Periodic Reporting for period 1 - FORGE (Development of novel and cost-effective coatings for high-energy processing applications)
Berichtszeitraum: 2020-11-01 bis 2021-10-31
A radical transformation is required in energy intensive industries in order for production to meet carbon neutral targets by 2050. Low carbon technologies and processes need to be able to address extreme and fluctuating conditions, but existing materials have inherent limitations in extreme conditions.
The FORGE Project aims to overhaul exiting materials and develop new materials creating methodologies and dataset to explore exploit the untap potential of the Compositionally Complex Materials.
These CCM are being developed to address surface degradation problems found in energy-intensive industries, with a particular focus on:
• Corrosion of metallic components from acidic, basic and reactive species
• Hydrogen embrittlement of high-strength steels from hydrolytic and process hydrogen
• Erosion of process plant from particulates, and wear from friction
• Thermal breakdown of ceramic vessel walls due to alkaline attack at pyrolytic temperatures
To solve these challenges the project will seek to design optimal high-performance coatings able to resist the specified set of degradation mechanisms as well as determining the best deposition methods. But the development of Compositionally Complex Materials requires Complex workflows. The ones that FORGE has defined, will bring the partners in a journey from theoretical material modelling to real industrial application. FORGE covers all the steps in between, and planned iterative cross-validations of its results to select down the most reliably performing coatings.
The field of Compositionally Complex Material (CCM) is vast, and Thermodynamic Calculations are not sufficient to provide enough information to identify suitable element composition. In FORGE Machine Learning models are used to support the initial composition selection and the following material synthesis and coating manufacturing.
The synthesis methods will be multiple: Induction Melting, PVD and Mechanical Alloying, providing insights on different aspects of the CCM composition and possibly generating results beyond the targets of FORGE, that is focused on material for the production of Coatings.
Apart the direct benefits from the involved industries, FORGE is working in setting up tools to expand the application of CCM to other sectors and applications, with tools to adapt the material composition and properties at each specific use case and calculation of the expected benefits in terms of LCA.
Finally FORGE is working also to establish procedures that guarantees the performance of its coatings, developing smart monitoring technologies that allows to program coating maintenance or replacement just when it is really needed.
The FORGE Project aims to overhaul exiting materials and develop new materials creating methodologies and dataset to explore exploit the untap potential of the Compositionally Complex Materials.
These CCM are being developed to address surface degradation problems found in energy-intensive industries, with a particular focus on:
• Corrosion of metallic components from acidic, basic and reactive species
• Hydrogen embrittlement of high-strength steels from hydrolytic and process hydrogen
• Erosion of process plant from particulates, and wear from friction
• Thermal breakdown of ceramic vessel walls due to alkaline attack at pyrolytic temperatures
To solve these challenges the project will seek to design optimal high-performance coatings able to resist the specified set of degradation mechanisms as well as determining the best deposition methods. But the development of Compositionally Complex Materials requires Complex workflows. The ones that FORGE has defined, will bring the partners in a journey from theoretical material modelling to real industrial application. FORGE covers all the steps in between, and planned iterative cross-validations of its results to select down the most reliably performing coatings.
The field of Compositionally Complex Material (CCM) is vast, and Thermodynamic Calculations are not sufficient to provide enough information to identify suitable element composition. In FORGE Machine Learning models are used to support the initial composition selection and the following material synthesis and coating manufacturing.
The synthesis methods will be multiple: Induction Melting, PVD and Mechanical Alloying, providing insights on different aspects of the CCM composition and possibly generating results beyond the targets of FORGE, that is focused on material for the production of Coatings.
Apart the direct benefits from the involved industries, FORGE is working in setting up tools to expand the application of CCM to other sectors and applications, with tools to adapt the material composition and properties at each specific use case and calculation of the expected benefits in terms of LCA.
Finally FORGE is working also to establish procedures that guarantees the performance of its coatings, developing smart monitoring technologies that allows to program coating maintenance or replacement just when it is really needed.
The activities programmed for the first period of the FORGE, corresponding to the first year, are all aiming in setting solid foundation for development of effective CCM material solutions.
All the partners were initially involved in the definition of the needs for the Energy Intensive Industries, with focus on those involved in FORGE. From this analysis emerged not only the definition of the use case demonstrator that will be utilised for the validation of the results, but also indication on other possible application scenarios that might benefit from the materials developed in FORGE, further increasing its exploitation potential.
From the initial analysis of the industrial needs, combined with the development and manufacturing capabilities in the project, the elements to be utilised in the formulation of Compositionally Complex Alloys and Compositionally Complex Ceramic was also defined, allowing to formulate eventually also the Key Performance Indicators for all the stages of the project.
The iterative work of material modelling and experimental validation also started this year, for both CCA and CCC a Machine Learning Algorithm were released once trained with data available from literature. The collection and organization of data from literature was a non-trivial activity, due to the scattered availability of data that required normalizations and implementation to be compatible and to avoid as much as possible biases.
The experimental activity took most of the effort, the CCA and CCC compositions realised were completely unknown before and each of them brought unexpected challenges that had to be solved.
Thirty CCA alloys, selected among the predictions of the first ML algorithm, were synthesized by Induction Melting, or Arc Melting where IM was not effective. Than processed by annealing, cutting and rolling to get specimens for the extensive characterization campaign that included, XRD, DSC, SEM, EDX, ICP-MS, LECO, H2 uptake, Corrosion via LPR and CPP, and Hardness.
Calphad calculations on phase formation and stability were also used as complement to the ML algorithm predictions, and the results has been backed up by the XRD and DSC data acquired on the experimental dataset, validating the use of Calphad phase calculation of CCA for future implementation of the ML algorithms.
Eighty CCC where formulated and realised with two techniques, Sol-Gel and direct dry powder mixing. This led to a set of about 160 samples that have been fired at increasing temperatures (1300, 1500, 1700) to identify which conditions lead to the formation of a single phase CCC. The characterization performed on CCC and CCA determines the dataset for the next iterations of the ML models.
Beside the activities dedicated to the identification of the CCA and CCC composition, also the preparatory activities for the coating deposition had started, in particular the development of methodologies for CCA powder production and the identification of luminescent taggants to be embedded in the coating for smart monitoring.
All the partners were initially involved in the definition of the needs for the Energy Intensive Industries, with focus on those involved in FORGE. From this analysis emerged not only the definition of the use case demonstrator that will be utilised for the validation of the results, but also indication on other possible application scenarios that might benefit from the materials developed in FORGE, further increasing its exploitation potential.
From the initial analysis of the industrial needs, combined with the development and manufacturing capabilities in the project, the elements to be utilised in the formulation of Compositionally Complex Alloys and Compositionally Complex Ceramic was also defined, allowing to formulate eventually also the Key Performance Indicators for all the stages of the project.
The iterative work of material modelling and experimental validation also started this year, for both CCA and CCC a Machine Learning Algorithm were released once trained with data available from literature. The collection and organization of data from literature was a non-trivial activity, due to the scattered availability of data that required normalizations and implementation to be compatible and to avoid as much as possible biases.
The experimental activity took most of the effort, the CCA and CCC compositions realised were completely unknown before and each of them brought unexpected challenges that had to be solved.
Thirty CCA alloys, selected among the predictions of the first ML algorithm, were synthesized by Induction Melting, or Arc Melting where IM was not effective. Than processed by annealing, cutting and rolling to get specimens for the extensive characterization campaign that included, XRD, DSC, SEM, EDX, ICP-MS, LECO, H2 uptake, Corrosion via LPR and CPP, and Hardness.
Calphad calculations on phase formation and stability were also used as complement to the ML algorithm predictions, and the results has been backed up by the XRD and DSC data acquired on the experimental dataset, validating the use of Calphad phase calculation of CCA for future implementation of the ML algorithms.
Eighty CCC where formulated and realised with two techniques, Sol-Gel and direct dry powder mixing. This led to a set of about 160 samples that have been fired at increasing temperatures (1300, 1500, 1700) to identify which conditions lead to the formation of a single phase CCC. The characterization performed on CCC and CCA determines the dataset for the next iterations of the ML models.
Beside the activities dedicated to the identification of the CCA and CCC composition, also the preparatory activities for the coating deposition had started, in particular the development of methodologies for CCA powder production and the identification of luminescent taggants to be embedded in the coating for smart monitoring.
Most of the project activities are still ahead, especially those more closely linked to the actual application of the newly developed materials into real industrial scenarios. Notwithstanding this the project already achieved important milestones:
• The first ML-algorithms for CCA and for CCC have been achieved
• The experimental activities on 30 CCAs compositions validated the ML algorithm providing data for its improvement
• An unbiased dataset of 160 CCCs has been obtained experimentally, solving issues related with biases on the data achievable from thermodynamic calculations
• Three CCAs has been synthesized in powder form, validating the methodologies for the upscale of the CCAs that will emerge from the development and modelling activities
• One luminiscent taggant has been selected among the initial 36 candidates, by passing all the characterization criteria set to assure the survival of the taggant also after the deposition process and in the extreme working condition of the coating
The results expected in the next periods builds upon these ones, bringing the consortium closer to the its final targets.
FORGE technology will be applied for different critical components in steel, aluminum, cement and ceramic manufacturing plants against four key performance indicators (KPIs) or performance targets (PTs), as detailed in the public deliverable released in this first period, to extend the service life of these components by making them more resistant to the harsh industrial working conditions.
• The first ML-algorithms for CCA and for CCC have been achieved
• The experimental activities on 30 CCAs compositions validated the ML algorithm providing data for its improvement
• An unbiased dataset of 160 CCCs has been obtained experimentally, solving issues related with biases on the data achievable from thermodynamic calculations
• Three CCAs has been synthesized in powder form, validating the methodologies for the upscale of the CCAs that will emerge from the development and modelling activities
• One luminiscent taggant has been selected among the initial 36 candidates, by passing all the characterization criteria set to assure the survival of the taggant also after the deposition process and in the extreme working condition of the coating
The results expected in the next periods builds upon these ones, bringing the consortium closer to the its final targets.
FORGE technology will be applied for different critical components in steel, aluminum, cement and ceramic manufacturing plants against four key performance indicators (KPIs) or performance targets (PTs), as detailed in the public deliverable released in this first period, to extend the service life of these components by making them more resistant to the harsh industrial working conditions.