Heat exchanger system with machine-learning based optimization
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Solution Overview
Problem
Existing cooling systems often operate inefficiently and consume excessive energy and water due to fixed rules-based control methods, failing to account for the interdependencies of system components and the costs of water and chemical usage.
Innovation Solution
A machine learning-based optimization system that utilizes sensors and processor circuitry to estimate energy consumption, water usage, and chemical consumption, determining optimal operating parameters for cooling systems to minimize energy, water, or cost, by predicting responses to various operating conditions and selecting parameters that satisfy target optimization criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed rules-based control methods are used to operate cooling towers, then the system operation is simple and predictable, but the system consumes excessive energy and water and operates inefficiently
Solution Approach 1:
The patent applies dynamics by transitioning from fixed rules-based control to dynamic machine learning models that continuously adapt cooling tower operation based on real-time environmental conditions, system state, and historical data. The ML models dynamically optimize fan speeds, water flow rates, and operating modes (wet/dry/adiabatic) to maximize cooling efficiency while minimizing energy and water consumption under varying conditions.
Solution Approach 2:
The patent implements parameter changes by using machine learning models to continuously adjust critical operating parameters including fan motor speeds, water pump flow rates, sump water levels, and heat exchanger temperatures. The system evaluates multiple potential parameter sets and selects optimal combinations that satisfy cooling demands while minimizing energy and water usage based on predicted environmental conditions and system performance.
2Loss of substance
If fixed rules-based control methods are used to operate cooling towers, then the control system is simple to implement, but the system consumes excessive water and chemicals
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring system performance parameters (temperatures, flow rates, energy consumption) and environmental conditions, then using this feedback to train and refine machine learning models. The system compares predicted versus actual performance, adjusts operating parameters accordingly, and iteratively improves water and chemical consumption optimization through learned patterns in system behavior and environmental responses.
Solution Approach 2:
The patent applies preliminary action by using machine learning models to predict future system state and optimal operating parameters before actual conditions occur. The system pre-calculates optimal fan speeds, water flow rates, and chemical dosing levels based on forecasted environmental conditions and historical performance patterns, allowing proactive optimization of water and chemical consumption rather than reactive adjustments.
3Use of energy by stationary object
If prior AI systems optimize only energy consumption, then energy efficiency is improved, but water and chemical consumption costs are not accurately accounted for
Solution Approach 1:
The patent implements universality by designing a multi-functional machine learning optimization system that simultaneously optimizes multiple resources including energy consumption, water consumption, and chemical usage. The system integrates multiple objective functions and constraints into a unified ML model framework that evaluates trade-offs between different resource types and selects operating parameters that optimize overall operating cost rather than single-resource efficiency.
Solution Approach 2:
The patent applies the composite materials principle by creating a composite optimization model that integrates multiple types of data and objective functions (energy costs, water costs, chemical costs, environmental conditions, system performance) into a unified cost function. This composite approach combines heterogeneous information sources and optimization criteria into a single framework that accurately represents total operating cost and enables comprehensive resource optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves efficient operation by optimizing energy consumption, water usage, and chemical consumption, providing a more accurate estimate of operating costs and improving overall performance by dynamically adjusting operating modes and parameters in real-time.
Implementation Method 1
cooling towers to transfer heat from a hotter process fluid to cooler ambient air
Implementation Method 2
the wet mode may involve the cooling tower distributing water onto an indirect heat exchanger of the cooling tower to utilize evaporative cooling to cool the process fluid
Implementation Method 3
indirect heat exchanger of the cooling tower
Data Source
AI summary
In one aspect, a heat exchanger system is provided that includes a cooling system and a sensor configured to detect a variable of the cooling system. The heat exchanger system includes processor circuitry configured to provide the variable and a plurality of potential operating parameters of the cooling system to a machine learning model representative of the cooling system to estimate at least one of energy consumption, water usage, and chemical usage for the potential operating parameters. The processor circuitry is further configured to determine, based at least in part on the estimated at least one of energy consumption, water usage, and chemical consumption, for the potential operating parameters, an optimal operating parameter of the cooling system to satisfy a target optimization criterion.