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Maneuverability is one of the most important sailing performances of vessels. In this article, a motion control model with three degrees of freedom (DoFs) for twin-waterjet propulsion vessel is proposed. The model is developed on the basis of maneuvering model group (MMG) maneuverability equations. A simulation environment is constructed on the Matlab Simulink platform. Standard turning tests and zig-zag tests are simulated by solving the motion control model, and the corresponding maneuverability parameters are calculated. Simulation results demonstrate that the maneuverability parameters are in accordance with the vessel maneuverability standard. The approach may be applied to the rapid prediction of vessel maneuverability.

In the two-step vitamin C fermentation process, its precursor 2-keto-L-gulonic acid was synthesized from L-sorbose by mixed culture of Gluconobacter oxydans and Bacillus megaterium. The interaction between Gluconobacter oxydans and Bacillus megaterium remains unclear and it is a challenge to mathematically model the mixed growth of these two strains. The Monod-type equations were previously proposed to describe the coupled growth of Gluconobacter oxydans and Bacillus megaterium. However, in this study, we modeled the interaction of these two strains in a macroscopic view by introducing the population theory. Taking account of the fact that the density or concentration of Gluconobacter oxydans or Bacillus megaterium was hardly to measure accurately in the mixed culture broth, the data of concentrations of the substrate and product were used to indirectly investigate the relation between these two strains. Three batch experiments were used to validate our model. And according to the values of identified parameters, the type of interaction between Gluconobacter oxydans and Bacillus megaterium was concluded to be predation, where Gluconobacter oxydans was predator, and Bacillus megaterium was prey.

This article presents a modeling approach for industrial 2-keto-L-gulonic acid (2-KGA) fed-batch fermentation by the mixed culture of Ketogulonicigenium vulgare (K. vulgare) and Bacillus megaterium (B. megaterium). A macrokinetic model of K. vulgare is constructed based on the simplified metabolic pathways. The reaction rates obtained from the macrokinetic model are then coupled into a bioreactor model such that the relationship between substrate feeding rates and the main state variables, e.g., the concentrations of the biomass, substrate and product, is constructed. A differential evolution algorithm using the Lozi map as the random number generator is utilized to perform the model parameters identification, with the industrial data of 2-KGA fed-batch fermentation. Validation results demonstrate that the model simulations of substrate and product concentrations are well in coincidence with the measurements. Furthermore, the model simulations of biomass concentrations reflect principally the growth kinetics of the two microbes in the mixed culture.

A set of kinetic models have been developed for the production of 2-keto-L-gulonic acid from L-sorbose by a mixed culture of Gluconobacter oxydans and Bacillus megaterium. A metabolic pathway is proposed for Gluconobacter oxydans, and a macrokinetic model has been developed for Gluconobacter oxydans, where the balances of some key metabolites, ATP and NADH are taken into account. An unstructured model is proposed for concomitant bacterium Bacillus megaterium. In the macrokinetic model and unstructured model, the mechanism of interaction between Gluconobacter oxydans and Bacillus megaterium is investigated and modeled. The specific substrate uptake rate and the specific growth rate obtained from the macrokinetic model are then coupled into a bioreactor model such that the relationship between the substrate feeding rate and the main state variables, such as the medium volume, the biomass concentrations, the substrate, and the is set up. A closed loop regulator model is introduced to approximate the induction of enzyme pool during lag phase after inoculation. Experimental results demonstrate that the model is able to describe 2-keto-L-gulonic acid fermentation process with reasonable accuracy.

A new spike sorting method based on the support vector machine (SVM) is proposed to resolve the superposition problem. The spike superposition is generally resolved by the template matching. Previous template matching methods separate the spikes through linear classifiers. The classification performance is severely influenced by the background noise included in spike trains. The nonlinear classifiers with high generation ability are required to deal with the task. A multi-class SVM classifier is therefore applied to separate the spikes, which contains several binary SVM classifiers. Every binary SVM classifier corresponding to one spike class is used to identify the single and superposition spikes. The superposition spikes are decomposed through template extraction. The experimental results on the simulated and real data demonstrate the utility of the proposed method.

An oxygen transfer model was established for Pichia pastoris growing on glycerol and methanol in a stirred tank bioreactor and expressing a recombinant human serum albumin (rHSA). This was based on pseudo-steady state mass balance, where the volumetric O2 transfer coefficient, k L a, was estimated as a function of power input per unit volume and aeration rate. Under pseudo-steady state, the O2 transfer rate model matched the O2 uptake rate obtained from a previous macrokinetic model. This procedure was also applied to estimate biomass concentration by using the on-line rolling identification approach.

The cell cycling model (CCM) for S. cerevisiae proposed earlier is modified and tested with our own experimental data. Although the original CCM was well verified in steady states and exponential growth with data available in literature, some discrepancies between model predictions and experiments were found for the dynamics of fed-batch culture. The redistribution pattern of the age distribution of daughter cells is suggested as cause of the model error. With an exponential type of redistribution, instead of the original linear one, the model behaviour in transients is improved. The modified model was verified with data of fraction of budding cells and cell number for five fed-batch cultivations. The model agreed well with the experimental data. The simulation results suggest that the cell cycling process indeed is essentially in a pseudo-steady state during fed-batch cultivation, as was assumed in the model. Due to the strong correlation between the quality of baker's yeast and the state of the population in the cell cycling process, the model was applied to optimize the feeding rate of a fedbatch process with consideration of final product quality. The optimal feeding was used succesfully in a laboratory experiment, which demonstrates the validity of the model.

A cell cycling model for unequal budding yeast Saccharomyces cerevisiae is proposed and verified by steady state data from experiments available in the literature. This model can be used to determine the relative fraction of the cells in any cycling phase or with any genealogical age during fermentation. As the quality of yeast is strongly influenced by the cycling process, the model could therefore be used to control the quality of the harvested yeast cells. The input of the cell cycling model is the specific growth rate μ, which is obtained from a metabolic model for S. cerevisiae proposed earlier. With this extended model system not only the quality control, but also the whole economical profit optimization can be carried out. Simulations were done to optimize the profit of a commercial scale baker's yeast production process by manipulating substrate feeding rate and substrate concentration under different aeration rates, fermentation periods and other conditions applied in industry.