# US 2019/0317459 A1 · Predictive reactor effluent air cooler maintenance > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2019/0317459 A1 - Document ID: US20190317459A1 - Status: Published application - Kind: A1 - Application number: 15/952,654 - Filed: 2018-04-13 - Published: 2019-10-17 - Earliest claimed: 2018-04-13 - Assignees: Honeywell International Inc. - Applicants: Honeywell International Inc. - Inventors: SOUMENDRA MOHAN BANERJEE; KISHEN MANJUNATH - Web page: https://patentgrep.com/patent/US20190317459A1 ## CPC - G05B 13/048: {using a predictor} - G05B 23/0283: {Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] (preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06)} - G05B 13/0285: {using neural networks and fuzzy logic} - G06Q 10/20: Administration of product repair or maintenance - G06Q 50/06: Energy or water supply - B01D 53/14: by absorption - G06F 30/00: Computer-aided design [CAD] - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - B01D: SEPARATION (separating solids from solids by wet methods B03B, B03D; by pneumatic jigs or tables B03B; by other dry methods B07; magnetic or electrostatic separation of solid materials from solid materials or fluids, separation by high-voltage electric fields B03C; centrifuges, vortex apparatus B04; presses per se for squeezing-out liquid from liquid-containing material B30B9/02; treatment of water C02F, e.g. softening by ion-exchange C02F1/42; {arrangements of air intake cleaners in gas turbine plants F02C7/05} ; arrangements or mounting of filters in air-conditioning, air-humidification or ventilation F24F13/28) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method of increasing reliability for a reactor effluent air cooler (REAC). The method includes providing a process facility computer communicatively coupled to at least one REAC including an air condenser with a plurality of field devices coupled thereto. The process facility computer includes a processor connected to a memory device storing a REAC predictive maintenance model. The REAC predictive maintenance model implements retrieving history data including at least one historical REAC value from the memory and receiving real-time data from the field devices including at least one real-time REAC value. A digital twin calculates a current reliability value for the REAC using the historical REAC data and the real-time REAC value. The method further includes comparing the current reliability value to a predetermined reliability value and generating an alert indicating that the REAC needs current maintenance whenever the current reliability value is less than the predetermined reliability value. ## Claims ### Claim 1 A method of increasing reliability for a reactor effluent air cooler (REAC), comprising: providing a process facility computer communicatively coupled to at least one REAC including an air condenser with a plurality of field devices coupled thereto in an industrial facility configured to run an industrial process, said process facility computer including a processor connected to a memory device storing a REAC predictive maintenance model comprising a digital twin of said REAC and a REAC adaptive model, said REAC predictive maintenance model implementing: retrieving history data including at least one historical REAC value from at least one of said memory device and a cloud computing-based data historian; receiving real time data from said plurality of field devices including at least one real-time REAC value, said digital twin calculating a current reliability value for said REAC using said history data including said historical REAC value and said real time REAC value; comparing said current reliability value to a predetermined reliability value, and generating an alert indicating that said REAC needs current maintenance whenever said current reliability value is less than said predetermined reliability value. ### Claim 2 (depends on 1) The method of claim 1, wherein said digital twin is created using said historical REAC value, an isometric layout and air condenser design mechanical details of said REAC. ### Claim 3 (depends on 1) The method of claim 1, wherein said process facility computer further executes: identifying at least one process parameter change to said industrial process to increase reliability of said REAC; and transmitting a signal to implement said process parameter change. ### Claim 4 (depends on 1) The method of claim 1, wherein said real time data includes at least one of: effluent pressure data; effluent flow rate data; water flow rate data; effluent temperature data; hydrogen sulfide concentration data; ammonia concentration data; and air temperature data. ### Claim 5 (depends on 1) The method of claim 1, wherein said history data includes at least one of: tube type data; replacement date data; inspection date data; ultrasonic inspection data; internal rotary inspection data; remote field eddy current inspection data; and fault mode data. ### Claim 6 (depends on 1) The method of claim 1, wherein said process facility computer further executes: retrieving a plurality of said REAC predictive maintenance models from said memory device, and calculating a reliability of said REAC using said plurality of REAC predictive maintenance models. ### Claim 7 (depends on 1) The method of claim 1, wherein said REAC adaptive model includes bayesian inference and long short term memory (LSTM) techniques based on input from fault mode data. ### Claim 8 A system of increasing reliability for a reactor effluent air cooler (REAC), comprising: a process facility computer communicatively coupled to at least one REAC including an air condenser with a plurality of field devices coupled thereto in an industrial facility configured to run an industrial process, said process facility computer including a processor connected to a memory device storing a REAC predictive maintenance model comprising a digital twin of said REAC and a REAC adaptive model, wherein said process facility computer is programmed to implement said predictive maintenance model causing said process facility computer to: retrieve history data including at least one historical REAC value from at least one of said memory device and a cloud computing-based data historian; receive real time data from said plurality of field devices including at least one real-time REAC value, said digital twin calculating a current reliability value for said REAC using said history data including said historical REAC value and said real time REAC value; compare said current reliability value to a predetermined reliability value; and generate an alert indicating that said REAC needs current maintenance whenever said current reliability value is less than said predetermined reliability value. ### Claim 9 (depends on 8) The system of claim 8 wherein said digital twin is created using said historical REAC value, isometric layout and air condenser design mechanical details of said REAC. ### Claim 10 (depends on 8) The system of claim 8 wherein said predictive maintenance model further causes said process facility computer to: identify at least one process parameter change to said industrial process to increase said reliability of said REAC; and transmit a signal to implement said process parameter change. ### Claim 11 (depends on 8) The system of claim 8 wherein said real time data includes at least one of: effluent pressure data; effluent flow rate data; water flow rate data; effluent temperature data; hydrogen sulfide concentration data; ammonia concentration data; and air temperature data. ### Claim 12 (depends on 8) The system of claim 8 wherein said history data includes at least one of: tube type data; replacement date data; inspection date data; ultrasonic inspection data; internal rotary inspection data; remote field eddy current inspection data; and fault mode data. ### Claim 13 (depends on 8) The system of claim 8 wherein said predictive maintenance model further causes said process facility computer to: retrieve a plurality of said REAC predictive maintenance models from said memory device, and calculate a reliability of said REAC using said plurality of REAC predictive maintenance models. ### Claim 14 (depends on 8) The system of claim 8 wherein said REAC adaptive model includes bayesian inference and long short term memory (LSTM) techniques based on input from fault mode data. ### Claim 15 A computer program product, comprising: a tangible data storage medium that includes program instructions executable by a processor to enable said processor to execute a method of increasing reliability for a reactor effluent air cooler (REAC); a process facility computer communicatively coupled to at least one REAC including an air condenser with a plurality of field devices coupled thereto in an industrial facility configured to run an industrial process, said process facility computer including said processor and said non-transitory data storage medium, said computer program product comprising: code for retrieving history data including at least one historical REAC value from at least one of a memory device and a cloud computing based data historian; code for receiving real time data from said plurality of field devices including at least one real-time REAC value, a digital twin calculating a current reliability value for said REAC using said history data including said historical REAC value and said real-time REAC value; code for comparing said current reliability value to a predetermined reliability value; and code for generating an alert indicating that said REAC needs current maintenance whenever said current reliability value is less than said predetermined reliability value. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein said digital twin is created using said historical REAC value, isometric layout and air condenser design mechanical details of said REAC. ### Claim 17 (depends on 15) The computer program product of claim 15, wherein said computer program product further comprises: code for identifying at least one process parameter change to said industrial process to increase reliability of said REAC; and code for transmitting a signal to implement said process parameter change. ### Claim 18 (depends on 15) The computer program product of claim 15, wherein said real time data includes at least one of: effluent pressure data; effluent flow rate data; water flow rate data; effluent temperature data; hydrogen sulfide concentration data; ammonia concentration data; and air temperature data. ### Claim 19 (depends on 15) The computer program product of claim 15, wherein said history data includes at least one of: tube type data; replacement date data; inspection date data; ultrasonic inspection data; internal rotary inspection data; remote field eddy current inspection data; and fault mode data. ### Claim 20 (depends on 15) The computer program product of claim 15, wherein said computer program product further comprises: code for retrieving a plurality of said REAC predictive maintenance models from said memory device, and code for calculating a reliability of said REAC using said plurality of REAC predictive maintenance models.