*2017 IFAC World Congress workshop on:* *Process Data Analytics* *Speakers:* Tongwen Chen, Biao Huang, Sirish L. Shah, Nina Thornhill and Jiandong Wang *For more details and registration visit link at:* https://www.ifac2017.org/workshops-and-tutorials *Time: Sunday, 9th July 2017; 9:00-17:30 * Workshop outline Process data analytic methods rely on the notion of sensor fusion whereby data from many sensors and alarm tags are combined with process information, such as physical connectivity of process units, to give a holistic picture of health of an integrated plant. The fusion of information from such disparate sources of data is the key step in devising methodologies for a smart strategy for process data analytics In the context of the application of analytics in the process industry, the objective in this workshop is to introduce participants to tools, techniques and a framework for seamless integration of information from process and alarm databases complemented with process connectivity information. The discovery of information from such diverse and complex data sources can be subsequently used for process and performance monitoring including alarm rationalization, root cause diagnosis of process faults, hazard and operability (Hazop) analysis, safe and optimal process operation. Such multivariate process data analytics involves information extraction from routine process data, that is typically non-categorical (as in numerical process data from sensors), plus categorical (or non-numerical or qualitative and binary) data from Alarm and Event (A&E) logs combined with process connectivity or topology information that can be inferred from the data through causality analysis or as obtained from piping and instrument diagrams of a process. The later refers to the capture of material flow streams in process units as well information flow-paths in the process due to control loops. *Target audience: *The intended audience for this workshop would be industrial practitioners of control including vendors working in the area of on-line data logging and archiving, graduate students with interests in statistical learning and data science and academics. *Workshop Program* The following topics will be discussed in this workshop. *Each topic will be accompanied by one or more industrial case study to convey the utilitarian value of the learning, discovery and diagnosis from process data.* · Overview of the broad analytics area with emphasis on its use in the process industry. · Basic definitions and introduction to supervised and unsupervised learning: simple regression, classification and clustering. · Data visualization methods; examination and analysis of data in a multivariate framework (in the temporal as well as the spectral domains). · Data quality assessment: Outlier detection; filtering and data segmentation. · Elements of statistical inference and learning including Bayesian methods. · Multivariate methods for data analysis: SVD, PCA, PLS, SVR. · Case studies on nearest neighbour methods for multivariate detection and diagnosis of transient disturbances. · Alarm data analysis: Detection and removal of nuisance alarms; root-cause analysis of alarms and alarm floods. · Data-based causality analysis for identification of process topology. · Future areas to explore in the use of statistical learning, data science and analytics for improved process operation.