Application of neuro-fuzzy PID controller for effective post-chlorination in water treatment plant

Hong, Seongbin; Lee, An-Kyu; Lee, Ho-Hyun; Park, No-Suk; Lee, Seung Hwan

doi:10.1080/19443994.2012.696810

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…10 Other works were mostly devoted to chlorine dosing control using different control strategies such as adaptive control 11 and PID control. 12 The above studies mainly concern the control of chlorine, lime and coagulant. To the best of the author's knowledge, study on the control of fluoride has not been reported in the literature.…”

Section: Introductionmentioning

confidence: 99%

Modelling and control in drinking water treatment plants: A case study using time‐varying feedforward control

Tan

Chai

Hussain

2021

Asia-Pacific J Chem Eng

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We used real plant data obtained from a water treatment plant in Peninsular Malaysia to develop a model to predict fluoride concentration in treated water.We validated the model with real plant data and demonstrated that the model yields good prediction, with mean absolute percentage error close to 10% or less. The validated model was then used to design an internal model control (IMC) tuned feedback controller, combined with a time-varying feedforward control approach by exploiting the predictable and repetitive pattern of the inlet water flow rate. Simulation studies using random and real disturbance data show that the proposed control has successfully prevented over fluoridation in treated water. The validated model, due to its simplicity, is suitable for future online implementation. It can also be readily applied to model-based control approaches (e.g., model predictive control) in future studies.

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Section: Introductionmentioning

confidence: 99%

Modelling and control in drinking water treatment plants: A case study using time‐varying feedforward control

Tan

Chai

Hussain

2021

Asia-Pacific J Chem Eng

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Chaoyong Li. easy-to-implement feedback control scheme that is widely used in the real-time automatic control of post-chlorine dosage [6]. The PID control scheme adjusts the chlorine dosage based on the difference between the actual measured value and the target value of the FCR.…”

Section: Introductionmentioning

confidence: 99%

Composite Control of Post-Chlorine Dosage During Drinking Water Treatment

Wang¹,

Xiang

2019

IEEE Access

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Chlorination is an effective disinfection method of drinking water treatment. Optimizing this process poses certain challenges, as the objectives of reaching the required disinfection effect and limiting the formation of disinfection by-products must be met simultaneously. These need adjusting the chlorine dosage in real time to cope with fluctuations in water quality and maintaining a free chlorine residual at the clear-water reservoir outlet that does not exceed regulatory limits. This paper proposes a composite control scheme that combines disturbance observer (DOB) and model predictive control (MPC) to meet the aforementioned objectives. MPC is employed for the feedback control of post-chlorine dosage, and DOB is adapted to monitor fluctuations in influent water quality. The estimated value from the DOB is applied in the feed-forward control of post-chlorine dosage. Both simulation and experimental results demonstrate that the proposed DOB-MPC composite control scheme provides a better disturbance rejection effect against fluctuations in influent water quality than MPC-only and PID-only control schemes, and it can effectively stabilize the quality of treated water. INDEX TERMS Post-chlorine dosage, composite control, disturbance observer, model predictive control.

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“…Water treatment plants (WTP) are disinfected with chlorine concentration in water, commonly known as chlorination process (Mouly et al, 2010;Zimocha & Łobos, 2014). In many WTPs, the process has been automated and the electronic equipment corrects the dosers to maintain the free residual chlorine established (Soyupak, Kilic, Karadirek, & Muhammetoglu, 2011;Hong, Lee, Lee, Park, & Lee, 2012;Liu, Rong, Xu, & Zhang, 2013). Automation processes of a WTP continually seek water quality by optimizing the uptake of chemical products (Lee, Shin, Hong, Choi, & Chun, 2016).…”

Section: Introductionmentioning

confidence: 99%

<b>Artificial neural networks to control chlorine dosing in a water treatment plant

2018

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Artificial neural networks in the multivariable control of chlorine dosing in the postchlorination stage in a water treatment plant in the Greater São Paulo, Brazil, are analyzed. The plant has constant fluctuations in chlorine demand caused by natural influences related to raw water from surface source. Modeling and computer simulation were implemented in MATLAB/Simulink ® environment, according to the physical and operational characteristics of the water treatment plant. Moreover, a Proportional-Integral (PI) controller was incorporated to provide better stability. Simulation results showed improved stability of free residual chlorine when compared to method currently employed, i.e. Proportional-Integral-Derivative (PID) controller that would reduce chlorine consumption in water treatment process.

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Application of neuro-fuzzy PID controller for effective post-chlorination in water treatment plant

Cited by 7 publications

References 8 publications

Modelling and control in drinking water treatment plants: A case study using time‐varying feedforward control

Modelling and control in drinking water treatment plants: A case study using time‐varying feedforward control

Composite Control of Post-Chlorine Dosage During Drinking Water Treatment

<b>Artificial neural networks to control chlorine dosing in a water treatment plant

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