Data-Driven Process Systems Optimization under Uncertain Environment

不确定环境下数据驱动的流程系统优化

基本信息

  • 批准号:
    RGPIN-2019-04584
  • 负责人:
  • 金额:
    $ 2.4万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2021
  • 资助国家:
    加拿大
  • 起止时间:
    2021-01-01 至 2022-12-31
  • 项目状态:
    已结题

项目摘要

For today's process industries, a solid foundation in data acquisition and storage has been developed based on the established process control and information technologies. While huge volumes of data are generated through daily operations, process industries are moving towards data-driven decision making. The analysis of operations and business data is used to support process, plant, and enterprise-wide optimization initiatives. To achieve this kind of transition, it requires seamlessly merging data analytics and process control aspects with operational optimization of planning and scheduling. The target is to create added value to the industrial process, increase the agility of processes to react to changes simultaneously focusing on energy efficiency and sustainability. In the past few years, we have made achievements in process operations optimization under uncertainty using the robust optimization and stochastic programming techniques. With a large amount of operational data available, it is possible to improve the process and uncertainty model and make more practical decisions towards systems optimization. In this research program, we plan to combine the fields of process optimization and process data analytics by implementing systematic methods for data-driven optimization under uncertainty. Specifically, we will: 1) investigate the data-driven process and uncertainty modeling with the aid of data analytics; 2) develop approaches for data-driven robust and adaptive optimization of complex process systems; 3) develop software tools to enable the automation of modeling and solution of the data-driven optimization problem. The proposed research will be utilized to optimize systems of systems, from a single process unit to an entire plant site. This will provide significant theoretic and computational support that enables managers, operators, and engineers in the process industry to collaborate and work together using real-time data and analysis in an information-driven environment.
对于今天的流程工业来说,基于已建立的过程控制和信息技术,已经在数据采集和存储方面奠定了坚实的基础。虽然海量的数据是通过日常运营产生的,但流程工业正在向数据驱动的决策方向发展。运营和业务数据分析用于支持流程、工厂和企业范围的优化计划。要实现这种过渡,需要将数据分析和流程控制方面与计划和调度的运营优化无缝结合。其目标是为工业流程创造附加值,提高流程的敏捷性,以同时对能源效率和可持续性做出反应。在过去的几年里,我们利用稳健优化和随机规划技术,在不确定条件下的过程操作优化方面取得了一定的成果。有了大量的运行数据,就有可能改进过程和不确定性模型,做出更实际的系统优化决策。在这项研究计划中,我们计划通过实施不确定情况下的数据驱动优化的系统方法,将过程优化和过程数据分析领域结合起来。具体地说,我们将:1)研究数据驱动的过程和借助数据分析的不确定性建模;2)开发数据驱动的稳健和自适应复杂过程系统优化的方法;3)开发软件工具,使数据驱动的优化问题的建模和求解自动化。拟议的研究将被用来优化系统系统,从单个工艺单元到整个工厂现场。这将提供重要的理论和计算支持,使流程行业的经理、操作员和工程师能够在信息驱动的环境中使用实时数据和分析进行协作和工作。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Li, Zukui其他文献

A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: III. Improving the Quality of Robust Solutions.
关于鲁棒对应物优化的比较理论和计算研究:iii。提高强大解决方案的质量。
A new methodology for the general multiparametric mixed-integer linear programming (MILP) problems
A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: II. Probabilistic Guarantees on Constraint Satisfaction.
Robust optimization for process scheduling under uncertainty
A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear Optimization and Robust Mixed Integer Linear Optimization.

Li, Zukui的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Li, Zukui', 18)}}的其他基金

Data-Driven Process Systems Optimization under Uncertain Environment
不确定环境下数据驱动的流程系统优化
  • 批准号:
    RGPIN-2019-04584
  • 财政年份:
    2022
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual
Robust Real-Time Optimization for Refinery Process Operations
炼油厂工艺操作的稳健实时优化
  • 批准号:
    555566-2020
  • 财政年份:
    2021
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Alliance Grants
Robust Real-Time Optimization for Refinery Process Operations
炼油厂工艺操作的稳健实时优化
  • 批准号:
    555566-2020
  • 财政年份:
    2020
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Alliance Grants
Data-Driven Process Systems Optimization under Uncertain Environment
不确定环境下数据驱动的流程系统优化
  • 批准号:
    RGPIN-2019-04584
  • 财政年份:
    2020
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual
Pipeline Operations Optimization using Data-Driven Model
使用数据驱动模型优化管道运营
  • 批准号:
    543444-2019
  • 财政年份:
    2019
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Engage Grants Program
Data-Driven Process Systems Optimization under Uncertain Environment
不确定环境下数据驱动的流程系统优化
  • 批准号:
    RGPIN-2019-04584
  • 财政年份:
    2019
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual
Systematic Management of Uncertainties in Process Operations
流程操作中不确定性的系统管理
  • 批准号:
    435906-2013
  • 财政年份:
    2018
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual
Systematic Management of Uncertainties in Process Operations
流程操作中不确定性的系统管理
  • 批准号:
    435906-2013
  • 财政年份:
    2017
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual
Process modeling and control algorithm development for flow metering valve
流量计量阀的过程建模和控制算法开发
  • 批准号:
    522294-2017
  • 财政年份:
    2017
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Engage Grants Program
Systematic Management of Uncertainties in Process Operations
流程操作中不确定性的系统管理
  • 批准号:
    435906-2013
  • 财政年份:
    2016
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual

相似国自然基金

Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
  • 批准号:
  • 批准年份:
    2024
  • 资助金额:
    万元
  • 项目类别:
    外国青年学者研究基金项目

相似海外基金

Developing a Novel Data-driven Modelling Strategy for Process Flow Diagram Optimisation
开发用于流程图优化的新型数据驱动建模策略
  • 批准号:
    2903759
  • 财政年份:
    2023
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Studentship
Decision support systems based on heterogeneous data driven models for a safe and optimal operation of industrial process systems
基于异构数据驱动模型的决策支持系统,用于工业过程系统的安全和优化运行
  • 批准号:
    RGPIN-2021-02929
  • 财政年份:
    2022
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual
Development of infiltration theory and data-driven process to realize melt-infiltration additive manufacturing
开发渗透理论和数据驱动流程以实现熔体渗透增材制造
  • 批准号:
    22K18285
  • 财政年份:
    2022
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Grant-in-Aid for Challenging Research (Pioneering)
Data-Driven Process Systems Optimization under Uncertain Environment
不确定环境下数据驱动的流程系统优化
  • 批准号:
    RGPIN-2019-04584
  • 财政年份:
    2022
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual
Decision support systems based on heterogeneous data driven models for a safe and optimal operation of industrial process systems
基于异构数据驱动模型的决策支持系统,用于工业过程系统的安全和优化运行
  • 批准号:
    DGDND-2021-02929
  • 财政年份:
    2022
  • 资助金额:
    $ 2.4万
  • 项目类别:
    DND/NSERC Discovery Grant Supplement
Developing Informed-consent Standards through a COmmunity-driven deliberative democracy process (DISCO) for Data to Suppression (D2S)
通过社区驱动的协商民主流程 (DISCO) 制定知情同意标准,以实现数据抑制 (D2S)
  • 批准号:
    10790266
  • 财政年份:
    2021
  • 资助金额:
    $ 2.4万
  • 项目类别:
Decision support systems based on heterogeneous data driven models for a safe and optimal operation of industrial process systems
基于异构数据驱动模型的决策支持系统,用于工业过程系统的安全和优化运行
  • 批准号:
    DGDND-2021-02929
  • 财政年份:
    2021
  • 资助金额:
    $ 2.4万
  • 项目类别:
    DND/NSERC Discovery Grant Supplement
Decision support systems based on heterogeneous data driven models for a safe and optimal operation of industrial process systems
基于异构数据驱动模型的决策支持系统,用于工业过程系统的安全和优化运行
  • 批准号:
    RGPIN-2021-02929
  • 财政年份:
    2021
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Grants Program - Individual
Decision support systems based on heterogeneous data driven models for a safe and optimal operation of industrial process systems
基于异构数据驱动模型的决策支持系统,用于工业过程系统的安全和优化运行
  • 批准号:
    DGECR-2021-00180
  • 财政年份:
    2021
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Launch Supplement
A System Behavioral Approach to Big Data-driven Nonlinear Process Control
大数据驱动的非线性过程控制的系统行为方法
  • 批准号:
    DP210101978
  • 财政年份:
    2021
  • 资助金额:
    $ 2.4万
  • 项目类别:
    Discovery Projects
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了