Data Centric Protein Extraction Process Optimization

Proteins are used in many products of our day-to-day life. The extraction and isolation of proteins from the raw source is a complex process in which every stage has multiple controlling parameters that need to be monitored and optimized to achieve predicted quality, functionality, and yield. The performance of the extraction process depends on the physical and chemical nature of the raw material and the equipment used. Currently, the trial and error method is used to optimize this process which results in wasted resources and time. The objective of this research is to develop machine learning models to predict the optimal operating conditions. The historical protein extraction process data will be used to develop the predictive models. Few of the challenges associated with this project are feature engineering, data structuring and organization, feature selection, and targeted data collection.

Faculty Supervisor:

Abdul Bais

Student:

Partner:

KeyLeaf

Discipline:

Engineering

Sector:

Agriculture; Professional, scientific and technical services

University:

University of Regina

Program: