US 12,487,567 B2
Chiller and air handler control using customizable artificial intelligence system
Jim Jingyue Gao, Seattle, WA (US); Vedavyas Panneershelvam, Burnaby (CA); Katherine Elizabeth Hoffman, Seattle, WA (US); Paritosh Mohan, London (GB); and Christopher R. Vause, Austin, TX (US)
Assigned to Phaidra, Inc., Seattle, WA (US)
Filed by Phaidra, Inc., Seattle, WA (US)
Filed on Dec. 13, 2023, as Appl. No. 18/538,913.
Application 18/538,913 is a continuation of application No. 17/525,694, filed on Nov. 12, 2021, granted, now 11,868,098.
Prior Publication US 2024/0111260 A1, Apr. 4, 2024
Int. Cl. G05B 13/02 (2006.01); G05B 13/00 (2006.01); G06N 5/022 (2023.01)
CPC G05B 13/028 (2013.01) [G05B 13/00 (2013.01); G05B 13/024 (2013.01); G06N 5/022 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A method, comprising:
determining a set of key performance indicators (KPIs) associated with an industrial process comprising a set of cooling equipment, wherein the set of cooling equipment comprises a chiller;
receiving process information for the industrial process that are associated with the set of KPIs from a user, wherein the process information comprises a set of semantic human-readable variable names and a mapping of the variable names to a set of industrial process data streams;
generating a custom objective function based on the process information;
using a reinforcement learning artificial intelligence agent comprising a set of hidden neural network layers, predicting a set of setpoints that optimize the set of KPIs based on the custom objective function and sensor data from the set of cooling equipment, wherein the sensor data comprise chiller data from the chiller; and
controlling the set of cooling equipment based on the set of setpoints, comprising selectively activating or deactivating the chiller.