INTELLIGENT BRANDS // Green Technology

Leveraging IoT

Pattni continued : “ Connected IoT sensors are already in use across the waste management value chain . Operators use the real-time data provided by these sensors to improve operational efficiencies – whether it ’ s by improving collections or planning their routes more sustainably . These sensors exist across the value chain , from their use in collection vehicles to deployment in waste facilities themselves .

“ In Norway , for example , sensors are used to monitor the amount of waste in bins so that collections can be better planned . In the winter , collections are kept to a minimum , while in the busier tourist summer months , the waste is collected more regularly .

That ’ s according to Pritesh Pattni , Chief Commercial Officer at ISB Global , the UK-based provider of resource planning software to waste management operators worldwide , who points to the three technologies as integral for operators to deal with the growing complexity of recycling and waste collection and processing , now and into the future .

Smarter sorting technology

“ Advances in sensor technology mean that waste companies can also detect when the wrong materials have been put in a bin . On-vehicle AI apps recognise and mark contaminated materials when household bins are emptied . The instant contaminated waste is identified , an alert , advice on best practice or a penalty can be administered on the spot , encouraging consumers to improve their recycling habits .

“ Sensor usage in trucks is becoming commonplace throughout the sector ,” Pattni went on . “ The data relayed by these devices informs an array of decisions that improve efficiency – such as minimising fuel use and allowing for predictive maintenance so that collection vehicle fleets stay in peak condition .”

Using Blockchain for traceability

AI and Machine Learning ( ML ) are set to radically improve the efficiency of automated waste sorting systems .

Pattni said : “ Sorting materials is often a bottleneck for waste management and recycling . The more waste that a system has to manage , the worse the problem becomes . That ’ s why making this process faster and more efficient is essential .

“ This is where AI and ML enter the conversation . Training ML models to recognise different materials and sort them efficiently means that sorting lines can run faster . There ’ s also less human error in the process and more waste streams can be sorted . Automated AI and ML-based sorters can work on a 24 / 7 basis if necessary .”

Around the world , governments are placing increasing requirements on businesses to prove that they have sustainable supply and waste management chains . These requirements are based on transparent reporting , which is often audited , with large fines and even custodial sentences for serious breaches .

Businesses therefore rely on their waste management operators to help them in a number of ways :

• Manage a range of waste collections efficiently and cost-effectively

• Provide full traceability of recycling and other waste management processes

• Transparent reporting p

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