Convolutional neural networks for solid waste segregation and prospects of waste-to-energy in ghana

Abstract

Waste management and practices is a pervasive world problem. This is mainly due to the continuous rise in urbanization which comes along with a rise in waste generation. Even though proper waste management has a vital role to play in the ecological environment by greening through the recovery of energy from waste, its management is a menace. Reports in Ghana indicate that about 5 million tons of Municipal Solid Waste (MSW) is generated annually and about 60% is organic. Out of this, the non-recyclable components constitutes about 20%, which indicates that 80% can be recovered and recycled, technically. Further, about 25% of the organic waste received at the material recovery and compost facility remains as compost for use in agricultural and other purposes. Considering the population of Ghana pegged at 30 million in 2019, and daily solid waste production of about 0.45 kg per person (Amoah, 2006). Proper management and greening of MSW is very much essential with increasing demand of energy and that is what this paper seeks to tackle. This paper mainly emphases on analyzing and classifying (segregating) solid waste using Convolutional Neural Networks (CNN) to productively process solid waste materials to enhance the separation process of converting waste to energy. Also, the potentials and prospects of organic waste to energy is exploited to reveal the technologies, socio-economic benefits as well as the challenges of implementing waste to energy plants in Ghana. Raspberry Pi Board, a camera, LEDs, an LCD screen and a buzzer are major components used. Results indicate that, the system can effectively segregate solid waste that is recyclable and can be converted to energy. Feasibility studies of waste to energy also indicates that, combustion and anaerobic process of conversion ia mostly applied in Ghana, which has improved the greening and advocacy for clean environment. Again, the prospects of waste to energy was analysed using SmartPLS 3.0 and results indicate that, jobs, socio-economic, tourism, environmental cleanliness and reduction of communicable diseases are the benefits of installation of waste to energy plants in Ghana.