Abstract: State-of-the-art systems used for plant monitoring and optimization fail to efficiently monitor and improve the performance of blast furnace ironmaking plants due to complexity of such plants. In addition, they attempt optimization without considering the operational stability of the blast furnace. The disclosure herein generally relates to industrial plant monitoring, and, more particularly, to a method and system for ironmaking plant optimization. The system determines an operational stability of the plant in terms of value of a determined Blast Furnace Stability Index (BFSI). Further, if the BFSI or one or more Key Performance Indicators (KPIs) of the plant deviates from corresponding threshold, then the optimization is done.

Abstract: This disclosure relates generally to for time lag identification in an industry. The disclosure proposes to monitor an industry continuously at real time to identify one or more parameters from plurality of sources (processes/units/plants) and a time delay or delayed performance or functional impact the identified parameter has on a plurality of Key Performance Indicator (KPI). The proposed time lag identification is performed using one-time lag identification from the proposed plurality of time lag identification techniques that include an individual time lag identification technique, a group-wise time lag identification technique and group-wise/individual time lag identification technique. Further the time lag identification is performed based on domain knowledge as well as data driven techniques.

Abstract: This disclosure relates generally to identification and analysis of regime shift. The identification and analysis of the regime shift includes regime shift identification (RSI), root cause analysis of the identified regime shift and a recommendation unit to rectify the identified regime shift. The disclosure proposes to monitor a system continuously to identify a regime shift at real-time as presence of regime shifts in any system decreases quality of process and products and makes the system less efficient. The regime shift is identified at real-time based on key performance indicators (KPIs), a set of relevant features and real time input data using machine learning techniques. Further the disclosure also proposes techniques for detecting at least one root cause for the identified regime shift and also recommends a rectification action to rectify the identified regime shift based on optimization techniques.