Iterative Model Based Experimental Design for Spherical Agglomeration Processes

A research dataset from Purdue University focuses on model-based design for spherical agglomeration, a pharmaceutical manufacturing process. The work involves identifying the best-fitting process model and developing an iterative experimental design strategy to minimize experiments for parameter estimation. The author is Kanjakha Pal, and the dataset is associated with an open-access research paper.

Use Cases

• Selecting optimal process models based on cross-validation experiments described in the paper.
• Applying D-optimal experimental design criteria to minimize the number of required lab experiments.
• Estimating parameters for high-fidelity models of nonlinear crystallization and granulation processes.
• Developing model-based control strategies for process intensification in drug manufacturing.

Strengths

• Focuses on a specific, high-impact process intensification strategy in pharmaceutical manufacturing.
• Describes a methodology for reducing capital and operating costs by merging unit operations.
• The associated research paper provides a clear theoretical framework for model identification and experimental design.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

Purdue University West Lafayette

Collection Method

Likely contains experimental or simulation data from research on spherical agglomeration processes.