Knowledge-based fuzzy system for diagnosis and control of an integrated biological wastewater treatment process

Abstract

A supervisory expert system based on fuzzy logic rules was developed for diagnosis and control of a laboratory- scale plant comprising anaerobic digestion and anoxic/aerobic modules for combined high rate biological N and C removal. The design and implementation of a computational environment in LabVIEW for data acquisition, plant operation and distributed equipment control is described. A step increase in ammonia concentration from 20 to 60 mg N/L was applied during a trial period of 73 h. Recycle flow rate from the aerobic to the anoxic module and bypass flow rate from the influent directly to the anoxic reactor were the output variables of the fuzzy system. They were automatically changed (from 34 to 111 L/day and from 8 to 13 L/day, respectively), when new plant conditions were recognised by the expert system. Denitrification efficiency higher than 85% was achieved 30 h after the disturbance and 15 h after the system response at an HRT as low as 1.5 h. Nitrification efficiency gradually increased from 12 to 50% at an HRT of 3 h. The system proved to react properly in order to set adequate operating conditions that led to timely and efficient recovery of N and C removal rates.