Coal Blending Advisory for Real-Time Power Plant Optimization
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Solution Overview
Problem
Current power plant operations face challenges in optimizing coal selection and blending due to inherent variations in coal properties, difficulty in real-time monitoring, and reliance on operator expertise, leading to suboptimal equipment performance and increased costs.
Innovation Solution
A system utilizing predictive models, including physics-based, knowledge-based, and data-driven models, to optimize coal selection and blending ratios based on real-time data and operating parameters, providing advisory modules for coal usage and performance optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If coal blending is done based on operator expertise and experience, then operational flexibility is maintained, but manufacturing precision and reliability of plant performance deteriorate
Solution Approach 1:
The system enables self-service through automated coal blending management. The optimization module automatically determines blending ratios based on real-time coal quality data and plant performance parameters, eliminating reliance on operator expertise while maintaining operational flexibility through configurable objective functions that can be adjusted by operators based on current priorities.
Solution Approach 2:
The patent replaces the mechanical decision-making process (operator expertise and experience) with an automated computational system. The system uses predictive models, optimization algorithms, and real-time data processing to determine optimal blending ratios, substituting human judgment with a more precise and reliable automated mechanism that continuously adapts to changing conditions.
2Manufacturing precision
If real-time monitoring of coal properties is implemented, then manufacturing precision improves, but device complexity and measurement difficulty increase
Solution Approach 1:
The system achieves multi-functionality by using a single integrated platform that performs multiple tasks: data acquisition from various sources, predictive modeling of coal properties, real-time optimization of blending ratios, and control signal generation. This universal system handles diverse coal quality parameters and plant performance metrics through a unified architecture, reducing overall complexity despite the comprehensive monitoring required.
Solution Approach 2:
The patent introduces an intermediary layer (the optimization system) between raw coal quality data and blending control decisions. This intermediary processes uncertain and variable coal property data through predictive models to generate reliable blending recommendations, mediating between the complexity of real-time monitoring and the simplicity of control actions required at the blending point.
3Manufacturing precision
If coal blending ratios are strictly controlled, then manufacturing precision improves, but adaptability to varying coal quality deteriorates
Solution Approach 1:
The system implements dynamic blending ratio control that continuously adapts to varying coal quality conditions. Rather than maintaining fixed blending ratios, the optimization module dynamically adjusts ratios in real-time based on actual coal properties measured or predicted from incoming coal shipments, ensuring both precision in achieving target blend specifications and adaptability to quality variations.
Solution Approach 2:
The patent incorporates feedback mechanisms where actual coal quality data and plant performance measurements are continuously fed back to the optimization system. This feedback loop enables the system to learn from past performance and adjust blending ratios to maintain precision while adapting to changing coal quality patterns, transforming the rigid control problem into a responsive adaptive system.
Data Source
AI summary
Performance optimization of power plants is one of the major challenges. Several machine learning based techniques are available which are used for optimization of the power plants. Coal selection and blending is critical to ensuring optimum operation of thermal power plants. The present disclosure provides a system and method for optimum coal selection for the power plant and power plant optimization. The system mainly comprises two components. First, a coal usage advisory module providing coal usage and blending ratio advice to the operators based on the available coal. The optimization is with respect to the entire power plant operation including its components. And second, a performance optimization advisory module provides operation instruction for boiler, SCR, APH and other power plant equipment based on the implemented coal blend in real-time.