Advanced Fermentation Control Strategies
A) Optimal genetic manipulations in batch bioreactor control
Advances in metabolic engineering have enabled bioprocess optimization at the genetic level. Large-scale systematic models are now available at a genome level for many biological processes. There is, thus, a motivation to develop advanced control algorithms, using these complex models, to identify optimal performance strategies both at the genetic and bioreactor level. In this method, the bi-level optimization framework is coupled with control algorithms to determine the genetic manipulation strategies in practical bioprocess applications. The bi-level optimization includes a linear programming problem in the inner level and a nonlinear optimization problem in the outer level. Both gradient-based and stochastic methods are used to solve the nonlinear optimization problem. Ethanol production in an anaerobic batch fermentation of Escherichia coli is considered in case studies that demonstrate optimization of ethanol production, batch time, and multi-batch scheduling.
B) Control strategies for intermittently mixed, forcefully aerated solid-state fermentation bioreactors based on the analysis of a distributed parameter model
This tests different control strategies based on classic proportional integral derivative (PID) and advanced dynamic matrix control (DMC) algorithms for an intermittently stirred, forcefully aerated solid-state fermentation bioreactor. This is done using a distributed parameter model to reproduce the main operating features of this type of bioreactor. There is a remarkable improvement in the bioreactor productivity when this control strategy is implemented. For this type of bioreactor, the temperature and water content of the substrate bed can be controlled by saturating the air at the air inlet but manipulating its temperature, coupled with a strategy of water replenishment when the water content of the bed falls below a threshold. Dynamic matrix control is superior to PID control; however, a specific convolution matrix for different stages of the fermentation is necessary due to the changing behaviour of the system. This shows the benefit of mathematical modelling, since the many different operating conditions investigated via simulations would not have been economically feasible to undertake experimentally with a large-scale bioreactor. This provides an excellent starting point for a large-scale experimental work.
C) Advanced controlling of anaerobic digestion by means of hierarchical neural networks
Several feed-forward back propagation neural networks (FFBP) are trained in order to model, and subsequently control, methane production in anaerobic digesters. To produce data for the training of the neural nets, four anaerobic continuous stirred tank reactors are operated in steady-state conditions at organic loading rates (Br) of about 2 kg mâˆ’3 dâˆ’1 chemical oxygen demand, and disturbed by pulse-like increase of the organic loading rate. For the pulses additional carbon sources like flour, sucrose, 1,2-diethylen glycol or vegetable oil are added to the basic feed, which consisted of surplus and primary sludge of a local waste-water treatment plant, to increase the chemical oxygen demand. Measured parameters are: gas composition, methane production rate, volatile fatty acid concentration, pH, redox potential, volatile suspended solids and chemical oxygen demand of feed and effluent. A hierarchical system of nets was developed and embedded in a decision support system to find out which is the best feeding profile for the next time steps in advance. A 3-3-1 FFBP simulated the pH with a regression coefficient of 0.82. A 9-3-3 FFBP simulated the volatile fatty acid concentration in the sludge with a regression coefficient of 0.86. And a 9-3-2 FFBP simulated the gas production and gas composition with a regression coefficient of 0.90 and 0.80, respectively. A lab-scale anaerobic continuous stirred tank reactor controlled by this tool is able to maintain a methane concentration of about 60% at a rather high gas production rate of between 5 and 5.6 m3 mâˆ’3 dâˆ’1.
D) A self-tuning adaptive control applied to an industrial large scale ethanol production
A multivariable adaptive self-tuning controller (STC) is developed for a biotechnological process application. Due to the difficulties involving the measurements or the excessive amount of variables normally found in industrial process, it is highly recommended to develop 'soft-sensors' which, in this case, is based fundamentally on artificial neural networks (ANN). These methods are especially suitable for the identification of time-varying and nonlinear models. An advanced control strategy based on STC is applied to a fermentation process to produce ethanol (ethyl alcohol) in industrial scale. The reaction rate considered for substratum consumption, cells and ethanol productions are validated with industrial data for typical operating conditions. The results obtained show that the procedure proposed in this work has a great for application.
E) Control of fed-batch fermentations
Fed-batch fermentation is used to prevent or reduce substrate-associated growth inhibition by controlling nutrient supply. Here we review the advances in control of fedbatch fermentations. Simple exponential feeding and inferential methods are examined, as are newer methods based on fuzzy control and neural networks. Considerable interest has developed in these more advanced methods that hold promise for optimizing fed-batch techniques for complex fermentation systems.
F) Fermentation control with personal computers
A cheap but efficient computer network system for advanced fermentation control is described. Two Commodore C-64 computers are connected to a single-drive floppy disk; possible bus conflicts are avoided by software programming. The first computer is dedicated to data acquisition and process control with conventional parameters whereas the second computer unit utilizes previously stored parameter values for on-line high-resolution colour graphics and for the evaluation of derived process variables such as growth rate and productivity. These variables can then be used for more refined process control.
MANTRA for improving batch reactor heat transfer applications
This kind of application uses a traditional chemical reactor or autoclave chamber that is enclosed by a heating/cooling jacket. The chemical reaction, typically a batch operation, is heated or cooled according to the batch recipe. The reactor is heated and cooled by a circulating heat transfer medium that needs to operate within a wide range of temperatures to meet the requirements of each phase of the recipe.
Heat Transfer Difficulties
â€¢ Each heating/cooling medium have different system dynamics, making difficult to tune the PID controller.
â€¢ During a disturbance or setpoint change, each heating/cooling medium are used sequentially, which can delay the control response.
â€¢ When the control output moves between ranges, the differences between the heating/cooling mechanisms can cause instability of control and cycling between ranges.
MANTRA for Bio Chemical Process Control
Requiring the knowledge of bioengineering, chemical engineering, fermentation engineering, enzyme engineering, and separation engineering, amino acid production is a good example of how a complex process can benefit from advanced control technology. ControlSoft's MANTRA advanced control system is used to control the complete process of amino acid production at a plant in Jiangxi, China.
ControlSoft's MANTRA's Advanced Process Control System is used to control the whole amino acid production process but particularly the temperatures and vacuum settings at desired levels to provide flexibility to meet different operating procedures. The goal of the MANTRA software is three-fold: reject any disturbances to the process, respond quickly to setpoint changes, and optimize the available controller outputs during steady-state control.
MANTRA for Dissolved Oxygen & Fermentation Control
Minimize waste of material and maximize quality of product by maintaining tight, responsive control of the interaction of the parameters of temperature, pressure, pH, and dissolved oxygen (DO2), which enables both anaerobes and organisms requiring high oxygen input to be grown.
ControlSoft's MANTRA's Coordinated Control (CC) function block is used to control dissolved oxygen content to desired levels. The goal of the CC block is three-fold: reject any disturbances to the process, respond quickly to setpoint changes, and optimize the available controller outputs during steady-state control.
Advanced Control Strategy to a Fermentation Process to Obtain Ethanol
Nowadays days with the globalisation, market competitively and environmental concern, there is a real need of products with high quality, low cost and low emission of pollutants. The process control is the main form to reach such objectives. That is the reason for the great advance on researches in the investigation of new methodologies and conceptions about process development and process control in all areas. The several control strategies have been tested and the proposed control algorithm has shown very good results, allowing the system to be operated in a high performance level and safe manner. As a case study it is considered an extractive alcoholic fermentation process. There is a great interest in the optimisation of the ethanol process, especially due the many advantages of using ethanol as fuel. In order to do that, it is necessary an efficient and robust control strategy because the control of biotechnological process is very difficult due to the complex nature of the microbial metabolism, as well as no linearity of its kinetics. The proposed control algorithm has shown to be robust for the analysed disturbances, presenting a great potential to be used in control strategies of large-scale systems. Particularly, the successful application of the proposed algorithm in the case study reveals its potential it's a large number of chemical processes of industrial interest. As a case study an extractive alcoholic fermentation process is considered, it that consists on four interlinked units: fermentor (ethanol production unit), centrifuge (cell separation unit), cell treatment unit, and vacuum vessel (ethanol -water separation unit), represented a non-linear process. The obtained results showed the potential of the proposed learning strategy for non-linear process.
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