报告人：黄彪，加拿大院士
时 间：5月30日下午2点
地 点：逸夫会议中心中心会议室
内容简介：
Operation of modern process industries is both a costly and technically complex business. It is of practical interest to investigate novel techniques to improve profitability while diligently maintaining environmental compliance. One of the proven approaches for finding solutions to achieve this objective is to develop innovative strategies for advanced monitoring and control of plant operations. Development and implementation of advanced monitoring and control techniques require real-time inference of critical process variables. However, on-line acquisition of such variables may involve difficulties due to the inadequacy of measurement techniques or low reliability of measuring devices. To overcome the shortcomings of traditional instrumentation, predictive inference has been designed to infer critical variables from real-time measurable secondary process variables. Predictive inference has become an emerging technology that has shown great potential in filling in the technological and financial gaps with little or no capital cost required. However, each inference is unique and there is no universal solution to the predictive inference problems. Hence, the novelty is reflected essentially in the solution strategies developed as each application poses its own challenges. Development of predictive inference mainly consists of four steps: 1) modeling, 2) prediction, 3) implementation and 4) monitoring. The main challenges are uncertainties involved in the development of predictive inference including uncertainty in data quality, in model parameters, in reference data and in operating conditions. These challenges call for establishment of a rigorous mathematical framework and practical rules. In this presentation, a general introduction to the main steps involved in predictive inference, mathematical principles behind robust modeling and inference, approaches to dealing with uncertainties and practical implementation is provided. The main challenges are discussed and solution strategies are illustrated through a number of successful industrial applications.
黄彪，加拿大阿尔伯特大学教授，现为加拿大工程院院士，加拿大国家自然科学与工程基金委员会（NSERC）油沙工业工程控制首席教授，阿尔伯塔省iCORE技术创新过程控制工业首席教授，加拿大化学工程学会会士及中国教育部海外长江学者。
黄彪院士于1983年和1986年在北航获得学士和硕士学位，1997年获得加拿大Alberta大学工学博士学位； 2003年7月晋升为Alberta大学教授。
黄彪院士获得过包括德国洪堡学者、加拿大化学工程学会Syncrude革新奖、阿尔伯塔大学McCalla教授奖和Killam教授奖、加拿大石油青年革新者奖（Petro-Canada Young Innovator Award）、《Journal of Process Control》最佳论文奖等多项殊荣。
黄彪院士是世界知名的过程控制专家，研究领域涉及控制器性能评价、系统辨识和过程故障诊断等方面。他在控制器性能评价方面做出了世界性的开创工作，相关成果在化工、石油、采矿和造纸等工业中得到广泛应用。黄彪院士学术成果斐然，目前出版4本专著，发表超过200篇期刊文章和超过140多篇会议论文，现为国际期刊《Control Engineering Practice》副总编，《Journal of Process Control》副编辑，《Canadian Journal of Chemical Engineering》副编辑。