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

我的研究计划旨在为偏远和资源有限的社区推进智能、弹性和强大的可再生能源水处理系统的设计。这项拟议研究计划的长期科学目标是对间歇运行过程中聚合物基膜内和膜上的结垢和污染机理有一个基本的了解，这在可再生能源系统中是常见的。无论水处理技术是间歇运行还是连续运行，这些基本知识对于支持智能、弹性和坚固耐用的水处理技术的工程都是必要的。 该研究计划的重点是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，该研究计划有以下三个短期目标： 1.基于机器学习的膜污染现场表征及预测性维护算法 2.了解间歇运行的可再生水处理系统的结垢和有机物的污染机理及其相互作用，以开发预测和最小化膜污染的工具。 3.开发一种新的智能、弹性和健壮的可再生能源水处理系统设计算法，考虑膜污染和预测性维护，以最大限度地减少膜污染 实现这些目标将对了解间歇运行过程中的膜污染机理、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做出重大贡献。这将代表着朝着设计智能、弹性和强大的可再生能源水处理系统以满足偏远社区的需求迈出了关键的一步。 创新的智能、弹性和坚固的可再生能源系统将适用于偏远社区和快速部署在救灾中。与土著社区建立的伙伴关系将是长期的、协作的，并将侧重于建设当地社区拥有和运作该系统的能力。此外，与土著社区的这些伙伴关系将侧重于将土著了解的方式纳入设计过程，并有助于编写关于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办法的文献。 还将为可靠的水处理系统的智能和优化控制开发新的方法。此外，机器学习方法、人工智能和大数据分析将应用于这一研究计划。优化设计方法也将适用于其他工程应用，如气候变化下的弹性基础设施和智能制造。

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.