Hybrid digital twins: Incorporating AI to advanced monitoring and optimization of energy recovery systems

混合数字孪生：将人工智能融入能量回收系统的高级监控和优化

• 批准号: 2894768
• 金额: --
• 依托单位: Heriot-Watt University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2894768
• 关键词: Hybrid; digital; twins; Incorporating; AI

The project focuses on studying the dynamic behavior of HEXs networks undergoing fouling. HEXs are traditionally designed in isolation and steady operation but fouling is a dynamic process; it builds up over time, gradually reducing the performance of individual units and networks, and causing a complex set of interactions at the process system level. The way HEXs foul is very sensitive to local conditions (temperature, flow regime or composition), which in practice deviate often, and substantially, from a design target (e.g. start-ups, shutdown, cleaning, formulation changes, bypasses, units far from the network delivering cold/hot streams). This makes history effects, neglected in a traditional design approach, of a critical importance to understand how real energy recovery systems evolve during service. In this project we will investigate how key performance indicators in a HEX network (e.g. fouling resistance, thermal conductivity, rate, thickness) respond to perturbations during the operation of real systems (e.g. properties of heat transfer fluids, operational procedures, varying or compounded fouling mechanisms). We will use advanced statistics (i.e. AI/machine learning) to rigorously characterize the uncertainty of existing digital twins and develop and train new hybrid models to monitor efficiency, predict its evolution (i.e. new deposition/removal models, new model identification and parameter estimation tools) and potentially, respond in real-time with an optimal control strategy. Industry is slowly transitioning from obsolete (steady) models into advanced (dynamic) digital twins to maximize the return of large-scale energy recovery systems. This project will lead the way in creating accurate, more responsive digital twins and reducing tens of thousands of tons of CO2e via intelligent monitoring of HEX networks.

该项目的重点是研究受到污染的HEXs网络的动态行为。HEX传统上设计为隔离和稳定运行，但结垢是一个动态过程;它会随着时间的推移而积累，逐渐降低单个单元和网络的性能，并在工艺系统层面引起一系列复杂的相互作用。HEX结垢的方式对局部条件（温度、流动状态或组成）非常敏感，其在实践中经常且实质上偏离设计目标（例如，启动、关闭、清洁、配方改变、旁路、远离输送冷/热流的网络的单元）。这使得在传统设计方法中被忽视的历史效应对于理解真实的能量回收系统在服务期间如何演变至关重要。在这个项目中，我们将研究如何在HEX网络的关键性能指标（如污垢阻力，热导率，速率，厚度）响应扰动在操作过程中的真实的系统（如传热流体的属性，操作程序，变化或复合污垢机制）。我们将使用先进的统计学（即人工智能/机器学习）来严格表征现有数字孪生的不确定性，并开发和训练新的混合模型来监测效率，预测其演变（即新的沉积/去除模型，新的模型识别和参数估计工具），并可能通过最佳控制策略实时响应。工业正在慢慢从过时的（稳定的）模型过渡到先进的（动态的）数字孪生模型，以最大限度地提高大规模能源回收系统的回报。该项目将通过智能监控HEX网络，在创建准确、响应更快的数字孪生模型和减少数万吨二氧化碳排放方面发挥主导作用。