Artificial Neural Network Modeling of Solvent-Free Extrusion Emulsification

无溶剂挤出乳化的人工神经网络建模

• 批准号: 538445-2018
• 负责人: Thompson, Michael
• 金额: $ 1.12万
• 依托单位: McMaster University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=675699
• 关键词: Artificial; Neural; Network; Modeling; Solvent

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.

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。我们正在提出一个为期两年的项目，研究人工智能（AI）技术的实施，以模拟制造过程，创建一个动态平台，以我们积累的知识增长，并用新配方测试控制方案。AI技术在这方面是有价值的，因为它们不需要物理机制来提供良好的预测，并且已知可以突出响应中的细节，这些细节通常可能不会被解释为系统机制的重要意义。目标是与公司赞助商分享这个模型，并提供足够的培训，以协助他们的生产需求，并为化学工程社区开发将人工智能技术应用于工艺的新信息。参与该项目的学生将参与人工智能技术和工业相关机械的研究，研究创新的过程和建模方法，并与财富500强公司接触。