Collaborative Control Applied to BSM1 for Wastewater Treatment Plants

Morales-Rodelo, Keidy; Francisco, Mario; Álvarez, Hernán; Vega, Pastora; Revollar, Silvana

doi:10.3390/pr8111465

Cited by 9 publications

(3 citation statements)

References 38 publications

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“…1 (BSM1), an anoxic–oxic treatment process simulation configured for nitrogen removal . As a simulation, the BSM1 benefits from well-defined influent patterns, a simple process configuration, and precise performance evaluation metrics, which enable the direct comparison of different control strategies. − The BSM1 is also well suited to the evaluation of model-free RL agents, which almost exclusively require a computer simulation in order to reach sufficient training requirements before being transferred to a physical environment, known as the simulation-to-real (Sim2Real) transfer. ,, …”

Section: Introductionmentioning

confidence: 99%

Systematic Performance Evaluation of Reinforcement Learning Algorithms Applied to Wastewater Treatment Control Optimization

Croll

Ikuma

Ong

et al. 2023

Environ. Sci. Technol.

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Treatment of wastewater using activated sludge relies on several complex, nonlinear processes. While activated sludge systems can provide high levels of treatment, including nutrient removal, operating these systems is often challenging and energy intensive. Significant research investment has been made in recent years into improving control optimization of such systems, through both domain knowledge and, more recently, machine learning. This study leverages a novel interface between a common process modeling software and a Python reinforcement learning environment to evaluate four common reinforcement learning algorithms for their ability to minimize treatment energy use while maintaining effluent compliance within the Benchmark Simulation Model No. 1 (BSM1) simulation. Three of the algorithms tested, deep Q-learning, proximal policy optimization, and synchronous advantage actor critic, generally performed poorly over the scenarios tested in this study. In contrast, the twin delayed deep deterministic policy gradient (TD3) algorithm consistently produced a high level of control optimization while maintaining the treatment requirements. Under the best selection of state observation features, TD3 control optimization reduced aeration and pumping energy requirements by 14.3% compared to the BSM1 benchmark control, outperforming the advanced domain-based control strategy of ammonia-based aeration control, although future work is necessary to improve robustness of RL implementation.

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Section: Introductionmentioning

confidence: 99%

Systematic Performance Evaluation of Reinforcement Learning Algorithms Applied to Wastewater Treatment Control Optimization

Croll

Ikuma

Ong

et al. 2023

Environ. Sci. Technol.

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“…In particular, MPC algorithms make it possible to obtain excellent control quality in the case of Multiple-Input Multiple-Output (MIMO) processes with constraints. As a result, MPC methods have been used to numerous industrial processes [7], e.g., chemical reactors [8], distillation columns [9], waste water treatment plants [10], solar power stations [11], cement kilns [12], pasteurization plants [13] and pulp digesters [14]. In addition to that, MPC algorithms are more and more popular in other areas; example applications are: fuel cells [15], active vibration attenuation [16], combustion engines [17], robots [18], synchronous motors [19], mechanical systems [20], freeway traffic congestion control [21] and autonomous driving [22].…”

Section: Introductionmentioning

confidence: 99%

Tuning of Multivariable Model Predictive Control for Industrial Tasks

Nebeluk

Ławryńczuk

2021

Algorithms

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This work is concerned with the tuning of the parameters of Model Predictive Control (MPC) algorithms when used for industrial tasks, i.e., compensation of disturbances that affect the process (process uncontrolled inputs and measurement noises). The discussed simulation optimisation tuning procedure is quite computationally simple since the consecutive parameters are optimised separately, and it requires only a very limited number of simulations. It makes it possible to perform a multicriteria control assessment as a few control quality measures may be taken into account. The effectiveness of the tuning method is demonstrated for a multivariable distillation column. Two cases are considered: a perfect model case and a more practical case in which the model is characterised by some error. It is shown that the discussed tuning approach makes it possible to obtain very good control quality, much better than in the most common case in which all tuning parameters are constant.

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“…These tools are related to several levels of the DCS, involving data acquisition solutions (level 0), primary control loops (level 1), as well as estimation (software sensors) and advanced control algorithms (level 2)-Figure 1. The need for such software tools and emulators is obvious as the implementation of advanced control techniques in WWTPs is quite poor [26,27]. In many cases, only empirical rules and primary controllers (PID, on/off) are used.…”

Section: Introductionmentioning

confidence: 99%

A Software Emulator for the Modelling and Control of an Activated Sludge Process in a Wastewater Treatment Plant

et al. 2021

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The design and implementation of a simulator, as a real-time application, for a complex process from the biological treatment stage of a wastewater treatment plant (WWTP), is addressed. More precisely, this emulator was achieved as a software tool that can be later integrated into a more complex SCADA (supervisory control and data acquisition) system of the WWTP Făcăi, Romania. The basic idea is to implement and validate a reduced-order model of the activated sludge process (ASP), initially simulated in the Matlab/Simulink environment (The MathWorks, Inc., Natick, MA, USA). Moreover, an advanced multivariable adaptive control scheme of the ASP is addressed. This software tool can be made to work in parallel with the evolution of the process and can have as input signals measured directly at the process level, possibly following parametric or model adaptations. The software emulator is developed in the LabWindows/CVI programming environment (National Instruments), which offers low-level access to hardware or software systems that have minimal open-architecture facilities. This environment provides versatile drivers and software packages that can facilitate the interaction with software tools developed within some earlier SCADA systems. The structure and the graphical interface of the emulator, some functionalities, experiments, and evolution of main variables are presented.

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Collaborative Control Applied to BSM1 for Wastewater Treatment Plants

Cited by 9 publications

References 38 publications

Systematic Performance Evaluation of Reinforcement Learning Algorithms Applied to Wastewater Treatment Control Optimization

Systematic Performance Evaluation of Reinforcement Learning Algorithms Applied to Wastewater Treatment Control Optimization

Tuning of Multivariable Model Predictive Control for Industrial Tasks

A Software Emulator for the Modelling and Control of an Activated Sludge Process in a Wastewater Treatment Plant

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