Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments-The Wastewater Treatment Plant Control Case

Altres ajuts: Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya i del Fons Social Europeu (2020 FI_B2 000)The evolution of industry towards the Industry 4.0 paradigm has become a reality where different data-driven methods are adopted to support industrial processes. One of them corresponds to Artificial Neural Networks (ANNs), which are able to model highly complex and non-linear processes. This motivates their adoption as part of new data-driven based control strategies. The ANN-based Internal Model Controller (ANN-based IMC) is an example which takes advantage of the ANNs characteristics by modelling the direct and inverse relationships of the process under control with them. This approach has been implemented in Wastewater Treatment Plants (WWTP), where results show a significant improvement on control performance metrics with respect to (w.r.t.) the WWTP default control strategy. However, this structure is very sensible to non-desired effects in the measurements-when a real scenario showing noise-corrupted data is considered, the control performance drops. To solve this, a new ANN-based IMC approach is designed with a two-fold objective, improve the control performance and denoise the noise-corrupted measurements to reduce the performance degradation. Results show that the proposed structure improves the control metrics, (the Integrated Absolute Error (IAE) and the Integrated Squared Error (ISE)), around a 21.25% and a 54.64%, respectively

LSTM-Based Wastewater Treatment Plants Operation Strategies for Effluent Quality Improvement

Wastewater Treatment Plants (WWTPs) are facilities devoted to managing and reducing the pollutant concentrations present in the urban residual waters. Some of them consist in nitrogen and phosphorus derived products which are harmful for the environment. Consequently, certain constraints are applied to pollutant concentrations in order to make sure that treated waters comply with the established regulations. In that sense, efforts have been applied to the development of control strategies that help in the pollutant reduction tasks. Furthermore, the appearance of Artificial Neural Networks (ANNs) has encouraged the adoption of predictive control strategies. In such a fashion, this work is mainly focused on the adoption and development of them to actuate over the pollutant concentrations only when predictions of effluents determine that violations will be produced. In that manner, the overall WWTP's operational costs can be reduced. Predictions are generated by means of an ANN-based Soft-Sensor which adopts Long-Short Term Memory cells to predict effluent pollutant levels. These are the ammonium (S-{NH,e}) and the total nitrogen (S-{Ntot,e}) which are predicted considering influent parameters such as the ammonium concentration at the entrance of the WWTP reactor tanks (S-{NH,po}), the reactors' input flow rate (Q-{po}), the WWTP recirculation rate (Q-{a}) and the environmental temperature (T-{as}). Moreover, this work presents a new multi-objective control scenario which consists in a unique control structure performing the reduction of S-{NH,e} and S-{Ntot,e} concentrations simultaneously. Performance of this new control approach is contrasted with other strategies to determine the improvement provided by the ANN-based Soft-Sensor as well as by the fact of being controlling two pollutants at the same time. Results show that some brief and small violations are still produced. Nevertheless, an improvement in the WWTPs performance w.r.t.The most common control strategies around 96.58% and 98.31% is achieved for S-{NH,e} and S-{Ntot,e}, respectively

Transfer learning in wastewater treatment plant control design : from conventional to long short-term memory-based controllers

In the last decade, industrial environments have been experiencing a change in their control processes. It is more frequent that control strategies adopt Artificial Neural Networks (ANNs) to support control operations, or even as the main control structure. Thus, control structures can be directly obtained from input and output measurements without requiring a huge knowledge of the processes under control. However, ANNs have to be designed, implemented, and trained, which can become complex and time-demanding processes. This can be alleviated by means of Transfer Learning (TL) methodologies, where the knowledge obtained from a unique ANN is transferred to the remaining nets reducing the ANN design time. From the control viewpoint, the first ANN can be easily obtained and then transferred to the remaining control loops. In this manuscript, the application of TL methodologies to design and implement the control loops of a Wastewater Treatment Plant (WWTP) is analysed. Results show that the adoption of this TL-based methodology allows the development of new control loops without requiring a huge knowledge of the processes under control. Besides, a wide improvement in terms of the control performance with respect to conventional control structures is also obtained. For instance, results have shown that less oscillations in the tracking of desired set-points are produced by achieving improvements in the Integrated Absolute Error and Integrated Square Error which go from 40.17% to 94.29% and from 34.27% to 99.71%, respectively

LSTM networks for the implementation of an internal model control strategy in WWTPs

Water resources are one of the most important environmental issues due to the vulnerability of water to contamination and its direct effect on human health. Improving their management depends largely on the treatment of wastewater. For that reason, there are strict pollution limits set for the effluent of wastewater treatment plants (WWTPs), which is discharged into streams or other receiving waters. In order to improve the treatment quality, meet the standards imposed by authorities and to maintain low cost of operations, control strategies are implemented in these plants. This project presents a control system based on internal model controllers (IMCs) adopting artificial neural networks (ANNs), as an alternative to the default control strategy of the Benchmark Simulation Model no. 1 (BSM1), a framework emulating the behavior of a general purpose WWTP that uses proportional integral (PI) controllers. With the proposed control approach, the real plant behavior is modeled with only influent and effluent data to take under control the dissolved oxygen and nitrate and nitrite nitrogen concentrations, also offering a better performance in terms of integral absolute error (IAE) and integral square error (ISE) with respect to the default controllers.Els recursos hídrics són un dels assumptes ambientals més importants degut a la vulnerabilitat de l'aigua a ser contaminada i el seu efecte directe en la salut humana. La millora de la seva gestió depèn en gran mesura del tractament d'aigües residuals. Per aquest motiu, hi ha estrictes límits de contaminació establerts per l'efluent de les estacions depuradores d'aigües residuals (EDAR), el qual ́es abocat en rierols o altres aigües receptores. Amb la finalitat de millorar la qualitat del tractament, complir les normes imposades per les autoritats i mantenir un baix cost de les operacions, s'apliquen estratègies de control en aquestes estacions. Aquest projecte presenta un sistema de control basat en controladors per model intern(IMC) que empren xarxes neuronals artificials (ANN), com alternativa a l'estratègia de control per defecte del Benchmark Simulation Model no. 1 (BSM1), un marc que emula el comportament d'una EDAR de propòsit general que utilitza controladors proporcionals integrals (PI). Amb el mètode de control proposat, el comportament real de l'estació es modela amb només les dades de l'influent i de l'efluent per tenir sota control les concentracions d'oxigen dissolt i de nitrat i nitrit de nitrogen, oferint també un millor rendiment en termes de la integral de l'error absolut (IAE) i de la integral de l'error quadràtic (ISE) respecte als controladors per defecte.Los recursos hídricos son uno de los asuntos ambientales más importantes debido a la vulnerabilidad del agua a ser contaminada y su efecto directo en la salud humana. La mejora de su gestión depende en gran medida del tratamiento de aguas residuales. Por este motivo, hay estrictos límites de contaminación establecidos para el efluente de las estaciones depuradoras de aguas residuales (EDAR), el cual es vertido en arroyos u otras aguas receptoras. Con elfin de mejorar la calidad del tratamiento, cumplir las normas impuestas por las autoridades y mantener un bajo costo de las operaciones, se aplican estrategias de control en estas estaciones. Este proyecto presenta un sistema de control basado en controladores por modelo interno (IMC) que emplean redes neuronales artificiales (ANN), como alternativa a la estrategia de control por defecto del Benchmark Simulation Model no. 1 (BSM1), un marco que emula el comportamiento de una EDAR de propósito general que utiliza controladores proporcionales integrales (PI). Con el método de control propuesto, el comportamiento real de la estación se modela con sólo los datos del influente y del efluente para tener bajo control las concentraciones de oxígeno disuelto y de nitrato y nitrito de nitrógeno, ofreciendo también un mejor rendimiento en términos de la integral del error absoluto (IAE) y de la integral del error cuadrático (ISE) con respecto a los controladores por defecto

Jätevedenpuhdistamojen prosessinohjauksen ja operoinnin kehittäminen data-analytiikan avulla: esimerkkejä teollisuudesta ja kansainvälisiltä puhdistamoilta

Instrumentation, control and automation are central for operation of municipal wastewater treatment plants. Treatment performance can be further improved and secured by processing and analyzing the collected process and equipment data. New challenges from resource efficiency, climate change and aging infrastructure increase the demand for understanding and controlling plant-wide interactions. This study aims to review what needs, barriers, incentives and opportunities Finnish wastewater treatment plants have for developing current process control and operation systems with data analytics. The study is conducted through interviews, thematic analysis and case studies of real-life applications in process industries and international utilities. Results indicate that for many utilities, additional measures for quality assurance of instruments, equipment and controllers are necessary before advanced control strategies can be applied. Readily available data could be used to improve the operational reliability of the process. 14 case studies of advanced data processing, analysis and visualization methods used in Finnish and international wastewater treatment plants as well as Finnish process industries are reviewed. Examples include process optimization and quality assurance solutions that have proven benefits in operational use. Applicability of these solutions for identified development needs is initially evaluated. Some of the examples are estimated to have direct potential for application in Finnish WWTPs. For other case studies, further piloting or research efforts to assess the feasibility and cost-benefits for WWTPs are suggested. As plant operation becomes more centralized and outsourced in the future, need for applying data analytics is expected to increase.Prosessinohjaus- ja automaatiojärjestelmillä on keskeinen rooli modernien jätevedenpuhdistamojen operoinnissa. Prosessi- ja laitetietoa paremmin hyödyntämällä prosessia voidaan ohjata entistä tehokkaammin ja luotettavammin. Kiertotalous, ilmastonmuutos ja infrastruktuurin ikääntyminen korostavat entisestään tarvetta ymmärtää ja ohjata myös eri osaprosessien välisiä vuorovaikutuksia. Tässä työssä tarkastellaan tarpeita, esteitä, kannustimia ja mahdollisuuksia kehittää jätevedenpuhdistamojen ohjausta ja operointia data-analytiikan avulla. Eri sidosryhmien näkemyksiä kartoitetaan haastatteluilla, joiden tuloksia käsitellään temaattisen analyysin kautta. Löydösten perusteella potentiaalisia ratkaisuja kartoitetaan suomalaisten ja kansainvälisten puhdistamojen sekä prosessiteollisuuden jo käyttämistä sovelluksista. Löydökset osoittavat, että monilla puhdistamoilla tarvitaan nykyistä merkittävästi kattavampia menetelmiä instrumentoinnin, laitteiston ja ohjauksen laadunvarmistukseen, ennen kuin edistyneempien prosessinohjausmenetelmien käyttöönotto on mahdollista. Operoinnin toimintavarmuutta ja luotettavuutta voitaisiin kehittää monin tavoin hyödyntämällä jo kerättyä prosessi- ja laitetietoa. Työssä esitellään yhteensä 14 esimerkkiä puhdistamoilla ja prosessiteollisuudessa käytössä olevista prosessinohjaus- ja laadunvarmistusmenetelmistä. Osalla ratkaisuista arvioidaan sellaisenaan olevan laajaa sovelluspotentiaalia suomalaisilla jätevedenpuhdistamoilla. Useiden ratkaisujen käyttöönottoa voitaisiin edistää pilotoinnilla tai jatkotutkimuksella potentiaalisten hyötyjen ja kustannusten arvioimiseksi. Jo kerättyä prosessi- ja laitetietoa hyödyntävien ratkaisujen kysynnän odotetaan tulevaisuudessa lisääntyvän, kun puhdistamojen operointi keskittyy ja paineet kustannus- ja energiatehokkuudelle kasvavat

Integrated Active Control Strategies and Licensing Approaches for Urban Wastewater Systems

The wastewater sector in the UK and other EU member states are facing stringent regulatory standards. The environmental water quality standards such as the EU-WFD, on the one hand, require a higher level of wastewater treatment which can result in increased GHG emissions and operational cost through higher energy use, chemical consumption, and capital investment. On the other hand, the Carbon Reduction Commitment Energy Efficiency scheme requires the water industries to reduce their GHG emission significantly. The research assesses the advantage of integrated active control of existing WWTPs, their optimisation and dynamic licensing approach to tackle this challenge while maintaining the quality of the receiving river. The dynamic licensing approach focuses on the design of control strategies based on the receiving river’s assimilative capacity. A simulation approach is used to test control strategies and their optimisation, interventions, and dynamic licensing approaches. The study developed an integrated UWWS model that fully integrate WWTP, sewer network, and receiving river, which enables the assessment of the advantage of integrated control strategies and dynamic licensing approach. The hybrid modelling approach uses mechanistic, conceptual and data-driven models in order to reduce computational cost while maintaining the model accuracy. Initially, the WWTP model was set up using average values of model parameters from the literature. However, this did not give a model with good accuracy. Hence, through, a careful design and identification of key parameters, a data campaign was designed to characterise influent wastewater, flow pattern, and biological processes of a real-world case study. The model accuracy was further improved using auto-calibration processes using a sensitivity analysis, identifying influential parameters to which the final effluent and oxidation ditch quality indicators are sensitive to. The sensitivity and auto-calibration were done using statistical measures that compare simulated and measured data points. Nash-Sutcliff coefficient (NSE) and root-mean-square-error (RMSE) measures show consistency in the sensitivity analysis, but correlation coefficient R2 showed a slight difference as it focusses on pattern similarity than values closeness. The combined use of NSE and RMSE gave the best result in model accuracy using fewer generation in the multi-objective optimisation using NSGA-II. Further local sensitivity analysis is used to identify the effect of varying control handles on GHG emissions (as equivalent CO2 emission), operational cost and effluent quality. The GHG emissions both from direct and indirect sources are considered in this study. The indirect GHG emissions consider the major GHG emissions (CO2, N2O, and CH4) associated with the use of electricity, sludge transport, and offsite degradation of sludge and final effluent. Similarly, the direct GHG emissions consider the emission of these major gases from different biological processes within the WWTP such as substrate utilisation, denitrification and biomass decay. This knowledge helps in the development of control strategies by indicating influential control handles and aids the selection of control strategies for optimisation purposes. It is found that multi-objective optimisation can reduce GHG emissions, operational cost while operating under the effluent quality standards. Multi-objective optimisation of control loops coupled with integrated active control of oxygen using final effluent ammonia concentration showed the highest reduction in GHG emissions and reduction in operational cost without violating the current effluent quality standard. Through dynamic licensing approach, the oxygen level in the oxidation ditch is controlled based on the assimilative capacity of the receiving river, which reduces the operational cost and effluent quality index without increased GHG emissions. However, to benefit from the dynamic licensing approach, a trade-off needs to be considered further between final effluent NO3 concentration and reduction in oxygen level in the oxidation ditch to reduce biomass decay which is responsible for higher GHG emission in this scenario

Probabilistic design and upgrade of wastewater treatment plants in the EU Water Framework directive context

The EU Water Framework Directive requires compliance with effluent and receiving water quality standards. This increased complexity implies that the evaluation of the impact of measures should be evaluated with adequate tools, both from the methodological point of view – by applying systems analysis investigations and modelling uncertainty assessment tools – and by making the developed methodology applicable in practice. Urban wastewater systems (UWWSs) are crucial components of river basins, since they usually contribute significantly to the pollution loads. They also have more flexibility in operation and management than other subsystems as agriculture. One part of this dissertation tries to answer the question “where” to improve the UWWS in a basin by means of systems analysis. A case study is tackled with the help of substance flow analysis (SFA) and of performance indicators. SFA allowed to identify the pressures on the receiving water. The indicators highlighted the critical structures in the basin. The spatial scale of the study was found to be of paramount importance. The other part of this dissertation deals with the question “how” to improve the UWWS, by proposing a systematic methodology to design correction measures, illustrated by the example of WWTP design and upgrade. The first step is the generation of influent time series to be fed to the WWTP models by means of a new phenomenological model of the draining catchment and sewer system. Ten different treatment process configurations were selected for the comparison. Further, eleven upgrade options were selected for evaluation, partly requiring real-time control (RTC) and partly the construction of additional treatment volume. For the immission-based evaluation, the integration of the WWTP model with a river model was made by means of the continuity-based interfacing method (CBIM). The propagation of the uncertainty on model parameters was performed with Monte Carlo simulations. Given the assumed boundary conditions, alternating systems show the best treatment cost-efficiency. RTC upgrades showed good potential for low-cost compliance, but with higher risk of limits exceedance. The immission-based evaluation revealed that considering the system from a holistic point of view can lead to substantial savings

MAB2.0 project: Integrating algae production into wastewater treatment

Different species of microalgae are highly efficient in removing nutrients from wastewater streams and are able to grow using flue gas as a CO2 source. These features indicate that application of microalgae has a promising outlook in wastewater treatment. However, practical aspects and process of integration of algae cultivation into an existing wastewater treatment line have not been investigated. The Climate-KIC co-funded Microalgae Biorefinery 2.0 project developed and demonstrated this integration process through a case study. The purpose of this paper is to introduce this process by phases and protocols, as well as report on the challenges and bottlenecks identified in the case study. These standardized technical protocols detailed in the paper help to assess different aspects of integration including biological aspects such as strain selection, as well as economic and environmental impacts. This process is necessary to guide wastewater treatment plants through the integration of algae cultivation, as unfavourable parameters of the different wastewater related feedstock streams need specific attention and management. In order to obtain compelling designs, more emphasis needs to be put on the engineering aspects of integration. Well-designed integration can lead to operational cost saving and proper feedstock treatment enabling algae growth