Building an adaptable biomanufacturing model for use throughout the engineering lifecycle

Challenges

• Lacked understanding of how multiple different data sets work together holistically
• Difficulty visualizing and evaluating the effect of process variances on batch cycle times
• Needed for more efficient CIP allocation and cleaning strategy
• Needed to make more cost-efficient capital investment decisions

This process engineering team lacked a holistic simulation model to analyze how unit operations worked together (or influenced one another) at each planned site. They were encountering difficulties, for example, in visualizing and evaluating the effect of process variances (e.g., processing times, flow rates, and pH adjustments) on batch cycle times. They also needed to find the optimum batch size to minimize delays resulting from bottlenecks at a key unit operation. In later design phases, the team needed to find the right water demands—for different grades of water—to ensure that these utilities were not over-dimensioned (potentially leading to increased capital expenditure and operating costs). Cleaning equipment processes and labor utilization were less than optimal, too. The team sought a way to better allocate CIP units to reduce cleaning equipment costs while ensuring that no processing unit was waiting beyond limitations to be cleaned. In addition, they needed to determine how many operators and resources were necessary to minimize delays for loading docks, room cleaning, and other daily maintenance tasks. These were unresolved questions that INOSIM was brought in to help answer.

Solutions

• Investigated the water distribution system for peak flow rates to robustly dimension pipe sizes
• Implemented shift calendars to test different shift models
• Determined the optimum production wheel schedule for the multi-product plant

The INOSIM team started with a model of the core process for one site, including representations of the equipment, recipes and variable buffers, water, and cleaning, to serve as a conceptual design. The team then extended this model so that it could be used to address new questions introduced by process development in the coming years, such as buffer preparation, utility systems, operator resources, CIP skids, and limited cleaning equipment availability. This modeling progression allowed for a more realistic model to be tailored to fit the other sites, including any site-specific changes, such as resource limitations for generation systems for highly purified water and buffer preparation recipes.

This model was further extended via INOSIM’s integrated scripting engine to implement specific rules for unit allocation and order prioritization. The result was a highly realistic digital twin capable of delivering more accurate KPIs. Shift calendars were also implemented to test different shift models to optimize operating for separate process steps to investigate if all must be run 24/7. These models also allow for the testing of different process equipment to CIP unit assignments to find a good assignment and allocation ratio. Finally, the INOSIM team investigated the water distribution system for peak flow rates to robustly design pipe sizes, considering every automated process step that uses water and adding stochastic water usage in the model for manual operations not related to the process, like room cleaning.

Results

• Saved millions of dollars on investment capital
• Increased capacity by 30%
• Informed more efficient CIP unit allocation and pipe sizing
• Optimized cleaning strategy

With the use of INOSIM simulation software and expert consultants, the customer designed a robust process for the entire biologics production flow. This included the holistic debottlenecking of processes, such as the supporting utility systems and labor requirements. Shift and Production Wheel modeling helped identify the optimal operating schedule, helping to increase capacity by 30% compared to the originally planned capacity output. This saved millions of investment capital dollars on CIP Skids, Pipework, Water Generation System, and Processing Vessels. For example, INOSIM simulations tested different allocation scenarios and showed that the process can run reliably with just 13 CIP units, instead of the 15 allotted based on the original plans. The simulations also revealed that only three-inch pipes were required to deliver maximum flow, resulting in a lower minimum flow, leading to lower energy consumption required for circulation. Based on these simulations, the customer purchased three WFI generators, instead of the four, while implementing a more efficient cleaning strategy that reduced delays by minimizing over-utilized skids. Finally, the implementation of shift calendars showed that some process steps did not need to be run 24/7 to reach the targeted production capacity.