Statistical Tools for Chemical Process and Product Design and Improvement

数学模型对于通过设计、监测和控制来推进产品和过程非常重要。这些模型在方程的数量、形式和相互联系方面往往很复杂。统计技术对于开发模型、估计参数以及确定我们改进了多少制造性能和产品特性非常重要。工程师和科学家需要统计技术来评估他们估计参数的能力，确定哪些是重要的，检查预测的质量，并设计额外的实验来提高模型性能和对物理的理解。产品改进带来了额外的统计需求，用于分析间接测量的特性（例如，聚合物的强度）或复杂的特性（例如，颗粒形状）。测量和表征产品的仪器的快速发展提供了新的数据种类，对统计分析提出了新的挑战。**该研究计划将开发新的统计工具，用于建立数学模型，探索实验结果，开发产品质量与制造因素相关的模型，并将通过应用于工业和实验室规模的问题来测试这些统计技术的实用性。该研究计划将开发更好的方法来估计时变行为模型的参数，当过程干扰是未知的；开发改进的方法来测试参数估计对模型预测性能的影响；开发改进的方法来调查和模拟随时间和地点变化的过程和产品行为；并开发处理更复杂类型数据的技术，如颗粒/细胞形状、光谱和产品性能曲线。**贡献将是新的统计工具，以支持化学过程和产品的设计，并帮助大学，工业和政府的科学家和工程师从他们的实验中提取更多的信息和理解。************

Mathematical models are important for advancing products and processes through design, monitoring, and control. These models are often complex, in the number, form and interconnectedness of equations. Statistical techniques are important for developing models, estimating parameters, and determining how much we have improved manufacturing performance and product characteristics. Engineers and scientists need statistical techniques to assess how well they can estimate parameters, to identify which are important, to check the quality of predictions, and to design additional experiments that improve model performance and physical understanding. Product improvement brings additional statistical needs for analyzing properties that are measured indirectly (e.g., strength of a polymer), or characteristics that are complex (e.g., particle shapes). Rapid advances in instruments for measuring and characterizing products have provided new kinds of data, with new challenges for statistical analysis. ** The research program will develop new statistical tools for building mathematical models, for exploring experimental results, for developing models relating product qualities to manufacturing factors, and will test the usefulness of these statistical techniques through application to industrial and lab-scale problems. The research program will develop better ways to estimate parameters in models of time-varying behaviour when process disturbances are unknown; develop improved ways to test the impact of parameter estimates on model prediction performance; develop improved ways for investigating and modeling process and product behaviour that changes with time and location; and develop techniques for dealing with more complex types of data such as particle/cell shapes, spectra and product performance curves. ** The contributions will be new statistical tools to support design of chemical processes and products, and to help university, industrial and government scientists and engineers extract more information and understanding from their experiments. ************