Development of robust estimation and learning techniques for new chemical formulations

Virox和Guelph大学之间拟议的合作将产生关于新化学配方的评估和机器学习战略的开发和实施。一般而言，评估策略利用系统模型和度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，本质上是一种输入-输出映射器，它基于大量数据创建“黑匣子”模型。这些开发的模型和技术将用于根据期望的产品设计限制(如抗菌活性、毒性、腐蚀性、产品保质期、成本等)预测新的化学配方。此外，Virox现有的过氧化氢配方记录库可能会在这一努力中发挥作用。该库包括数以万计的配方和数据点，这些配方和数据点是通过手动测试创建和记录的。此外，拟议的技术可以进行微调，以调查大型化学成分库(如美国环境保护局提供的那些)，并从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提高混合物中过氧化氢的抗微生物活性。Virox和Guelph大学都将从拟议的研究项目和合作中受益匪浅。在更短的时间内开发新配方的能力将降低Virox用于新产品开发的研发成本，并将使其具有更大的经济竞争力。此外，圭尔夫大学团队将开发新的估计和机器学习技术，并将就相关工业项目培训高素质人员(HQP)。

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.