Evaluation of rule-based control strategies according to process state diagnosis in A2/O process

Abstract In this study, rule-based control strategies were proposed according to the result of the process state diagnosis. Multivariate statistical techniques were used to derive the diagnosis results that included comprehensive information for the current process state. Based on the diagnosis result, the quantitative control setpoint could be calculated by using the optimized mathematical model. The developed process diagnosis procedure and the control strategies were applied in a pilot-scale A 2 /O (anaerobic/anoxic/oxic) process. The target variables in the proposed rule-based control strategies according to the process state diagnosis were the effluent NH 4 –N and NO X –N components. From the application of these rule-based control strategies according to the process state diagnosis, the percentages of groups 3 and 4, which were considered the abnormal process state, were decreased by about 53.8% compared to the no-control case. In addition, the maintenance interval of the control action ranged from 4 h to 25 h. Effluent NH 4 –N and NO X –N concentrations lower than the target values were maintained by applying the proposed rule-based control strategies according to the process state diagnosis. Moreover, frequent changes of the process operating state were minimized and the electrical equipment load was reduced.

[1]  J C Costa,et al.  Knowledge-based fuzzy system for diagnosis and control of an integrated biological wastewater treatment process. , 2006, Water science and technology : a journal of the International Association on Water Pollution Research.

[2]  C W Kim,et al.  Identification of process operating state with operational map in municipal wastewater treatment plant. , 2009, Journal of environmental management.

[3]  Gustaf Olsson,et al.  Instrumentation, Control and Automation in Wastewater Systems , 2015 .

[4]  J R Kim,et al.  Identification of the adverse effect of nitrate on the phosphate release rate and improvement of EBPR process models. , 2006, Water science and technology : a journal of the International Association on Water Pollution Research.

[5]  Monique Polit,et al.  Fuzzy control of dissolved oxygen in a sequencing batch reactor pilot plant , 2005 .

[6]  M Martínez,et al.  Case-based reasoning, a promising tool to face solids separation problems in the activated sludge process. , 2006, Water science and technology : a journal of the International Association on Water Pollution Research.

[7]  Hong Wang,et al.  Improvement of nitrogen removal and reduction of operating costs in an activated sludge process with feedforward–cascade control strategy , 2008 .

[8]  U Jeppsson,et al.  Demonstration of a tool for automatic learning and re-use of knowledge in the activated sludge process. , 2006, Water science and technology : a journal of the International Association on Water Pollution Research.

[9]  Jean-Pierre Corriou,et al.  Model predictive control for wastewater treatment process with feedforward compensation , 2009 .

[10]  M Poch,et al.  A knowledge-based approach to the deflocculation problem: integrating on-line, off-line, and heuristic information. , 2003, Water research.

[11]  H Bae,et al.  Knowledge-based control and case-based diagnosis based upon empirical knowledge and fuzzy logic for the SBR plant. , 2006, Water science and technology : a journal of the International Association on Water Pollution Research.

[12]  J Lafuente,et al.  Improving the nitrogen removal efficiency of an A2/O based WWTP by using an on-line knowledge based expert system. , 2002, Water research.

[13]  I. Takács.  A dynamic model of the clarification-thickening process , 1991 .

[14]  Miquel Sànchez-Marrè,et al.  Designing and building real environmental decision support systems , 2004, Environ. Model. Softw..

[15]  Ni-Bin Chang,et al.  Mining the fuzzy control rules of aeration in a Submerged Biofilm Wastewater Treatment Process , 2007, Eng. Appl. Artif. Intell..

[16]  Emil Petre,et al.  Adaptive and robust-adaptive control strategies for anaerobic wastewater treatment bioprocesses , 2013 .

[17]  G. Olsson,et al.  Dissolved oxygen controller based on on-line measurements of ammonium combining feed-forward and feedback. , 2002, Water science and technology : a journal of the International Association on Water Pollution Research.

[18]  Juan A. Baeza,et al.  Knowledge-based supervision and control of wastewater treatment plant: a real-time implementation , 2000 .

[19]  S Kim,et al.  Rule-based fuzzy inference system for estimating the influent COD/N ratio and ammonia load to a sequencing batch reactor. , 2006, Water science and technology : a journal of the International Association on Water Pollution Research.

[20]  Ruey-Fang Yu,et al.  Possible control approaches of the Electro-Fenton process for textile wastewater treatment using on-line monitoring of DO and ORP , 2013 .

[21]  Miriam G Rodríguez,et al.  Process analysis for treatment of industrial plating wastewater: simulation and control approach , 2005 .

[22]  Seong-Pyo Cheon,et al.  Evaluation of model-based control strategy based on generated setpoint schedules for NH4–N removal in a pilot-scale A2/O process , 2012 .