Digitalization of Agroindustry to optimize water management – DIGICAT
August 2017 – March 2018
Agrofood sector is the biggest industrial water consumer, and reaches 8-15% of water consumed in European Industry. DIGICAT chose meat industry as it is the main subsector of agroindustry in terms of sales.
Nowadays industry has prioritize energy or transport in the optmization of its processes as its costs are higher than water. However, water management at industry is nowadays a relevant concern due to changes in terms of regulation, scarcity, environmetnal impact and its real costs (when analyzing its real costs including hot water).
DIGICAt aims at developing a new software based in artificial inteligence (Big Data Analytics) applied to agrofood industry to improve its water management. Big data will allow to add predictive functionalities to enhance decission making processes to optimize water management process in real time.
- ZINNAE (Coordinador)
- COGNIT (Coordinador técnico)
Innovative Business Associations by MINECO
This project received funding support under the “Innovative Business Associations” framework (AEIs). This program supports to strenghten innovation clusters and is also alligned with the european strategy for competitiveness through innovation.
Only clusters from the National Registry of Clusters can benefit from this funding call. These are clusters with innovation potential and critical mass. ZINNAE is member of this Registry since 2010.
This project is the stage 1 of an ambitious initiative to develop an artificial inteligence software for water management control at industrial level. The software will determine patterns and predict performance with a certain production data, being able to anticipate to water standards and ensure water efficiency at all stages of the process.
The specific objectives of Strand 1 of the project are:
- Identify the needs for water monitoring and control of the Agrofood industry – FRIBIN
- Characterization and visualization of water lines
- Data analysis to ensure water efficiency through an optimal water consumption and water discharge
- Determine water patterns with regards to the industrial process
- Identify predictive index and needs
- Development of predictive algorithms
- Analysis of the added value for the software
- Analysis of the market uptake and project continuation
The project is coordinated by ZINNAE with the technical coordination support of COGNIT. The project plan identifies five work packages:
- WP1 – Coordination and dissemination
- WP2 – Water Cycle analysis at FRIBIN
- WP3 – Water optimization
- WP4 – Fast prototype of Big Data Analytics solutions to water efficiency processes
- WP5 – Added value and market analysis
More information and contacts
More information and opportunities for collaboration through cpresa(@)zinnae.org, j.milan(@)cognit.es, ccalidad(@)fribin.es, jsantacruz(@)contazara.es and csaviron(@)itainnova.es