Artificial Neural Network Modeling of Solvent-Free Extrusion Emulsification

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Liquid-solid dispersions containing micro- or nano-sized polymer particles are used for a broad range of products from Canadian companies, spanning foods, pharmaceuticals, cosmetics, oil drilling/ recovery, to name a few examples and thus have a major impact on our environment and economy. Few of the polymers used can be dispersed without use of environmentally-harmful solvents. Solvent Free Extrusion Emulsification (SFEE) is a new approach based on greener manufacturing philosophies where any polymer melt of sufficient viscosity can be converted into an aqueous dispersion by making unconventional use of a twin-screw extruder. The approach redesigns the entire manufacturing process, eliminating many downstream unit operations, lowering energy utilization, and avoiding the necessity of harmful solvents. This new process, however, lacks any deep investigation and modeling, with few successful products prepared to date. It currently relies entirely on the skills of highly experienced processors and several years of trial-and-error. Because of its high sensitivity displayed to a great many variables, the process is difficult to control and scale. We are proposing a two-year project to study the implementation of artificial intelligence (AI) techniques to model the manufacturing process, creating a dynamic platform to grow with our accumulated knowledge, and test control schemes with new formulations. AI techniques are valuable in this regards since they do not need a physical mechanism to give good predictions and are known to highlight details in the responses that might not be normally interpreted as significant of the system's mechanism. The goal is to share this model with the company sponsor, with sufficient training to assist in their production needs and to develop new information for the Chemical Engineering community on applying AI techniques to processes. The student working on the project will be involved in studies of AI techniques and industrially relevant machinery, looking at an innovative process and modeling method, and having contact with a Fortune 500 company.