Smart production technologies that are implemented today have dramatically intensified data generation and collection through networked information-based technologies throughout the chemical industry and other manufacturing enterprises. The data generation and collection are so fast-paced that humans have to rely on computers for consuming as well as processing the data. This, in turn, leads to an ever increasing pace for the development of algorithms and methods to improve process performance and facilitate process monitoring. The algorithms and methods should, at first, be able to extract significant information from the large datasets. Second, they should provide accurate means to reduce process variability and boost performance. Third, they should allow discovery of the underlying process dynamics that can substantially improve decision-making. Finally, steps can then be taken to move towards recommending preemptive actions (preventive decisions that are made before a failure occurs or is even observed).
展开▼
