Systematic design of membership functions for fuzzy-logic control: A case study on one-stage partial nitritation/anammox treatment systems.

A methodology is developed to systematically design the membership functions of fuzzy-logic controllers for multivariable systems. The methodology consists of a systematic derivation of the critical points of the membership functions as a function of predefined control objectives. Several constrained optimization problems corresponding to different qualitative operation states of the system are defined and solved to identify, in a consistent manner, the critical points of the membership functions for the input variables. The consistently identified critical points, together with the linguistic rules, determine the long term reachability of the control objectives by the fuzzy logic controller. The methodology is highlighted using a single-stage side-stream partial nitritation/Anammox reactor as a case study. As a result, a new fuzzy-logic controller for high and stable total nitrogen removal efficiency is designed. Rigorous simulations are carried out to evaluate and benchmark the performance of the controller. The results demonstrate that the novel control strategy is capable of rejecting the long-term influent disturbances, and can achieve a stable and high TN removal efficiency. Additionally, the controller was tested, and showed robustness, against measurement noise levels typical for wastewater sensors. A feedforward-feedback configuration using the present controller would give even better performance. In comparison, a previously developed fuzzy-logic controller using merely expert and intuitive knowledge performed worse. This proved the importance of using a systematic methodology for the derivation of the membership functions for multivariable systems. These results are promising for future applications of the controller in real full-scale plants. Furthermore, the methodology can be used as a tool to help systematically design fuzzy logic control applications for other biological processes.

[1]  Gürkan Sin,et al.  Sensitivity analysis of autotrophic N removal by a granule based bioreactor: Influence of mass transfer versus microbial kinetics. , 2012, Bioresource technology.

[2]  J. B. Copp,et al.  Benchmark Simulation Model No 2: finalisation of plant layout and default control strategy. , 2010, Water science and technology : a journal of the International Association on Water Pollution Research.

[3]  R. Jager,et al.  Fuzzy Logic in Control , 1995 .

[4]  José Ferrer,et al.  Energy saving in the aeration process by fuzzy logic control , 1998 .

[5]  W. Verstraete,et al.  Strategies to mitigate N2O emissions from biological nitrogen removal systems. , 2012, Current opinion in biotechnology.

[6]  Ulf Jeppsson,et al.  Dynamic influent pollutant disturbance scenario generation using a phenomenological modelling approach , 2011, Environ. Model. Softw..

[7]  Krist V. Gernaey,et al.  A novel control strategy for single-stage autotrophic nitrogen removal in SBR , 2015 .

[8]  Dezhi Sun,et al.  Study on emission characteristics and reduction strategy of nitrous oxide during wastewater treatment by different processes , 2015, Environmental Science and Pollution Research.

[9]  Hisbullah,et al.  Design of a Fuzzy Logic Controller for Regulating Substrate Feed to Fed-Batch Fermentation , 2003 .

[10]  Norhaliza Abdul Wahab,et al.  Multivariable PID control design for activated sludge process with nitrification and denitrification , 2009 .

[11]  Manel Poch,et al.  Fuzzy model and decision of COD control for an activated sludge process , 1998, Fuzzy Sets Syst..

[12]  Susanne Lackner,et al.  Full-scale partial nitritation/anammox experiences--an application survey. , 2014, Water research.

[13]  Yoshiyasu Okaniwa,et al.  A Direct Ammonium Control System Using Fuzzy Inference in a High-Load Biological Denitrification Process Treating Collected Human Excreta , 1992 .

[14]  P A Vanrolleghem,et al.  Controlling the nitrite:ammonium ratio in a SHARON reactor in view of its coupling with an Anammox process. , 2006, Water science and technology : a journal of the International Association on Water Pollution Research.

[15]  J Horiuchi,et al.  Industrial application of fuzzy control to large-scale recombinant vitamin B2 production. , 1999, Journal of bioscience and bioengineering.

[16]  Krist V. Gernaey,et al.  Development of novel control strategies for single-stage autotrophic nitrogen removal: A process oriented approach , 2014, Comput. Chem. Eng..

[17]  Anna Katrine Vangsgaard,et al.  An operational protocol for facilitating start-up of single-stage autotrophic nitrogen-removing reactors based on process stoichiometry. , 2013, Water science and technology : a journal of the International Association on Water Pollution Research.

[18]  Krist V. Gernaey,et al.  Calibration and validation of a model describing complete autotrophic nitrogen removal in a granular SBR system , 2013 .

[19]  M. B. Beck,et al.  Fuzzy control of the activated sludge wastewater treatment process , 1980, Autom..

[20]  Krist V. Gernaey,et al.  Extending the benchmark simulation model n°2 with processes for nitrous oxide production and side-stream nitrogen removal , 2015 .

[21]  Krist V. Gernaey,et al.  Aeration control by monitoring the microbiological activity using fuzzy logic diagnosis and control. Application to a complete autotrophic nitrogen removal reactor , 2015 .

[22]  A. Seco,et al.  A supervisory control system for optimising nitrogen removal and aeration energy consumption in wastewater treatment plants. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.

[23]  L. J. P. Snip,et al.  Challenges encountered when expanding activated sludge models: a case study based on N2O production. , 2014, Water science and technology : a journal of the International Association on Water Pollution Research.

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

[25]  Thomas F. Edgar,et al.  Process Dynamics and Control , 1989 .

[26]  P A Vanrolleghem,et al.  Benchmark simulation model no 2: general protocol and exploratory case studies. , 2007, Water science and technology : a journal of the International Association on Water Pollution Research.