Debasis Sarkar

Optimisation of fed-batch bioreactors using genetic algorithms

An optimisation procedure based on genetic algorithm approach is developed for the determination of substrate feed profiles for the optimal operation of fed-batch bioreactors. The problem specific knowledge generated through the rigorous application of the optimal control theory is used to formulate the set of decision variables representing the qualitative and quantitative aspects of the feed rate profile. A customized genetic algorithm with suitable genetic operators is used for generating the optimal feed profiles. Even though the optimal control theory is not explicitly used, the feed rate policies thus evolved are shown to retain the characteristics of the profiles generated through the application of optimal control theory. The efficiency of the proposed algorithm is demonstrated with two fermentation processes: secreted protein and yeast cell mass production.

Optimization of fed-batch bioreactors using genetic algorithm: multiple control variables

The determination of optimal feed rate profiles for fed-batch bioreactors with more than one feed rates is a numerically difficult problem involving multiple singular control variables. A solution strategy based on genetic algorithm approach for the determination of optimal substrate feeding policies for fed-batch bioreactors with multi-control variables is proposed. The multiplier updating method is introduced in the proposed method to handle inequality constraints on state variables. The efficiency of the algorithm is demonstrated for two case studies on fed-batch bioreactors with two control variables taken from literature. The control policies obtained retain the characteristics of the profiles generated through rigorous application of control theory.

ANNSA: a hybrid artificial neural network/simulated annealing algorithm for optimal control problems

This paper introduces a numerical technique for solving nonlinear optimal control problems. The universal function approximation capability of a three-layer feedforward neural network has been combined with a simulated annealing algorithm to develop a simple yet efficient hybrid optimisation algorithm to determine optimal control profiles. The applicability of the technique is illustrated by solving various optimal control problems including multivariable nonlinear problems and free final time problems. Results obtained for the different case studies considered agree well with those reported in the literature.

Modelling and experimental validation of ultrasound assisted unseeded batch cooling crystallization of L-asparagine monohydrate

A population balance model for ultrasound-assisted unseeded batch cooling crystallization of L-asparagine monohydrate (LAM) from its aqueous solution is developed and validated. The model considers nucleation, growth, and breakage phenomena and is solved by high resolution finite volume discretization with a flux limiter. An additional kinetic expression is introduced for induced nucleation due to ultrasound irradiation. The kinetics are extracted using a nonlinear optimization technique that uses solute concentration data measured by high performance liquid chromatography and mean crystal size measured by laser diffraction. The developed model shows good quantitative agreement with experimental observations on solute concentration and final crystal size distribution (CSD). Experimental data and model simulations show that ultrasound significantly reduces the metastable zone width for LAM, enhances the nucleation rate, and induces early nucleation.

Dynamic flux balance analysis of batch fermentation: effect of genetic manipulations on ethanol production

In silico optimization of bioethanol production from lignocellulosic biomasses is investigated by combining process systems engineering approach and systems biology approach. Lignocellulosic biomass is an attractive sustainable carbon source for fermentative production of bioethanol. For enhanced ethanol production, metabolic engineering of wild-type strains—that can metabolize both hexose and pentose sugars or microbial consortia consisting of substrate-selective microbes—may be advantageous. This study presents a detailed in silico analysis of bioethanol production from glucose-xylose mixtures of various compositions by batch mono-culture and co-culture fermentation of specialized microbes. Dynamic flux balance models based on available genome-scale reconstructions of the microorganisms have been used to analyze bioethanol production, and the maximization of ethanol productivity is addressed by computing optimal aerobic–anaerobic switching times. Effects of ten metabolic engineering strategies that have been suggested in the literature for ethanol overproduction, have been evaluated for their efficiency in enhancing the ethanol productivity in the context of batch mono-culture and co-culture processes.

Nucleation kinetics from metastable zone widths for sonocrystallization of l-phenylalanine

This study investigates the effect of ultrasound on metastable zone width (MSZW) during crystallization of l-phenylalanine from aqueous solution. The solubility of l-phenylalanine in water was measured gravimetrically in the temperature range of 293.15-333.15K. The MSZW was measured by conventional polythermal method for four different cooling rates at five different saturation temperatures in absence and presence of ultrasound. The MSZW increased with increase in cooling rates and decreased with increase in saturation temperature. The application of ultrasound considerably reduced the MSZW for all the experiments. The obtained MSZW data are analysed using four different approaches to calculate various nucleation parameters. In presence of ultrasound, the apparent nucleation order decreased and nucleation rate constant increased significantly.

In Silico Analysis of Bioethanol Overproduction by Genetically Modified Microorganisms in Coculture Fermentation

Lignocellulosic biomass is an attractive sustainable carbon source for fermentative production of bioethanol. In this context, use of microbial consortia consisting of substrate-selective microbes is advantageous as it eliminates the negative impacts of glucose catabolite repression. In this study, a detailed in silico analysis of bioethanol production from glucose-xylose mixtures of various compositions by coculture fermentation of xylose-selective Escherichia coli strain ZSC113 and glucose-selective wild-type Saccharomyces cerevisiae is presented. Dynamic flux balance models based on available genome-scale metabolic networks of the microorganisms have been used to analyze bioethanol production and the maximization of ethanol productivity is addressed by computing optimal aerobic-anaerobic switching times. A set of genetic engineering strategies for ethanol overproduction by E. coli strain ZSC113 have been evaluated for their efficiency in the context of batch coculture process. Finally, simulations are carried out to determine the pairs of genetically modified E. coli strain ZSC113 and S. cerevisiae that significantly enhance ethanol productivity in batch coculture fermentation.

Optimisation of fed-batch bioreactors using genetic algorithms: Two control variables

Abstract The determination of optimal feed rate profiles for fed-batch bioreactors with two feed rates is a numerically difficult problem involving two singular control variables. A solution strategy based on genetic algorithm approach for the determination of optimal substrate feeding policies for fed-batch bioreactors with two control variables is proposed. The efficiency of the algorithm is demonstrated for a fed-batch bioreactor for the production of foreign protein production by recombinant bacteria. The control policies obtained retain the characteristics of the profiles generated through the application of control theory

Constructing regions of attainable sizes and achieving target size distribution in a batch cooling sonocrystallization process

The application of ultrasound to a crystallization process has several interesting benefits. The temperature of the crystallizer increases during ultrasonication and this makes it difficult for the temperature controller of the crystallizer to track a set temperature trajectory precisely. It is thus necessary to model this temperature rise and the temperature-trajectory tracking ability of the crystallizer controller to perform model-based dynamic optimization for a given cooling sonocrystallization set-up. In our previous study, we reported a mathematical model based on population balance framework for a batch cooling sonocrystallization of l-asparagine monohydrate (LAM). Here we extend the previous model by including energy balance equations and a Generic Model Control algorithm to simulate the temperature controller of the crystallizer that tracks a cooling profile during crystallization. The improved model yields very good closed-loop prediction and is conveniently used for studies related to particle engineering by optimization. First, the model is used to determine the regions of attainable particle sizes for LAM batch cooling sonocrystallization process by solving appropriate dynamic optimization problems. Then the model is used to determine optimal operating conditions for achieving a target crystal size distribution. The experimental evidence clearly demonstrates the efficiency of the particle engineering approach by optimization.

Particle engineering by optimization for the unseeded batch cooling crystallization of L-asparagine monohydrate

Due to the stochastic nature of the nucleation event, producing the desired crystal size distribution (CSD) consistently is a challenging task in an unseeded crystallization process. A predictive dynamic model based on a population balance framework was developed for the unseeded batch cooling crystallization of L-asparagine monohydrate (LAM) from its aqueous solution. The nucleation and growth kinetic parameters were estimated simultaneously from the experimental data by combining the population balance model with a nonlinear parameter estimation technique. The model was then used to solve various optimization problems related to particle engineering. Both single objective and multi-objective optimization problems are solved to determine the optimal operating conditions for various objective functions reflecting the different desired attributes of the CSD. Finally, an attempt is made to achieve the desired target shape of the CSD by solving an appropriate dynamic optimization problem. The experimental implementation of the obtained optimal operating conditions clearly demonstrates the predictive ability of the developed model and the use of the optimization approach as a useful tool for particle engineering.