncover "hidden" information contained in the on-line data and thereby creating correlations to the multitude of highly specific biochemical off-line data. Modelling enables the continuous prediction of otherwise discrete off-line data whereby critical process states can be more easily detected. The challenging issue of this concept is to establish significant on-line and off-line data sets. In this context, online sensor systems are reviewed with respect to commercial availability in combination with the suitability of offline analytical measurement methods. In a case study, the aptitude of the concept to exploit easily available online data for prediction of complex process variables in a recombinant E. coli fed-batch cultivation aiming at the improvement of monitoring capabilities is demonstrated. In addition, the perspectives for model-based process supervision and process control are outlined.

A hybrid method of differential evolution with application to optimal control problems of a bioprocess system

Abstract: A hybrid method of differential evolution is developed in this study. Two additional operations (accelerated phase and migrating phase) are embedded into the original version of differential evolution. These two phases are used for improving the convergence speed without reducing the diversity among the individuals. The method of multiplier updating, incorporated in the proposed method, is introduced to solve the constrained optimization problems. The method is then extended to solve the simultaneous optimal control and optimal parameter selection problems of a bioprocess system.