Hybrid filtering of feed stream noise from oscillating yeast cultures by combined Kalman and neural network configurations.
Large continuous flow bioreactors are often under the influence of noise in the feed stream(s). Prior removal of noise is done by filters based either on specific algorithms or on artificial intelligence. Neither method is perfect. Hybrid filters combine both methods and thereby capitalize on their strengths while minimizing their weaknesses. In this study, a number of hybrid models have been compared for their ability to recover nearly noise-free stable oscillations of continuous flow Saccharomyces cerevisiae cultures from aberrant behavior caused by noise in the feed stream. Each hybrid filter had a different neural network in conjunction with an extended Kalman filter (EKF). The choice of the best configuration depended on the performance index. All hybrid filters were superior to both the EKF and purely neural filters. Along with previous studies of monotonic fermentations, the present results establish the suitability of hybrid neural filters for noise-affected bioreactors.
Patnaik
Pratap R
PR
Institute of Microbial Technology, Sector 39-A, Chandigarh, India. pratap@imtech.res.in
D000465:Algorithms; D019149:Bioreactors / Q000382:microbiology* / Q000706:statistics & numerical data*; D001709:Biotechnology; D003491:Cybernetics; D005285:Fermentation; D016571:Neural Networks, Computer; D012441:Saccharomyces cerevisiae / Q000254:growth & development / Q000378:metabolism*