Utilizing the CESMII SMIP to Optimize Product Quality for Mixer/Filler Systems in the Food Industry
Project Lead: Rensselaer Polytechnic Institute
Partners: G-W Process Optimization, Krinos
Member % Cost Share: 50%
CESMII % Cost Share: 50%
Duration: 6 Months
Krinos Foods and other SME food manufacturers face issues of product uniformity, consistency, and off-spec quality that can result in lower productivity and waste (of product and time). Without real-time quality control the company cannot predict problems or intervene in a timely fashion. A strategic long-term benefit lies in the new product development of high value-added foods that require very careful monitoring and in-situ process feedback to assure acceptable quality.
To utilize the CESMII SMIP to collect and contextualize data from two innovative sensors that will interface with the mixer and filler assets; create and implement data driven models that predict product quality based on operational performance and when conditions are suboptimal; and advise what interventions are needed to assure on-spec product quality.
- Determine avail. measurements/operating param. of the 2 new sensors to characterize & profile assets
- Create and test software for Optical Smart Sensor (OSS) and Resistive Power Draw Sensor (RPDS)
- Install sensors first on the RPI mixer and then at the Krinos mixer/filler locations
- Create SMIP profiles for the (i) mixer and (ii) filler using tools provided by the CESMII SMIP
- Create an instance of the CESMII SMIP to capture real-time data from the mixer and filler sensors
- Generate Status/Warning/Help prediction models for the mixer and filler
- Create dashboards to visualize; verify integrated operation; utilize the predictive model to reap benefits
Key Tasks & Milestones
- Develop Mixer & Filler SM profiles (M1-M3)
- Install SMIP instance and connect to Mixer/Filler system data sources (M3-M4)
- Configure Prediction Models and Develop Dashboards (M5)
- Demonstrate use of SM Profiles and Prediction Models in SMIP (M6)
- Recommend Best Fit of Project Deliverables with Food Industry at large (M6)
For Krinos – real-time monitoring of product consistency, uniformity, color, correct ingredient additions enabling timely intervention that will result in repeatable products, improved product quality, higher throughput, and less waste.
For food industry – A cost-effective real-time, on-line QC system utilizing the SMIP and novel sensors for mixers and fillers that will allow them to optimize throughput and be more competitive. Additionally, higher sensitivity real-time QC will enable new, more complex products to be systematically developed.
Reusable SM Profiles that can be configured and/or adopted by other food-related manufacturers, for development of SM solutions. Real-time predictive models in the SM Marketplace to optimize quality in the Food Industry. Industry use case example of SMIP and SM Profiles. A manufacturing methodology available for CESMII members for new product development of high-value-added, complex food products, enabled by the new sensors, predictive models, and the newly developed profiles.