{"id":55,"date":"2013-06-28T16:50:52","date_gmt":"2013-06-28T14:50:52","guid":{"rendered":"http:\/\/www.capelab.dii.unipd.it\/?page_id=55"},"modified":"2025-12-01T11:00:10","modified_gmt":"2025-12-01T10:00:10","slug":"product-design-and-quality-control","status":"publish","type":"page","link":"https:\/\/research.dii.unipd.it\/capelab\/research-2\/product-design-and-quality-control\/","title":{"rendered":"Process modeling and optimization"},"content":{"rendered":"
\r\n
\r\n
\r\n
\r\n \"\"\r\n<\/div>\r\n
\r\n\r\n

\r\n A deep understanding of any physical system can be captured and summarized through a mathematical model<\/strong> \u2014 a powerful tool that enables both simulation<\/strong> and optimization<\/strong> of system behavior. To ensure that such models accurately represent reality, controlled experiments<\/strong> must be conducted to investigate and validate their performance. The reliability<\/strong> of a model depends critically on two factors: the choice of an appropriate model structure<\/strong> and the accurate estimation of its parameters<\/strong>.\r\n <\/p>\r\n\r\n

\r\n However, model development can become a highly complex and resource-intensive task when experiments are not planned with a solid scientific rationale. To address this, CAPE-Lab<\/strong> has developed specialized optimal experiment design<\/strong> techniques<\/strong> that maximize the information extracted from experimental data<\/strong> while minimizing the time and cost<\/strong> required for experimentation.\r\n <\/p>\r\n\r\n

\r\n These optimal design methods<\/strong> (model-based design of experiments) have been successfully applied across a broad range of fields in process<\/strong> and biomedical engineering<\/strong>. Applications include:\r\n <\/p>\r\n\r\n