Estratègies de control i decisió en les plantes de tractament d'aigües residuals per a la millora de l'operació

Investigadors de la UAB publiquen un llibre que ens mostra els beneficis de l'aplicació de l'Enginyeria de control de tractament d'aigües, on s'examina el funcionament de les estacions depuradores d'aigües residuals (EDAR). Per a això s'empren dos models basats en plantes reals habitualment utilitzats en investigacions a nivell internacional amb la finalitat que els lectors puguin reproduir els resultats, alhora que poden implementar les seves pròpies solucions.Investigadores de la UAB publican un libro que nos muestra los beneficios de la aplicación de la Ingeniería de control de tratamiento de aguas, donde se examina el funcionamiento de las estaciones depuradoras de aguas residuales (EDAR). Para ello se emplean dos modelos basados en plantas reales habitualmente utilizados en investigaciones a nivel internacional con la finalidad de que los lectores puedan reproducir los resultados, al tiempo que pueden implementar sus propias soluciones.Researchers at the UAB publish a book that shows us the benefits of applying water treatment control engineering, which analyses the operation of wastewater treatment plants (WWTPs). The study is carried out in two models based on real plants usually used in international research. Readers can reproduce the results and test their own solutions

A Recurrent Neural Network for Wastewater Treatment Plant Effuents' Prediction

[Abstract] Wastewater Treatment Plants (WWTP) are industries devoted to process water coming from cities' sewer systems and to reduce their contamination. High-pollutant products are generated in the pollutant reduction processes. For this reason, certain limits are established and violations of them are translated into high economic punishments and environmental problems. In this paper data driven methods are performed to monitor the WWTP behaviour. The aim is to predict its effluent concentrations in order to reduce possible violations and their derived costs. To do so, an alarm generation system based on the application of Artificial Neural Networks (ANNs) is proposed. The proposed system shows a good prediction accuracy (errors around 5%) and a reduced miss-detection probability (30%).[Resumen] Las Plantas de tratamiento de aguas residuales (PTAR) son industrias dedicadas a procesar el agua que proviene de los sistemas de alcantarillado de las ciudades y reducir su contaminación. Los productos de alta contaminación se generan en los procesos de reducción de contaminantes. Por esta razón, se establecen ciertos límites y sus violaciones se traducen en castigos económicos elevados y problemas ambientales. En este documento, se realizan métodos controlados por datos para monitorizar el comportamiento de la EDAR. El objetivo es predecir sus concentraciones de efluentes para reducir las posibles violaciones y sus costos derivados. Para ello, se propone un sistema de generación de alarmas basado en la aplicación de Redes Neuronales Artificiales (ANN). El sistema propuesto muestra una buena precisión de predicción (errores en torno al 5%) y una probabilidad de detección errónea reducida (30%).Ministerio de Economía y Empresa; DPI2016-77271-

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

New approach for regulation of the internal recirculation flow rate by fuzzy logic in biological wastewater treatments

Altres ajuts: Acord transformatiu CRUE-CSICMarian Barbu acknowledge the support of the project " EXPERT ", Contract no. 14PFE/17.10.2018.The internal recirculation plays an important role on the different biological processes of wastewater treatment plants because it has a great influence on the concentration of pollutants, especially nutrients. Usually, the internal recirculation flow rate is kept fixed or manipulated by control techniques to maintain a fixed nitrate set-point in the last anoxic tank. This work proposes a new control strategy to manipulate the internal recirculation flow rate by applying a fuzzy controller. The proposed controller takes into account the effects of the internal recirculation flow rate on the inlet of the biological treatment and on the denitrification and nitrification processes with the aim of reducing violations of legally established limits of nitrogen and ammonia and also reducing operational costs. The proposed fuzzy controller is tested by simulation with the internationally known benchmark simulation model no. 2. The objective is to apply the proposed fuzzy controller in any control strategy, only replacing the manipulation of the internal recirculation flow rate, to improve the plant operation.Therefore, it has been implemented in five operation strategies from the literature, replacing their original internal recirculation flow rate control, and simulation results are compared with those of the original strategies. Results show improvements with the application of the proposed fuzzy controller of between 2.25 and 57.94% in reduction of total nitrogen limit violations, between 55.22 and 79.69% in reduction of ammonia limit violations and between 0.84 and 38.06% in cost reduction of pumping energy

Global Internal Recirculation Alternative Operation to Reduce Nitrogen and Ammonia Limit Violations and Pumping Energy Costs in Wastewater Treatment Plants

The internal recirculation plays an important role in different areas of the biological treatment of wastewater treatment plants because it has a great influence on the concentration of pollutants, especially nutrients. A usual manipulation of the internal recirculation flow rate is based on the target of controlling the nitrate concentration in the last anoxic tank. This work proposes an alternative for the manipulation of the internal recirculation flow rate instead of nitrate control, with the objective of avoiding limit violations of nitrogen and ammonia concentrations and reducing operational costs. A fuzzy controller is proposed to achieve it based on the effects of the internal recirculation flow rate in different areas of the biological treatment. The proposed manipulation of the internal recirculation flow rate is compared to the application of the usual nitrate control in an already established and published operation strategy by using the internationally known benchmark simulation model no. 2 as a working scenario. The results show improvements with reductions of 59.40% in ammonia limit violations, 2.35% in total nitrogen limit violations, and 38% in pumping energy costs

Nitrous oxide reduction in wastewater treatment plants by the regulation of the internal recirculation flow rate with a fuzzy controller

Altres ajuts: acords transformatius de la UABThe reduction of greenhouse gas emissions due to anthropogenic causes is one of the world's main challenges to face climate change. Wastewater treatment plants are necessary to improve the quality of wastewater before it is discharged into the receiving environment, but they have the disadvantage of generating nitrous oxide emissions during the biological treatment, which is a potent greenhouse gas. Avoiding partial nitrification by increasing dissolved oxygen is one of the ways to reduce these emissions. However, this article proposes to face a reduction of nitrous oxide emissions by reducing oxygen to minimum levels causing heterotrophic microorganisms to reduce nitrous oxide to dinitrogen. To achieve this objective, the present work proposes a regulation of the internal recirculation flow rate of the biological treatment by means of a fuzzy controller. This regulation is added to a usual control strategy in wastewater treatment plants, which achieves satisfactory results with respect to water quality and operational costs but that generates high nitrous oxide emissions. The Benchmark Simulation Model no. 2 Gas is used as working scenario, which includes the two main nitrous oxide emission pathways: heterotrophic denitrification and ammonia oxidizing bacteria denitrification. The proposed internal recirculation manipulation is shown to achieve nitrous oxide reductions of 26.70 and 30.83 % in different time periods with a slight effluent quality improvement and an operational cost reduction

Review: Presence, distribution and current pesticides used in Spanish agricultural practices

To guarantee an adequate food supply for the world's growing population, intensive agriculture is necessary to ensure efficient food production. The use of pesticides helps maintain maximum productivity in intensive agriculture by minimizing crop losses due to pests. However, pesticide contamination of surface waters constitutes a major problem as they are resistant to degradation and soluble enough to be transported in water. In recent years, all groups of pesticides defined by the World Health Organization have increased their use and, therefore, their prevalence in the different environmental compartments that can have harmful effects. Despite this effort, there is no rigorous monitoring program that quantifies and controls the toxic effects of each pesticide. However, multiple scientific studies have been published by specialized research groups in which this information is disseminated. Therefore, any attempt to systematize this information is relevant. This review offers a current overview of the presence and distribution of the most widelyused pesticides (insecticides, herbicides, and fungicides) by crop type and an evaluation of the relationships between their uses and environmental implications in Spain. The data demonstrated that there are correlations between the presence of specific pesticides used in the main crops and their presence in the environmental compartments. We have found preliminary data pointing to existing associations between specific pesticides used in the main crops and their presence in environmental compartments within different geographical areas of Spain; this should be the subject of further investigation

Aplicació d'una xarxa neuronal artificial per donar suport a l'operació de plantes en la indústria de les aigües residuals

En els últims anys, l'aparició del paradigma Indústria 4.0, així com l'ús de la intel·ligència artificial i especialment de les xarxes neuronals, està impulsant un canvi en la forma d'entendre i d'actuar amb diferents processos industrials. En aquest aspecte, algunes línies d'investigació proposen l'ús de les xarxes neuronals en el desenvolupament d'elements de control atesa la seva capacitat per modelar sistemes no lineals i d'alta complexitat. D'aquesta manera, una xarxa neuronal és capaç de modelar el comportament de processos difícils d'abordar mitjançant processos de control convencional.En los últimos años, la aparición del paradigma Industria 4.0, así como el uso de la inteligencia artificial y especialmente de las redes neuronales, está impulsando un cambio en la forma de entender y de actuar con diferentes procesos industriales. En este aspecto, algunas líneas de investigación proponen el uso de las redes neuronales en el desarrollo de elementos de control dada su capacidad para modelar sistemas no lineales y de alta complejidad. De este modo, una red neuronal es capaz de modelar el comportamiento de procesos difíciles de abordar mediante procesos de control convencional.In the last decade, the incursion of the Industry 4.0 paradigm, as well as the adoption of artificial intelligence and specially the artificial neural network, has arisen a change in the the vision and management of the industrial processes. In that sense, some research lines have been proposed to adopt neural networks in the development of different control strategies due to their ability in modelling highly no-lineal and complex systems. Therefore, a neural network is able to model the behaviour of processes which are difficult to manage when conventional control strategies are considered

Applicability domains of neural networks for toxicity prediction

In this paper, the term "applicability domain" refers to the range of chemical compounds for which the statistical quantitative structure-activity relationship (QSAR) model can accurately predict their toxicity. This is a crucial concept in the development and practical use of these models. First, a multidisciplinary review is provided regarding the theory and practice of applicability domains in the context of toxicity problems using the classical QSAR model. Then, the advantages and improved performance of neural networks (NNs), which are the most promising machine learning algorithms, are reviewed. Within the domain of medicinal chemistry, nine different methods using NNs for toxicity prediction were compared utilizing 29 alternative artificial intelligence (AI) techniques. Similarly, seven NN-based toxicity prediction methodologies were compared to six other AI techniques within the realm of food safety, 11 NN-based methodologies were compared to 16 different AI approaches in the environmental sciences category and four specific NN-based toxicity prediction methodologies were compared to nine alternative AI techniques in the field of industrial hygiene. Within the reviewed approaches, given known toxic compound descriptors and behaviors, we observed a difficulty in being able to extrapolate and predict the effects with untested chemical compounds. Different methods can be used for unsupervised clustering, such as distance-based approaches and consensus-based decision methods. Additionally, the importance of model validation has been highlighted within a regulatory context according to the Organization for Economic Co-operation and Development (OECD) principles, to predict the toxicity of potential new drugs in medicinal chemistry, to determine the limits of detection for harmful substances in food to predict the toxicity limits of chemicals in the environment, and to predict the exposure limits to harmful substances in the workplace. Despite its importance, a thorough application of toxicity models is still restricted in the field of medicinal chemistry and is virtually overlooked in other scientific domains. Consequently, only a small proportion of the toxicity studies conducted in medicinal chemistry consider the applicability domain in their mathematical models, thereby limiting their predictive power to untested drugs. Conversely, the applicability of these models is crucial; however, this has not been sufficiently assessed in toxicity prediction or in other related areas such as food science, environmental science, and industrial hygiene. Thus, this review sheds light on the prevalent use of Neural Networks in toxicity prediction, thereby serving as a valuable resource for researchers and practitioners across these multifaceted domains that could be extended to other fields in future research

Application of control strategies in wastewater treatment plants for effluent quality improvement, costs reduction and effluent limits violations removal

En este trabajo se aplican diferentes estrategias de control en las plantas de tratamiento de aguas residuales. El primer objetivo es la mejora del rendimiento de control. Básicamente, esto sirve como una prueba de que la estrategia de control propuesta se ha aplicado correctamente. El objetivo final es el efecto de la estrategia de control aplicada sobre el rendimiento de la planta. En concreto, mejorar la calidad del efluente, reducir costes de operación y evitar violaciones de los límites establecidos en el efluente. La evaluación de las diferentes estrategias de control se lleva a cabo en primer lugar con el Benchmark Simulation Model No. 1 (BSM1), y en segundo lugar con Benchmark Simulation Model No. 2 (BSM2). BSM1 se centra en el tratamiento biológico mediante reactores de lodos activos, y la evaluación se basa en una semana de simulación. BSM2 es una versión extendida del BSM1, agregando el tratamiento de lodos y proporciona un afluente más elaborado y variable, con un año de evaluación. Los enfoques de control se basan en Control Predictivo basado en Modelo, control difuso, funciones que relacionan las variables de entrada y las manipuladas, y Redes Neuronales Artificiales. El Control Predictivo basado en Modelo se propone para una mejora del tracking. El control difuso y las funciones se implementan para mejorar los procesos de desnitrificación o de nitrificación en base a los objetivos propuestos. Sus parámetros de sintonía se seleccionan mediante análisis trade-off. Las Redes Neuronales Artificiales se aplican para detectar riesgo de violaciones y obtener una selección automática de la estrategia de control adecuada. Se muestran los resultados y se comparan con las estrategias de control por defecto y con la literatura. Para el rendimiento de control, se obtiene una mejora satisfactoria. En cuanto al rendimiento de la planta, en la mayoría de los casos se evitan violaciones de los límites establecidos de nitrógeno total y de amonio y nitrógeno amoniacal, mientras que también se consigue una mejora de la calidad del efluente y una reducción de costes de operación.In this work different control strategies are applied in wastewater treatment plants. The first objective is the control performance improvement. Basically, this serves as a proof that the proposed control strategy has been applied properly. The final objective is the effect of the applied control strategy on the plant performance. Specifically, the effluent quality improvement, costs reduction and avoiding violations of the established effluent limits. The evaluation of the different control strategies is carried out first with Benchmark Simulation Model No. 1 (BSM1), and secondly with Benchmark Simulation Model No. 2 (BSM2). BSM1 is focused on the biological wastewater treatment by activated sludge reactors, and the evaluation is based on a week of simulation. BSM2 is extended adding the sludge treatment and provides a more elaborated and variable influent with an assessment of one year. The control approaches are based on Model Predictive Control, Fuzzy Control, functions that relate the input and manipulated variables and Artificial Neural Networks. Model Predictive Control is proposed for tracking improvement, Fuzzy Controllers and functions are implemented to improve the denitrification or nitrification processes based on the proposed objectives. Their tuning parameters are selected by trade-off analyses. Artificial Neural Networks are applied to detect risk of violations for an automatic selection of the suitable control strategy. The results are presented and compared with the default control strategy and with the literature. For the control performance, a satisfactory improvement is obtained. Regarding the plant performance, in most of the cases, violations of the established limits of total nitrogen and ammonium and ammonia nitrogen are avoided, as well as an effluent quality improvement and cost reduction are achieved