	US 12,462,901 B2
	Method and system for performance optimization of flue gas desulphurization (FGD) unit
Rajan Kumar, Pune (IN); Pallavi Venugopal Minimol, Pune (IN); Sagar Srinivas Sakhinana, Pune (IN); Abhishek Baikadi, Pune (IN); Duc Doan, Tokyo (JP); Vishnu Swaroopji Masampally, Pune (IN); and Venkataramana Runkana, Pune (IN)
Assigned to Tata Consultancy Services Limited, Mumbai (IN)
Appl. No. 17/597,133
Filed by Tata Consultancy Services Limited, Mumbai (IN)
PCT Filed Jun. 27, 2020, PCT No. PCT/IN2020/050558 § 371(c)(1), (2) Date Dec. 27, 2021, PCT Pub. No. WO2020/261300, PCT Pub. Date Dec. 30, 2020.
Claims priority of application No. 201921025745 (IN), filed on Jun. 27, 2019.
Prior Publication US 2022/0246248 A1, Aug. 4, 2022
Int. Cl. G16C 20/10 (2019.01); G05B 13/04 (2006.01); G16C 20/70 (2019.01)

CPC G16C 20/10 (2019.02) [G05B 13/048 (2013.01); G16C 20/70 (2019.02)]

15 Claims

1. A processor implemented method for optimization of a Flue Gas Desulphurization (FGD) process, comprising:

collecting a plurality of plant data using a plurality of sensors, from a FGD process being monitored, as input data, via one or more hardware processors, wherein the plurality of plant data comprises real-time and non-real-time data;

pre-processing the input data, comprising removing one or more unwanted components from the input data via the one or more hardware processors;

performing a dimensionality reduction on pre-processed input data, via the one or more hardware processors, comprising:

performing a feature selection, wherein the feature selection comprising identifying a plurality of features affecting each of a plurality of Key Performance Indicators (KPIs) of the FGD process, from the pre-processed input data, wherein the plurality of KPIs are a plurality of Process Variables (PVs) representing a running state of the FGD plant and in turn the FGD process being monitored, and wherein the plurality of PVs comprise chimney inlet Sulphur dioxide (SO₂) concentration, absorption tower level, absorption tower pH, booster upper fan power consumption, limestone slurry concentration inside tower, and gypsum conversion; and

extracting the plurality of features by performing a feature extraction;

generating a plurality of predictive models based on the plurality of features extracted, for each KPI, via the one or more hardware processors;

selecting one of the plurality of predictive models as a predictive model for processing the input data, via the one or more hardware processors;

compensating for one or more unmeasured parameters of the selected predictive model using information generated using one or more soft-sensors, via the one or more hardware processors, wherein the one or more unmeasured parameters comprise one or more PVs among the plurality of PVs;

performing the optimization of the FGD process, via the one or more hardware processors, comprising:

simulating the operation of the FGD process using the selected predictive model, comprising predicting a plurality of FGD process parameters by the selected predictive model;

estimating a plurality of optimal set points of operation, in real time, from the predicted plurality of FGD process parameters;

determining at least one performance lapse in the FGD process, based on the estimated plurality of optimal set points of operation; and

generating at least one recommendation to optimize the FGD process, in response to the determined at least one performance lapse, wherein the at least one recommendation comprise an optimal set points of a plurality of Manipulated Variables (MVs) of the FGD plant, wherein the plurality of MVs comprise limestone slurry flow rate, limestone slurry concentration, air flow for oxidation, gypsum purging, number of recirculation pumps, and elevation of spray for the pumps, and wherein the plurality of MVs are adjusted by a control system of the FGD plant to bring desirable effects in the plurality of PVs;

continuously pushing the optimal set points of the plurality of MVs into the FGD plant at predefined time intervals to optimize operation of the FGD process; and

detecting deviation in working of one or more equipment in the FGD plant by comparing the selected predictive model with one or more working models of the one or more equipment, wherein upon detecting deviation an alarm is generated to alert a user about a potential equipment failure.