Wiener modelling and model predictive control for wastewater applications

The research presented in this paper aims to demonstrate the application of predictive control to an integrated wastewater system with the use of the wiener modeling approach. This allows the controlled process, dissolved oxygen, to be considered to be composed of two parts: the linear dynamics, and a static nonlinearity, thus allowing control other than common approaches such as gain-scheduling, or switching, for series of linear controllers. The paper discusses various approaches to the modelling required for control purposes, and the use of wiener modelling for the specific application of integrated waste water control. This paper demonstrates this application and compares with that of another nonlinear approach, fuzzy gain-scheduled control

Multiplicity of solutions in model‑based multi objective optimization of wastewater treatment plants

Wastewater treatment process design involves the optimization of multiple conflicting objectives. The detection of different equivalent solutions in terms of objective values is crucial for designers in order to efficiently switch to the new optimal operation policies if changes in the process conditions or new constraints occur. In this work, the dynamic multi-objective optimization of a municipal wastewater treatment plant model is carried out. The aim is to simultaneously optimize an economic cost term and an effluent quality index. The selected process variables for the optimization are (1) an aeration factor in the aerated tank previous to the clarifier, and (2) an internal recycle flow rate. Their time profiles are approximated using the control vector parameterization technique. To solve the multi-objective problem and find the Pareto front, the NSGA-II algorithm has been used. The simulation of different realistic scenarios which impose operational constraints (e.g., maintenance operations) reveals that, indeed, multiple solutions exist at least in some areas of the Pareto front. It is observed that different control profiles can produce nearly identical results in terms of Pareto solutions. The a priori knowledge of these equivalent solutions for different scenarios provides the decision makers with alternative choices to be adapted to their organizations policies when events altering decision variables bounds or adding new constraints to the process model occur.The authors are grateful to Ministry of Science, Innovation and Universities (MICINN) and FEDER for their fnancial support (Projects DPI2016-77538-R and RTI2018-099139-B-C21

On optimizing a WWTP design using multi-objective approaches

In this paper, the multi-objective formulation of an optimization problem arising from an activated sludge (AS) system of a wastewater treatment plant (WWTP) design optimization is solved through a multi-objective genetic algorithm. Two multi-objective approaches are proposed. First, a solution to the WWTP design is provided, regardless of its location, date of construction or the involved unit operations. The variables that mostly influence the cost of the system define the objectives and are simultaneously optimized. Second, two crucial objectives for the correct operation of the AS system are simultaneously optimized: the investment and operation costs are minimized and the effluent quality is maximized. Since the objectives are conflicting, several trade-offs between objectives are obtained through the optimization process. The direct visualization of the trade-offs through Pareto curves assists the decision-maker in the selection of crucial design and operation variables. The numerical results show that the proposed methodology produces improved results with physical meaning when compared with previous work.Fundação para a Ciência e a Tecnologia (FCT

Towards the synthesis of wastewater recovery facilities using enviroeconomic optimization

The wastewater treatment industry is undergoing a major shift towards a proactive interest in recovering materials and energy from wastewater streams, driven by both economic incentives and environmental sustainability. With the array of available treatment technologies and recovery options growing steadily, systematic approaches to determining the inherent trade-off between multiple economic and environmental objectives become necessary, namely enviroeconomic optimization. The main objective of this chapter is to present one such methodology based on superstructure modeling and multi-objective optimization, where the main environmental impacts are quantified using life cycle assessment (LCA). This methodology is illustrated with the case study of a municipal wastewater treatment facility. The results show that accounting for LCA considerations early on in the synthesis problem may lead to dramatic changes in the optimal process configuration, thereby supporting LCA integration into decision-making tools for wastewater treatment alongside economical selection criteria

Optimisation-based methodology for the design and operation of sustainable wastewater treatment facilities

The treatment of municipal and industrial wastewaters in conventional wastewater treatment plants (WWTPs) requires a significant amount of energy in order to meet ever more stringent discharge regulations. However, the wastewater treatment industry is undergoing a paradigm shift from a focus on waste-stream treatment and contaminant removal to a proactive interest in energy and resource recovery facilities, driven by both economic and environmental incentives. The main objective of this thesis is the development of a decision-making tool in order to identify improvement opportunities in existing WWTPs and to develop new concepts of sustainable wastewater treatment/recovery facilities. The first part of the thesis presents the application of a model-based methodology based on systematic optimisation for improved understanding of the tight interplay between effluent quality, energy use, and fugitive emissions in existing WWTPs. Plant-wide models are developed and calibrated in an objective to predict the performance of two conventional activated sludge plants owned and operated by Sydney Water, Australia. In the first plant, a simulation-based approach is applied to quantify the effect of key operating variables on the effluent quality, energy use, and fugitive emissions. The results show potential for reduced consumption of energy (up to 10-20%) through operational changes only, without compromising effluent quality. It is also found that nitrate (and hence total nitrogen) discharge could be signficantly reduced from its current level with a small increase in energy consumption. These results are also compared to an upgraded plant with reverse osmosis in terms of energy consumption and greenhouse gas emissions. In the second plant, a systematic model-based optimisation approach is applied to investigate the effect of key discharge constraints on the net power consumption. The results show a potential for reduction of energy (20-25%), without compromising the current effluent quality. The nitrate discharge could be reduced from its current level to less than 15 mg/L with no increase in net power consumption and could be further reduced to <5 mg/L subject to a 18% increase in net power consumption upon the addition of an external carbon source. This improved understanding of the relationship between nutrient removal and energy use for these two plants will feed into discussions with environmental regulators regarding nutrient discharge licensing.The second part of the thesis deals with the application of a systematic, model-based methodology for the development of wastewater treatment/resource recovery systems that are both economically and environmentally sustainable. With the array of available treatment and recovery options growing steadily, a superstructure modeling approach based on rigorous mathematical optimisation provides a natural approach for tackling these problems. The development of reliable, yet simple, performance and cost models is a key issue with this approach in order to allow for a reliable solution based on global optimisation. it is argued that commercial wastewater simulators can be used to derive such models. The superstructure modeling framework is also able to account for wastewater and sludge treatment in an integrated system and to incorporate LCA with multi-objective optimisation to identify the inherent trade-off between multiple economic and environmental objectives. This approach is illustrated with two case studies of resource recovery from industrial and municipal wastewaters. The results establish that the proposed methodology is computationally tractable, thereby supporting its application as a decision support system for selection of promising wastewater treatment/resource recovery systems whose development is worth pursuing. Our analysis also suggests that accounting for LCA considerations early on in the design process may lead to dramatic changes in the configuration of future wastewater treatment/recovery facilities.Open Acces

Optimization methodology for high COD nutrient-limited wastewaters treatment using BAS process

Optimization of biofilm activated sludge (BAS) process via mathematical modelling is an entangle activity since economic, environmental objective and technical decision must be considered. This paper presents a methodology to optimize the operational conditions of BAS process in four steps by combining dynamic simulation techniques with non-linear optimization methods and with operative decision-making criteria. Two set of variables are separately prioritized in the methodology: essential variables related to physical operation to enforce established process performance, and refinement variables related to biological processes that can generate risks of bulking, pin-point floc and rising sludge. The proposed optimization strategy is applied for the treatment of high COD wastewater under nutrient limitation using an integrated mathematical model for COD removal that include predation, hydrolysis and a simplified approach to the limiting solids flux theory in the secondary clarifier in order to facilitate the convergence of the optimization solver. The methodology is implemented in a full-scale wastewater treatment plant for a cellulose and viscose fibre mill obtaining (i) improvement of the effluent quality index (Kg pollution/m3) up to 62% and, (ii) decrease the operating cost index (€/m3) of the process up to 30% respect the regular working operational conditions of the plant. The proposed procedure can be also applied to other biological treatments treating high COD nutrient-limited industrial wastewater such as from textile and winery production among others

Multi-objective optimal control of small-size wastewater treatment plants

In this work, a multi-objective dynamic optimization of the operating strategy of a small-size wastewater treatment plant is carried out. In-situ incineration of the excess sludge produced for electricity production is investigated in order to reduce the operating costs. The trade-offs between the treatment quality and the operating costs are characterized. Compared to the literature, emphasis is put on a more rigorous formulation of the problem and an accurate modeling of the underlying phenomena so as to get physically relevant solutions. Thus, from a mathematical perspective, the problem is formulated so that the solution is less sensitive to the – arbitrarily chosen – plant initial conditions. Modeling of physical phenomena e.g. the detrimental effect of the concentration of suspended solids in the mixed liquor, on oxygen transfer rate, has been included in the model. Several constraints are added to the problem so as to maintain the optimal solutions within the limits of validity of the mathematical model. The results provided a clear picture about the trade-offs between the treatment quality and the exploitation costs. Sludge incineration was shown to be of a high energetic profit, but it does not allow the plant to be electrically autonomou

Invited review: Models for the optimization of regional wastewater treatment systems

European Journal of Operational Research, nº 73 (1994)The problem of the optimization of regional wastewater systems may be generally formulated as follows: to define the transport and treatment system, in a region or water basin, which assure compliance with given pollution control criteria, with minimum cost. In addition, one may try to satisfy other objectives, such as minimum environmental impact, better effluent reuse or adequate phasing. From the optimization point of view, the two main problems that render the solution difficult are the dimensionality and the concavity of cost functions. The matter has been dealt with by many authors, who have produced varied techniques to try to solve this problem. This paper begins with a brief review of the work of those authors who have produced models specifically designed to study the problem. Then, solution strategies are discussed concerning three major items: definition of the objective function and constraints, optimization method and practical aplicability of the models. The paper concludes with the discussion of topics for future research

MODEL DEVELOPMENT AND SYSTEM OPTIMIZATION TO MINIMIZE GREENHOUSE GAS EMISSIONS FROM WASTEWATER TREATMENT PLANTS

As greenhouse gas emissions (GHG) reduction has drawn considerable attention, various methods have been established to estimate greenhouse gas emissions from wastewater treatment plants (WWTPs). In order to establish a design and operational strategy for GHG mitigation, accurate estimates are essential. However, the existing approaches (e.g. the IPCC protocol and national greenhouse gas inventories) do not cover emissions from all sources in WWTPs and are not sufficient to predict facility-level emissions. The ultimate goal of this research was to improve the quantification of GHG emissions from WWTPs. This was accomplished by creating a new mathematical model based on an existing activated sludge model. The first part of the research proposed a stepwise methodology using elemental balances in order to derive stoichiometry for state variables used in a mass balance based whole-plant wastewater treatment plant model. The two main advantages of the elemental balance method are the inclusion of carbon dioxide (CO2) into the existing model with no mass loss and ease of tracking elemental pathways. The second part of the research developed an integrated model that includes (1) a direct emission model for onsite emissions from treatment processes and (2) an indirect emission model for offsite emissions caused by plant operation. A sensitivity analysis of the proposed model was conducted to identify key input parameters. An uncertainty analysis was also carried out using a Monte Carlo simulation, which provided an estimate of the potential variability in GHG estimations. Finally, in the third part, the research identified an optimal operational strategy that resulted in minimizing operating costs and GHG emission, while simultaneously treating the wastewater at better levels. To do this, an integrated performance index (IPI) was proposed to combine the three criteria. The IPI was then incorporated into an optimization algorithm. The results obtained in this research demonstrated that the variation of GHG emissions is significant across the range of practical operational conditions. With system optimization, however, WWTPs have the potential to reduce GHG emissions without raising operating costs or reducing effluent quality. Further research should include a mechanistic examination of processes that produce methane (CH4) in the wastewater treatment stream and nitrous oxide (N2O) in the sludge treatment stream