Reduction in operator supervision time using a modified malfunction detection system with fuzzy inference in an...

A malfunction detection system based on fuzzy inference, in which a fermentation process is categorized into three states (normal, intermediate and abnormal) according to the computation of malfunction degree, was modified to reduce the supervision tasks required for an industrial-scale production of alkaline cellulase. The time lag of the culture, tau, was determined from the difference in the cumulated amount of CO2 evolved between the actual and standard values. By employing tau, the deviations of the actual values of process data (pH of broth and the amount of antifoaming agent supplied) from the standard values could be compensated for in terms of a delayed or forward effect in fermentation time. In the detection tests conducted for a series of 200 runs using the modified method, two runs and a single run were categorized as belonging to the abnormal and intermediate states, respectively, and all of the other runs were recognized to be in a normal state. As a result, the total supervision time for operators was reduced by the modification.