Optimization of Energy Consumption in the Biological Reactor of a Wastewater Treatment Plant by Means of Oxy Fuzzy and ORP Control

Aeration of the biological reactor in wastewater treatment plants (WWTPs) represents one of the major cost items, which may account for more than 50% of the total energy consumption. Therefore, airflow rate must be supplied based on the real needs of the biological reactions and the goals to be achieved in terms of removal efficiency and effluent quality. Among the different strategies available to optimize energy consumption of air supply, the Oxy Fuzzy logic and oxidation reduction potential (ORP)-based control systems have proven to be efficient and reliable. The present study compares the effects of these two control systems in terms of energy consumption and efficiency of COD and ammonia oxidation in the activated sludge reactors of two WWTPs for domestic sewage. Both systems allowed to largely comply with the limits set on the effluent for COD and ammonia in spite of the dynamic pattern of the influent load. The Oxy Fuzzy system led to reducing energy consumption by 13% while the ORP control system only by 2%, as average per year. The Oxy Fuzzy system showed higher flexibility, being more capable of adapting the set-points in relation to the influent load. The ORP system seemed to be more suitable for plants where the influent load does not change significantly: the set-points are fixed and the input load can be properly managed only for limited variations.

[1]  J. Comas,et al.  Energy Saving in a Wastewater Treatment Process: an Application of Fuzzy Logic Control , 2005, Environmental technology.

[2]  Awwa,et al.  Standard Methods for the examination of water and wastewater , 1999 .

[3]  Rui Araújo,et al.  Dissolved oxygen control of the activated sludge wastewater treatment process using stable adaptive fuzzy control , 2012, Comput. Chem. Eng..

[4]  Ken J. Hall,et al.  Real-Time Control of Wastewater Treatment Systems Using ORP , 1993 .

[5]  M. A. Jaramillo,et al.  New contributions to the ORP & DO time profile characterization to improve biological nutrient removal. , 2012, Bioresource technology.

[6]  Bengt Carlsson,et al.  Nonlinear and set-point control of the dissolved oxygen concentration in an activated sludge process , 1996 .

[7]  Enrico Benetto,et al.  Integrating fuzzy multicriteria analysis and uncertainty evaluation in life cycle assessment , 2008, Environ. Model. Softw..

[8]  Ramon Vilanova,et al.  Applying variable dissolved oxygen set point in a two level hierarchical control structure to a wastewater treatment process , 2015 .

[9]  Rajendra Akerkar,et al.  Knowledge Based Systems , 2017, Encyclopedia of GIS.

[10]  M. Campanelli,et al.  Consumi elettrici ed efficienza energetica nel trattamento delle acque reflue , 2013 .

[11]  Oliver J. Hao,et al.  Alternating aerobic-anoxic process for nitrogen removal : process evaluation , 1996 .

[12]  A. E. Greenberg,et al.  Standard Methods for the Examination of Water and Wastewater seventh edition , 2013 .

[13]  Sara Weiss,et al.  Wastewater Engineering Treatment And Resource Recovery , 2016 .

[14]  F. A. Koch,et al.  Oxidation-Reduction Potential – A Tool for Monitoring, Control and Optimization of Biological Nutrient Removal Systems , 1985 .

[15]  Marta Schuhmacher,et al.  A fuzzy expert system for soil characterization. , 2008, Environment international.

[16]  Daniel R. Thévenot,et al.  RELATION BETWEEN REDOX POTENTIAL AND OXYGEN LEVELS IN ACTIVATED-SLUDGE REACTORS , 1989 .

[17]  A. Chao,et al.  Applying the Nernst equation to simulate redox potential variations for biological nitrification and denitrification processes. , 2004, Environmental science & technology.