PI and Fuzzy Control for P-removal in Wastewater Treatment Plant

Xu, Hongyang; Vilanova, Ramón

doi:10.15837/ijccc.2015.6.2081

Cited by 20 publications

(5 citation statements)

References 20 publications

(23 reference statements)

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“…The higher‐level control loops compute optimized set points (Guerrero, Guisasola, Comas, Rodríguez‐Roda, & Baeza, 2012; Guerrero, Guisasola, Vilanova, & Baeza, 2011; Rojas, Baeza, & Vilanova, 2011). (Xu & Vilanova, 2013; 2015; Hongyang et al, 2018) used BSM1‐P as a simulation platform and used three different control frameworks consisting of PI, Fuzzy, and MPC. These control strategies showed significant improvement in the effluent quality with increased operating costs.…”

Section: Resultsmentioning

confidence: 99%

Supervisory control configurations design for nitrogen and phosphorus removal in wastewater treatment plants

Sheik

Seepana

Rao

2021

Water Environment Research

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Model predictive control (MPC) and Fuzzy controllers are designed in a two‐level hierarchical supervisory control framework for control of activated sludge‐based wastewater treatment plants (WWTP) in order to efficiently remove nitrogen and phosphorus. Benchmark simulation model No.3 with a bio‐phosphorus (ASM3bioP) module is used as a working platform. The hierarchical control framework is used to alter the dissolved oxygen in the seventh reactor (DO7) to control ammonia. Lower‐level PI, MPC, and Fuzzy are used to control the nitrate levels in the fourth reactor (SNO4) by manipulating internal recycle (Qintr) and DO7 in the seventh tank by manipulating mass transfer coefficient (KLa7). MPC and Fuzzy are designed in the supervisory layer to alter the DO7 set‐point based on the ammonia composition in the seventh reactor (NH7). From the analysis, it is observed that the effluent quality is improved with a decrease in ammonia, TN, and TP. Though a little difference was observed in the cost for all the control strategies, a trade‐off is maintained between cost and percentage improvement of effluent quality. MPC‐MPC combination showed significant removal in ammonia and better effluent quality when compared to other control strategies. Practitioner points Developed novel strategies in hierarchical configurations for better nutrient removal with optimal costs in an A2O process. Lower level control strategies deals with dissolved oxygen in last aeration tank and nitrate in fourth anoxic tank (PI/MPC) Higher level control strategy deals with ammonia in the last aeration tank (MPC/Fuzzy). Average and violations of nutrient removal, economy and overall effluent quality for three weather conditions (Dry, Rain and Strom) are studied. A trade‐off is observed between EQI and OCI.

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Section: Resultsmentioning

confidence: 99%

Supervisory control configurations design for nitrogen and phosphorus removal in wastewater treatment plants

Sheik

Seepana

Rao

2021

Water Environment Research

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“…This is achieved by employing fuzzy rules that are identical to those used in human inference design. FLC is used on the WWTP in this study [19,23]. Figure S4 depicts the FLC controller in the wastewater flow diagram.…”

Section: Design Of Fuzzy-logic Controller (Flc)mentioning

confidence: 99%

“…Different plant-wide models are studied in the literature, which includes sludge control approaches, biogas production in primary settler, handling of the anaerobic digester, and phosphorus modeling with interactions of sulfur and iron cycles [10][11][12][13][14][15][16][17]. In the literature, many control applications like fuzzy logic controller (FLC), model predictive controller (MPC), proportional-integral (PI), and ammonia-based aeration control (ABAC) with different hierarchical combinations of PI, MPC, and fuzzy were studied, and it is observed that there is a trade-off between operational cost and effluent quality [18][19][20][21]. Maheswari et al (2020) designed the nested control loop on three-stage biological treatment for ammonia changes and they observed that Effluent Quality Index (EQI) is improved with higher operational costs [22].…”

Section: Introductionmentioning

confidence: 99%

Design of Feedback Control Strategies in a Plant-Wide Wastewater Treatment Plant for Simultaneous Evaluation of Economics, Energy Usage, and Removal of Nutrients

et al. 2021

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Simultaneous removal of nitrogen and phosphorous is a recommended practice while treating wastewater. In the present study, control strategies based on proportional-integral (PI), model predictive control (MPC), and fuzzy logic are developed and implemented on a plant-wide wastewater treatment plant. Four combinations of control frameworks are developed in order to reduce the operational cost and improve the effluent quality. As a working platform, a Benchmark simulation model (BSM2-P) is used. A default control framework with PI controllers is used to control nitrate and dissolved oxygen (DO) by manipulating the internal recycle and oxygen mass transfer coefficient (KLa). Hierarchical control topology is proposed in which a lower-level control framework with PI controllers is implemented to DO in the sixth reactor by regulating the KLa of the fifth, sixth, and seventh reactors, and fuzzy and MPC are used at the supervisory level. This supervisory level considers the ammonia in the last aerobic reactor as a feedback signal to alter the DO set-points. PI-fuzzy showed improved effluent quality by 21.1%, total phosphorus removal rate by 33.3% with an increase of operational cost, and a slight increase in the production rates of greenhouse gases. In all the control design frameworks, a trade-off is observed between operational cost and effluent quality.

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“…Fig. 3 illustrates the process of obtaining z output from a Mamdani two-rule fuzzy inference system with 2 inputs, x and y [18] [16]. There are several defuzzification methods, including center of area (COA) or centroid, center average, maximum membership principle, and min-max membership (middle of maxima) [6].…”

Section: 3mentioning

confidence: 99%

Analysis of Fuzzy Logic Modification for Student Assessment in e-Learning

Wardoyo

Yuniarti

2020

IJID

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The phenomenon of the rapid transfer of learning to online systems, such as e-Learning, has occurred massively. Institutions must ensure that student assessments run well. The characteristics of learning in e-Learning require an appropriate assessment method. The fuzzy logic method can be an option. Research shows that fuzzy logic is capable of providing flexible and objective performance evaluation. Fuzzy logic is a method that can overcome the uncertainty of transparency and objectivity of student assessments. In general, fuzzy logic applications are carried out by standards. Modification is an attempt to reveal the flexibility and to optimize the use of fuzzy logic. This study presents an analysis of fuzzy logic modification for the assessment of Algorithm and Data Structures courses held in e-Learning. These modifications include (i) modification of the parameter score with score compatibility, (ii) consequent modification of the fuzzy rules and (iii) modification of the implication process. The study results show that although the use of fuzzy logic requires more complicated procedures and tools, it can present various kinds of assessment as an option for educators to assess students in e-Learning.

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PI and Fuzzy Control for P-removal in Wastewater Treatment Plant

Cited by 20 publications

References 20 publications

Supervisory control configurations design for nitrogen and phosphorus removal in wastewater treatment plants

Supervisory control configurations design for nitrogen and phosphorus removal in wastewater treatment plants

Design of Feedback Control Strategies in a Plant-Wide Wastewater Treatment Plant for Simultaneous Evaluation of Economics, Energy Usage, and Removal of Nutrients

Analysis of Fuzzy Logic Modification for Student Assessment in e-Learning

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