Abstract
Today, in modern large-scale industrial processes, each step-in manufacturing produces a bulk of variables, which are highly precise in nature. However, great challenges are faced under different real-time operating conditions when using just the basic data-driven methods. One of the sultriest research points for convoluted process control is the usage of big data analytics. The aim of big data analytics is to take full advantages of the large amounts of obtained process data and mine helpful details present within. Compared to the well-developed model-based approaches, usage of big data analytics provides productive elective answers for various modern issues under different working conditions. Majority of the modelling in process control in a closed loop system is based on varying the command input to obtain desired controlled output. However, modelling of the process control in a closed loop system based on the disturbance using conventional methods is time consuming since disturbance data is too big and too complex. Utilization of advanced big data analytical methods to mine the disturbance data can lead towards more informed decisions to model the process control in the system. Thus, relevant solutions can be obtained to some of the challenges in the modeling of process control using big data analytics.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
