Periodic Reporting for period 3 - FORGE (Development of novel and cost-effective coatings for high-energy processing applications)
Reporting period: 2023-05-01 to 2024-10-31
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.
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.
The first year of the FORGE project aimed to build solid basis for developing effective CCM material solutions. All the partners were involved in defining the needs for the Energy Intensive Industries, especially those represented in FORGE. From this analysis, the project defined the use case demonstrator and other potential application scenarios for the materials developed in FORGE. The project also defined the elements to be use for the formulation of Compositionally Complex Alloys and Compositionally Complex Ceramic and the Key Performance Indicators for the project stages.
The iterative work of material modelling and experimental validation for both CCA and CCC provided the first Machine Learning Algorithms, which were released once trained with data available from the literature.
The experimental activity took most of the effort to complete the datasets, with the challenges implied in the synthesis and characterization of CCA and CCC compositions completely unknown before.
Thirty CCA alloys, selected among the predictions of the first ML algorithm, were synthesized by Induction Melting, or Arc Melting. Tens of material libraries were synthesized by PVD, with more than 60 different compositions in each material library.
Eighty CCC were formulated and realized with Sol-Gel and direct dry powder mixing techniques. 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.
The identification of promising CCA and CCC allowed FORGE to move into the second phase of the project, which involved the synthesis of the new material, by Mechanical Alloying, and the production of thick coating by multiple deposition techniques: Laser Cladding, HVOF, HVAF, Cold Spraying.
The processes for the synthesis of the compositionally complex material as feedstock and their following deposition were developed and optimised to work at a pilot industrial scale, enabling eventually the production components and coupons to be tested in the harsh environments of Energy energy-intensive industries.
High Sliding Wear during Aluminium Extrusion, Abrasion in the piping of Cement production plants, the Corrosive environment found in plants for Carbon Capture and Storage, the high temperature of ceramic Kilns and the embrittlement caused by H2 used in Steel industries were all part of the field test stand by FORGE's materials.
The iterative work of material modelling and experimental validation for both CCA and CCC provided the first Machine Learning Algorithms, which were released once trained with data available from the literature.
The experimental activity took most of the effort to complete the datasets, with the challenges implied in the synthesis and characterization of CCA and CCC compositions completely unknown before.
Thirty CCA alloys, selected among the predictions of the first ML algorithm, were synthesized by Induction Melting, or Arc Melting. Tens of material libraries were synthesized by PVD, with more than 60 different compositions in each material library.
Eighty CCC were formulated and realized with Sol-Gel and direct dry powder mixing techniques. 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.
The identification of promising CCA and CCC allowed FORGE to move into the second phase of the project, which involved the synthesis of the new material, by Mechanical Alloying, and the production of thick coating by multiple deposition techniques: Laser Cladding, HVOF, HVAF, Cold Spraying.
The processes for the synthesis of the compositionally complex material as feedstock and their following deposition were developed and optimised to work at a pilot industrial scale, enabling eventually the production components and coupons to be tested in the harsh environments of Energy energy-intensive industries.
High Sliding Wear during Aluminium Extrusion, Abrasion in the piping of Cement production plants, the Corrosive environment found in plants for Carbon Capture and Storage, the high temperature of ceramic Kilns and the embrittlement caused by H2 used in Steel industries were all part of the field test stand by FORGE's materials.
The FORGE project has successfully completed its ambitious agenda of designing, producing, and testing compositionally complex materials tailored for energy-intensive industries. Through extensive material research and development, several key milestones were achieved, setting the stage for future advancements in material science and industrial applications.
Upscaled Synthesis Processes: The project successfully adapted an upscaled process to synthesize CCA/CCC advanced materials, achieving a feedstock productivity of 10 kg/day. This significant upscaling demonstrates the project’s potential for industrial-scale production.
Parameter Identification and Specimen Realization: Parameters for the deposition of new compositionally complex materials were identified, resulting in the realization of over 200 coating specimens. This extensive experimentation has expanded our understanding of how different compositions behave under various conditions.
Advanced Monitoring Capabilities: The project validated new smart monitoring technologies, including taggant and sensors capable of quick readings at temperatures up to 1700°C. These advancements provide crucial data for monitoring the coating degradation when exposed to harch industrial environments.
Optimizing Deposition Processes: Four industrial metal deposition processes were rigorously tested and evaluated, each specific to different performance targets. This comprehensive approach ensured that the most effective deposition techniques were identified and implemented.
Training Numerical Models: Five numerical models were trained to predict material performance, allowing for educated decision-making. These models have provided valuable predictions for over 500,000 alloy compositions, assessing their phase, hardness, and pitting potential.
Extensive Data Collection: With experimental data on more than 500 compositionally complex materials collected, the project has created a big database that will serve as a foundation for future research and development.
It is important to recognize that while the introduction of a new advanced material may not immediately revolutionize processes in energy-intensive industries, there is significant potential for niche applications to benefit from dedicated material design. By focusing on specific material performance goals or applications, we can leverage the extensive dataset generated by the FORGE project to identify promising starting points for new material synthesis and validation. The methodologies refined during this project provide a solid framework for continued innovation and progress in material science.
Upscaled Synthesis Processes: The project successfully adapted an upscaled process to synthesize CCA/CCC advanced materials, achieving a feedstock productivity of 10 kg/day. This significant upscaling demonstrates the project’s potential for industrial-scale production.
Parameter Identification and Specimen Realization: Parameters for the deposition of new compositionally complex materials were identified, resulting in the realization of over 200 coating specimens. This extensive experimentation has expanded our understanding of how different compositions behave under various conditions.
Advanced Monitoring Capabilities: The project validated new smart monitoring technologies, including taggant and sensors capable of quick readings at temperatures up to 1700°C. These advancements provide crucial data for monitoring the coating degradation when exposed to harch industrial environments.
Optimizing Deposition Processes: Four industrial metal deposition processes were rigorously tested and evaluated, each specific to different performance targets. This comprehensive approach ensured that the most effective deposition techniques were identified and implemented.
Training Numerical Models: Five numerical models were trained to predict material performance, allowing for educated decision-making. These models have provided valuable predictions for over 500,000 alloy compositions, assessing their phase, hardness, and pitting potential.
Extensive Data Collection: With experimental data on more than 500 compositionally complex materials collected, the project has created a big database that will serve as a foundation for future research and development.
It is important to recognize that while the introduction of a new advanced material may not immediately revolutionize processes in energy-intensive industries, there is significant potential for niche applications to benefit from dedicated material design. By focusing on specific material performance goals or applications, we can leverage the extensive dataset generated by the FORGE project to identify promising starting points for new material synthesis and validation. The methodologies refined during this project provide a solid framework for continued innovation and progress in material science.