On-line state estimation and adaptive optimization using state equations for continuous production of bioethanol
Abstract On-line optimization of bioprocesses is a difficult task due to the absence of accurate mathematical models and complex non-linear dynamic behaviour. In the present investigation, state equations were used to develop an algorithm to estimate unmeasurable state variables and time-varying parameters which were then used for adaptive, dynamic optimization of a continuous bioreactor. The manipulated variable was adjusted based on the steepest-descent technique to continuously drive the process to the optimal operating conditions in a reasonable time period. The estimation algorithm is shown to predict the unmeasured state variables to within ± 5% during dynamic fluctuations caused by both sudden changes in dilution rate and gradual changes in nutrient feed supply. The control algorithm is shown to quickly and smoothly adjust the manipulated variable to keep the bioreactor operating at its highest productivity as compared to steady state conditions even during inverse-response dynamic conditions.