Dust Suppression System Using Kriging Extrapolation
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Solution Overview
Problem
Current dust abatement methods fail to provide comprehensive and accurate real-time monitoring of dust and moisture levels along routes, leading to inefficient and often incorrect assessments of dust conditions, resulting in either unsprayed or oversprayed areas, which can cause environmental harm and increased costs.
Innovation Solution
A system that collects environmental data from sensors and historical data to extrapolate dust and moisture levels using the Kriging method, integrating vehicle telematics and air systems, allowing for targeted dust abatement measures to be calculated and implemented dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional dust monitoring methods are used, then dust abatement can be performed, but the monitoring is not comprehensive or accurate enough, leading to incorrect assessments of dust conditions
Solution Approach 1:
The route is divided into multiple monitored segments, each equipped with sensors. This segmentation allows comprehensive coverage of the entire route while distributing the complexity across multiple manageable units rather than requiring a single complex monitoring system.
Solution Approach 2:
A computer system acts as an intermediary that collects data from multiple sensors, processes the information using historical data and environmental factors, and generates comprehensive dust level assessments. This intermediary consolidates the complexity of integrating multiple sensor inputs into a unified decision-making system.
2Reliability
If comprehensive sensor coverage is deployed along the entire route, then accurate dust monitoring is achieved, but the cost and complexity of the system increases significantly
Solution Approach 1:
Instead of continuous sensor deployment along the entire route, the route is segmented into specific monitored sections. Sensors are strategically placed at key locations where dust generation is most likely to occur, providing reliable assessments without requiring comprehensive coverage at every point.
Solution Approach 2:
The system uses historical data and environmental conditions to identify which segments require monitoring. Rather than deploying sensors uniformly across the entire route, monitoring is applied partially to the segments that most need it, based on historical dust patterns and current environmental conditions.
3Object-affected harmful factors
If dust suppressants are applied to all areas along the route, then dust suppression is ensured, but resources are wasted in areas that do not require suppression
Solution Approach 1:
Dust suppression measures are applied locally to specific segments where dust conditions are actually detected or predicted. The system identifies which segments require suppression based on real-time sensor data and historical patterns, applying suppressants only to those locations rather than uniformly across the entire route.
Solution Approach 2:
The system continuously monitors dust levels and suppressant application effectiveness, using this feedback to adjust future suppression decisions. Historical data on suppressant performance in different conditions is stored and used to optimize future applications, reducing waste by learning from past experiences.
4Measurement precision
If frequent monitoring and suppression actions are taken, then dust control is improved, but the cost of suppressant application and system operation increases
Solution Approach 1:
Instead of continuous suppressant application, the system uses periodic monitoring and suppression actions triggered by detected dust conditions. Sensors monitor dust levels continuously, but suppressants are applied only periodically when dust thresholds are exceeded, reducing operational costs while maintaining effective dust control.
Solution Approach 2:
The system uses historical data and environmental conditions to predict when dust problems are likely to occur, allowing preliminary suppression actions to be taken before dust levels become problematic. This preventive approach reduces the frequency of emergency suppression applications, lowering operational costs.
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for dust suppression is provided. The present invention may include collecting, from a plurality of sensors, environmental data pertaining to one or more monitored segments comprising a route; based on the environmental data and historical data, identifying a moisture level and a dust level of the one or more monitored segments; based on the environmental data and the historical data, extrapolating a moisture level and dust level of one or more unmonitored segments comprising the route; and based on the historical data, the moisture levels and the dust levels for the monitored segments and unmonitored segments, determining one or more abatement measures for the route.