Towards Smart, Resilient & Robust Renewable Powered Water Treatment Systems for Remote Communities

迈向智能、有弹性

• 批准号: RGPIN-2020-06087
• 负责人: FreireGormaly, Marina
• 金额: $ 1.68万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717315
• 关键词: Towards; Smart; Resilient; Robust; Renewable

My research program aims to advance the design of smart, resilient and robust renewable powered water treatment systems for remote and resource-constrained communities. The long-term scientific objective of this proposed research program is to develop a fundamental understanding of scaling and fouling mechanisms in and on polymer-based membranes during intermittent operation which is common of renewable powered systems. This basic knowledge is necessary to support the engineering of smart, resilient and robust water treatment technologies, be they intermittently or continuously operated. Beginning with a focus on improving drinking water systems safety, reliability, and sustainability by developing more efficient methods for their design and operation, the research program has the following three short-term objectives: 1. To characterize membrane fouling in-situ and develop a predictive maintenance algorithm using machine learning 2. To understand the fouling mechanisms by scaling and organics, alone and their interactions for intermittently operated renewable powered water treatment systems to develop tools for predicting and minimizing membrane fouling. 3. To develop a new design algorithm for smart, resilient and robust renewable powered water treatment systems considering membrane fouling and predictive maintenance to minimize membrane fouling Accomplishing these objectives will culminate in significant contributions to knowledge regarding the understanding of membrane fouling mechanisms during intermittent operation, control of membrane fouling using predictive maintenance by relying on machine learning algorithms, and development of a new design method. This will represent a crucial step forward toward the engineering of smart, resilient and robust renewable powered water treatment systems to meet the needs of remote communities. The innovative smart, resilient and robust renewable powered systems will be applicable both for remote communities and for quick deployment in disaster relief. Partnerships developed with Indigenous communities will be long-term, collaborative and will focus on building local capacity for community ownership and operation of the system. As well, these partnerships with Indigenous communities will focus on incorporating Indigenous Ways of Knowing into the design process and contribute to the literature on responding to the calls to action in the Truth & Reconciliation Commission of Canada and approaches to sustainable community capacity building. New methods will also be developed for the smart and optimal control of reliable water treatment systems. As well, machine learning methods, artificial intelligence and big data analytics will be applied in this research program. The optimization and design methods will also be applicable to other engineering applications, such as resilient infrastructure under climate change and smart manufacturing.

我的研究项目旨在为偏远和资源有限的社区推进智能、有弹性和强大的可再生动力水处理系统的设计。该拟议研究计划的长期科学目标是对间歇运行期间聚合物膜内部和表面的结垢和污垢机制有一个基本的了解，这在可再生能源动力系统中很常见。这些基础知识对于支持智能、有弹性和稳健的水处理技术的工程设计是必要的，无论这些技术是间歇运行还是连续运行。 该研究项目首先关注通过开发更有效的设计和运行方法来提高饮用水系统的安全性、可靠性和可持续性，有以下三个短期目标： 1. 原位表征膜污染并使用机器学习开发预测维护算法 2. 了解间歇运行的可再生动力水处理系统中结垢和有机物的单独结垢机制及其相互作用，以开发预测和最大程度减少膜结垢的工具。 3. 为智能、有弹性和稳健的可再生动力水处理系统开发一种新的设计算法，考虑膜污染和预测性维护，以最大限度地减少膜污染 实现这些目标最终将为了解间歇运行期间的膜污染机制、依靠机器学习算法使用预测性维护控制膜污染以及开发新的设计方法等方面的知识做出重大贡献。这将是朝着智能、有弹性和强大的可再生动力水处理系统工程迈出的关键一步，以满足偏远社区的需求。 创新的智能、弹性和强大的可再生能源供电系统将适用于偏远社区和救灾中的快速部署。与原住民社区建立的伙伴关系将是长期的、协作性的，并将重点放在社区所有权和系统运营的当地能力建设上。此外，与原住民社区的这些伙伴关系将侧重于将原住民的认知方式纳入设计过程，并为响应加拿大真相与和解委员会的行动呼吁以及可持续社区能力建设方法做出贡献。 还将开发新方法来对可靠的水处理系统进行智能和优化控制。此外，机器学习方法、人工智能和大数据分析也将应用于该研究项目。优化和设计方法也将适用于其他工程应用，例如气候变化下的弹性基础设施和智能制造。