Modeling and optimizations of mixing and aeration processes in bioreactors with activated sludge

Mixing aimed at homogenization of the volume of bioreactors with the activated sludge is of great importance for the proper course of the wastewater treatment process. It affects both the efficiency of pollutants removal and the properties of the activated sludge related to its sedimentation. The mixing process in bioreactors can be carried out in different ways. In batch bioreactors in the aeration phase or flow bioreactors in aerobic chambers, mixing is carried out through aeration systems. These systems should aerate the activated sludge flocs for efficient biological treating of wastewater, as well as effectively homogenize the volume of the bioreactor. Hence, it is important to choose such a design of the aeration system and its operation settings that provide the amount of air ensuring the exact amount of oxygen for the implementation of technological processes, counteract sedimentation of sludge at the bottom of the reactor, are reliable as well as economical in operation (demand of electric energy). The paper presents the model studies aimed at optimization of the design and settings of aeration and mixing systems used in active sludge bioreactors.

Dynamic Modeling of the Impact of Temperature Changes on CO2 Production during Milk Fermentation in Batch Bioreactors

Knowledge of the mathematical models of the fermentation processes is indispensable for their simulation and optimization and for the design and synthesis of the applicable control systems. The paper focuses on determining a dynamic mathematical model of the milk fermentation process taking place in a batch bioreactor. Models in the literature describe milk fermentation in batch bioreactors as an autonomous system. They do not enable the analysis of the effect of temperature changes on the metabolism during fermentation. In the presented extensive multidisciplinary study, we have developed a new mathematical model that considers the impact of temperature changes on the dynamics of the CO2 produced during fermentation in the batch bioreactor. Based on laboratory tests and theoretical analysis, the appropriate structure of the temperature-considered dynamic model was first determined. Next, the model parameters of the fermentation process in the laboratory bioreactor were identified by means of particle swarm optimization. Finally, the experiments with the laboratory batch bioreactor were compared with the simulations to verify the derived mathematical model. The developed model proved to be very suitable for simulations, and, above all, it enables the design and synthesis of a control system for batch bioreactors.

Enzyme-constrained models predict the dynamics of Saccharomyces cerevisiae growth in continuous, batch and fed-batch bioreactors

Genome-scale, constraint-based models (GEM) and their derivatives are commonly used to model and gain insights into microbial metabolism. Often, however, their accuracy and predictive power are limited and enable only approximate designs. To improve their usefulness for strain and bio-process design, we studied here their capacity to accurately predict metabolic changes in response to operational conditions in a bioreactor, as well as intracellular, active reactions. We used flux balance analysis (FBA) and dynamic FBA (dFBA) to predict growth dynamics of the model organism Saccharomyces cerevisiae under different industrially relevant conditions. We compared simulations with the latest developed GEM for this organism (Yeast8) and its enzyme-constrained version (ecYeast8) herein described with experimental data and found that ecYeast8 outperforms Yeast8 in all the simulations. EcYeast8 was able to predict well-known traits of yeast metabolism including the onset of the Crabtree effect, the order of substrate consumption during mixed carbon cultivation and production of a target metabolite. We showed how the combination of ecGEM and dFBA links reactor operation and genetic modifications to flux predictions, enabling the prediction of yields and productivities of different strains and (dynamic) production processes. Additionally, we present flux sampling as a tool to analyze flux predictions of ecGEM, of major importance for strain design applications. We showed that constraining protein availability substantially improves accuracy of the description of the metabolic state of the cell under dynamic conditions. This therefore enables more realistic and faithful designs of industrially relevant cell-based processes and, thus, the usefulness of such models

Adaptive Control of CO2 Production during Milk Fermentation in a Batch Bioreactor

The basic characteristic of batch bioreactors is their inability to inflow or outflow the substances during the fermentation process. This follows in the simple construction and maintenance, which is the significant advantage of batch bioreactors. Unfortunately, this characteristic also results in the inability of the current industrial and laboratory batch bioreactors to control fermentation production during the process duration. In some recent studies, it was shown that changing the temperature could influence the execution of the fermentation process. The presented paper shows that this phenomenon could be used to develop the closed-loop control system for the fermentation production control in batch bioreactors. First, based on theoretical work, experiments, and numerical methods, the appropriate structure of the mathematical model was determined and parameters were identified. Next, the closed-loop control system structure for batch bioreactor was proposed, and the linear and adaptive control system based on this structure and the derived and identified model were developed. Both modeling and adaptive control system design are new and represent original contributions. As expected, due to the non-linearity of the controlled plant, the adaptive control represents a more successful approach. The simulation and experimental results were used to confirm the applicability of the proposed solution.

Optimizing anaerobic co-digestion of goat manure and cotton gin trash using biochemical methane potential (BMP) test and mathematical modeling

Anaerobic co-digestion is widely adopted to enhance process efficacy by balancing the C/N ratio of the feedstock while converting organic wastes to biomethane. Goat manure (GM) and cotton gin trash (CGT) were anaerobically co-digested in triplicate batch bioreactors. The process was optimized and evaluated utilizing mathematical equations. The liquid fraction of the digestate was analyzed for nitrate and phosphate. The co-digestions with 10 and 20% CGT having the C/N ratios of 17.7 and 19.8 yielded the highest and statistically similar 261.4 ± 4.8 and 262.6 ± 4.2 mL/gvs biomethane, respectively. The biodegradability (BD) of GM and CGT was 94.5 ± 2.7 and 37.6 ± 0.8%, respectively. The BD decreased proportionally with an increase in CGT percentage. The co-digestion having 10% CGT yielded 80–90% of biomethane in 26–39 d. The modified Gompertz model-predicted and experimental biomethane values were similar. The highest synergistic effect index of 15.6 ± 4.7% was observed in GM/CGT; 30:70 co-digestion. The concentration of nitrate and phosphate was lower in the liquid fraction of digestate than the feedstocks, indicating that these nutrients stay in the solid fraction. The results provide important insights in agro-waste management, further studies determining the effects of effluent application on plants need to be conducted.

System characterization of dynamic biological systems through improved data analysis.

Determining the functional development and dominant competitive strategy in microbial community enrichments is complicated by the extensive measurement campaigns required for off-line system analysis. This study demonstrates that detailed system characterization of aerobic pulse fed enrichments can be established using on-line measurements combined with automated data analysis. By incorporating the physicochemical processes in on-line data processing with a Particle Filter and kinetic process model, an accurate reconstruction of the dominant biological rates can be made. We hereby can differentiate between storage compound production and biomass growth in sequencing batch bioreactors. The method proposed allows for close monitoring of changes in functional behavior of long-running enrichment cultures, without the need for off-line samples, therewith enabling the identification of new insights in process dynamics with a minimal experimental effort. Even though a specific example application of the method proposed is described here, the approach can readily be extended to a wide range of dynamic experimental systems that can be characterized based on on-line measurements.