● Exact Problem: Inefficient coal transportation management lacks real-time tracking, quality control, and environmental monitoring. 

● Proposed solution: The solution combines Artificial Intelligence (AI), data analytics, and a mobile application to revolutionize the coal transportation industry. 

 Idea: ◆ Deploying IoT sensors on coal-carrying vehicles. 

           ◆ Creating a mobile app for stakeholders.   

           ◆ Weather notification in the mobile application to support effective transportation of coal.

           ◆ The quality of coal is predicted using ML, and the user is alerted about transporting the coal in suitable weather conditions. 

           ◆ The optimized route with predicted arrival and departure time is also notified for transportation with time effectiveness coal transportation industry faces multifaceted challenges, from inefficient processes to a lack of real-time visibility and pressing safety concerns. Load management issues and disruptions caused by unpredictable weather further compound the industry's struggles, resulting in delays and escalating operational costs.

USE CASES 

 Mining operation Management: Tracking coal-holding vehicles from the mine to the power plant. 

 Efficient Energy Production: Timely coal deliveries to power plants and energy facilities. Consistent energy production through reliable coal supply. 

Public Transports Tracking: By tracking the vehicle's time of traveling and safe traveling can be ensured

 Our proposed solution represents a transformative leap forward, introducing a comprehensive system that seamlessly integrates cutting-edge technologies. Each coal-hauling vehicle is outfitted with a GPS module, not only enabling real-time tracking for heightened visibility but also facilitating the optimization of transportation routes. Continuous monitoring of load weight using advanced load cells addresses safety risks associated with overloading or underloading, ensuring a streamlined and secure operational process. Weather and environmental conditions are diligently tracked, allowing for proactive schedule management to mitigate the impact of unfavorable conditions. The incorporation of the DHT11 sensor, bolstered by Machine Learning capabilities, offers a sophisticated approach to monitoring coal quality. Moreover, route optimization, dynamically adapting to real-time traffic and road conditions, serves to reduce both fuel consumption and transportation times. A pivotal aspect of our solution is the user-friendly mobile application, providing operators with a centralized interface for real-time updates. This includes critical information such as remaining coal weight, distribution details, weather forecasts, and optimized routes. In essence, our project aims to revolutionize the coal transportation landscape by not only enhancing efficiency and safety but also by fostering environmental suitability. By providing operators with the tools needed for informed decision-making, we envision a future where the industry operates with heightened effectiveness, safety, and a keen commitment to environmental responsibility.

DEPENDENCIES 

Network bandwidth: If the network bandwidth is insufficient, the data will not be able to transmitted to the cloud in a timely manner and the ML models will not be able to train or be deployed. 

SHOW STOPPER

Data Analytics: To identify coal-carrying vehicles that are at high risk of accidents. Machine Learning:To identify areas where operational efficiency can be improved.

Check out our animation video of this project: https://drive.google.com/file/d/1aFtheNYldfciwXiiRdeTAue6I307xg_y/view?usp=sharing