Dynamic strategies formonitoring quality controlat a complex bioengineering facility

Abstract

Introduction: Stream processing of the data generated by bioengineering facilities is still an unresolved issue during real time identification of significant patterns in them. As a result, the lack of a unified approach to diagnose the macrostates of awater purification system has led to a significant complication of correct predictive analytics and untimely precursor detection of undesirable situations. Purpose:To develop a dynamic procedure for a real-time selection of the most preferred set of diagnostic features from a variety of all possible sets that are obtained during the training and that are replenished during the operation of the system, the above-mentioned procedure providing a flexible optimization strategy for its monitoring and control. Methods: We use a new procedure to form preference relations for a set of alternatives by group accounting for their relative superiority for the huge volume of continuously updated data. Results:Using the dynamic decision rule for switching between sets of criteria, which depends on the actual data, we obtain correct quantitative estimates of the state for a certain type of a purification system. The key idea of a dynamic decision rule is to take into account the dependence of the number of state classification errors obtained at the historical data on the criteria set used at the current control moment. The new algorithm provides a gain in the quality of macrostatediagnosing (by 15–20%) and an exponential decrease in decision-making time compared with the classical stationary model. Practical relevance: The results of this study are used to develop a monitoring plan for anaerobic treatment systemsand to create and maintain a data base with an essential reduction of the time of historical data processing and of computational resources for an industrial hybrid bioreactor.