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.

含有微或纳米大小的聚合物颗粒的液体固体分散液用于加拿大公司的各种产品，跨越食品，药品，化妆品，油钻/恢复，仅举几个例子，因此对我们的环境和经济产生了重大影响。 不用使用环保溶剂，几乎没有使用的聚合物可以分散。 溶剂不自由挤出乳化（SFEE）是一种基于绿色制造哲学的新方法，任何具有足够粘度的聚合物融化可以通过非常规使用双螺旋式挤出器来转化为水分散体。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，并避免了有害溶剂的必要性。但是，这个新过程缺乏任何深入的研究和建模，迄今为止，很少有成功的产品准备。目前，它完全依赖经验丰富的处理器的技能和数年的反复试验。由于其高灵敏度显示出许多变量，因此很难控制和扩展该过程。 我们提出了一个为期两年的项目，以研究人工智能（AI）技术的实施，以建模制造过程，创建一个动态平台，以我们的累积知识成长，并使用新的配方进行测试控制方案。在此方面，AI技术很有价值，因为它们不需要物理机制来提供良好的预测，并且已知可以在响应中突出细节，这些响应通常不会被解释为系统机制的重要性。目的是与公司赞助商分享该模型，并通过足够的培训来协助其生产需求，并为化学工程社区开发新信息，以将AI技术应用于流程。从事该项目的学生将参与研究AI技术和与工业相关的机械的研究，研究创新过程和建模方法，并与Fortune 500公司联系。