Autonomous Estimation and Control for Stochastic Nonlinear Chemical and Biological Processes

随机非线性化学和生物过程的自主估计和控制

• 批准号: 341778-2012
• 负责人: Gopaluni, Bhushan
• 金额: $ 2.11万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=572364
• 关键词: Autonomous; Estimation; Control; Stochastic; Nonlinear

The ever increasing energy, environment and quality demands on Canadian chemical and biological industries are subjecting them to a high level of economic pressure. Hence, increasing the all-round efficiency of these industries is vital for their economic well-being. While a variety of factors determine their efficiency, in this project we will focus on development of novel methods to improve efficiency through a mechanism of adaptive information processing. In particular, the objectives of this project are (1) to develop a mechanism for online adaptive modeling of nonlinear processes, (2) to develop a strategy for adaptive experiment design and (3) to use the adaptive modeling mechanism for generating actionable information. A lot of information, in the form of data and a priori knowledge, is available from chemical and biological processes. This data are often generated through batch experiments and processed in a batch fashion to capture them in the form of models. The data are also used to identify discrepancies between the model and the measurements. However, industrial processes are often changing and batch processing of data is suboptimal. In this project, we will develop adaptive data processing techniques. In particular, nonlinear, noisy, and time varying nature of real processes will be accounted for in the mechanisms developed. The developed methods will enable Canadian economic drivers such as oil & gas, pulp & paper, and biotech industries to operate their processes at optimal level. The project will also help us train highly qualified personnel in an important field of engineering.

加拿大化工和生物工业对能源、环境和质量的要求不断提高，使它们面临着很大的经济压力。因此，提高这些行业的综合效率对它们的经济健康至关重要。虽然多种因素决定了它们的效率，但在这个项目中，我们将专注于开发新的方法，通过自适应信息处理机制来提高效率。具体地说，该项目的目标是(1)开发一种对非线性过程进行在线自适应建模的机制，(2)开发一种自适应实验设计策略，以及(3)使用自适应建模机制来生成可操作的信息。 许多信息，以数据和先验知识的形式，可以从化学和生物过程中获得。这些数据通常是通过批量实验生成的，并以批量方式进行处理，以模型的形式捕获它们。这些数据也被用来确定模型和测量之间的差异。然而，工业过程经常在变化，数据的批处理不是最优的。在这个项目中，我们将开发自适应数据处理技术。特别是，实际过程的非线性、噪声和时变特性将在所开发的机制中考虑在内。 开发的方法将使加拿大的经济驱动力，如石油和天然气、纸浆和造纸以及生物技术行业，能够以最佳水平运行其流程。该项目还将帮助我们在一个重要的工程领域培养高素质的人才。