Modeling and control of fed batch bioreactor

Abstract

Bioprocesses are gaining increased attention because of their enormous potential for the production of high value products, especially in human health care. Modern methods of enzyme optimization and metabolic engineering are powerful tools for the development of novel efficient biocatalysts. The development of new bio-processes is enhanced by the newlineapplication of modern process modeling and simulation techniques, combined with assessment methods that are applied systematically in the process development.Effective monitoring and control of bioprocess improves process efficiency, yield and minimizes overall production cost. The environmental conditions of a bioreactor such as temperature, pH, Dissolved Oxygen (DO) levels, and agitation rate need to be closely monitored and controlled in order to provide optimal growth conditions. Maintaining and controlling these newlineprocess parameters is a bit challenging task. The major bottlenecks in the modeling and control of a fed-batch bioprocess are the dynamic behavior is inherently nonlinear, accurate process models are rarely available due to the complexity of the process, the model parameters vary in an unpredictable manner and reliable biosensors to measure important key variables and intercellular activities are rarely available. Due to the complexity of the biological systems the basic bioprocess models are not able to reflect the real situations. Artificial Neural Networks (ANN) based soft sensor have the capability to learn nonlinearity of the process using experimental plant data and thus can be used to estimate the state of bioprocess such as biomass concentration product concentration. newline newline