RECENT DEVELOPMENTS IN MODELING OF MONITORING IN THE ANAEROBIC BIOREACTOR SYSTEM

Abstract

Monitoring in the anaerobic bioreactor system is requiring understanding the occurred situation in the bioreactor process. Bioreactor is complex designed to accelerate waste degradation by combining attributes of the aerobic and anaerobic bioreactors involves many variables. Multivariate Statistical Process Control (MSPC) models are a statistical solution to the problem of directly calculating physical and biological properties of molecules from their physical structure. QSAR model is utilized to extract information from a set of numerical descriptors characterizing molecular structure and use this information to develop inductively a relationship between structure and property. The goal of a (MSPC) model is to replace the conventional methods univariate Statistical Process Control (SPC) to analyze the state of the multivariate process of anaerobic bioreactor. The objective of the sequential aerobic-anaerobic treatment is to cause the rapid biodegradation of degradable waste in the aerobic stage in order to reduce the production of organic acids in the anaerobic stage resulting in the earlier onset of methanogenesis. The monitoring of process uses principal component analysis (PCA) to reduce multivariate data. Further, hotelling T² values were used to monitor the quality of the bioreactor operating condition. Hence, fuzzy logic was used to determine the present condition of the bioreactor based on the value of T² related. The simulation results indicate that the offered method is able to determine four bioreactor process states, i.e. normal, organic overload, hydraulic overload, and fluctuations in temperature, with the success rate 100%.

Keywords

• Bioreactor
• Multivariate statistical process control
• Principal component analysis
• Fuzzy logic.