Jaipur Intelligence – Empowering Your Waste-to-Energy Plant
Take control of your Waste-to-Energy operations like never before. Jaipur Intelligence leverages patent-pending technology, developed through years of dedicated research in computer vision and deep learning, specifically engineered for waste recognition and management in plant bunkers. Our solution transforms the complexity of waste streams into clear, actionable insights, enabling you to confidently make optimal decisions — anytime, under any conditions.
Oversized and dangerous item detection
Jaipur Intelligence accurately identifies hazardous or problematic waste items directly within the bunker and at truck unloading bays, preventing operational disruptions and protecting plant efficiency.
Crane operators receive instant, real-time alerts whenever potentially harmful or suspicious materials are detected. Alerts are organized into three default categories:
- Hard & Oversized Items: Large or heavy materials that could cause mechanical blockages (e.g., concrete blocks, logs, metal bars).
- Dangerous Items: Materials that pose an explosion or fire risk (e.g., gas tanks, lithium batteries).
- Non-compliant Items: Waste specifically prohibited by plant regulations (e.g., construction debris, tyres).
This classification system can be fully customized to meet plant-specific requirements.
Additionally, Jaipur Intelligence detects waste even when it is partially covered or hidden beneath other materials, ensuring comprehensive protection.
Our solution requires no initial calibration, is compatible with bunkers of any size, and delivers near-zero false positives from day one. Management teams gain clear insights and historical data through detailed, intuitive reporting and analytics, allowing for continuous improvement and proactive decision-making.
Calorific value mapping
Ensuring stable combustion is critical for the efficient operation of any Waste-to-Energy plant. Achieving this stability requires optimal mixing of waste materials within the bunker to maintain consistent calorific values, reduce emissions, and optimize energy output.
Jaipur Robotics addresses this challenge by providing the industry’s most precise and comprehensive calorific value mapping (Lower Heating Value – LHV) of waste. Our advanced technology leverages deep learning algorithms and computer vision to empower crane operators, enabling them to effectively mix waste before it is introduced into the combustion chambers. Operators receive clear, real-time visual guidance through an intuitive heat map that accurately highlights areas of higher and lower calorific values within the bunker.
Unlike traditional systems that offer only surface-level analysis, Jaipur Robotics’ solution delivers detailed calorific value insights throughout the entire bunker volume. By accurately detecting waste characteristics even below the surface, our technology ensures that operators have a comprehensive understanding of the waste composition at all times.
Moreover, our calorific value mapping solution seamlessly integrates with existing plant operations, requiring minimal setup and no ongoing calibration. As a result, plant operators can immediately benefit from enhanced visibility, reduced operational uncertainty, and optimal waste handling.
Crane tracking
Jaipur Robotics’ advanced crane tracking solutions provide precise, real-time monitoring and prediction of the position and operational status of one or multiple overhead cranes within the bunker of a Waste-to-Energy plant.
Leveraging cutting-edge software and intelligent analytics, our solution continuously tracks and evaluates crane movements as well as waste-mixing activities in the bunker. By doing so, we enable plant operators to optimize crane trajectory planning, significantly minimize unnecessary or inefficient crane movements, and accurately measure and manage operational downtime.
By offering detailed visibility and actionable insights into crane operations, Jaipur Robotics empowers Waste-to-Energy plants to improve their overall operational efficiency, reduce maintenance and operational costs, and consistently achieve higher productivity through informed, data-driven decision-making.