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intelligent Water Treatment Technologies for water preservation combined with simultaneous energy production and material recovery in energy intensive industries

Deliverables

Deep Learning system implementation.

The Deep Learning algorithm will be developed using a DL framework tensorflowkeras or pytorch and will be specifically designed for general purpose process control

Smart Monitoring System Design document.

Smart Monitoring System Design document

Set up of the project management collaborative tool

Set up of the project management collaborative tool

Integrated sensors platform design.

The implementation of a modular general purposed system is envisaged for monitoring key process indicators This system will be able to integrate different types of sensors MEMS sensors traditional sensors laser sensors fiber optic sensors etc which will be customized in relation to the different application to which they are intended The great advantage of the proposed system will be to allow high efficiency dynamic monitoring capable of increasing the quantity and quality of data available in IoT perspective in order to evaluate the presence of analytes in the aqueous sample before and after treatment and to evaluate the process parameters

Sensors datasheets and features.

For each sensor a datasheet will be drawn up containing communication protocol power supply full scale resolution precision linearity POLITO FUEL Techedge Especially for the PLC based controller a study will be contacted towards migrating the design to the cloud

Periodic Risk Monitoring Report

This report will include the identification of new potential risks and the definition of mitigation measures as well as the monitoring of the implementation of such measures

Plan for the Exploitation and Dissemination of Results (PEDR).

The main outcome of this deliverable will be the Plan for the Exploitation and Dissemination of Results PEDR

Training plan.

The main outcome of this deliverable is a plan describing all the training activities that will contribute to professional development through advanced training of researchers and other key staff research managers industrial executives and potential users of knowledge generated by the project

Quality Assurance Plan.

Quality Assurance Plan that will include rules for preparing the deliverables and the ways of verification

Report on scalability of ion exchange and MD/MCr membranes

Polyvinylidenefluoride PVDF and the more hydrophobic copolymer Polyvinylidenefluoride Hexafluoropropylene PVDFHFP will be used for the fabrication of MDMCr membranes Drywet spinning and the wetspinning phase inversion methods will be applied for HF type membrane while dry casting will be employed for the flat sheet configuration The produced membranes will be characterized in terms of membrane distillation performance pore structure and fouling resistance In collaboration with THK and with support from CUT the appropriate modules for the application will be implemented

Open Research Data Pilot and Data management Plan

Report on Open Research Data Pilot and Data management Plan Periodic updates along with the projects reporting periods

Lab scale RED/MD unit

Lab scale development integration and optimization of the RED MD treatment process

Lab scale HRRO unit for integration with IX

Lab scale HRRO unit approx 05 m3hr output for integration with IX

Project Website.

Development of and regular updating of the intelWATT web site and social media presence including LinkedIn ResearchGate FB and Twitter

Project graphic identity (LOGO), leaflet and poster

The specific deliverable includes among others the development of project public website intelWATT leaflet intelWATT posters as well as the creation of the project graphic identity Logo

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