Livrables
Definition of the Process Flow Diagram PFD and subsequent Piping and Instrument Drawing PID plus functional description of the pilot unit Design documents for the construction of the pilot and programming of the control software
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 ReportThis 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
Report on the water use for all the intelWATT's case studies applicationsIn this task a complete physicochemical characterisation of every process and wastewater effluent will be performed Indicative analytical method that will be used include ICP AAS UVVis HPLC Ionic chromatography etcAdditionally the operating conditions will be analysed in detail in order to explore water preservation potentials in every intelWATTs application
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
Report on scalability of AWC-NF membranes for nanofiltration.Report on scalability of AWCNF membranes for nanofiltration
Report on the selection of membranes for Case Studies 1 and 2Report on the selection of membranes for Case Studies 1 and 2 WP4 5
First report on synthesis and characterization of graphene-based membrane (POLITO, M6)Graphene oxide will be considered for the fabrication of ionically selective membrane Commercial GO powder will be used to fabricate labscale GO membrane by vacuum filtration of GObased water dispersion At the same time alternative membrane fabrication process will be investigated screen printing calendaring doctorblade spin coating as preliminary step for subsequent scaling up The GO membranes will be functionalized in order to obtain a selfpolarization of the membrane residual positive and negative charge for anion and cationexchange membranes respectively The GO membranes will be characterized both by SEM Raman XPS and TEM
Report on sensors requirements in order to achieve quantitative water analysis.The technical specifications of commercial water monitoring sensors will be studied such as colorimetric sensors portable spectrophotometric sensors electrical conductivity meter turbidity meter assessing their applicability and efficiency Politecnico di Torino will also investigate the development of MEMS type sensors which are particularly reliable and low energyconsuming combining high consistency and efficiency
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 the physicochemical properties for all feed and waste water streams and key quality monitoring parametersDetermination of the physicochemical properties for all feed and waste water streams including chemical compositions temperatures and flow ratesDefinition of the most representative key quality parameters for on line monitoring
Lab scale assessment of highly selective artificial water channels membranes AWC-MD for membrane distillationLab scale assessment of highly selective artificial water channels membranes AWCMD for membrane distillation
Report on fabrication of highly selective artificial water channels membranes AWC-NF for nanofiltrationReport on fabrication of highly selective artificial water channels membranes AWCNF for nanofiltration
Report on optimizations and control of porosity and surface chemistry in graphene-based membraneThe porous materials will be infiltrated with low cost IEM eg SPEEK SPES dissolved in a suitable solvent in order to get a compenetrated multilayer reducing the impedance associated to the thick membrane commonly used in membraneassisted processes maximizing the interface and allowing to higher energy conversion efficiency Additional infiltration can be obtained by ALD process in order to have atomiclayer thick metaloxides inside the GO channel Moreover interlayer distance will be controlled and partially tuned by acting on the drying state and exploiting functional link acting as spacers Other 2D materials such as MXenes will be considered for the fabrication of composite membranes
Report on results from lab systems on fouling/scaling studiesThe fouling tendency of all the membranes that will be used in intelWATTs processes will be evaluated by first analyzing the relevant feedwater quality properties These parameters include a Biological organic indices Microbial ATP Bacterial growth potential BGP LCOCD Total Organic Carbon TOC Total Nitrogen TN and Orthophosphate b Particulate fouling indices SDI045 MFI045 MFI10 KDa and c Transparent exopolymer particles TEP10KDa Identification of fouling types will be performed based on the membrane autopsy of the fouled membrane Based on the results the best pretreatment options will be suggested
Report on scalability of ion exchange and MD/MCr membranesPolyvinylidenefluoride 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
Data Lake Design Document.Data Lake Design Document
Report on optimization and characterization of tubular pretreatment UF membraneThe UF tubular membrane modules already used as standard at CUT are optimized based on the requirements of WP2 and findings of WP3 The corresponding improvements will include both the tubular nonwoven which serves as the membrane support and the actual membranes
The Deep Learning algorithm will be developed using a DL framework tensorflowkeras or pytorch and will be specifically designed for general purpose process control
Deep Learning system design document.Deep Learning system design document
Smart Monitoring System Design document.Smart Monitoring System Design document
Set up of the project management collaborative toolIMPACT platform integrated.
This platform will be able to implement the following tasks: a) manage sensors subscription southbound and b) Collect and interchange data using LWM2M, LWPA (NB-IoT), MQTT, TR-069 or other industrial protocols like MODBUS, apply security, data privacy and segregation policies for IoT applications and enterprises.
Functional cloud-based platform user interface dashboardAnalysis on dashboard data and functionalities, optimization of integrated systems and redesign of sensors and final optimization of design based on performance, connectivity, edge computing functionality and power supply. Optimization and finalization of machine learning process based on testing of the pilot system.
NI Platform integrated.The integration between NI-Connect platform the PLCs and Impact platform will be analyzed. NI will undertake to design any modification needed in order to maintain compatibility with the other systems. The NI-Connect will be adapted and deployed in the landscape in order to be integrated with the other components of the smart control system
Sensor data ingestion system Implementation.Sensor data ingestion system Implementation
Membrane Simulator implementedThe simulator component will be developed in order to provide input and output data to train the deep learning control system before the real scenario will be deployed. Simulator will model the membrane behavior (input and output data) through three different way; (1) mathematical models of current membranes, (2) mathematical models of membranes and (3) historical data of similar scenarios. This component will be able to score the fitting of each model and balance the contribution for the overall result of the simulation.
Dashboard Design Document.Dashboard Design Document
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
Design of Sensor data ingestion mechanism.Design of Sensor data ingestion mechanism
Data Lake System Implementation.The data lake will able to store the incoming data from the sensors and actuators in the three demonstration plants. The “data lake” will store the raw information as it is sent from the devices and provide a data processing pipeline that will validate, clean, homogenize, aggregate and transfer the data to a data storage that will serve as the data source for the digital twin and the representation dashboards, as well as, the Deep Learning algorithms. The data store will also provide a semantic data layer that will enrich the information with annotations to be used in the dashboard representations and the digital twins. This semantic data will be provided by the data processing pipeline and act as an abstraction layer between the raw data coming from the sensors and the representation layers, allowing to have a common representation model for the several processes involved.The data lake will be a common infrastructure to the three case studies and will be hosted in a public or private cloud environment accessible to all interested parties under high security provided by the IMPACT platform. Data processing will be carried by elastic infrastructure components based on big data techniques such as Spark and Hadoop and running on containerized workload management environments such as Kubernetes.
Engineering & automation detailed design for treatment prototype.Engineering & automation detailed design for HRRO/IX treatment prototype
Delivery of PVDF based membranes for MD/MCr (100 m2 with minimum water flux @ ΔT 60oC 20LMH and >99.5% salt rejection)
Lab scale RED/MD unitLab scale development integration and optimization of the RED MD treatment process
Delivery of optimized tubular UF membrane modulesThe optimized Tubular UF modules will be delivered to CNR-ITM, NCSR and THK and for use in the case studies (WP’s 5, 6 and 7).
Smart Monitoring System Integrate.Smart Monitoring System Integrated
Lab scale CTBD treatment unitIntegration and evaluation of the conventional (pressure, temperature, conductivity, turbidity flowmeter etc), as well as, the customized (Fe2+, SO42+ and Cl-) sensors will be performed ensuring alignment with the KPI’s set in WP1. Using membranes (4-inch diameter) and modules provided by NI and CUT, along a dedicated pre-treatment testing system (CUT), process conditions and configurations of the CTBD lab unit will be optimized. Zero liquid discharge will be introduced though membrane distillation, crystallisation subprocess developed by NCSRD, THK & CUT.
Lab scale HRRO unit for integration with IXLab scale HRRO unit approx 05 m3hr output for integration with IX
Lab scale HRRO/IX unitLab scale development, integration and optimization of the hybrid HRRO/IX treatment process for metal plating effluents
Dashboard System Implementation.The dashboard functionality will provide information about the signals, both of their status and their history and future trends, as well as allowing to analyse the state of the predictive model and its performance over time. On one hand, Techedge will create several dashboards that allow to visualize in a centralized way the current state of the input and output signals, from which it will be possible to validate the current state of each scenario under study and the possible incidents related to the different signs. Dashboard will be able to visualize the historical information of the signals and, through different analytical models, will be able to visualize the tendency of those signals in the future to be able to act preventively. The other scenario will allow analysing the effectiveness of predictive models over time and visualize how the actions carried out by deep learning models have influenced the system positively or negatively. From these dashboards, it will be possible to discover how the retraining performed by the system can improve the results, making the environment more and more efficient.
Report on Open Research Data Pilot and Data management Plan Periodic updates along with the projects reporting periods
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 posterThe 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
Publications
Auteurs:
Roxanne Engstler; Jan Reipert; Somayeh Karimi; Josipa Lisičar Vukušić; Felix Heinzler; Philip Davies; Mathias Ulbricht; Stéphan Barbe
Publié dans:
Membranes; Volume 12; Numéro 9; Pages: 853, Numéro 1, 2022, Page(s) 853, ISSN 2077-0375
Éditeur:
Molecular Diversity Preservation International
DOI:
10.3390/membranes12090853
Auteurs:
Ebrahim Hosseinipour; Somayeh Karimi; Stéphan Barbe; Kiho Park; Philip A. Davies
Publié dans:
Desalination, Numéro Volume 544, 15 December 2022, 116126, 2022, ISSN 0011-9164
Éditeur:
Elsevier BV
DOI:
10.1016/j.desal.2022.116126
Auteurs:
Enrica Fontananova, Valentina Grosso, Elvira Pantuso, Laura Donato, Gianluca Di Profio
Publié dans:
Journal of Membrane Science, Numéro Volume 676, 15 June 2023, 121585, 2023, ISSN 0376-7388
Éditeur:
Elsevier BV
DOI:
10.1016/j.memsci.2023.121585
Auteurs:
Bohr, Sven Johann; Wang, Fei; Metze, Michael; Vukušić, Josipa Lisičar; Sapalidis, Andreas; Ulbricht, Mathias; Nestler, Britta; Barbe, Stéphan
Publié dans:
Frontiers in Sustainability, Numéro Volume 4,2023, 2023, ISSN 2673-4524
Éditeur:
Frontiers Media S.A
DOI:
10.3389/frsus.2023.1093911
Auteurs:
Ioannis Tournis; Dimitris Tsiourvas; Zili Sideratou; Lamprini G. Boutsika; Aggeliki Papavasiliou; Nikos K. Boukos; Andreas A. Sapalidis
Publié dans:
Environmental Science Water Research & Technology, Numéro 8, 2022, 2373, 2022, Page(s) 2373–2380, ISSN 2053-1419
Éditeur:
Royal society of Chemistry
DOI:
10.1039/d2ew00407k
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