Pre-study Preventor - automatic process supervision
Reference number | |
Coordinator | ACOSENSE AB |
Funding from Vinnova | SEK 476 900 |
Project duration | September 2015 - February 2016 |
Status | Completed |
Venture | Strategic innovation programme for process industrial IT and automation – PiiA |
Call | Strategic innovation program PiiA - summer 2015 |
Important results from the project
The pre-study has quantified the market potential for the product Preventor which automatically indicates desired/undesired process variations with the patented technology Active Acoustic Spectroscopy. A requirement specification was needed to identify the customers´ needs of communication between the operator and the instrument which was summed up in a document. Another purpose of the pre-study was to also identify mathematical methods for robustly presenting small variations in big amounts of data over a long period of time. The found methods were tested on real site data.
Expected long term effects
The pre-study showed that there is a great need for a product in the process industry like the Preventor. It was shown that the potential is even higher, when different process variations can be identified (quality A, B, C, ) The pre-study has addressed the challenges in the user-interface for two-way communication. The mathematics needed have been found and can be implemented for identifying desired/undesired process variation. Based on the pre-study, the goals in the implementation project can be more precise and the consortium will apply for a medium size project within PiiA.
Approach and implementation
The pre-study contained 5 work-packages market analysis, user interface, robust data collection and handling variations and outliers over time. Market analysis and the user interface have been investigated by surveys and in deep interviews with Acosense customers and are presented in reports. The remaining areas have been handled in cooperation with FCC through tests of algorithms and solutions FCC resolved based on two earlier data-sets of two positions prior to this project.