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
Problem Statement
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.
Project Goal
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.
Technical Approach
- 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)
Potential Impact
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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.
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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.
Benefits
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.
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