AI Generative Design Tool for Low-Cost District Heating Networks (Phase 2)

低成本区域供热网络的人工智能生成设计工具（第二阶段）

• 批准号: 10015328
• 金额: $ 57.33万
• 依托单位: CITY SCIENCE CORPORATION LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10015328
• 关键词: AI; Generative; Design; Tool; Low

Our project objective is to build a generative design tool that will automate the optimal design of district heat energy networks. Currently, design is costly and time-consuming and often lacks sufficiently detailed data to ensure system efficiency. In many cases this leads to over-sizing of equipment which increases capital costs which are ultimately passed on to the end consumer or can make projects unviable.By developing a new, cutting-edge, generative design tool our project will accelerate district heat deployment, and enable the UK to meet its 2050 vision of 20% of heating demand being met by heat networks.1) Using an automated, computer-driven process, we will radically reduce the up-front costs of modelling and system sizing;2) Using the latest AI techniques combined with detailed geographic data we will optimise system design to increase the affordability of heat networks and maximise their viability.Our solution will radically reduce the cost of district heat and improve its viability. End consumers will benefit through lower heating costs, warmer homes/buildings and reduced fuel poverty. Taking into account multiple design options will allow for greater focus on zero-carbon fuel sources (including hydrogen) which will promote a new generation of true zero-carbon district heat. End users that stand to benefit include Local Authorities, Major Heat Users, Social Landlords, and suppliers of Heat. Direct users that will benefit from improved productivity and accuracy include Engineering Consultancies, Local Authorities, Network Operators and Suppliers/manufacturers.

我们的项目目标是建立一个生成式设计工具，将自动化的区域热能网络的优化设计。目前，设计成本高，耗时长，而且往往缺乏足够详细的数据来确保系统效率。在许多情况下，这会导致设备尺寸过大，从而增加资本成本，最终转嫁给终端用户，或者使项目无法实施。通过开发一种新的、尖端的、生成式的设计工具，我们的项目将加快区域供热部署，并使英国能够实现其2050年20%的供热需求由热力网络满足的愿景。1）使用自动化、计算机驱动的流程，我们将从根本上降低建模和系统规模的前期成本;2）使用最新的人工智能技术与详细的地理数据相结合，我们将优化系统设计，以提高供热网络的可负担性并最大限度地提高其可行性。我们的解决方案将从根本上降低区域供热的成本并提高其可行性。最终消费者将受益于更低的供暖成本、更温暖的房屋/建筑物和减少燃料贫困。考虑到多种设计方案将允许更多地关注零碳燃料来源（包括氢），这将促进新一代真正的零碳区域供热。受益的最终用户包括地方当局、主要热用户、社会房东和热供应商。直接用户将受益于提高生产力和准确性，包括工程咨询，地方当局，网络运营商和供应商/制造商。