The increasing demand for protein-rich products coupled with a growing interest in resource efficiency and sustainability make the processing of whey from cheese and yogurt production nowadays highly promising. Whey, long considered a low-value by-product, can be transformed into value products in a number of ways that significantly boost the dairy industry’s environmental and economic bottom line.
This use case describes the application of INOSIM Simulation Software to a facility for processing whey from various pre-processes into the main products protein and lactose. In the project, an existing plant of the customer was modeled. The model is used in production and capacity planning, as well as for the evaluation of measures for capacity expansion.
While whey processing offers numerous benefits, there are several challenges associated with extracting protein and lactose from whey. A major factor is the inconsistency of raw material composition. Parameters such as dry mass content can vary significantly between suppliers, making flexible and easily adaptable process control an absolute must.
Furthermore, the customer faces complexities caused by quality standards (Organic, Halal, Conventional), multi-product production, material expiry, utility shortages and a high failure rate of equipment. Taking these complex yet essential dependencies into account led to considerable difficulties in earlier approaches to modeling the process with other simulation software. However, the customer saw the urgent need to enhance production and capacity planning, as the conventional approach was time-consuming and inflexible, making it impossible to respond quickly to changes in the supply chain or unplanned equipment downtime.
With the help of targeted support from INOSIM experts, the customer created a tailored model for the entire whey processing plant. In addition to the main process, parts of the supply chain, the development of product inventory, important utilities such as chilled water, and the CIP infrastructure are considered. The model accounts for varying raw material parameters and target product concentrations with the ability to automatically change production recipes (e. g., one-stage concentration vs. two-stage concentration) and adjust process parameters (e. g., ultrafiltration membrane flow or concentration settings). Heuristics for pooling raw materials and for a priority system in batch schedules are adapted from real plant operations and implemented in the model to minimize issues with material expiry. Input data, such as raw material supply lists and product inventory, are automatically read in via an IT/OT interface.
The model user can optionally interfere and make manual adjustments to the dedication of equipment and the target production planning. Thus, the model user is enabled to quickly react to equipment breakdown or short-term changes in the supply chain. In addition to the built-in INOSIM visualizations, interactive dashboards were created using the modern business intelligence solution Salesforce Tableau. These dashboards are fed and distributed automatically via INOSIM BICON, ensuring all relevant stakeholders are always up-to-date.
During the project, other departments became interested in the simulation model. This gave rise to the idea of supplementing the model with future plant expansions from various early-stage engineering projects to evaluate different scenarios and better assess their impact on capacity.
The new INOSIM model intuitively visualizes the production and enables the customer to foresee bottlenecks due to equipment allocation, utility shortages or supply chain issues. The ability to simulate sudden equipment failures, anticipate the effect on production and make adjustments in planning as needed has improved the robustness of crisis management significantly. Due to the more accurate prediction of the processes and the ability to react quickly to uncertainties, material expiry issues and the development of inventory can be foreseen much more reliably. As a result, communication with logistics has been sustainably improved.
The model has been applied in decision-making in several engineering projects. For example, when planning a plant expansion, the model was used to evaluate the increased load on the chilled water system and CIP structures. Subsequently, the simulation results were used to assess scenarios for expanding the chilled water and CIP infrastructure, leading to a cost-effective and robust design.
During local business hours
Germany +49 231 97 00 250
USA +1 214 663 3101
India +91 9766 331 092