US 11,860,617 B2
Forecasting industrial aging processes with machine learning methods
Nataliya Yakut, Ludwigshafen am Rhein (DE); Simeon Sauer, Heidelberg (DE); Mihail Bogojeski, Berlin (DE); Franziska Horn, Berlin (DE); and Klaus-Robert Mueller, Berlin (DE)
Assigned to Technische Universitaet Berlin, Berlin (DE)
Appl. No. 17/779,737
Filed by BASF SE, Ludwigshafen am Rhein (DE)
PCT Filed Nov. 25, 2020, PCT No. PCT/EP2020/083425
§ 371(c)(1), (2) Date May 25, 2022,
PCT Pub. No. WO2021/105246, PCT Pub. Date Jun. 3, 2021.
Claims priority of application No. 19211393 (EP), filed on Nov. 26, 2019.
Prior Publication US 2023/0028276 A1, Jan. 26, 2023
Int. Cl. G05B 23/02 (2006.01)
CPC G05B 23/0283 (2013.01) [G05B 23/0254 (2013.01)] 17 Claims
OG exemplary drawing
 
1. A computer-implemented method for predicting a progress of degradation of a chemical production plant, comprising:
a) receiving via an input interface currently measured process data indicative of a current process condition for a current operation of at least one chemical process equipment of the chemical production plant, wherein the at least one chemical process equipment has one or more degradation key performance indicators, KPIs, for quantifying the progress of degradation of the at least one chemical process equipment;
b) receiving via the input interface one or more expected operating parameters indicative of a planned operating condition of the at least one chemical process equipment within a prediction horizon;
c) applying by a processor a data driven model to an input dataset comprising the currently measured process data and the one or more expected operating parameters to estimate a future value of the one or more degradation KPIs within the prediction horizon, wherein the data driven model is parametrized or trained according to a training dataset, wherein the training dataset is based on sets of historical data comprising process data and the one or more degradation KPIs; and
d) providing via an output interface the future value of the one or more degradation KPIs within the prediction horizon which is usable for monitoring and/or controlling,
wherein the degradation comprises at least one of:
deactivation of heterogeneous catalysts due to coking, sintering, and/or poisoning;
plugging of a chemical process equipment on process side due to coke layer formation and/or polymerization;
fouling of a heat exchanger on water side due to microbial and/or crystalline deposits; and
erosion of an installed equipment in a fluidized bed reactor.