报告人：新加坡国立大学王笑楠助理教授
地点：实验十五楼207
题目：Smart Energy, Smart Manufacturing, Smart Decision Making
报告时间：4月25日（周四）14:00-15:30
报告摘要:
The grand challenges facing human societies are all closely interconnected with the sustainable provisioning of energy, water, and material resources for constantly growing and developing populations, as well as the subsequent processing and management of wastes and pollutions. In this talk, Dr Xiaonan Wang will first introduce the decision-making platform her team developed combining comprehensive database, agent based simulation, and resource technology network optimization, to deal with the challenges ofenergy, water, food, and waste systems through a nexus manner for a circular economy. As key components of smart city development strategies, the open-source, data-driven, systematic tools and models can simulate different constraints and targets seamlessly with respect to environmental, economic and social costs and benefits in a bottom-up approach to inform infrastructure planning, investment and decision making for city regions globally.
From macro-level down to detailed systems, smart energy technologies, operational strategies, and market design enhanced by AI, IoT, and Blockchain technologies will be discussed. Current energy and manufacturing systems are facing a revolutionary transformation from both supply and demand side. Compared with traditional operation of urban systems, the on-line process that combines real-time prediction and optimization, model predictive control, demand-responsive scheme and other novel decentralized market strategies will improve energy and environment systems performance. The recent rapid development of AI and blockchain technologies has brought tremendous new opportunities to many fundamental domains, which will also be discussed in this talk. Blockchain and smart contract for hybrid renewable energy systems and demand management are developed. Moreover, the smart systems engineering research in advanced manufacturing and healthcare is also briefly presented. It is promising to take advantage of data and machine intelligence to open new research directions in many frontier fields supported by the presented results.
报告人介绍:
王笑楠博士是新加坡国立大学助理教授（Assistant Professor），博士生导师，智慧系统工程研究中心创始人，领导十余人的国际团队开展基于人工智能和优化控制的智慧城市，能源，材料项目研究。其团队在多能源系统综合利用，城市能源互联网和储能系统优化，废物回收产能等领域有一系列高被引论文和开源软件产出。此外作为系统工程领域资深研究员，与政府和工业界（如美国加州电网，英国国际发展署，及全球各大制药公司）紧密合作，担任顾问。同时领导新加坡最大的材料基因组加速开发计划（Accelerated Materials Development programme），总研究经费超一亿人民币。
王笑楠博士于2011年获得清华大学化学工程与工业生物过程本科学士学位，受加州大学系统硕博连读全额奖学金资助于2012年和2015年取得加州大学戴维斯分校化学工程与控制科学的硕士和博士学位，多次获得全美控制会议（ACC）最佳报告奖，博士论文获得UC Davis工程学院最佳论文提名。2015-2017年在帝国理工学院系统工程研究中心从事博士后研究，同时担任未来能源实验室讲师。与合作教授Nilay Shah院士的团队一同开发了用于大型城市系统（包括水，能源和其他经济资源）的开源优化计算平台，利用多智能体模型和资源技术优化网络向政府和规划部门提供环境和经济最优化的解决方案，与全球多个研究中心和政府机构合作，将模型运用于中、英、新、澳和非洲部分区域，促进智能城市的发展。王笑楠博士作为美国化学工程师学会(AICHE) senior member和电气电子工程师学会(IEEE) member多年担任核心期刊（如Applied Energy, Processes）客座编委，二十余期刊审稿人和多个国际会议组织者、主席。受邀在国际会议上作30余次学术报告，获得IChemE Global Awards -Young Researcher finalist奖项。