Development of robust estimation and learning techniques for new chemical formulations

开发新化学配方的稳健估计和学习技术

• 批准号: 520258-2017
• 负责人: Gadsden, StephenAndrew
• 金额: $ 7.29万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=651317
• 关键词: Development; robust; estimation; learning; techniques

The proposed collaboration between Virox and the University of Guelph will yield the development and implementation of estimation and machine learning strategies on novel chemical formulations. Generally speaking, estimation strategies make use of system models and measurements to predict system states and outcomes. Machine learning methods, also known as artificial neural networks, are essentially input-output mappers that create 'black box' models based on large sets of data. These developed models and techniques will be used to predict new chemical formulations based on desired product design constraints (such as antimicrobial activity, toxicity, corrosion, product shelf life, cost, and others). Additionally, Virox's existing library of peroxide formulation records may be leveraged in this endeavor. The library includes tens of thousands of formulations and data points that have been created and recorded through manual testing. Furthermore, the proposed techniques could be fine-tuned to survey large libraries of chemical ingredients (such as those available with the US Environmental Protection Agency), and theoretically isolate further inert chemical additives that could synergistically increase the antimicrobial activity of hydrogen peroxide in blends. Both Virox and the University of Guelph will greatly benefit from the proposed research project and collaboration. The ability to develop novel formulations in a shorter period of time will reduce Virox's R&D costs for new product development, and will enable greater economic competitiveness. Furthermore, new estimation and machine learning techniques will be developed by the University of Guelph team, and highly qualified personnel (HQP) will be trained on a relevant industrial project.

Virox和圭尔夫大学之间拟议的合作将产生新的化学制剂的估计和机器学习策略的开发和实施。一般来说，估计策略利用系统模型和测量来预测系统状态和结果。机器学习方法，也被称为人工神经网络，本质上是输入输出映射器，基于大量数据创建“黑匣子”模型。这些开发的模型和技术将用于根据所需的产品设计约束（如抗菌活性，毒性，腐蚀，产品保质期，成本等）预测新的化学配方。此外，Virox现有的过氧化物制剂记录库可用于此奋进。该库包括通过手动测试创建和记录的数万个配方和数据点。此外，可以对所提出的技术进行微调，以调查大型化学成分库（例如美国环境保护局提供的那些），并在理论上分离出进一步的惰性化学添加剂，这些添加剂可以协同增加混合物中过氧化氢的抗微生物活性。Virox和圭尔夫大学都将从拟议的研究项目和合作中受益匪浅。在较短时间内开发新配方的能力将降低Virox新产品开发的研发成本，并将提高经济竞争力。此外，圭尔夫大学团队将开发新的估计和机器学习技术，并将对相关工业项目的高素质人员（HQP）进行培训。