Insight into the large-scale upstream fermentation environment using scaled-down models

Scaled-down models are small-scale bioreactors, used to mimic the chemical (pH, nutrient and dissolved oxygen) and physical (pressure, viscosity and temperature) gradients known to occur in the large-scale fermenter. Conventionally, before scaling up any bioprocess, small-scale bioreactors are used for strain selection, characterisation and optimisation. The typical small-scale environment is homogenous, hence all the cells held within the small-scale bioreactor can be assumed to experience the same condition at any point in time. However, for the large-scale bioreactor, this is not the case, due to its inhomogeneous environment. Three different scaled-down models are reviewed here, and the results suggest that a bacterium responds to changes in its environment rapidly and the magnitude of response to environmental oscillations is organism-specific. The reaction and adaption of a bacterium to an inhomogeneous environment in most cases result in productivity and quality losses. This review concludes that consideration of fermentation gradients should be paramount when researchers screen for high yielding mutants in bioprocess development and doing this would help mitigate performance loss on scale-up. © 2018 Society of Chemical Industry     

Functional unfold principal component regression methodology for analysis of industrial batch process data

This work proposes a methodology utilizing functional unfold principal component regression (FUPCR), for application to industrial batch process data as a process modeling and optimization tool. The methodology is applied to an industrial fermentation dataset, containing 30 batches of a production process operating at Novozymes A/S. Following the FUPCR methodology, the final product concentration could be predicted with an average prediction error of 7.4%. Multiple iterations of preprocessing were applied by implementing the methodology to identify the best data handling methods for the model. It is shown that application of functional data analysis and the choice of variance scaling method have the greatest impact on the prediction accuracy. Considering the vast amount of batch process data continuously generated in industry, this methodology can potentially contribute as a tool to identify desirable process operating conditions from complex industrial datasets. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1986–1994, 2016     

Efficient MILP formulations for the simultaneous optimal peptide tag design and downstream processing synthesis

Novel and efficient linear formulations are developed for the problem of simultaneously performing an optimal synthesis of chromatographic protein purification processes, and the concomitant selection of peptide purification tags, that result in a maximal process improvement. To this end, two formulations are developed for the solution of this problem: (1) a model that minimizes both the number of chromatographic steps in the final purification process flow sheet and the composition of the tag, by use of weighted objectives, while satisfying minimal purity requirements for the final product; and (2) a model that attempts to find the maximal attainable purity under constraints on the maximum number of separation techniques and tag size. Both models are linearized using a previously developed strategy for obtaining optimal piecewise linear approximations of nonlinear functions. Proposed are models to two case studies based on protein mixtures with different numbers of proteins. Results show that the models are capable of solving to optimality all the implemented cases with computational time requirements of under 1 s, on average. The results obtained are further compared with previous nonlinear and linear models attempting to solve the same problem, and, thus, show that the approach represents significant gains in robustness and efficiency. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

Artificial neural networks to infer biomass and product concentration during the production of penicillin G acylase from Bacillus megaterium

BACKGROUND: Production of microbial enzymes in bioreactors is a complex process including such phenomena as metabolic networks and mass transport resistances. The use of neural networks (NNs) to infer the state of bioreactors may be an interesting option that may handle the nonlinear dynamics of biomass growth and protein production. RESULTS: Feedforward multilayer perceptron (MLP) NNs were used for identification of the cultivation phase of Bacillus megaterium to produce the enzyme penicillin G acylase (EC. 3.5.1.11). The following variables were used as input to the net: run time and carbon dioxide concentration in the exhausted gas. The NN output associates a numerical value to the metabolic state of the cultivation, close to 0 during the lag phase, close to 1 during the exponential phase and approximately 2 for the stationary phase. This is a non‐conventional approach for pattern recognition. During the exponential phase, another MLP was used to infer cellular concentration. Time, carbon dioxide concentration and stirrer speed form an integrated net input vector. Cellular concentrations provided by the NN were used in a hybrid approach to estimate product concentrations of the enzyme. The model employed a first‐order approximation. CONCLUSION: Results showed that the algorithm was able to infer accurate values of cellular and product concentrations up to the end of the exponential growth phase, where an industrial run should stop. Copyright © 2008 Society of Chemical Industry     

Biotechnological production of lactic acid integrated with potato wastewater treatment by Rhizopus arrhizus

This paper describes a feasibility study of a for lactic acid production integrated with are treatment of wastewater from an industrial starch plant. Rhizopus oryzae two strains, Rhizopus arrhizus and Rhizopus oligosporus were tested with respect to their capability to carry out simultaneous saccharification and fermentation to lactic acid using potato wastewater. Rhizopus arrhizus DAR 36017 was identified as a suitable strain that demonstrated a high capacity for starch saccharification and lactic acid synthesis. The optimal conditions, in terms of pH, temperature and starch concentration, for lactic acid production were determined. The selected fungal strain grew well in a pH range from 3.0 to 7.0. The addition of CaCO₃10 g dm^?3 maintained the pH at 5.0–6.0 and significantly enhanced lactic acid production. Kinetic study revealed that almost complete starch saccharification and a lactic acid yield of 450g kg^?1 could be achieved in 20 h and 28 h cultivation, respectively. The maximum lactic acid production 21 g dm^?3 and mycelial biomass (1.7 g dm^?3) were obtained at 30 °C. Besides the multiple bioproducts, total removal of suspended solids and 90% reduction of COD were achieved in a single no‐aseptic operation. Copyright © 2003 Society of Chemical Industry     

甘油生物歧化为1,3-丙二醇过程的H∞控制

A robust controller is designed by using the bilinear transformation and H∞ mixed sensitivity method for bio-dissimilation process of glycerol to 1,3-propanediol. Under the controller the system works near an optimal steady-state for the volumetric productivity of 1,3-propanediol attaining its maximization. The design procedure is carried out by tuning the transformation parameter and DC gain of the performance weighted function, which is an iterative and optimal search process. Simulation results are presented which show that the designed robust controller not only ensures the robust stability of the system in face of the parametric variations in the model, but also makes the system have a favourable robust tracking performance. The validity of the proposed H∞ controller has been tested.