2,378 research outputs found

    Water quality indicator interval prediction in wastewater treatment process based on the improved BES-LSSVM algorithm

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    This paper proposes a novel interval prediction method for effluent water quality indicators (including biochemical oxygen demand (BOD) and ammonia nitrogen (NH3-N)), which are key performance indices in the water quality monitoring and control of a wastewater treatment plant. Firstly, the effluent data regarding BOD/NH3-N and their necessary auxiliary variables are collected. After some basic data pre-processing techniques, the key indicators with high correlation degrees of BOD and NH3-N are analyzed and selected based on a gray correlation analysis algorithm. Next, an improved IBES-LSSVM algorithm is designed to predict the BOD/NH3-N effluent data of a wastewater treatment plant. This algorithm relies on an improved bald eagle search (IBES) optimization algorithm that is used to find the optimal parameters of least squares support vector machine (LSSVM). Then, an interval estimation method is used to analyze the uncertainty of the optimized LSSVM model. Finally, the experimental results demonstrate that the proposed approach can obtain high prediction accuracy, with reduced computational time and an easy calculation process, in predicting effluent water quality parameters compared with other existing algorithms.Peer ReviewedPostprint (published version

    Lagoon Wastewater Effluent Impacts Stream Metabolism in Red River Tributaries

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    Lagoons are the most common form of sewage treatment for rural Canadian communities and may therefore be a major source of pollution to local waterways. However, the environmental effects of pulse releases of lagoon effluent are largely unknown. This study reports on changes in physicochemical conditions and stream metabolism occurring as result of summer lagoon effluent releases into Red River tributaries, Manitoba, Canada. We calculated metrics of stream metabolism using the single-station, open water method. We found that an effluent release results in a significant short-term increase in physicochemical (i.e., water nutrients, stream discharge) conditions which had a subsidy effect on stream metabolism. We also found that stream metabolism was significantly greater in effluent exposed versus unexposed reaches; however, our results suggest the degree of effect depends on whether the release occurred early or late in the summer. The findings of this study have implications for lagoon management and future stream monitoring projects aimed at evaluating the effects of lagoon wastewater effluent

    Design of feedback control strategies in a plant-wide wastewater treatment plant for simultaneous evaluation of economics, energy usage, and removal of nutrients

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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 trans-fer 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

    Bioreactor Simulation with Quadratic Neural Network Model Approximations and Cost Optimization with Markov Decision Process

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    The efficient operation of activated sludge type wastewater treatment plants is an ongoing topic for the utility providers, where electric energy consumption shares are high, giving cca. 30 % of total operational costs. Intervention methods for intensification include fine tuning of aeration settings, sludge removal and the adjustment of recirculation rates. In order to analyze the effects of various process control strategies, activated sludge models (ASM) are used for the purpose of biokinetic modeling. In practice, most model simulators do not incorporate optimization and necessary auto-calibration of the latter, due to high computational demand of timeseries evaluation. In this paper, a new mathematical model is presented, which makes biokinetic simulations suitable for the use in decision support systems. Namely, the ASM model is approximated with a computanional inexpesive quadratic model solution, fed into a set of mass-balance corrected neural networks. Cost optimization is achieved with Markov decision process model. The developed method was illustrated for a case of Hungarian, large wastewater treatment plant. It was proven, the model is able to find better aeration schemes for the plant in aspect of cost of operation and nitrogen removal efficiency. The model can be used to find cost-optimal policies under arbitrary defined conditions. As a benefit, results can be implemented into industrial logic controllers

    Implementing Early Warning Systems in WWTP. An investigation with cost-effective LED-VIS spectroscopy-based genetic algorithms

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    Measuring how the pollution load evolves in real time along sewer networks is key for proper management of water resources and protecting the environment. The technique of molecular spectroscopy for water characterization has increasingly widespread use, as it is a non-invasive technique that leads to the correlation of the physical-chemical conditions of wastewater with spectroscopic surrogates by a series of mathematical estimation models. In the present research work, different symbolic regression models obtained with evolutive genetic algorithms are evaluated for the estimation of chemical oxygen demand (COD); five-day biochemical oxygen demand (BOD5); total suspended solids (TSS); total phosphorus (TP); and total nitrogen (TN), from the spectral response of samples measured between 380 and 700 nm and without the use of chemicals or pre-treatment. Around 650 wastewater samples were used in the campaign, from 43 different wastewater treatment plants (WWTP) in which both, raw/influent and treated/effluent, were examined through 18 models composed of Classical Genetic Algorithm (CGA), the Age-Layered Population Structure (ALPS), and Offspring Selection (OS) by mean of HeuristicLab software, to make a comparison among them and to determine which models and wavelengths are most suitable for the correlation. Models are proposed considering both raw and treated samples together (15) and only with tertiary treated wastewater reclaimed for agriculture irrigation effluent (3). The Pearson correlation coefficients were in the range of 67–91% for the test data in the case of the combined models. The results conform the first steps for a real-time monitoring of WWTP.The author Daniel Carreres Prieto wishes to thank the financial support received from the Seneca Foundation of the Región de Murcia (Spain) through the program devoted to training novel researchers in areas of specific interest for the industry and with a high capacity to transfer the results of the research generated, entitled: “Subprograma Regional de Contratos de Formación de Personal Investigador en Universidades y OPIs” (Mod. B, Ref. 20320/FPI/17). The present research has been funded by the project MONITOCOES: New intelligent monitoring system for microorganisms and emerging contaminants in sewage networks. Reference: RTC2019-007115-5 by the Ministry of Science and Innovation - State Research Agency, within the RETOS COLABORACIÓN 2019 call, which supports cooperative projects between companies and research organizations, whose objective is to promote technological development, innovation and quality research. The authors wish to thank the help and availability received from the company Munuera Laboratories during the field campaign

    Primary sedimentation tank model with characterized settling velocity groups

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    Primary sedimentation involves the separation of solids and liquid in primary settling tanks (PSTs) of wastewater treatment systems. These physical processes are described by various settling conditions such as discrete and flocculent settling, along with other phenomena such as flocculation, coagulation, ammonification or hydrolysis. The modelling of primary sedimentation has often been overlooked because (i) it involves various intricacies that are difficult to replicate and (ii) primary sedimentation has been assumed to be an input to most of the main unit process models, including the activated sludge (AS) system and the anaerobic digestion (AD) models. Though there has been a wide range of proposed mathematical models to describe how PSTs function, the need to correctly disaggregate the total suspended solids (TSS) into realistic fractions of unbiodegradable particulate organics (UPO), biodegradable particulate organics (BPO) and inorganic settleable solids (ISS), remains. This is because PST models that are unable to correctly split the TSS into its characteristic components make incorrect assumptions. These assumptions lead to inconsistencies in predicting the compositions of the primary sludge (PS) that is fed to the AD unit and the settled wastewater (settled WW) that is treated in the AS system. Hence, it becomes difficult to correctly simulate the entire system (plant-wide) towards a holistic evaluation of system strategies. In this study, a realistic PST model was developed, with characterized settling velocity groups, within a plant-wide setting, for municipal wastewater. This involved the improvement of a current TSS-based model into a more accurate and realistic model that could account for the settling of raw wastewater particles. The model was therefore expected to predict the composition of the PS that is treated in the AD system and the composition of the settled WW that is going to the AS unit processes. This could be achieved by splitting the TSS into UPO, BPO and ISS fractions. In developing preparation of such a realistic PST model, the following objectives were established: 1. Disaggregate the TSS into realistic UPO, BPO and ISS fractions, by means of discrete particle settling modelling (Kowlesser, 2014) and the particle settling velocity distribution (PSVD) approach of Bachis et al. (2015). 2. Verify that the model is internally consistent with wastewater treatment plant (WWTP) data, by means of mathematical material mass balances and other specific scenarios. 3. Demonstrate the application and impact of such a model by performing steady state plant-wide simulations. Using the discrete particle settling approach of Kowlesser (2014), a discrete particle settling model was developed in Microsoft Excel and implemented into a dynamic PST framework in WEST® (Vanhooren et al., 2003). The discrete particle settling model was described using steady state and dynamic calculations and the insights obtained from these calculations were implemented in the current TSS-based PST model of Bachis et al. (2015). This was performed towards developing the University of Cape Town Primary Sedimentation Unit (UCTPSU). The influent raw wastewater TSS was fractionated into UPO, BPO and ISS fractions and settling proportions of these fractions were assigned to five settling velocity groups. In addition, a distinct settling velocity was assigned to each settling velocity group. Previous studies data from WRC (1984) and Ekama (2017), were used in the discrete particle settling model, which was able to reproduce PS and settled WW outputs, through steady state and dynamic calculations and under strict material mass balances. As a result, UPO, BPO and ISS settling proportions as well as settling velocities, were extracted from these calculations and used as input parameters into the UCTPSU model. This dynamic model was rigorously verified to be internally consistent with regards to strict material mass balances. The verification scenarios also included variations of high and low settling velocities as well as a combination of both high and low velocities and checking that the model was behaving as expected. The application and impact of the UCTPSU model were demonstrated using plant-wide scenarios in proposing a preliminary integration, under steady state conditions. It showed how incorrect disaggregation of the TSS into UPO, BPO and ISS fractions can lead to incorrect predictions in terms of the settled WW composition, the AS system capacity, the effluent quality, as well as the energy consumption and generation in the AS system and AD unit respectively. The investigation also revealed the need to measure key wastewater parameters such as particle settling velocities and the unbiodegradable particulate COD fraction, when it comes to realistically modelling of primary sedimentation of municipal wastewater, with the view of optimizing plant operations and tactical decision making. The study thereafter recommended the need to conduct an extensive experimental campaign to measure in-situ diurnal data, mainly in terms of settling velocities and settling proportions of UPO, BPO and ISS. It was also suggested to use the settleometer as a tool to extract these settling velocities and settling proportions, after performing biodegradability tests. As such, the data collected from the experimental campaign and the biodegradability tests could be used in calibrating the UCTPSU model and validation could be undertaken by means of full plant scale data

    Model refinements in view of wastewater treatment plant optimization : improving the balance in sub-model detail

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    Water is a very vulnerable resource and needs to be protected. In order to optimise wastewater treatment technology, we need to better understand the processes taking place in them. Mathematical modelling is a powerful tool to build knowledge about complex processes as it can exploit the power of computation. In this work wastewater treatment plant process optimization was pursued through the development of new models. In order to describe/model a WWTP it is mandatory to describe all of the processes in a sufficiently detailed manner (i.e. not overly complex nor oversimplified). Indeed, it does not make sense to use an overly detailed bio-kinetic model including hundreds of components and to oversimplify hydraulics, chemical reactions, aeration or settling behaviour. At this point WWTP models consist of highly detailed bio-kinetic models but often lack detail of other critical processes (hydraulics, chemical processes, gas-liquid transfers, aeration, energy consumption…). Emphasis is given to sub processes that are known to have a large impact on the overall process performance, i.e. influent characterization, primary sedimentation, aeration and energy consumption. The gathered knowledge is a step forward towards improving the way we design and operate our wastewater treatment infrastructure

    Editorial: Integrated water management for enhanced water quality and reuse to create a sustainable future

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    Safe drinking water and sanitation are very important for the survival of human life. With the rapid proliferation of industries, growth in population and different forms of pollution, i.e. in water, air, soil and sediments, the living environment and the ecosystem is constantly polluted. In this context, integrating different water resources for enhanced water quality and reuse is important to solve the persisting problems and challenges in developing and the developed nations. Integrated water management offers environmental, economic and social benefits because it aims at maximizing the existing resources and prevents further depletion of the ecosystem
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