Advances in control technologies for wastewater treatment processes: status, challenges, and perspectives

Iratni, Abdelhamid; Chang, Ni‐Bin

doi:10.1109/jas.2019.1911372

Cited by 77 publications

(44 citation statements)

References 191 publications

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“…A review of control technologies applied to water reclamation facilities was performed by Iratni and Chang (2019). Fermentate addition and ABAC were implemented to maintain long‐term nitritation in batch laboratory scale operated sequentially under anaerobic, aerobic, and anoxic conditions (Melin & Coats, 2019).…”

Section: Control and Automationmentioning

confidence: 99%

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (2019) DIRECT

Sweeney

Kabouris

2020

Water Environment Research

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Section: Control and Automationmentioning

confidence: 99%

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (2019) DIRECT

Sweeney

Kabouris

2020

Water Environment Research

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“…5 To address those challenges in wastewater treatment plants (WWTPs), numerous control strategies have been put forward over the past years. Typical methods include proportional-integral-derivative (PID) control, 6,7 multi-objective optimal control, 8 multivariable control, [9][10][11] and nonlinear model predictive control (NMPC). [12][13][14] The PID control, a commonly used approach in industrial processes, is also popular in wastewater treatment processes.…”

Section: Introductionmentioning

confidence: 99%

“…However, nonlinearities and disturbances greatly degrade the performance of PID. 6 Multivariable control 9 can get satisfied effects. However, it largely depends on the accuracy of a neural network model, and the structure of neural network is determined by trial and error.…”

Section: Introductionmentioning

confidence: 99%

“…For example, it simply integrates constraints into an optimization problem. 6 However, NMPC is a data-driven control, whose robustness may be deteriorated owing to uncertainties in the WWTPs. 16 Actually, if nonlinearities, uncertainties, and external disturbances-such as coupling, influent temperature, influent rate, and constituents-are considered as time-varying disturbance, desired performance can be obtained by estimating and eliminating disturbance by an advanced control algorithm.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Disturbance rejection control for the dissolved oxygen in a wastewater treatment process

Wei

Chen

Zuo

et al. 2020

Measurement and Control

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In wastewater treatment processes, the concentration of dissolved oxygen affects the performance of wastewater treatment directly. It is one of the key factors that determines effluent quality of the wastewater treatment. However, a simple closed-loop control has a high-energy consumption, and it cannot guarantee the effluent quality due to large perturbations in wastewater treatment plants, such as the influent rate, the temperature, and the complex biochemical reactions. In this paper, a new disturbance rejection controller is designed to address those perturbations. Dynamics of dissolved oxygen is transformed into a controllable canonical form. Discrepancy between the dissolved oxygen dynamics and the controllable canonical form is estimated by a disturbance observer and compensated by a control law. Stability and the bound of tracking errors are obtained. Finally, numerical results on the benchmark simulation model number 1 are presented to confirm the proposed method.

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Extended moving horizon estimation for chemical processes under non‐Gaussian noises

Valipour

Ricardez‐Sandoval

2021

AIChE Journal

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Studies on moving horizon estimation (MHE) for applications featuring process uncertainties and measurement noises that follow time-dependent non-Gaussian distributions are absent from the literature. An extended version of MHE (EMHE) is proposed here to improve the estimation for a general class of non-Gaussian process uncertainties and measurement noises at no significant additional computational costs. Gaussian mixture models are introduced to the proposed EMHE to approximate offline the non-Gaussian densities of these random variables. Moreover, the proposed EMHE-based estimation scheme can be updated online by re-approximating the corresponding Gaussian mixture models when the distributions of noises/uncertainties change due to sudden or seasonal changes in the operating conditions. These updates are not expected to increase the central processing unit times considerably. Illustrative case studies featuring open-loop operation and closed-loop control using nonlinear model predictive control have shown that the practical features offered by EMHE resulted in significant improvements in state estimation and online control.

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Advances in control technologies for wastewater treatment processes: status, challenges, and perspectives

Cited by 77 publications

References 191 publications

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (2019) DIRECT

Modeling, instrumentation, automation, and optimization of water resource recovery facilities (2019) DIRECT

Disturbance rejection control for the dissolved oxygen in a wastewater treatment process

Extended moving horizon estimation for chemical processes under non‐Gaussian noises

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