The terms and acronyms used are given here to make it easier to read and understand the website.
- SbD – Safety by Design
- TRL - Technology Readiness Level
- EBD – Evidence Based Decision
- MPC – Model Predictive Control
SbD – Safety by Design
SbD is the process of addressing safety issues in both the R&D (Research & Development) and design phases of technologies (e.g. nanotechnology), through the integration of hazard identification and risk assessment methods early in the design process in order to eliminate or minimise the risks of harm through the construction and life of the product being designed. It is a modifying process through received scientific evidence in order that the products where SbD is applied are safer and do not endanger the public.
A main target of SABYDOMA project for Cnano, besides the reduction of wasted and shift to more safer processes, is the improvement of coating’s quality. Already based on the preliminary results, a significant improvement of our nano-composite coatings has been observed based on the following SEM images. On the left side, the surface of a Ni/SiC nano-composite coating with the SoA method. On the right side, the surface of the corresponding coating produced from the SABYDOMA plating apparatus, in which a better distribution of nanoparticles is evident
TRL-Technology Readiness Level
Technology Readiness Levels (TRLs) are indicators of the maturity level of particular technologies. This measurement system provides a common understanding of technology status and addresses the entire innovation chain. There are nine technology readiness levels; TRL 1 being the lowest and TRL 9 the highest.
Technology Readiness Levels (according to EU H2020 guideline):
TRL 1 – Basic principles observed
TRL 2 – Technology concept formulated
TRL 3 – Experimental proof of concept
TRL 4 – Technology validated in lab
TRL 5 – Technology validated in relevant environment (industrially relevant environment in the case of key enabling technologies)
TRL 6 – Technology demonstrated in relevant environment (industrially relevant environment in the case of key enabling technologies)
TRL 7 – System prototype demonstration in operational environment
TRL 8 – System complete and qualified
TRL 9 – Actual system proven in operational environment (competitive manufacturing in the case of key enabling technologies; or in space)
EBD – Evidence Based Decision
EBD is a safety philosophy which aims to develop refined and appropriate decisions on the basis of scientific evidence.
MPC – Model Predictive Control
Model Predictive Control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. It uses a model of the system that is being analysed, to make predictions about its future behaviour. The philosophy of MPC can be described simply as follows: Predict future behaviour using a system model, given measurements or estimates of the current state of the system and a hypothetical future input trajectory or feedback control policy.
In this framework future inputs are characterized by a finite number of degrees of freedom, which are used to optimize a predicted cost. Only the first control input of the optimal control sequence is implemented, and, to introduce feedback into this strategy, the process is repeated at the next time instant using newly available information on the system state. This repetition is instrumental in reducing the gap between the predicted and the actual system response (in closed-loop operation). It also provides a certain degree of inherent robustness to the uncertainty that can arise from imperfect knowledge or unknown variations in the model parameters (referred to as multiplicative uncertainty), as well as to model uncertainty in the form of disturbances appearing additively in the system dynamics (referred to as additive uncertainty).
(with minor adaptations from: Kouvaritakis, Basil, and Mark Cannon. “Introduction.” In Model Predictive Control: Classical, Robust and Stochastic, edited by Basil Kouvaritakis and Mark Cannon, 1–9. Cham: Springer International Publishing, 2016. https://doi.org/10.1007/978-3-319-24853-0_1)