Artificial Neural Network Modeling of Solvent-Free Extrusion Emulsification

含有微或纳米级聚合物颗粒的液固分散体广泛用于加拿大公司的产品，包括食品、制药、化妆品、石油钻探/采收等，因此对我们的环境和经济产生了重大影响。使用的聚合物很少能在不使用对环境有害的溶剂的情况下分散。无溶剂挤出乳化（SFEE）是一种基于绿色制造理念的新方法，任何足够粘度的聚合物熔体都可以通过非传统的双螺杆挤出机转化为水分散体。该方法重新设计了整个制造过程，消除了许多下游单元操作，降低了能源利用率，避免了有害溶剂的使用。然而，这种新工艺缺乏任何深入的调查和建模，迄今为止准备好的成功产品很少。目前，它完全依赖于经验丰富的处理器的技能和几年的反复试验。由于该方法对许多变量的灵敏度很高，因此该过程难以控制和缩放。

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.