Controlo inteligente do sistema de distribuição de água em Alicante

El sistema de telemedida permite monitorizar y gestionar las infraestructuras y la red en alta en tiempo real. También contiene una herramienta (ModopENE) útil para mejorar y optimizar la eficiencia de las bombas de impulsión. Por último, contiene un módulo que permite sectorizar la red de abastecimiento y gestionarla. El sistema de telegestión y telemedida genera alertas y avisos en tiempo real que reciben los operarios o el responsable.

La replicabilidad de estas actuaciones radica en la sensorización de la zona. Es una solución técnicamente avanzada que permite replantear los sistemas de gestión eficientes en otras zonas con contextos similares de escasez hídrica y condiciones de aridez.

Responsible entity

La Diputación de Alicante, a través de su Área de Ciclo Hídrico, se ocupa de caracterizar la situación de los recursos hídricos provinciales y dar asesoramiento técnico a las administraciones locales, así como de construir infraestructuras hidráulicas que proporcionen servicio a los municipios, como corresponde al papel de la Institución Provincial como «Ayuntamiento de Ayuntamientos».

https://ciclohidrico.com/ 

The focus of the firm Ampos21 is to propose efficient, innovative, integrated and transversal solutions applying a quantitative method to any solution.  The consultancy has acquired extensive experience in the evaluation, analysis and modelling of a wide range of processes in geological media, including hydrogeology, geochemistry, geomechanics and coupled reactive transport. In this context they visualize hydrological resources as an integrated solution, which synthesizes all relevant aspects of the hydrological cycle for a given project.

Detailed explanation

Aqualearning is a solution based on artificial intelligence (AI) supported by IML’s own software. Its main function is to transform data into valuable information. The process followed can be broken down into the following fields:

In the first place, the analysis and preparation of the data, evaluating the variables, finding correlations, discriminating the data that provide information from those that do not.

A part of the data is used to ‘train’ the algorithm. In this sense, once the algorithm is ‘trained’, its predictions are validated. Validation uses unused data to see if it has the ability and robustness to generate predictions as accurately as possible. When the predictions are already reliable, the algorithm is prepared to generate future alerts or decision making. Aqualearning contains different algorithms developed using open source (TensorFlow technology).

Aqualearning benefits from the cause-effect relationship inherent in natural resource-related processes to manage them using AI.
The software was applied in different fields:

First, for the prediction of minority elements in the aquifer of Forsmark (Sweden) where it is planned to build a deep store of nuclear waste. From physicochemical data and majority elements (collected since 1994), by means of a decision tree (algorithm typology) it was possible to correlate the appearance of minority elements with the presence of some majority elements. The hypothesis was correct, with a high coefficient of determination and allowed to predict the concentrations of barium (97% average accuracy) and uranium and molybdenum (>85% average accuracy).

It was also applied to the water resource affected by mining in Chile and Peru. In these countries, a part of the mining waste generates effluents of strong acidifiers. Currently the experimental process to evaluate the effect of the pH of the waters of each new exploitation, or expansion of this on the environment is slow (30 weeks). From the data of the first weeks and the concentrations of some elements (copper), the Aqualearning software is able to predict the effect of the pH of the waters at the end of the experiment. The result was achieved with more than 90% accuracy (using 4230 data), while copper concentrations (85% accuracy) were predicted. This contribution makes it possible to speed up the sizing of mine treatment plants in Peru in advance. Although it does not replace the experimental process, the results of Aqualearning have been a great help in the planning of actions and investments in the cases tested.

Finally, another application of this predictive methodology was made in a groundwater pumping field with 14 active wells. It is known that some of them interfere, but due to the geology and the arrangement of the fractures it is not identified which of them they are. Using Aqualearning it was possible to predict the levels of the wells by means of the flow data extracted during the previous days, obtaining a satisfactory adjustment (ICRA, 2022), (IDAEA, 2022).