SCHOOL OF ENGINEERING AND TECHNOLOGY
DEPARTMENT OF INDUSTRIAL AND MANUFACTURING ENGINEERING
TITLE: PREDICTION OF SOLID WASTE GENERATION AND ROUTE OPTIMIZATION
FOR GARBAGE COLLECTION
STUDENT NAME: TANAKA PUTSAI
REGISTRATION NUMBER: H150147N
SUPERVISORS: MR T KANYOWA AND MR G.V NYAKUJARA
A project submitted as a partial fulfillment of the requirements of a Bachelor of
Technology Honors Degree in Industrial and Manufacturing Engineering 2019
MAY 2019
i
COPYRIGHT
Copyright ©2018. All rights reserved.
All rights reserved. No part of this research may be reproduced, stored in any retrieval system, or
transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or
otherwise for purpose without the prior written permission of the author.
ii
DECLARATION
I ……………………………..………. registration number………………. do hereby declare that
this research project is original
Harare institute of Technology
Department of Industrial and Manufacturing Engineering
P.O. Box Be277
Belvedere
Harare
Zimbabwe
STUDENT ………………………………………………... DATE………………………….
SUPERVISOR SIGNATURE ……………………………...DATE………………………….
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DEDICATION
I dedicate this research paper to my family and mentors. I also dedicate this project to my
mentors, Mr. G.V. Nyakujara and Mr. T. Kanyowa whose passion for engineering drew my
interest, and all the Harare Institute of Technology faculty who assisted and enlightened me on
impartation of knowledge leading to the finalization of this paper.
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ACKNOWLEDGEMENTS
I would like to take this opportunity to thank all the people who assisted in coming up with this
project. My utmost gratitude to Mr. T. Kanyowa and Mr. G Nyakujara for enhancing my
knowledge on the subject.
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ABSTRACT
The current system being used by the waste collection service providers in Zimbabwe involves a
lot of manual work. The main issue with the garbage bins at public places is that they get
overflowed in advance before the commencement of the next cleaning process. This however,
poses hazardous scenarios to the public and ecosystem. The purpose of this project is to develop
a waste collection system based on location. The system will also be integrated with an android
application designed for route optimization and prediction model to further help in knowing the
future demands of waste services. The system gives an alert to the waste management
organizations for instant cleaning with the proper authentication on level of waste in garbage
bins. The ultrasonic sensor is interfaced with the microcontroller to check level of waste in
dustbin and also the heat detection sensor, LM35 is also interfaced to the microcontroller to
check for any fires that are lit in the garbage bins. After alerting the waste management service
providers, the location of the filled garbage bin is known through the Global System for Mobile
Communications (GSM) that sends the message to the service providers and GPS that provides
the coordinates (latitude and longitude). The message sent consists of the waste level status and
the location’s coordinates. The coordinates are then sent to the driver who inputs them into the
android application and route optimization occurs. The data is collected and stored in the
database. The data is retrieved from the database and inputted into the predictive model to
forecast the number of bins to be collected in the future and further help in inventory
management.
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TABLE OF CONTENTS
COPYRIGHT .................................................................................................................................. ii
DECLARATION ........................................................................................................................... iii
DEDICATION ............................................................................................................................... iv
ACKNOWLEDGEMENTS ............................................................................................................ v
ABSTRACT................................................................................................................................... vi
TABLE OF CONTENTS .............................................................................................................. vii
TABLE OF FIGURES ............................................................................................................... xi
CHAPTER 1: INTRODUCTION ............................................................................................... 1
1.0
Introduction .......................................................................................................................... 1
1.1 Problem background ................................................................................................................. 1
1.2 Problem statement ..................................................................................................................... 2
1.3 Aim ........................................................................................................................................... 2
1.4 Objectives ................................................................................................................................. 2
1.5 Justification ............................................................................................................................... 2
1.6 Scope ......................................................................................................................................... 3
1.7 Research questions .................................................................................................................... 3
1.8 Expected results ........................................................................................................................ 3
1.9 Conclusion ................................................................................................................................ 3
CHAPTER TWO: LITERATURE REVIEW ............................................................................. 4
2.0 Introduction ............................................................................................................................... 4
2.1Waste collection equipment ....................................................................................................... 4
2.2 Bin Location.............................................................................................................................. 4
2.3 Population density..................................................................................................................... 4
2.4Existing solutions ....................................................................................................................... 5
vii
2.4.1 Smart garbage disposal system .............................................................................................. 5
2.4.2 Hardware description of the smart waste system ................................................................... 8
2.4.2.1 Load sensor ......................................................................................................................... 8
2.4.2.2 Global Positioning System (GPS)....................................................................................... 9
2.4.2.3 Global System for Mobile communication (GSM) .......................................................... 10
2.4.2.4 Fire detecting sensor ......................................................................................................... 11
2.4.2.5 Level detecting sensor....................................................................................................... 11
2.4.2.6 Microcontroller ................................................................................................................. 11
2.5 Route Optimization for garbage collection trucks .................................................................. 12
2.5.1 Capacitated Arc Routing Problem ....................................................................................... 12
2.5.2 GIS based Route Optimization ............................................................................................ 13
2.5.3 Other Approaches Used On Route Optimization................................................................. 13
2.6 Waste generation models ........................................................................................................ 14
2.6.1 Prediction of Solid Waste Generated Using Artificial Neural Network .............................. 15
2.6.2 Prediction of Solid Waste Generated Using Regression Models......................................... 15
2.6.2 Other Approaches ................................................................................................................ 16
2.7 Steeple Analysis ...................................................................................................................... 16
2.8 Gap Analysis ........................................................................................................................... 17
2.9 Conclusion .............................................................................................................................. 18
CHAPTER THREE: METHODOLOGY ................................................................................. 19
3.0 Introduction ............................................................................................................................. 19
3.1 Project flowchart ..................................................................................................................... 19
3.2Problem Identification ............................................................................................................. 20
3.3 Data Collection ....................................................................................................................... 20
3.3.1 Permission seeking from the Harare City Council .............................................................. 21
viii
3.3.2 Literature Review................................................................................................................. 21
3.3.3 Observations ........................................................................................................................ 21
3.3.4 Interviews ............................................................................................................................. 21
3.4 Conceptual Design .................................................................................................................. 22
3.4.1 Embodiment design ............................................................................................................. 22
3.4.2 Detailed Design .................................................................................................................... 22
3.5 Engineering tools used ............................................................................................................ 22
3.5.1 Microsoft Office....................................................................Error! Bookmark not defined.
3.5.2 Statistical Package for Social Sciences (SPSS) ....................Error! Bookmark not defined.
3.5.3 E-Draw ..................................................................................Error! Bookmark not defined.
3.5.4 Proteus...................................................................................Error! Bookmark not defined.
3.5.5 Matlab ...................................................................................Error! Bookmark not defined.
3.6 Conclusion .............................................................................................................................. 23
CHAPTER FOUR: DESIGN DEVELOPMENT ................................................................. 24
4.0 Introduction ............................................................................................................................. 24
4.1 Design considerations ............................................................................................................. 24
4.1.1 Concept 1 ..................................................................................................................... 24
4.1.2 Concept 2 ..................................................................................................................... 25
4.1.3 Concept 3 ..................................................................................................................... 26
4.2 Concept Selection ................................................................................................................... 28
4.2.1 Concept screening ................................................................................................................ 28
4.2.2 Concept Scoring ................................................................................................................... 28
4.3
Design development of the system .................................................................................... 30
4.3.1 Design of the electronic part (waste level detection system) ............................................... 30
4.3.2 Design of the fire detection part of the system .................................................................... 31
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4.3.3Circuit design ........................................................................................................................ 31
4.3.3.1 Circuit calculations ........................................................................................................... 32
4.4
Route Optimization formulation ........................................................................................ 34
4.5 Android Application Development for Route Optimization .................................................. 35
4.6 Development of the Predictive model..................................................................................... 35
4.7 Economic Analysis ................................................................................................................. 36
4.7.1Material cost.......................................................................................................................... 37
4.7.2 Projected sales ...................................................................................................................... 37
CHAPTER FIVE: RESULTS, CONCLUSION AND RECOMMENDATIONS .................... 40
5.0 Introduction ............................................................................................................................. 40
5.1 Overview of objectives ........................................................................................................... 40
5.2 Prediction of solid waste generated ........................................................................................ 40
5.3 Waste level detection system .................................................................................................. 40
5.4 Android application for route optimization ............................................................................ 40
5.5 Recommendations ................................................................................................................... 41
REFERENCES ............................................................................................................................. 43
APPENDIX A: WASTE LEVEL DETECTION SYSTEM CODE ............................................. 46
APPENDIX B: ROUTE CODE MAP ACTIVITY ...................................................................... 49
APPENDIX C: ROUTE CODE MAIN ACTIVITY .................................................................... 51
APPENDIX D: ROUTE CODE.................................................................................................... 53
APPENDIX E: DIAGRAM FOR THE SUGGESTED BIN IN 3D MODEL .............................. 54
APPENDIX F: SYSTEM SIMULATION USING PROTEUS .................................................... 55
APPENDIX G: DATASET COLLECTED FROM MUNICIPALITY ........................................ 56
APPENDIX H: ANDROID APPLICATION INTERFACES ...................................................... 58
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TABLE OF FIGURES
FIGURE 2. 1 Internet Of Things Based Waste Management System (Amrutha P. V,et al, 2017) 5
FIGURE 2.2 SMART WASTE MANAGEMENT SYSTEM (N NATHRANI, et, al, 2018) ........ 6
FIGURE 2.3 Smart Waste Management System (Rupa, et al, 2018) ............................................. 7
FIGURE 2.4 Design of a Smart Waste Management and Bin Collection Services (S.S.
Navghane, et al, 2016) .................................................................................................................... 8
FIGURE 2. 5 Global Positioning System ....................................................................................... 9
FIGURE 2. 6 Global System for Mobile Communication Nikita Nathrani, et al, 2018 ............... 10
FIGURE 2. 7 Level detecting sensor (Ultrasonic sensor)............................................................. 11
Figure 3. 1Project flow chart………………………………………………………….……..19
FIGURE 3. 2 GRAPH OF AVERAGE NUMBER OF BINS ...................................................... 20
FIGURE 4. 1 CONCEPT 1………………………………………………………………………25
FIGURE 4. 2 CONCEPT 2 ........................................................................................................... 26
FIGURE 4. 3 CONCEPT 3 ............................................................Error! Bookmark not defined.
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CHAPTER 1: INTRODUCTION
1.0 Introduction
Waste management or waste disposal is the activities and actions required to manage waste from
its inception to its final disposal. It is one of the operational problems currently being faced in
Zimbabwe. Due to rapid population growth and lack of public awareness, waste management has
become an issue of national concern. The collection is being done in an adhoc manner hence
higher solid waste collection costs. Hence this chapter serves to highlight mainly the aims and
objectives of the study as well as the scope and some other important issues.
1.1 Problem background
Figure 1.1 Overflow bin captured by researcher in Budiriro
Waste management has been poorly done in Harare for the past decade with a number of
problems cited by the responsible authority. Waste is a major concern around the world as it
directly impacts the environment. Since 1980, Zimbabwe’s population tremendously increased
by 115% from 7164172 to 16913261 people. This population increment and the congestion of
cities such as Harare have led to production of high volumes of waste which is difficult to
handle. There have been poor collection services, absence of waste monitoring system at
designated disposal areas and also difficulty in monitoring the bins at designated areas as it
1
involves a lot of manual work to check the bin levels. As a result of that bins get full and waste
starts to overflow posing a lot of dangers to people and environment. The overflowing waste
constitutes of various dangerous products including broken glasses, smelly organic compounds,
to mention a few, and these pose dangers to the citizens and animals. The problem is not
affecting Harare only but major cities and even small towns countrywide, but for the period
Harare CBD is used for the research. There have been an incremental on environmental pollution
which have been caused by lack of tracking the designated bins at the designated areas where
waste disposal is at its peak.The garbage collection vehicles are assigned to certain areas without
any serious demand analysis and the route formation is left for the drivers.
1.2 Problem statement
Data shortage on collection time, location and delays in collection of waste causes overflow of
bins resulting in environmental pollution and diseases.
1.3 Aim
To develop a smart waste collection system and prediction model for solid waste generation
1.4 Objectives
•
To develop a predictive model for solid waste generation
•
To develop a waste level detection system
•
To develop an android application for route optimization
1.5 Justification
The routes proposed:
•
Ensures that waste is collected on time
•
Easy access to the bin location as the system reports its location
•
Provides shortest possible path to the bin
•
Reduces unnecessary trips of garbage collection vehicle
•
Reduces the overall expenditure associated with the garbage collection
2
1.6 Scope
The project is more focused on obtaining the correlation between the population density and
waste disposal rate, and route optimization of capacitated garbage collection trucks in Harare
CBD Area.
1.7 Research questions
•
How does the community and the council link?
•
Which areas are given the first priority in terms of waste collection and why?
•
How does population increase vary with waste disposal?
•
What algorithms can be used to achieve shortest possible routes?
•
What parameters are to be taken into consideration during formulation of the
optimum route?
•
What is the average population density for given area for example per square
kilometer?
•
What equipment does the council have now that enables efficient waste collection?
•
What are the costs incurred during waste collection services?
1.8 Expected results
•
To have a prediction of solid waste generation
•
To have an algorithm that provides the optimum route for the garbage truck to follow
1.9 Conclusion
Waste that is well managed saves the entire community from deadly diseases. Hence there is
need to introduce smart cities
3
CHAPTER TWO: LITERATURE REVIEW
2.0 Introduction
This chapter helps in acknowledging the existing solutions that are in place. It gives an overview
of the current waste collection systems, available resources to ensure that there is smooth waste
collection, statistical data of the areas under study, scheduling that is followed and efforts by
other researchers to control the problem.
2.1Waste collection equipment
The citizens from different areas use a wide range of bins to dispose waste. Mostly the disposal
bags are mainly used in the high-density areas which comprises of medium to low income
people. The commonly used are the disposal plastic bags, buckets, and any plastic bag that can
be found to be suitable. The large bins are mainly found at designated areas where citizens can
reach and pre-dispose their waste in times when the council delays to collect refuse. 95% of the
citizens in high density areas use the black disposal plastics.
2.2 Bin Location
The bin locations were obtained from the data provided by the Waste management department
and this information was then used to calculate the required information like the Euclidean
distance and also to take into account the aspect of road networks in the Harare Central Business
District. The Waste Management Department also provided their sequence of collecting the
waste and disposing it in the Pomona dumping site. The areas that contain the locations are
mainly the pick and drop places for public transport for example Market square rank, Copa
Cabana rank, Fourth street rank. This information gathered helps to model the routes and to
know the exact places where the waste will be collected.
2.3 Population density
Population density of residential areas in the study area was collected from the Zimbabwe
Statistics Department. The data was then used to predict the solid waste generation rate in the
residential areas.
4
2.4Existing solutions
2.4.1 Smart garbage disposal system
Amrutha P.V, et al, 2017, designed an Internet of things based waste management system using
smart bin. They build an automatic open dustbin that opens each time it detects people who want
to put litter in it. The dustbins have sensors mounted on them that can easily check the waste
level and the tracking the weight as well and each time the threshold is surpassed, the message is
sent to the municipal (Amrutha P.V, et al, 2017). As a way to eradicate the decaying smell, the
bins have sprinklers that spray the harmless chemicals to avoid the decaying smell. The system
consisted of an eclipse Juno tool, Aurdino tool with java language, motor driver, LCD display,
load cell, playback IC, speaker, IR sensors, smell sensors, bread board, power supply and
raspberry pi.
FIGURE 2.1 Internet Of Things Based Waste Management System (Amrutha P. V, et al, 2017)
N Nathrani, et al, 2018, designed a smart system whereby the system has a microcontroller, WiFi module, GSM, DC motor, ultrasonic sensor and telnet protocol used on the internet or local
area network to assist in bidirectional communication for storing as well as retrieving data.
Ultrasonic sensors are placed all over the bin and they detect waste level and after detecting they
inform the microcontroller on the status of the bins (N Nathrani, et al, 2018).
5
FIGURE 2.2 SMART WASTE MANAGEMENT SYSTEM (N NATHRANI, et, al, 2018)
Rupa, et al, 2018, designed a much similar system to the one designed by Rupa, et al, 2018,
which had an embedded IOT based device clinged to the dustbin. The clinged device
continuously monitored the level of the dustbins and updating the information to the website
(Rupa, et al, 2018). The bin level was detected using ultrasonic sensors, and each time the bin if
full or passes a certain limit then the information is updated to the designed website. The system
also consisted of a timer which was set to a fixed number such that when that time elapses while
the bin is not attended to, the system would then forward the message to the higher authority
(Rupa, et al, 2018). During the time when the bin is waiting to be emptied, the system will be in
waiting state and once the bin is cleaned the system comes out of the waiting state.
6
FIGURE 2. 3 Smart Waste Management System (Rupa, et al, 2018)
S.S. Navghane, et al, 2016, also designed an internet of things based smart garbage and waste
collection bin which is interfaced by microcontroller based systems that have IR wireless
systems and also central system that shows the level of bins on a mobile web browser with html
page by Wi-Fi. Their main aim was to eliminate human resources and efforts hence enhancing
the smart city vision (S.S. Navghane, et al, 2016).
7
FIGURE 2.4 Design of a Smart Waste Management and Bin Collection Services (S.S.
Navghane, et al, 2016)
2.4.2 Hardware description of the smart waste system
2.4.2.1 Load sensor
The load sensor was used such that when the required weight is met the information is relayed by
the microcontroller. The micro controller then checks with the ultrasonic sensor such that when
the required weight is reached and also the waste level then the message is relayed.
8
2.4.2.2 Global Positioning System (GPS)
FIGURE 2.5 Global Positioning System
The global positioning system (GPS) is used to give the exact location of where the filled bin is
and the coordinates are sent using the GSM to the control room. Once the coordinates are known,
they are inputted by the driver into the android application and the best route is developed.
9
2.4.2.3 Global System for Mobile communication (GSM)
FIGURE 2.6 Global System for Mobile Communication Nikita Nathrani, et al, 2018
Nikita Nathrani, et al, 2018, used a quad-band GSM/GPRS which was provided by SIM800 with
a SMT type customer application embedded system. Quad-band 850/900/1800/1900MHz is
supported bySIM800. Transmission of voice, SMS and data information is done with low power
consumption using the GSM Module. The module size is 24*24*3 mm, featuring Bluetooth and
embedded AT (Nikita Nathrani, et al, 2018).
Ms. Rupa, et al, 2018, used The SIM800 modem which has the receiver and transmitter pins to
connect the SIM800 modem with microcontroller using the UART at 9600 baud rate, which is
the default baud rate of this modem. Once a serial connection is open through your
microcontroller you can start sending the AT commands (Ms. Rupa, et al, 2018). When you
10
send AT commands for example: "AT\r" you should receive back a reply from the SIM800
modem saying "OK" or other response depending on the command send.
2.4.2.4 Fire detecting sensor
Fire alarm systems are very essential for security purpose. Fire alarm and protection system is a
combination of electrical and electronics instruments. Thermistors are the most use sensing
device which is used in fire detection and prevention systems.
2.4.2.5 Level detecting sensor
FIGURE 2.7 Level detecting sensor (Ultrasonic sensor)
Ultrasonic sensors are used for detecting the level of waste in this scenario. They are transmitting
and receiving ultrasonic signals which have a frequency range from 65 kHz up to 300 kHz. A
short ultrasonic pulse is transmitted at the time 0, reflected by an object. The sensor receives this
signal and converts it to an electric signal. The next pulse is transmitted when the echo is faded
away.
2.4.2.6 Microcontroller
Mrs.D.Anuradha, et al, 2017, used the mega 2560 as a microcontroller board based on
theATmega2560.It consist of 54 digital input and output pins in which 15 can be used as PWM
11
output, 16 analog input, 4UARTswhich is a hardware serial port, 16 MHz crystal oscillator, a
USB connection (Mrs.D.Anuradha, et al, 2017). It also has reset button power jack and ICSP
header. It has the sensor to detect the temperature and humidity and axis digital accelerometer.
The board contains battery shield and connector cables (Mrs.D.Anuradha, et al, 2017). In this
coding is embedded in the kit. The coding contains information used to determine the
temperature. Then HTML code is used for display the output (Mrs.D.Anuradha, et al, 2017).
2.5 Route Optimization for garbage collection trucks
The method on collection of waste and the various parameters considered are analyzed on citing
a number of researchers. The approaches are different in every area and the scheduling differs.
2.5.1 Capacitated Arc Routing Problem
Thelma P.B, et al, 2015, published a paper on sequential approach for the optimization of truck
routes for solid waste collection. They carried the study in Brazil. The main objective of their
study was to present the sequential approach that involved phases of optimization problem of
truck routes for the collection of solid waste. The research was carried in three phases. The first
phase executed the grouping of arcs based on adapted model of the p-median problem
formulated as a model of the Binary Integer Linear Programming (BILP) (Thelma P.B, et al,
2015). Phase two denoted on the development of a model for the solution to the Capacitated Arc
Routing Problem (CARP), formulated as a Mixed Integer Linear Programming (MILP) problem.
The last phase carried out the application of an improved procedure of Hierholzer for sequencing
the arcs obtained in the previous phase. The proposed methodology was verified using real data
and proficiently solved the problem. The results led to a reduction in the distances traveled by
trucks, which could promote money savings for the public coffers, as well as a reduction in
carbon dioxide emissions (Thelma P.B, et al, 2015).
In addition to that, Ogwueleka, T.C, 2009, defined the problem objective as to minimize the
overall cost based on the distance travelled by vehicle. He proposed a heuristic method to
generate possible solution to an extended capacitated arc routing problem (CARP) on undirected
network, inspired by the refuse collection problems in Nigeria. The heuristic procedure consisted
of route first, cluster second method. The computational experience with the heuristic in Onitsha
12
was presented. The technique was compared with the existing schedule with respect to cost, time
and distance travelled. The adoption of the proposed heuristic in Onitsha resulted in reduction of
the number of existing vehicles, a 22.86% saving in refuse collection cost and 16.31% reduction
in vehicle distance travelled per day. Ogwueleka added that the result revealed a good
performance of the proposed heuristic method, which would be useful in vehicle scheduling.
2.5.2 GIS based Route Optimization
Hoang LAN Vu, Kelvin Tsun Wai Ng, Damien Bolingbroke, 2017, used geographic information
systems whereby a GIS-based dual phase model integrated the handcart pre-collection phase and
truck collection phase for a study area located in Hai Phong, Vietnam and the total system cost
was estimated. Initially the impermanent collection points were first identified using both the
maximize coverage and minimize facility location-allocation tools from a list of candidate
temporary collection points and constraints. Afterwards, two vehicle routing problems were then
separately modeled for hand cart and truck routes. 30 situations were modeled in order to assess
the interrelationships between the model parameters, with respect to the total operation costs and
maintenance system costs. The scenario with 11 temporary collection points and a maximum
handcart collection distance of 500 m gave the lowest overall cost in the study area. The results
suggested that a single temporary collection point in the study was able to serve about 2,590
people in an area of 0.11square kilometers. Compared to the status quo condition, a 13.76%
reduction in truck travel distances was attainable using the proposed model (Damien
Bolingbroke, et al, 2017). They also concluded that the number and distribution of temporary
collection points greatly affected the cost effectiveness in both pre-collection and collection
phases.
2.5.3 Other Approaches Used On Route Optimization
M Dotoli, et al, 2017, researched on route optimization and published a paper entitled a vehicle
routing technique for hazardous waste collection. M Dotoli, et al, 2017, presented a vehicle
routing and scheduling technique for the collection and disposal of hazardous waste. The method
combined clients’ requests for waste removal in order to find routinely the best trade-off between
reduction of travelled distances, maximization of amount of collected waste, and maximization
of commercial value of extractions. They considered constraints on vehicles’ capacity i.e. mass
13
and volume, and availability, lunch break requirements, working hours’ limits, the need to keep
the different type of hazardous waste separated, and the time restrictions that typically limit the
collection of waste at commercial and industrial sites (M Dotoli, et al, 2017). The short
computational time made the approach more suitable for planning, evaluation of alternative plans
and management of late orders or urgencies. M Dotoli, et al, 2017, suggested that in future
research to include the integration of the proposed method with some heuristics to reduce the
computational complexity in case of a very large number of requests to service.
In addition to the fight for proper waste management, Töania Rodrigues Pereira Ramos, Carolina
Soares de Morais, Ana Paula Barbosa-Povoa, 2017, conducted a research on the smart waste
collection routing problem, alternative operational management approaches. In order to reduce
uncertainty on the waste levels in bins, they installed sensors that were capable of reading and
reporting the bins’ level in real-time. This contributed to the classification of dynamic routes to
collect the more demanding bins resulting in an improvement of the operations´ efficiency. Three
different operational management approaches were studied to deal with the information
transmitted by the sensors (Ana Paula Barbosa-Povoa, et al, 2017). The first was a limited
approach, in which a minimum fill-level rule was imposed followed by a smart collection
approach, that used a model to decide the best bins and the best sequence to collect them and
finally a smarter collection approach, that used a combination of a heuristic method to decide the
best days to collect the bins, with the smart collection model developed in the previous stage. To
confirm the three approaches, data from a real case-study was used and for each approach, the
impacts on profit, distance travelled, kg per km ratio and vehicle usage rate were assessed and
compared to the company’s current results. The results show that the third operational
management approach is the most efficient of the three, leading to a potential company’s profit
increase of 7%, which represents a meaningful managerial insight for practical implementations
(Ana Paula Barbosa-Povoa, et al, 2017).
2.6 Waste generation models
Waste generation production rate have been a key subject studied by researchers to try to predict
the amounts produced for different communities, cities and countries. Predictive statistical tools
are mostly used to predict the generated amounts for example regression analysis, time series
models and artificial neural networks.
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2.6.1 Prediction of Solid Waste Generated Using Artificial Neural Network
Miyuru Kannangara, et al, 2017 did a study and its main objective of this study was to develop
models for accurate prediction of municipal solid waste (MSW) generation and diversion based
on demographic and socio-economic variables, with planned application of generating Canadawide MSW inventories. Models were generated by mapping residential solid waste quantities
against the socio economic and demographic parameters of 220 municipalities in the province of
Ontario, Canada. Decision trees and neural networks were used to develop the models. The data
was obtained from Canadian Census at regional and municipal levels (Miyuru Kannangara, et al,
2017). Datasets were generated using Matlab software. Results indicated that the algorithms can
be used to predict waste generation (Miyuru Kannangara, et al, 2017).
In addition to that, Sama Azadi, et al, 2015, analyzed on the prediction of mean seasonal
municipal solid waste generation. They developed an artificial neural network model and
multiple linear regression model for the verification of the results. MAE, MAPE, RMSE and R
were the performance measures used for the verification. The multiple regression model showed
poor prediction performance as opposed to the ANN model (Sama Azadi, et al, 2015). They
concluded that in order to have a cost effective strategy for the waste management in the future,
the ANN model should be adopted.
2.6.2 Prediction of Solid Waste Generated Using Regression Models
S.M. Al-Salem et al, 2018, analyzed on the accurate prediction of solid waste generation the
study was done in Kuwait and six inputs were considered for development of the multi variable
regression models. The statistical analysis confirmed the reliability of the regression models that
were developed. S.M. Al-Salem et al, 2018, stated that the results obtained indicated that the
predictions were very accurate. The standard errors (SE) ranged from 3.52% to 10.46% for the
multiple regressions predictive models while the mean standard errors for the dependent
variables were between 0.125 to 1.09% (S.M. Al-Salem et al, 2018). Hence the regression
models can be used to predict the individual solid waste components that can be used in decision
making and also policies for long term solid waste management policies (S.M. Al-Salem et al,
2018).
15
Daniel Starobin, et al, 2016, developed a study on prediction of solid waste management using
gradient boosting regression model. The data was supplied from the New York City Department
of Sanitation and was collaborated with other data sets to forecast the solid waste generation. The
model was trained using data range from 2005 to 2011 and the evaluation was done both
temporally and spatially (Daniel Starobin, et al, 2016). The model accurately forecasted on the
weekly solid waste generation tonnages for all the sections in the city. It also captured very short
timescale fluctuations associated with holidays, special events, seasonal variations and weather
related events (Daniel Starobin, et al, 2016).
2.6.2 Other Approaches
Maryam Abbasi, et al, 2015, used a variety of approaches to estimate on the future waste
generation amounts. The main objective of their study was to develop a model to be used for
accurate forecasting of solid waste management. However, the algorithms used were adaptive
neuro-fizzy inference system, artificial neural network, k-nearest neighbor and support vector
machine (Maryam Abbasi, et al, 2015). The study was carried out in Ogan City Council region in
Queensland, Australia and the results showed that artificial intelligence models have good
prediction performance. The results also suggested that ANFIS system produced the most
accurate forecasts of the peaks while k-nearest neighbor was successful in predicting the monthly
average of waste amounts (Maryam Abbasi, et al, 2015).
In conclusion as a researcher, there is need to optimize the routes followed by the garbage
collection trucks and also to predict the waste generated in Harare CBD area. Other researches
solely touched the two on separate occasions but as a researcher there is a necessity two do the
same once such that before optimizing routes, the amount of waste generated will be known
through estimation and also it shows on time of noticeable changes to optimize the routes then.
However, the researcher went on to do the steeple analysis to check on the accountability of the
project.
2.7 Steeple Analysis
➢ Social
16
Society will benefit from the waste vehicle route optimization since the pollution will be
reduced.
➢ Technological
The route optimization of the waste collection vehicles will be done using the models and also
algorithms in the software and web application.
➢ Environmental
Route optimization of the waste collection vehicles is ecofriendly since the number of vehicles
used on particular routes will be optimized hence the carbon dioxide produced will be decreased
as the fuel consumed decreases.
➢ Economic
The main objective of this route optimization is to reduce transportation costs hence considering
the Municipality council large sums of money will be saved and can be used for other purposes.
➢ Legal and Political
The implementation of the project is in accordance with the government regulations.
➢ Ethical
The route optimization results in a friendly and cleaner environment.
2.8 Gap Analysis
The researcher gathered all the relevant information on the research done towards the
optimization of delivering the waste management services. From the previous work done by the
other researchers, a lot has been done and the researcher acknowledged them. However, the
researcher saw the need to develop an android application that shows the optimized route and
included navigation so that the driver can actually follow the correct path. Also the researcher
added fire detecting sensors on the waste level detecting system something that had been
neglected by other researchers. The prediction of the waste generated was also taken into account
by the researcher.
17
2.9 Conclusion
The chapter looked at the work done by the other researchers in developing smart waste
management systems. The researcher also did gap analysis to further see some improvements
that are to be done.
18
CHAPTER THREE: METHODOLOGY
3.0 Introduction
This chapter focuses is the primary guideline of the researcher on the development of the waste
prediction models, the smart waste collection system and the route optimization of the garbage
collection trucks. It contains the various research methods and the data collection techniques
used by the researcher during the course of the development of the project. The chapter contains
the project routine in form of a flow chart which consists of steps followed in course of the
project.
3.1 Project flowchart
The researcher used Pahl and Beitz design model as it was the most feasible to use since it
highlights each stage and allows for its evaluation before proceeding to the next stage. The
design process consists of the steps that are followed chronologically that are one after the other.
The steps highlighted on the flowchart include identification of the problem, literature review,
data collection, conceptual designs formulation, concept selection, detailed design, development
of the model, validation of the developed prototype, correction of the prototype and the finally
development of the actual product. The flowchart is shown below.
Figure 3.1 project flowchart
19
3.2Problem Identification
This is the first stage that was done by the researcher. After navigating around the Harare Central
Business District, CBD, the researcher’s attention was drawn towards by the amount of full bins
lying idle waiting for collection and also the overflowing of the waste from the bins posing
health hazards to the people and the environment at large. This prompted the researcher to look
for an engineering solution to reduce the problem hence the idea of developing the system that
notifies the City Fathers each time a bin is full, its actual location and the optimal path to be
followed. The researcher then went on to propose the developing of the system.
3.3 Data Collection
After the initial step, the researcher then went on to research for the relevant information to use
for the development of the system. On this stage various techniques were employed to gather
data and these included the use of observations, electronic journals, interviews, waste
management reports, internet sources, permission seeking from the Harare City Council and
informal conversations with randomly picked people.
Below are the graphs showing some of the data the researcher obtained from the Harare City
Council Waste Management Department in Granite side.
FIGURE 3.2 GRAPH OF AVERAGE NUMBER OF BINS
20
3.3.1 Permission seeking from the Harare City Council
The researcher sought permission from the Harare City Council to conduct primary research at
the Harare City Council Waste Management Department in Granite side (Pagomba). The
researcher sought permission to have access to collect information from the drivers, their waste
management reports and also to undergo some field trips with them.
3.3.2 Literature Review
The researcher looked for the relevant information to use for the development of the system. This
information included how other researchers had tried to mitigate the problem, the relevant
components used and their working principles, and the gaps that were left by other researcher
that were addressed by the researcher in the development of the system. Past research papers
published by other researchers were used to extract information from by the researcher and also
internet sources were used by the researcher. The researcher used all the information collected to
develop the system.
3.3.3 Observations
The observations were carried out by the researcher on how the waste was collected and from the
observations done it showed that no systematic approach was done in terms of scheduling for the
waste collection rather they worked on assumptions that the bin at certain place is full leading to
miscalculations in terms of correct collection of bins giving room for overflowing of bins. In
some cases the trucks can go and return without providing service as the bin can be found to be
still empty or at very low level. The researcher also looked at how the waste collectors lost
control of the waste and brainstormed on what can be done. After observing the researcher
compared the proposed system against the old conventional way of collecting waste.
3.3.4 Interviews
The researcher conducted interviews with the responsible people at the Waste Management
Department. From the interviews conducted it showed that bins collected each day in the Harare
Central Business District ranged from 11 bins to 15 bins a day. The rise from 8 bins to 10 bins a
day from the previous years was caused by the increase in activities in the Harare Central
21
Business District. Statistical data was provided and graphs were plotted to show how the
fluctuations go about in waste collection services per week. Problems faced with the keeping of
the bins were also highlighted as they talked on rapid fires being started in the bins by the street
children. The cost increment of services was also highlighted during the interviews as they said
that since there are no optimized routes, drivers tend to use their own judgment and follow their
own routes so long they provide the routes hence this made the researcher to also develop the
android application for route optimization such that costs are reduced.
3.4 Conceptual Design
After gathering all the relevant information, the researcher then proceeded to the system
development stage. During this stage, the researcher developed numerous designs that all had
different working principles but all meeting the same objectives and the aim of the project. The
concepts were described together with their working principles and also the components needed.
Factors were named that were to be considered during the development of the system. These
factors included Prediction accuracy/performance, memory requirements, safety, cost,
maintainability and effectiveness.
3.4.1 Embodiment design
At this stage, the concepts were analyzed and concept screening and scoring was done to remain
with the last solution that provided all the benefits. Strengths and weaknesses were analyzed and
the remaining solution proceeded to the next stage of detailed design with some features being
borrowed from other concepts suggested.
3.4.2 Detailed Design
In this stage, the details of the system to be developed are then stated and all the design
considerations are taken into account such that they provide for the system developed. The
details also guide with the objectives and the aim of the project.
3.5 ‘;p
22
3.6 Conclusion
The chapter looked on how the researcher undertook the project and also how data was collected.
The chapter also covered on the various research methods used and the techniques employed by
the researcher.
23
CHAPTER FOUR: DESIGN DEVELOPMENT
4.0 Introduction
Chapter four focuses on the concepts and solutions developed which are used to solve the
problem. The concepts developed should use the current and advanced technology to overcome
the waste being produced in the city with minimum effort and cost. This chapter contains the
generation and selection of the concepts. The solution with the highest score is then deemed the
chosen solution. The design development of the chosen solution and also the economic analysis
is covered in this chapter.
4.1 Design considerations
Prediction accuracy/performance, memory requirements, complexity, learning rate, modeling
ease, input-output correlation are the factors influencing the concept generation as described
below.
4.1.1 Concept 1
This concept is a metaheuristic approach and is used on the periodic vehicle route optimization
(PVRP). The algorithm that is used is the Tabu Search algorithm. It works in such a way that
when the initial data is collected based on the collection routes and schedules, an initial solution
is formed. After forming an initial solution, small alterations start to be computed in the need to
find the best alternative route that can be used. Availability of waste is the major constraint for
this concept. The small changes that are made include changes in collection points, routes that
might be initially linked, containers that exists on similar road networks and changes in
collection schedules. This concept is done using the Transcad software.
24
FIGURE 4.1 CONCEPT 1
4.1.2.1 Advantages
•
Accurate
•
Room for visualization and analysis of transport problems
4.1.2.2 Limitations
•
More complex
4.1.2 Concept 2
This is an ANN predictive model with a radial basis neural network (RBNN). The neural
network has no weights between the input and output layers and the hidden layer. The network
has the transfer function fixed as to be radically symmetrical. This concept consists of a heuristic
approach for capacitated vehicle routing problem (CVRP). The algorithm used in this concept is
the adaptive large neighborhood search (ALNS). The algorithm is a framework where different
algorithms compete to modify the current solution. The algorithm then destroys the solution and
another algorithm chosen repairs the solution. This concept works in such a way that the vehicles
start from the depot and they should all return to the depot after completion of the scheduled
25
work. The waste collected should not be greater than the capacity the vehicle can carry. This
concept is done using the LogVRP.
FIGURE 4.2 CONCEPT 2
4.1.3.1 Advantages
•
less complex
•
requires less memory for computation
4.1.3.1 Disadvantages
•
Have a limited number of transfer (activation) functions.
•
Less accurate
•
Poor relative network training
4.1.3 Concept 3
This concept consists of a waste level detection system, predictive model and an android
application for the route optimization. The waste level detection consists of sensors that sense the
26
level of waste and also the weight of the waste such that when the threshold value is reached, a
message is sent to the waste management control room and the sent information is stored in the
database. From the stored information, the registration number and the driver responsible for that
zone highlighted in the message then is given the coordinates of the location of where the bin is
and the driver assuming that they all use the android phones then input the coordinates into the
android application and the optimal path is formed. As soon as the bin is emptied, a message is
then send again showing the bin status.
Figure 4.3 CONCEPT THREE
27
4.2 Concept Selection
The researcher used the two-stage phase concept selection critea. The concepts were compared
and analyzed and a quantitative evaluation was done through screening and scoring.
4.2.1 Concept screening
Analysis was done on the concepts made by the researcher. A reference scale of +, - and 0 was
used by the researcher. The positive sign referred to a good feature, while a negative sign to a
bad feature and 0 as a neutral. After screening the remaining concepts proceeded to concept
scoring.
Table 4.1 CONCEPT SCREENING
Criteria
Reference Concept 1 Concept 2
Concept 3
Availability
0
+
+
+
Cost
0
0
-
+
Accuracy
0
-
0
+
Complexity
0
-
+
+
Learning rate
0
+
+
+
Memory requirements
0
+
+
0
Safety and ergonomics
0
+
+
+
Processing speed
0
+
0
+
Total number of positives
0
4
5
7
Total number of negatives 0
2
1
0
Net
2
4
7
Ranking
3
2
1
Decision
Proceed to next stage Proceed to next stage
4.2.2 Concept Scoring
After concept screening, the researcher proceeded to do concept scoring with two concepts
remaining that is concept 2 and concept 3. The researcher developed a rating scale to use to do
concept scoring. The concept with the highest score was then developed. The rating was done
against the criteria stated.
28
Rating scale used by the researcher
1. Excellent 9-10
2. Good 7-8
3. Fair 5-6
4. Poor 3-4
5. Unsatisfactory 0-2
Table below shows the scoring for the ANN model concepts.
Table 4.2 CONCEPT SCORING
Concept 2
Criteria
Weight Rating
Concept 3
Weighted
Rating
score
Weighted
score
Availability
0.15
5
0.75
7
1.05
Cost
0.10
6
0.60
5
0.50
Accuracy
0.15
5
0.75
6
0.90
Complexity
0.10
5
0.50
6
0.60
Learning rate
0.10
6
0.60
7
0.70
Memory
0.10
4
0.40
5
0.50
and 0.15
5
0.75
6
0.90
0.15
5
0.75
7
1.05
requirements
Safety
ergonomics
Processing
speed
Total score
5.10
6.20
Rank
2
1
Decision
Reject
Develop
29
4.3 Design development of the system
The design detailed below covers from the electronic part of the waste level detection, design of
the android application and design of the predictive model.
4.3.1 Design of the electronic part (waste level detection system)
This section gives the detailed design of the circuit and also algorithms to be followed in route
optimization. The researcher is using proteus software that contains ultrasonic, gsm and gps
libraries. Java programming language is used to program the system. Below is the algorithm for
waste level detection part of the system.
Algorithm 1
Inputs: waste in bin
Outputs: message indicating bin status
1 Install ultrasonic sensor at the top of the bin
2 Fix the threshold distance as 10cm
3 Measure the waste level in each bin after every 10s
4 If distance from sensor to top of waste level is less than 10cm then send the message to
the control room or else return step number 3.
5 End
The ultrasonic sensor senses the waste level in the form of ping time which is from the ECHO
pin such that the microcontroller calculates the distance between the waste level and sensor. Ping
time is defined as the time taken by the ultrasonic sound from the point of reaching waste level
and returning back to the ECHO pin. The distance is calculated as shown below:
•
Distance is the vertical distance between top of waste level and ultrasonic sensor
•
Time is the ping time and is denoted by (t)
•
V is the speed of ultrasonic sound which is 340m/s at rtp and pressure
•
Assuming that the vertical length of the bin is 1.5m, 𝑙 is the level of waste and 𝑙𝑜 is the
vertical length of the bin
30
𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = 𝑠𝑝𝑒𝑒𝑑 × (
𝑡𝑖𝑚𝑒
)
2
= 𝑠𝑝𝑒𝑒𝑑 × 𝑝𝑖𝑛𝑔 𝑡𝑖𝑚𝑒
=𝑣×
𝑡
2
= 340 ×
𝑡
2
Change from distance to level of waste generated
𝑙𝑒𝑣𝑒𝑙 𝑜𝑓 𝑤𝑎𝑠𝑡𝑒 (𝑙)𝑎𝑠 𝑎 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒 𝑖𝑠 𝑔𝑖𝑣𝑒𝑛 𝑏𝑦 =
=
𝑙0 − 170𝑡
× 100%
𝑙0
=
1.5 − 170𝑡
× 100%
1.5
𝑙𝑜 − 𝑑
× 100%
𝑙0
The will be used by the ping time comes from the ECHO pin hence the equation above will be
used by the microcontroller to calculate waste level.
4.3.2 Design of the fire detection part of the system
The fire detection part of the system consists of the Thermistors, transistor, indicator and also
battery.
4.3.3Circuit design
This section covers the design of the circuit and also the assembly part of components. There is
full wave rectification where by the AC is stepped down and after that it is then rectified and
finally smoothened by the capacitor. The voltage from the socket is stepped down to at least 9V
and this is deemed as the supply voltage to the components. As shown from the circuit, the trig
pin on the ultrasonic sensor is controlled by the microcontroller and the ping time is read and the
waste level is calculated based on the mathematical model of the microcontroller. After all that
the distance is then send to the GSM and the GPS also sends the address of the bin to the GSM
also. The GSM then sends the message to the control room showing bin status level and location.
31
4.3.3.1 Circuit calculations
This helps the researcher in knowing actual components to be used and the right specific data.
Calculations also help in knowing the amounts needed so as to avoid the sudden failure of the
circuit. Below is the detailed workings of the electronics components.
Firstly, the transformer steps down the 240V AC to 9 volts. From the datasheet, the
microcontroller and the ultrasonic sensor works at5Volts DC with a recommended supply
voltage of 7-12 volts.
1 Transformer calculations
Given,
Primary voltage 𝑉𝑝 =220
Secondary voltage 𝑉𝑠 =9
Secondary winding 𝑁𝑠
Primary winding 𝑁𝑝
𝑉𝑝 𝑁𝑝
𝑉𝑠
=𝑁
𝑠
220
9
80
= 3
Select, Trans-80P3S.
Voltage smoothening
Capacitor
Given,
Frequency (f) = 50Hz
𝑉𝑟𝑖𝑝𝑝𝑙𝑒 = 0.0063 the allowable ripple across the load
𝐼
𝑙𝑜𝑎𝑑
𝑉𝑟𝑖𝑝𝑝𝑙𝑒 = 𝑓×𝐶
32
𝐼
𝑙𝑜𝑎𝑑
𝐶= 𝑓×𝑉
150
= 50×0.0063
= 474.1904
Say 470 µF
Resistance (Ohms Law)
R=
∆𝑉
𝐼
Where,
I-operating current
R-resistance
∆ 𝑉- voltage drop
Resistance for the AT Mega micro controller and its peripherals
The operating voltage and current of the components is obtained from the datasheet of the
components.
∆ 𝑉 = 9-5 V
I = 40mA
R=
∆𝑉
𝐼
(9−5)𝑉
= 40𝑚𝐴
=100 000
= 100K Ω
Resistance for Ultrasonic sensor
∆ 𝑉 = (9-5) V
33
I = 15mA
∆𝑉
R= 𝐼
(9−5)𝑉
= 15𝑚𝐴
= 266 666.3
=270 000
= 270k Ω
Resistance for the GSM module
∆ V = 9-3.3 V
I = 160mA
𝑅 =
∆𝑉
𝐼
(9−3.3)𝑉
= 160𝑚𝐴
=38 000
= 30𝑘 Ω
4.4 Route Optimization formulation
This section covers the route optimization formulation. Route optimization is done using an
android application such that when the coordinates are received from the GPS, they are inputted
into the application and an optimal path is created that minimizes costs incurred, distance
travelled and also resource utilization. Below is an algorithm used in the created android
application.
Algorithm 2
Inputs: location coordinates
Output: optimized path
34
1 Fetch the data from the sensors
2 Send the bin location to the driver
3 Driver inputs bin location coordinates into application
4 Calculate the best route using genetic algorithm and travelling salesman algorithm
5 Assign Id for each location randomly
6 Compare current location with p1 to find distance
7 For first location to last location
➢ Randomly select one location
➢ Compare distance between current location and selected location
➢ If distance is less replace p1 with new selected one
8 Return the best positions in the order of distance from small to large
9 The genetic algorithm groups the locations with same distance
10 Calculates fitness value that is considered as the cost in travelling salesman algorithm
11 Using travelling salesman algorithm, it traverse the groups to find the best route
12 Optimal route is mapped
4.5 Android Application Development for Route Optimization
The Android Application was developed using Android Studio. The application is used by the
users to see the optimized route. The latitude and longitude are inputted into the application.
After inputting the map appears and the exact position of the user is shown on the map and after
clicking on the navigation pane the route simulation starts. The android application exactly
shows the time to be taken to reach the destination and the total distance as well.
4.6 Development of the Predictive model
A systematic approach is taken during the development of the predictive model. Factors such as
network architecture, adequate model inputs, parameter estimation and model validation are
taken into consideration. The model was build based on the multilayer feed-forward network.
The Levenberg-Marquardt training algorithm was used during the training process and it
combines the Grade and Gauss-Newton methods. Statistical error measures like MAPE, RMSE
35
and R2 are computed. Below are the formulas used for the computation of the statistical error
measures. The inputs include the total waste collected per day, number of trucks the council have
assigned to the zones, the
𝑛
𝑆𝑊𝐺(𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑) − 𝑆𝑊𝐺(𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑)
1
𝑀𝐴𝑃𝐸 = ( ∑ |
|)
𝑛
𝑆𝑊𝐺(𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑)
𝑖=1
𝑛
1
2
𝑅𝑀𝑆𝐸 = √( ∑|𝑆𝑊𝐺(𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑) − 𝑆𝑊𝐺(𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑) | )
𝑛
𝑖=1
2
∑𝑛𝑖=1|𝑆𝑊𝐺(𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑) − 𝑆𝑊𝐺(𝑝𝑟𝑒𝑑𝑖𝑐𝑡𝑒𝑑) |
))
𝑅 = (1 − (
𝑆𝑊𝐺(𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑)
2
4.7 Economic Analysis
The economic analysis highlights the costs to be incurred from procurement of materials,
development of the system and the profits to be experienced. The total cost for the system will be
the combined amount of material costs and the manufacturing costs.
36
4.7.1Material cost
Below is a table termed the bill of materials highlighting the components that have been
procured and their respective costs. Also cost analysis is done for mass production of these
systems.
Table …..
Part Description
Number of units Unit price ($) Per units ($) Per 1000 units ($)
GPS module
1
47
47
47000
GSM SIM 900A
1
35
35
35000
Ultrasonic sensor
1
6
6
6000
Microcontroller (Atmega 16)
1
20
20
20000
System casing
1
5
5
5000
Android phone
1
250
250
250000
Voltage regulator
1
3
3
3000
Capacitor
1
0.50
0.50
500
Resistors
3
0.50
1.50
1500
Circuit board
1
3
3
3000
Laptop
1
600
600
600000
Sim card
1
2
2
2000
Transformer
1
12
12
12000
Rectification diodes
4
2
8
8000
Max 232
1
6
6
6000
Lcd screen 16”
1
10
10
10000
1009
997000
Total labor
Total cost for
Overhead costs +
cost ($)
1000 units
overhead @ 25%
$4/hour
32
32000
$7/hour
35
35000
Total cost
4.7.2 Projected sales
Operation
Android
Personnel
Programmer
application
Programming
Time
8
Rate
hours
Programmer
5
37
software
hours
Circuit
Electronics
2
development
expert
hours
Soldering
Electronics
2
expert
hours
$7/hour
14
14000
$4/hour
8
8000
89
89000
Total cost
22250
Total for producing one unit = total labor cost for one unit + total unit cost
= $89 + $1009
= $1098
Total cost for producing 1000 units = total labor costs @ 1000 units + total unit cost @ 1000
= $89000 + $997000
=$1 086 000
Total unit cost in mass production = $1086000/1000
= $1086
The sales price estimated for the product is $1200
Therefore contribution margin = revenue cost – variable cost
= $1200 - $1086
= $114
Cost of investment for production cost = total material (mass) + total labor (overhead)
= $89000 + $997000
= $1086000
Fixed cost at 40% of capital investment for production = = 0.4 × $1086000
= $434 400
Initial capital investment = total hardware cost + total development cost
38
= $1009 + $89
= $1098
Total cost = Initial capital investment + Additional operational cost
= $1098 + $150
= $1248
System selling price
Assuming the mark-up price of 25%
Initial capital investment = $1098
Therefore mark up price = 0.25 * $1098
= $274.5
Therefore selling price = $1098 + $274.5
= $1372.50
Therefore the payback period = $1098/$1372.50
= 0.8years * 12months
= 9.6months
39
CHAPTER FIVE: RESULTS, CONCLUSION AND RECOMMENDATIONS
5.0 Introduction
This chapter highlights the results obtained from the system developed and the prediction model.
It also looks at overview of the stated aim and objectives from chapter and whether if they were
met.
5.1 Overview of objectives
The objectives stated in chapter 1 were as follows:
•
To develop a predictive model for solid waste generation
•
To develop a waste level detection system
•
To develop an android application for route optimization
5.2 Prediction of solid waste generated
5.3 Waste level detection system
The waste level detection system was developed and it performed as per the intention. The
ultrasonic sensor detected the waste level and continuously reported to the microcontroller giving
feedback on the level of waste. Once the level surpassed the threshold limit, the GSM sent a
message of the waste level and also the exact location of the bin to the waste management
control department. Also in the event of fire, the system also notified the responsible authority
since a fire detecting sensor was also coupled to the system (LM35 sensor).
5.4 Android application for route optimization
The android application was developed using Android Studio and Dart programming languages.
The application was installed in an android device and worked very well. Once the message was
received, the coordinates were sent to the driver and the route simulation was done showing the
optimized route to be followed. Route navigation was also added to the application so the driver
40
could simply watch the route before driving. See appendices for the screenshots taken for the
android application.
5.5 Recommendations
The scope of the project was limited to just development of smart waste system, route
optimization and prediction of number of bins collected. However, much work still needs to be
done like the system should also in future be necessitated to finding the bins that fill first and
giving solutions as to when they should be visited. The system should also be integrated with the
machine vision aspect of being able to detect the different types of waste and giving back report.
41
42
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APPENDIX A: WASTE LEVEL DETECTION SYSTEM CODE
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APPENDIX B: ROUTE CODE MAP ACTIVITY
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APPENDIX C: ROUTE CODE MAIN ACTIVITY
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APPENDIX D: ROUTE CODE
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APPENDIX E: DIAGRAM FOR THE SUGGESTED BIN IN 3D MODEL
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APPENDIX F: SYSTEM SIMULATION USING PROTEUS
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APPENDIX G: DATASET COLLECTED FROM MUNICIPALITY
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APPENDIX H: ANDROID APPLICATION INTERFACES
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