Nordic Sugar in Nakskov has identified an optimization opportunity by adopting a more data-driven approach to their crystallization equipment. As part of the project, Nordic Sugar and Neurospace formed a task force to explore the possibility of using machine learning to provide data-based feedback to the operator and thereby reduce energy consumption in production.
Facts about the company
Nordic Sugar A/S was founded back in 1883 and is today part of the Nordzucker group. The factory plays a central role in advancing a greener agenda in sugar production in Europe and has a strategic goal of reducing its emissions by 70% by 2030.
Summary
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Nordic Sugar in Nakskov aims to reduce energy consumption and thus its environmental footprint.
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Crystallization equipment in sugar production is a major energy consumer, as the process is carried out in batches, and it is difficult to reuse the residual heat.
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Nordic Sugar identified untapped potential in the fact that the cycle time around the crystallization equipment was often not fully utilized.
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The goal is to use data to predict the optimal time to switch between high and low-pressure steam as soon as the model has gathered sufficient data under the specific boil.
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A potential increase between 7% and 43% in stage 5 steam (low-pressure steam) has been estimated.
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It is still possible to scale to new, more efficient, crystallization equipment and to adjust the model with minimal effort. Modeling is expected to improve across all devices the more devices it scales to.
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The model was implemented in January 2024, and it has already had a positive impact on production.
Basic knowledge about crystallization equipment
“Crystallization equipment in sugar production is used to concentrate the sugar solution under pressure and temperature. The vacuum allows the water in the sugar solution to evaporate at lower temperatures, so the sugar is not damaged. The resulting concentrated sugar solution then undergoes the crystallization process, where sugar crystals are formed.
The steam used in the crystallization equipment is recycled from previous production stages where higher temperature steam is needed. Therefore, the energy needs of the crystallization equipment are covered by recycled residual heat from evaporation (stage 4 and stage 5) at the factory. The steam can no longer be used, as the cooling of the steam itself is used to create the vacuum inside the equipment.
By reusing steam from previous processes, the factory’s overall energy efficiency is increased, thus strengthening Nordic Sugar’s goal of greener sugar production”
The Project
Nordic Sugar’s production runs 24/7 from the first beet being dug up to the last one being turned into sugar. This period is also described as a “campaign.” Production should be seen as a single-line system, and each step in the production has a cycle time that must be adhered to. When production runs around the clock, it is essential that all cycle times are met to ensure that there are no disruptions in production.
Operators in production have identified untapped potential in the fact that the cycle time around the crystallization equipment was often not fully utilized. This is because the sugar solution often boils faster than the 3-hour cycle time for A-product. In practice, this meant that too much stage 4 steam (high energy) was used, and there was potential to use more stage 5 steam (low energy) to fully utilize the cycle time with lower power.
Nordic Sugar and Neurospace held a kickoff meeting where the challenge was highlighted, and knowledge sharing was primarily aimed towards Neurospace to ensure that the necessary domain knowledge was present to be able to start working on the project. There was no single answer to how the task should be solved. However, it turned out that it was important to involve domain experts, who in this case were the operators and relevant technicians. The experts could provide insight into how production was carried out and which factors were important to consider. Based on this knowledge and data from previous production campaigns, a machine learning model was developed to predict when it was necessary to switch from stage 5 steam to stage 4 steam.
Results
The model was implemented in January 2024, and it has already had a positive impact on production. In the first two test campaigns, the estimated optimization potential ranged from 7% to 43%. In addition to reducing energy consumption, the project has also had a positive impact on efficiency and steam balance at the factory.
| Optimization potential on test pan measured per campaign | ||
|---|---|---|
| Increase in stage 5 steam | 7% | 43% |
| Power requirement | 6.349 kWh | 39.004 kWh |
| Diesel | 634 liter | 3897 liter |
| CO2 | 1690 kg | 10.385 kg |
The potential should be seen as a dynamic solution as it depends on various factors such as the size of the equipment, type, piping, and the operator’s chosen switching time. This was also the reason why the OWLS project was chosen, as it was intended to be possible to scale the model to all crystallization equipment regardless of type and location.
Scaling potential
The OWLS project was given priority over other machine learning projects as it would provide a basic foundation so that the model only needs fine-tuning before it can be implemented on untested crystallization equipment in the rest of the factory and the group. There was also a focus on the fact that if Nordic Sugar were to upgrade their current crystallization equipment to more energy-efficient models, it would still be possible to use the OWLS project without starting over