6,346 research outputs found
Neural Observer Based Hybrid Intelligent Scheme for Activated Sludge Wastewater Treatment
Activated sludge wastewater treatment plants have received considerable attention due to their efficiency to eliminate biodegradable pollution and their robustness to reject disturbances. Different control strategies have been proposed, but most of these techniques need sensors to measure process main variables. This paper presents a discrete-time recurrent high order neural observer (RHONO) to estimate substrate and biomass concentrations in an activated sludge wastewater treatment plant. The RHONO is trained on-line with an extended Kalman filter (EKF)-based algorithm. Then this observer is associated with a hybrid intelligent system based on fuzzy logic to control the substrate/biomass concentration ratio using the external recycle flow rate and the injected oxygen as control actions. The intelligent system and neural observer performance
is illustrated via simulations
A simple and efficient feedback control strategy for wastewater denitrification
Due to severe mathematical modeling and calibration difficulties open-loop
feedforward control is mainly employed today for wastewater denitrification,
which is a key ecological issue. In order to improve the resulting poor
performances a new model-free control setting and its corresponding
"intelligent" controller are introduced. The pitfall of regulating two output
variables via a single input variable is overcome by introducing also an
open-loop knowledge-based control deduced from the plant behavior. Several
convincing computer simulations are presented and discussed.Comment: IFAC 2017 World Congress, Toulouse, Franc
Municipal wastewater treatment with pond technology : historical review and future outlook
Facing an unprecedented population growth, it is difficult to overstress the assets for wastewater treatment of waste stabilization ponds (WSPs), i.e. high removal efficiency, simplicity, and low cost, which have been recognized by numerous scientists and operators. However, stricter discharge standards, changes in wastewater compounds, high emissions of greenhouse gases, and elevated land prices have led to their replacements in many places. This review aims at delivering a comprehensive overview of the historical development and current state of WSPs, and providing further insights to deal with their limitations in the future. The 21st century is witnessing changes in the way of approaching conventional problems in pond technology, in which WSPs should no longer be considered as a low treatment technology. Advanced models and technologies have been integrated for better design, control, and management. The roles of algae, which have been crucial as solar-powered aeration, will continue being a key solution. Yet, the separation of suspended algae to avoid deterioration of the effluent remains a major challenge in WSPs while in the case of high algal rate pond, further research is needed to maximize algal growth yield, select proper strains, and optimize harvesting methods to put algal biomass production in practice. Significant gaps need to be filled in understanding mechanisms of greenhouse gas emission, climate change mitigation, pond ecosystem services, and the fate and toxicity of emerging contaminants. From these insights, adaptation strategies are developed to deal with new opportunities and future challenges
Crossing the death valley to transfer environmental decision support systems to the water market
Environmental decision support systems (EDSSs) are attractive tools to cope with the complexity of environmental global challenges. Several thoughtful reviews have analyzed EDSSs to identify the key challenges and best practices for their development. One of the major criticisms is that a wide and generalized use of deployed EDSSs has not been observed. The paper briefly describes and compares four case studies of EDSSs applied to the water domain, where the key aspects involved in the initial conception and the use and transfer evolution that determine the final success or failure of these tools (i.e., market uptake) are identified. Those aspects that contribute to bridging the gap between the EDSS science and the EDSS market are highlighted in the manuscript. Experience suggests that the construction of a successful EDSS should focus significant efforts on crossing the death-valley toward a general use implementation by society (the market) rather than on development.The authors would like to thank the Catalan Water Agency (Agència Catalana de l’Aigua), Besòs River Basin Regional Administration
(Consorci per la Defensa de la Conca del Riu Besòs), SISLtech, and Spanish Ministry of Science and Innovation for providing funding
(CTM2012-38314-C02-01 and CTM2015-66892-R). LEQUIA, KEMLG, and
ICRA were recognized as consolidated research groups by the Catalan
Government under the codes 2014-SGR-1168, 2013-SGR-1304 and
2014-SGR-291.Peer ReviewedPostprint (published version
Enhancing Operation of a Sewage Pumping Station for Inter Catchment Wastewater Transfer by Using Deep Learning and Hydraulic Model
This paper presents a novel Inter Catchment Wastewater Transfer (ICWT) method
for mitigating sewer overflow. The ICWT aims at balancing the spatial mismatch
of sewer flow and treatment capacity of Wastewater Treatment Plant (WWTP),
through collaborative operation of sewer system facilities. Using a hydraulic
model, the effectiveness of ICWT is investigated in a sewer system in Drammen,
Norway. Concerning the whole system performance, we found that the S{\o}ren
Lemmich pump station plays a vital role in the ICWT framework. To enhance the
operation of this pump station, it is imperative to construct a multi-step
ahead water level prediction model. Hence, one of the most promising artificial
intelligence techniques, Long Short Term Memory (LSTM), is employed to
undertake this task. Experiments demonstrated that LSTM is superior to Gated
Recurrent Unit (GRU), Recurrent Neural Network (RNN), Feed-forward Neural
Network (FFNN) and Support Vector Regression (SVR)
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