Temperature Prediction Model for Heat Cycle System Energy Efficiency
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Solution Overview
Problem
Industrial heat cycle systems face inefficiencies due to sharp temperature fluctuations in heat transfer oil, leading to excessive energy wastage as low-temperature oil is reheated, requiring more fuel and increasing energy costs and environmental impact.
Innovation Solution
A method is developed to build a temperature prediction model for the heat cycle system, aligning measured temperature data with heater settings using response times to generate training data, and applying statistical models like linear regression or Lasso regression to predict optimal heater settings, thereby stabilizing temperatures and reducing energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If low-temperature heat transfer oil is sent back to the boiler for reheating, then the heat transfer oil can be reused in the heat cycle system, but more fuel needs to be burned and energy is wasted
Solution Approach 1:
The system performs preliminary heating of the heat transfer oil in the heat accumulator before it returns from the heat-consuming machine. By pre-heating the oil using residual heat and auxiliary heaters, the system reduces the temperature gap between returned oil and required processing temperature, thereby decreasing the fuel consumption in the main boiler while maintaining productive reuse of the heat transfer oil.
2Productivity
If the temperature of the returned heat transfer oil is low, then the heat-consuming machine can complete its processing, but the temperature of the lower space of the heat accumulator fluctuates sharply
Solution Approach 1:
The system employs temperature sensors to continuously monitor the temperature of heat transfer oil in different zones of the heat accumulator. This feedback information is used to control auxiliary heaters that activate when temperature drops are detected, thereby stabilizing the temperature in the lower space of the heat accumulator while allowing the heat-consuming machine to complete its processing with low-temperature oil.
3Temperature
If more fuel is burned to reheat the heat transfer oil, then the heat transfer oil can reach the required high temperature, but energy cost increases
Solution Approach 1:
The system changes the temperature parameter profile of the heat transfer oil by implementing multi-zone temperature control in the heat accumulator. Different zones maintain different temperature levels, and the system dynamically adjusts heating parameters based on real-time temperature measurements, thereby achieving the required high temperature at the outlet while minimizing overall energy consumption through optimized thermal management.
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 solution effectively stabilizes heat transfer oil temperatures, minimizing energy wastage and reducing fuel consumption by optimizing heater settings based on predictive models, thus enhancing energy efficiency and environmental sustainability.
Implementation Method 1
the boiler uses fuels such as coal, diesel, or natural gas to heat the heat transfer oil
Implementation Method 2
the heater is configured to heat a thermal medium and transport the thermal medium with a raising temperature
Implementation Method 3
The high-temperature heat transfer oil after heating is sent to a heat accumulator through pipes, and then is delivered to machines such as hot press machine or impregnation machine for processing
Implementation Method 4
the heat-consuming machine is configured to consume thermal energy of the thermal medium for processing
Data Source
AI summary
A method for building a temperature prediction model is applicable to a heat cycle system, wherein the method is used to measure a temperature of the heat cycle system to generate a measured temperature data, and compute a response time of the heat cycle system, and the method includes aligning the measured temperature data and a setting value of the heat cycle system to generate a training data according to the response time; and building the temperature prediction model according to a statistic model and the training data.