Evaluation of rule-based control strategies according to process state diagnosis in A 2 /O process H.S. Kim a , T.S. Moon b , Y.J. Kim a , M.S. Kim a , W.H. Piao a , S.J. Kim a , C.W. Kim a,⇑ a Dept. of Civil and Environmental Eng., Pusan National University, Busan 609-735, Republic of Korea b Environsoft Co., Ltd., #511 Industry-University Co., Bld., Pusan National University, Busan 609-735, Republic of Korea highlights " We develop the procedure for diagnosis of the current process state. " The proper control action is deduced by using the mathematical model. " The rule-based control strategies develop based on the process state diagnosis results. " The frequent changes of the process operating state are minimized. " The electrical equipment load is reduced. article info Article history: Received 23 October 2012 Received in revised form 14 February 2013 Accepted 18 February 2013 Available online 28 February 2013 Keywords: Rule-based control strategy Process state diagnosis Modified ASM3 + Bio-P model NH 4 –N and NO X –N removal A 2 /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 com- prehensive information for the current process state. Based on the diagnosis result, the quantitative con- trol 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 pro- cess operating state were minimized and the electrical equipment load was reduced. Crown Copyright Ó 2013 Published by Elsevier B.V. All rights reserved. 1. Introduction Due to the complex and non-linear dynamic characteristics of wastewater treatment processes, stable long-term process opera- tion is difficult. To examine the quality of the wastewater treat- ment process, the factors that can affect the process state significantly should be identified and the composite correlation of these factors should be analyzed. The process diagnosis is one of the ICA (instrumentation, control and automation) technologies that can conduct these analyses and provide useful information to human operators. Additionally, when the proper control strategies based on the diagnosis result for the process state are decided and applied in the target process, stable long-term process operation is possible regardless of the influent disturbance or the internal prob- lem of the process. In order to maintain stable process operation, a lot of real-time control strategies and systems have been suggested [1–6]. How- ever, because of the frequent change of the process state and the electrical equipment load according to the real-time control action, the improvement of performance has been required in these strat- egies and systems for stable long-term process operation. To improve the real-time control system, the knowledge-based expert system [7–10], case-based reasoning system [11,12] and fuzzy inference system [13–17] have been introduced. However, the knowledge-based expert system suffers the disadvantage that the judgment for deciding the proper control action is subjective based on expert knowledge. To use the case-based reasoning system, reli- 1385-8947/$ - see front matter Crown Copyright Ó 2013 Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.cej.2013.02.078 ⇑ Corresponding author. Tel.: +82 10 8507 2416; fax: +82 51 515 5347. E-mail addresses: h-sukim@pnu.kr (H.S. Kim), tsmoon@pnu.kr (T.S. Moon), yjkim@pnu.kr (Y.J. Kim), daniel@pnu.kr (M.S. Kim), piaowenhua@pnu.kr (W.H. Piao), rotsmile@pnu.kr (S.J. Kim), cwkim@pnu.kr (C.W. Kim). Chemical Engineering Journal 222 (2013) 391–400 Contents lists available at SciVerse ScienceDirect Chemical Engineering Journal journal homepage: www.elsevier.com/locate/cej