PROJECT LEAD: Auburn University
PARTNERS: Rayonier Advanced Materials
PROBLEM STATEMENT: The US pulp and paper industry offers huge opportunity for CESMII to develop and deploy SM technologies to significantly improve the energy efficiency and the global competitiveness of the industry
PROJECT GOAL: Radically accelerate the development and adoption of advanced sensors, controls, platforms, and models to enable SM in the US pulp and paper industry. Statistics pattern analysis (SPA) enhanced ML soft sensor (SPA- ML) for entrained air content and the corresponding advanced multi- objective model predictive control (MPC) for defoamer dosing, wash water flow, and washed pulp quality of brownstock washing will be developed, tested and analyzed using data collected from Rayonier Advanced Material Jesup Plant, the largest cellulose specialties manufacturing plant in the world with several sets of brownstock washer lines
TECHNICAL APPROACH: Using SPA framework to build the SPA-ML soft sensor which is then used to enable multi-objective model MPC for brownstock washing and carrying out techno-economic analysis (TEA) to quantify its energy savings and cost benefits.
KEY TASKS AND MILESTONES:
- Develop ML enabled soft sensor algorithm for entrained air content
- Develop multi-objective MPC solution for brownstock washing
- Implementation and optimization at Rayonier
- Techno-economic analysis
- Develop and deploy SM solutions to ALL major processes in pulp and paper manufacturing that are reusable for process modeling and control across a wide range of high-energy consumption manufacturing processes, building on the SM Platform core technologies.
- Train several Ph.D. students on SM in the areas of advanced ML sensing, data analytics, and model-based control and optimization of pulp and paper processes in close collaboration with our industry partners throughout this project
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