Ecopost and Austrian polyolefins producer Borealis, have agreed on a partnership that will promote Ecopost’s work in Kenya, said Sustainableplastics.
As an integrated waste management company, Ecopost collects and sorts wasterecycles plastic waste plastic into recycled plastic lumber profiles used in applications varying from fencing to road signage to outdoor furniture.
As a social enterprise, Ecopost is addressing the problem of plastic pollution, as well as issues such as chronic youth unemployment, deforestation and climate change, by employing local labour and recycled waste to produce their products.
Kenya is estimated to generate some 22,000 tons of waste per day. Nairobi alone produces over 2,400 tons of solid waste on a daily basis. Poverty is rampant, with 36% of Kenyans living below the poverty line, earning less than USD1.90 a day; deforestation has denuded the land.
Ecopost has developed a model through which it addresses all three challenges. The company works with marginalised youth and women in the community who collect, sort, shred and prepare waste material for producing pellets and plastic lumber. Ecopost not only ensures they are paid a fair and regular income, the company incorporates training and capacity building across the value chain to achieve impact at scale: creating jobs for operators, plastic waste collectors, and distributors.
In this way, Ecopost helps to divert plastic waste from open burning, dumping in waterways, sewers and landfilling and offers alternatives to wood and virgin plastics for applications like fencing, signage & building material.
The collaboration agreed between Borealis and Ecopost will see Borealis providing funding for Ecopost’s activities to boost waste recycling in Kenya and to promote a circular economy in line with the UN Sustainable Development Goals.
We remind, Borealis will implement Honeywell’s UniSim Live software as early adopters to build process models for optimizing operations through virtual process simulation. UniSim Live will allow Borealis to extend the utility of process models to near real-time process monitoring and focus on early event detection by using digital twins to improve plant reliability.
mrchub.com