Energy Utility Modeling for Optimal Driving Force Distribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current energy recovery systems face challenges in optimizing energy consumption and utility selection due to limitations in existing software, which struggle with variable driving force distribution and require manual iteration, especially in large-scale processes with multiple material streams having different operational attributes.
Innovation Solution
A system and method that utilize a computer-based energy utility modeling program to optimize energy recovery by determining global energy utility targets and optimal driving force distributions through stream-specific minimum approach temperatures, allowing for flexible input of operational attribute ranges and calculating optimal energy consumption values without manual enumeration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single global ΔTmin value is used in existing software, then the system is simple to operate, but it requires manual trial and error iteration to optimize energy consumption
Solution Approach 1:
The system dynamically adjusts the ΔTmin parameter from a fixed single value to variable stream-specific values. The software automatically modifies ΔTmin for different process streams based on optimization algorithms, eliminating manual trial-and-error iteration while maintaining ease of operation through automated dynamic parameter adjustment.
Solution Approach 2:
The software performs self-optimization by automatically adjusting ΔTmin values for different streams without requiring manual user iteration. The system serves itself by implementing automated optimization algorithms that continuously refine energy consumption without human intervention in the parameter tuning process.
2Loss of energy
If stream-specific minimum approach temperatures are implemented, then energy optimization is improved, but software complexity increases
Solution Approach 1:
The system segments the single global ΔTmin parameter into multiple stream-specific ΔTmin values. Each process stream can have its own optimized minimum approach temperature, allowing precise energy optimization for individual streams while the software manages the complexity through automated segmentation and assignment of parameters.
Solution Approach 2:
The software automatically changes the ΔTmin parameter from a fixed global value to variable stream-specific values. This parameter transformation enables energy optimization across different streams with varying thermal characteristics, while the automated parameter management system handles the increased complexity without requiring additional user effort.
3Adaptability or versatility
If manual trial and error approach is used to tweak stream attributes, then flexibility in adjustment is maintained, but productivity is reduced
Solution Approach 1:
The software performs preliminary automated optimization by pre-calculating optimal ΔTmin values for all streams before the user needs to make adjustments. This preliminary action maintains flexibility in parameter adjustment while dramatically increasing productivity by eliminating the iterative trial-and-error process, providing optimized values ready for immediate implementation.
Solution Approach 2:
The system implements automated feedback loops that continuously monitor energy consumption and adjust ΔTmin values accordingly. This feedback mechanism maintains adaptability by allowing dynamic parameter adjustment while improving productivity through automated closed-loop optimization that eliminates manual iteration and provides rapid convergence to optimal settings.
Data Source
AI summary
A system, methods, and user-friendly program product to calculate global energy utility targets and define optimal driving force distribution for a process or cluster of processes under all possible process changes and streams specific minimum temperature approach values, simultaneously, and without enumeration, are provided. The program product can utilize stream-specific minimum temperature approach values ΔTmini, where the superscript i represents the specific hot stream, as the optimization parameters instead of the single global ΔTmin currently used, in addition to identifying the optimal operating conditions. The program product can define optimal process conditions and an optimal driving force distribution in heat recovery systems, and can produce an optimal Pareto-curve that shows the rigorous trade off between energy cost and capital cost for any energy recovery system.