CARTIF Projects
LIXIV-IA
AI and big data based solutions for the optimisation of autonomous plants used for leachate treatment
Description
LIXIV_IA propone el desarrollo de soluciones basadas en inteligencia artificial (IA) y big data para la optimización de la operación de plantas autónomas contenerizadas usadas en el tratamiento de Lixiviados procedentes de diferentes orígenes (residuos sólidos urbanos y aguas residuales).
Objectives
-
- Improve the digitalisation of the plant through the implementation of new sensors (e.g. pH for inlet correction) and improvement of data acquisition systems.
- Develop new data processing techniques to obtain patterns that enable optimisation of the key leachate processes (evaporation-condensation 1 and direct osmosis).
- Determination of indicators and KPIs (Key Performance Indicators) to evaluate the performance of the plant in relation to the improvements made.
- Develop models based on artificial intelligence techniques of the most important modules of the installation.
Validate the tools developed in an autonomous leachate treatment plant.
Actions
- Collaboration on monitoring requirements.
- Collaboration in the definition of the logging architecture.
- Generation of state of the art AI tools appropriate for Leachate issues.
- Configuration of the OPC-based acquisition platform. Definition of the database structure on the client company’s server. Quality assurance of online data logging in the different initial experiments.
- Processing of data capture and creation of data visualisation tool from proprietary tool. Transfer of knowledge to the company on the process of configuration and registration of data from the plant based on OPC on database structure and updating of the data visualisation tool.
Expected Results
- Exploratory data analysis of the relevant data for the plant’s key equipment (EC1 and Osmosis). Generation of models of different nature (statistical, time series, input/output) to relate the variables affected in the key equipment of the plant.
- Correlation of key variables with the different leachate qualities based on laboratory analytical information provided by the company. Updating of the visualisation tool to ensure the updating of relevant analytics for data exploitation (box-plot, two-by-two correlations and time series).
- Update of the data models to collect the laboratory analytics in the visualisation interface.
- Definition of the experimentation to be carried out together with the company to undertake modelling processes based on step input response analysis.
- Theoretical optimisation of the key elements of the plant based on the models generated in WP3. Updating of the models based on the results of the experimental validation of the optimisation by the client.
R&D Line
- Desarrollo de sistemas de aquisición y monitorización de datos y creación de modelos basados en técnicas de inteligencia artificial para la optimización de procesos.
Client
R&D CDTI Projects
Total Budget: 224,422€
Duration: 01/01/2023 – 31/12/2024
Responsible
Clemente Cárdenas
Industrial and Digital Systems Division
Networking
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