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Monday, 1 February 2021

Using RAI to support waste management

 Robotics and Artificial Intelligence are significant globalised technologies applicable to every sector. The UK’s extensive industrial and academic research base has immense potential to impact on home and global markets. The Robotics & Artificial Intelligence Innovation Network addresses this opportunity, enabling UK RAI innovators to connect, access markets and showcase their capability.

 This webinar aims to inform material recovery facilities (MRFs) of the current state of RAI to increase the recycling rate to meet UK’s recycling targets. The UK’s 2020 waste targets will soon be obsolete as recycling rates have plummeted to around 44%, despite the target set at 50% by the year 2020. This means that 22 million tons of waste are sent to landfill every year in the UK and are not recycled. In order to support MRFs to meet the UK targets, a closer look needs to be taken at recent developments in robotics and AI. Waste sorting robots using computer vision or other sensory methods are becoming more established in countries like USA and start-ups have emerged tackling the issue of waste contamination at earlier stages in the recycling process. This webinar aimed to connect people from the waste management sector to innovators who are using robotics and AI to improve waste management.






In US news and current events today, NowThis News is exploring how these trash sorting industrial robots are changing recycling with machine learning and artificial intelligence. 2019 has been a banner year for environmental action and change. Waste management and how we recycle plastic, paper, and more is crucial to saving the environment and fighting the climate crisis. With machine learning algorithms, these artificial intelligence robots are sorting recycling faster than humans ever could. The future of artificial intelligence-fueled trash sorting robots is bright.

how Sensoneo smart waste management solutions enable cities and businesses to manage their waste smartly. Sensoneo help you to manage the waste cost-efficiently and improve the environment and wellbeing of people. See the experience and results of Nitra town - region of 150K+ inhabitants who managed to save over 30% of waste collection costs thanks to Sensoneo

QuadMinds - IoT Solutions

The Internet of Things (IoT) is a technology and market development based on connectivity between everyday objects and applications. See how IoT can improve waste management.

Guardforce's Smart Waste Bin

SMART CITY BIN FOR WASTE MANAGEMENT (WASTE COLLECTION TECHNOLOGY)

If you walk along our streets, you often see litter on the ground and not in the bins. Rubbish doesn’t fit in the bins and gets blown around by the wind, creating unsanitary conditions that attract dogs, birds and rodents. The city has to spend a lot of money on a large fleet of vehicles to service all these bins. Taking this into account, as well as a number of other environmental and economic factors, we have developed a new state-of-the-art litter bin. Binology bins are radically different from standard bins: - they use solar energy and don’t need to be connected to the electricity network - they are equipped with a compactor so they can hold 8 times more rubbish than an ordinary bin - software provides information on how full each bin is and this, together with road condition reports, is used to allocate drivers to the most efficient waste collection routes. The advantages provided by state-of-the-art Binology technology include: - up to 90 % reduction in the cost of waste collection - a significant reduction in the number of collections, thereby reducing the number of waste collection trucks required - managed and reduced fuel costs - reduced CO2 emissions - by installing smart bins, recovered materials can be separated and recycled - animals can’t get into the bins - the level of waste in each bin is monitored in % - waste collection routes are generated taking into account road conditions - improved waste management through system reporting. In addition, advertising materials can be placed on the bins to provide an additional source of income. This technology will give us modern Clean cities that will make everybody happy.   

The UK's first AI waste sorting robot - Green Recycling

Intelligent Waste Recycling.

Green Recycling is the first UK Waste Management company to make use of Artificial Intelligence to sort waste materials.

A Novel IOT and AI based Smart Waste Management System

Prepared by Team LOGICTRONS for the 2018 edition of TCS EngiNX. Idea mainly focuses on managing the urban waste by collecting and disposing of the waste materials in a city in order to enrich the livelihood of the citizens by preventing all the ill effects caused by irresponsible unhandled wastes.

Greyparrot uses deep vision for waste management

Greyparrot is building computer vision systems for the waste management industry. The TC Top Picks program showcases outstanding early-stage startups across these categories: AI/Machine Learning, Biotech/Healthtech, Blockchain, Fintech, Mobility, Privacy/Security, Retail/E-commerce, Robotics/IoT/Hardware, SaaS and Social Impact & Education.

ECO fl-AI : AI for waste management SMART Plastic Waste Sorting And Segregating Machine Using Artificial intelligence system

Solid waste disposal problem is the one of the main issue in both urban and rural areas. So we can't get proper plant to disposal it. And it also required to segregate the plastic in terms of Thermoplastic and Thermosetting plastic, based on which we can reuse plastic for generating electricity, for making bonded rods and also for light wait materials... It is comes under swachh bharat abhiyana, Which helps to achieve good environment and also economical to the industries. Also we get accurate results compare to human assistance using this AI System...

EU's eREC Event: Waste Management 4.0 & Tech Trends

For decades, the United States and other wealthy countries have been dependent on China to buy and process almost half of the world’s plastic waste. Now, stricter recycling standards in China mean its cheaper for some US cities to simply send recyclables to the dump rather than pay a higher fee to process them. That’s why a number of researchers and tech companies are working on robots to make US recycling more efficient.

Role of Artificial Intelligence in Environmental sustainability Smart Recycling System
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AI:Create a frameworks using various humanoid robotics with the help of various neural networks and LLMs on the occasiun of Diwali night from late night to peak hours to earley morning detection of fireworks reside, the pollution noice, voice and various traces of scraps found there along with fire broken and taming calming the fire broken in real time in the various neural networks of remote backward areas tow, cities, states countries and continents of the US,UK, Canada, Midle East, Africa, Korea, China, Europe, Asia and Oceania in real time. IoT-Based Automated Fire Detection and Alert System Overview This project automates fire detection and alert systems using IoT technology. It replaces traditional methods of fire reporting, ensuring quick and reliable emergency responses even in the absence of human intervention. The system leverages NodeMCU microcontrollers, environmental sensors, and the MQTT protocol to monitor conditions and trigger alarms in case of abnormalities. Features Automated Fire Detection: Monitors temperature and gas levels using DHT11 and gas sensors. Real-Time Alerts: Sends location-based notifications to receivers for immediate action. Scalable MQTT Communication: Efficient communication using a single MQTT topic for all nodes. Localized Alarm System: Receiver nodes trigger buzzers for the corresponding locations. System Architecture Source Nodes: NodeMCUs (Source_Beleghata, Source_London) equipped with: DHT11 Sensor for temperature monitoring. Gas Sensor for detecting smoke or hazardous gases. Publishes location-based messages upon detecting abnormal conditions. Receiver Nodes: NodeMCUs (Receiver_Beleghata, Receiver_London) equipped with buzzers. Subscribes to a shared MQTT topic and triggers the buzzer if the location matches incoming data. Communication Protocol MQTT Protocol: All nodes are connected to the internet for real-time communication. A shared MQTT topic ensures scalability and simplicity. Demonstration Setup Nodes: Source_Beleghata (with sensors) Receiver_Beleghata (with buzzer) Source_London (with sensors) Receiver_London (with buzzer) Workflow: Source nodes detect abnormal conditions and publish the affected location ("Beleghata" or "London") to the MQTT topic. Receiver nodes subscribe to the topic and trigger an alarm if the message matches their location. Requirements Hardware: 4 NodeMCU boards DHT11 sensors (2 units) Gas sensors (2 units) Buzzers (2 units) Software: Arduino IDE MQTT broker (e.g., Mosquitto) Internet connection for NodeMCU devices Installation and Usage Hardware Setup: Connect sensors to the Source NodeMCUs. Connect buzzers to the Receiver NodeMCUs. Software Configuration: Flash the provided code onto the NodeMCU devices using the Arduino IDE. Set up an MQTT broker and configure the nodes to publish/subscribe to the shared topic. Testing: Simulate abnormal conditions to test the system. Verify that alarms are triggered at the appropriate receiver nodes. Future Enhancements Integration with mobile applications for remote monitoring and alerts. Expansion to support additional locations and sensors. Advanced machine learning algorithms for anomaly detection.

  Global AI–IoT Humanoid Robotics Framework for Diwali Night Fireworks, Pollution, Waste and Fire Emergency Detection Proposed...