Developing advanced filtration technologies to recover value-added bioproducts from wastewater.

Biotech+AI

Project AFTERLIFE: Advanced filtration technologies for the recovery and later conversion of relevant fractions from wastewater.

AFTERLIFE proposes a flexible, cost- and resource-efficient process framed in the zero-waste and circular economy approach for the recovery and valorization of the relevant fractions from wastewater. 

The first step of such process is an initial step consisting of a cascade of membrane filtration units for the separation of the totally of solids in wastewater. Then, the concentrates recovered in each unit will be treated to obtain high-pure extracts and metabolites or, alternatively, to be converted into value-added biopolymers (polyhydroxyalkanoates). Moreover, the outflow of the process is an ultra-pure water stream that can be directly reused. 

The outcomes of the project are focused on:

– Demonstration of an integrated pilot using real wastewater from three water intensive food processing industries (fruit processing, cheese, and sweets manufacturing)

– Demonstration of the applicability of the recovered compounds and the value added bioproducts in manufacturing environments

The design and optimization of the AFTERLIFE process following a holistic approach aims to contribute to improve performance and reduce the costs associated to wastewater treatment by maximizing the value recovery.


Our main tasks

  • Project coordination
  • Development of a holistic mathematical model of the process
  • Multidisciplinary optimization (MDO)
  • Evaluation of the optimization problems results
  • Particularization of the model and distribution of the optimal results
  • Process conceptual design and basic engineering


Partners

BIO BASED EUROPE PILOT PLANT | CELABOR | CITROMIL | CSIC | CTC | HERITAGE 1466 | IDENER.AI | INNOVEN | JAKE | LUREDERRA | MI-PLAST | NOVA-INSTITUTE | NOVA.ID.FCT | VTT


Start date – finish date

09 / 2017 - 02 / 2022


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nº 745737