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National Labs explore energy-efficient solutions for paper industry

Researchers at two national laboratories are developing new computer simulations that could help papermakers improve energy efficiency and cut energy costs. The project is part of the Department of Energy’s HPC4Mfg initiative, a multi-lab effort to use high-performance computing to address complex challenges in US manufacturing. Project outcomes could have significant benefits; the papermaking industry ranks third among the nation’s largest energy users, behind only petroleum-refining and chemical production, according to the US Energy Information Administration.

Working together
According to press information from Lawrence Berkeley National Laboratory (LBNL), researchers at Lawrence Livermore National Laboratory (LLNL) and LBNL are using the national labs’ supercomputing capabilities to look at more energy efficient and cost-saving ways to make paper, targeting wet-pressing—where water is removed by mechanical pressure from the wood pulp into the press felts before drying. The researchers hope to develop a model for flow and deformation of the wet porous paper during the process, saving both energy and money.

A computer simulation framework developed at LLNL, and a full-scale microscale flow model, developed at Berkeley Lab, are being used to model the complex pore structures in paper manufacturing with the goal of reducing energy use by 20 percent in the next 3 years. Credit: David Trebotich, Lawrence Berkeley National Laboratory

The project is one of the seedlings for the DoE’s HPC4Mfg initiative, which is headed by LLNL. “The major purpose is to leverage our advanced simulation capabilities, high performance computing (HPC) resources and industry paper press data to help develop integrated models to accurately simulate the water papering process,” said Yue Hao, an LLNL scientist and a co-principal investigator on the project. “If we can increase the paper dryness after pressing and before the drying (stage), that would provide the opportunity for the paper industry to save energy.”

The team recently released its final report on the first phase of the pilot project for the Agenda 2020 Technology Alliance, a consortium of paper manufacturers with a roadmap to reduce their energy usage by 20 percent by 2020. Hao said if manufacturers could increase the paper’s dryness by 10-15 percent, it would save paper manufacturers up to 20 percent of the energy used in the drying—up to 80 trillion BTUs (thermal energy units) per year and as much as US$250 million for the industry annually.

Hao admits that improving the dewatering process is no easy task. Yet by leveraging the DOE national laboratories’ advanced simulation capabilities and HPC resources, along with sufficient experimental measurements and paper machine data, researchers feel confident they can develop the computational models needed to optimize paper press processes and achieve the goals set by Agenda 2020.

“The scientific challenge is that we need to develop a fundamental understanding of how water flows and migrates,” Hao said. “All the physical phenomena involved make this problem a tough one because the dewatering process isn’t fully understood due to a lack of sufficient data. This is a collective effort and we really need every piece of the contribution.”

LLNL developed the simulation framework integrating mechanical deformation and two-phase flow models, while LBNL developed a full-scale microscale flow model for the complex pore structures in the press felts utilizing sophisticated modeling capabilities.

From lab to industry
“This was true ‘HPC for manufacturing,’” said David Trebotich, a computational scientist in the Computational Research Division at LBNL and co-principal investigator on the project. “We used 50,000-60,000 cores at NERSC (National Energy Research Scientific Computing Center) to do these simulations. It’s one thing to take a research code and tune it for a specific application, but it’s another thing to make it effective for industry purposes. Through this project, we have been able to help engineering-scale models be more accurate by informing better parameterizations from micro-scale data.”

Researchers said in order to create a more accurate and reliable computational model and develop a better understanding of a complex phenomenon, they would need to acquire more complete data from the industry such as paper material properties, high-resolution micro-CT images of paper and experimental data derived from scientifically-controlled dewatering tests.
The study was conducted with funding from the DoE’s Advanced Manufacturing Office within the Energy Efficiency and Renewable Energy Office. Other researchers on the project include Wei Wang of Lawrence Livermore and Jun Xu and David Turpin of Agenda 2020.


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