Abstract

Insufficient landfills problem had increased the needs to decrease the waste and recycling them. However, despite the efforts done by the government and local authorities on promoting recycling culture by introducing new laws and regulations, the awareness and willingness among the community is still low. One of the possible reasons to this is lack of effort to categorize the waste into the designated category which are paper, glass, plastic and metal. In order to address this problem, it is important to design a system that will ease the process of categorizing the waste. This can be achieve by the automation of the said process. In this work, a system consist of an algorithm and hardware to automatically categorize recyclable waste is proposed. The proposed system are utilizing weight sensor and ultrasonic sensors in order to capture the characteristics of the waste item, which are weight and size so that it can be categorized into paper, glass, plastic and metal. Here, a sytem to automatically separate household waste item is presented by combining an algorithm with a set of hardware consist of minimal number of sensors, conveyer belt and servor motors.

Highlights

  • The raising number of population and development of an area comes with various side effect

  • In Malaysia, due to the demographic behavior, waste are sent to landfill rather than being disposed using incinerator [3]

  • Figure 8 shows the prototype of the waste separation system

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Summary

Introduction

The raising number of population and development of an area comes with various side effect. One of it is the amount of waste generated [1], which most of will be sent to the landfill [2, 3] for disposal. Malaysian government through Ministry of Urban Wellbeing, Housing and Local Government had initiated the Separation at Source beginning from 1 September 2015 at several states in Malaysia. This require all premises to separate their solid waste at source consist of recyclable and residual waste [7]. The recycling rate is expected to be at 22% by the year 2020 [9], which is still a low percentage

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