Created March 28, 2020 © GPL3+

Smart and Sustainable Waste Management System

A better way of managing both Dry and Wet waste!

IntermediateFull instructions provided24 hours134

Smart and Sustainable Waste Management System

Things used in this project

Hardware components

Bolt IoT Bolt WiFi Module

SparkFun Ultrasonic Sensor - HC-SR04

Arduino UNO

Jumper wires (generic)

DFRobot Gravity: Analog LM35 Temperature Sensor For Arduino

Software apps and online services

Arduino IDE

Bolt IoT Bolt Cloud

Twilio SMS Messaging API

Story

INTRODUCTION

Waste Management System

In simple terms, waste management is the separation of wet waste and dry waste. The generation of waste is unavoidable, and the materials carried in this waste impacts human and environmental health. Naturally, waste management is something that must be carried out, and one way to do this meticulous segregation of wet and dry waste, so that dry waste can be recycled and wet waste can be composted.

When we segregate waste, there is reduction of waste that reaches landfills and occupies space. Air and water pollution rates are considerably reduced, and makes it easier to apply different processes – composting, recycling and incineration can be applied to different kinds of waste.

Waste management starts at the household level, and is not that difficult to achieve. Even a few minor changes can go a long way. Firstly, have two garbage disposal bins at home, one for dry waste and one for wet waste. Items like aluminium foils, tetra packs, glass, paper, plastics, metals, etc. fall under the dry waste category, whereas kitchen waste such as stale food, fruits and vegetables come under wet waste.

It is important to make sure that wet waste is thrown out of the house on a daily basis. Dry waste can be discarded twice or thrice a week. Ensure that plastic containers thrown in the dry waste bin are void of any food residue.

Besides taking measures at an individual level, try involving like-minded people and form a community solely dedicated to waste management in your apartment complex. Introduce two separate disposal drums on your complex ground, and explain to people the importance of this segregation. The process of waste segregation should be thoroughly explained to family and neighbours in your apartment building. Create awareness amongst the staff in the apartment building to help make the process easier.

The importance of waste segregation in the world cannot be understated. Waste Segregation is the first step in a compliant waste management plan that will help the save the environment and improve the quality of the atmosphere we live in. It really does matter which bin you put the garbage into.

If done in a proper manner, waste management not only eliminates the surrounding waste, but also will reduce the intensity of the greenhouse gases like methane, carbon monoxide which gets emitted from the wastes accumulated. The depth of the existing landfills will be also curbed, thereby cutting down whatever is toxic to the environment. The number of fossil fuels will also get reduced in this manner, leading to a cleaner and a greener environment. This project helps in managing both dry and wet waste in a smart way.

DRY WASTE MANAGEMENT

1. Problem Statement

To implement a smart bin built on a micro controller based platform Arduino Uno board which is interfaced with BOLT IoT WiFi module and Ultrasonic sensor which can notify the status of the waste present in the dustbin to the municipal authority.

2. System Flow Chart

Flow Chart

The flow diagram of the proposed system is as shown above (can be understood easily)

3. About Hardware

3.1 Bolt IoT Platform

Bolt IoT Wi-Fi module

Bolt is an IoT platform which let you control your devices and sensors through the internet with ease. With firmware built into it, you can connect the bolt device to any Wi-Fi near you with the bolt app. With bolt you can control your device and monitor from any part of the world and the best part is, it has its own cloud and you can deploy the code and analyse by sitting anywhere on the Earth.

for more details please visit: https://www.boltiot.com/

3.2 Ultrasonic Sensor

Ultrasonic Sensor

An Ultrasonic sensor is a device that measures the distance of an object with the help of sound waves. It measures distance through sending out a sound wave at a particular frequency and listening for that wave to bounce back. It is possible to measure the distance between the sensor and that object by recording the elapsed time between the sound wave being generated and the sound wave bouncing back. In other words, the sensor head emits an ultrasonic wave and receives the wave that is reflected back from the target. The distance can be calculated with the following formula: Distance = 1/2 × T × C Where T is the time between the emission and reception, and C is the speed.

3.3 Twilio Messaging API

Twilio API Logo

Twilio is a cloud communications platform as a service company based in San Francisco, California. Twilio allows software developers pro grammatically to make and receive phone calls, send and receive text messages, and perform other communication functions using its web service APIs. It that enables powerful communication between mobile devices, applications, services, and systems throughout the business in order to bridge the gap between conventional communication. Twilio seeks to rid businesses of the messy telecom hardware by providing a telephony infrastructure web service via a globally available cloud API, allowing web developers to use standard web languages to integrate phone calls, text messages and IP voice communications into their web, mobile and traditional phone applications.

4. Hardware Setup

Circuit Diagram

The Ultrasonic sensor is kept on the top of the dustbin to constantly check the level of the trash. The Arduino sends this trash level constantly to the Bolt device and the python code written for the Bolt device checks whether the level is less than the given limit in the code. If it is less than the given limit, it send an SMS alert and telegram message to the mobile phone.

5. Software Setup

Interfacing the ultrasonic sensor as mentioned in the above link in the blog would be quite mathematical. So, we could the ultrasonic sensor library by Erick Simoes which abstracts all the mathematical formulas of the sensor. Also I have given the github link to download the Arduino and Bolt IoT interfacing library

Here is the link to download library:

https://github.com/Inventrom/boltiot-arduino- helperhttps://github.com/ErickSimoes/Ultrasonic

The Arduino is programmed to check the distance of the garbage and send the distance constantly to the bolt device constantly (See working of Ultrasonic Sensor above). The python code written for the bolt device will check whether the distance less than the required. If it is less than the required then it sends an alert through Twilio and Telegram.

6. Working/ Demo

Hardware Setup in - action

The project is tested by filling the dry waste. The ultrasonic sensor was able to give the details of the amount of trash filled in percentage. I was also able to receive the message from Twilio API when the dustbin was filled (I have attached the screen shot of the message received below) After receiving the message the municipality authority can come and pick up the dry waste. But, this cannot be done in case of wet waste because it produces bad odour which is unpleasant for human-beings. Let us see how can we encounter this problem using the same module.

Message Received Using TWILIO API

WET WASTE MANAGEMENT

Dry waste can be managed easily but in case of wet waste it is not so easy. Now, let us see how can we fix it.

For doing that we can determine the microbial succession of the dominating taxa and functional groups of microorganisms and the total microbial activity during the composting of bio waste in a monitored process. The wet waste emits bad odour because of microbial activities which takes place inside the bin, it is because, certain gasses are released in to the environment such as methane and carbon monoxide. During the microbial activities there will be sudden raise (normally it will be the room temperature but during the microbial activities it reaches to 60- 70 degree Celsius) in the temperature. If the temperature if the bin is constantly being monitored and if we can predict the temperature inside the bin in the earlier stages we can dispose the waste inside the bin earlier, which protects the bin in emitting the bad odour. For doing that we need Machine Learning algorithm to predict the temperature. Hence I am going to use Bolt IoT WiFi module for the experiment. Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. Nowadays, machine learning are being used in various field ie., medical, IT, Industrial, space, etc. Today we are focused on building a project based on polynomial regression to predict the temperature inside the wet dustbin. For that I am using Polynomial Regression. Now, What is Polynomial Regression?

Polynomial Regression

In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in x.

Prediction

Machine learning is a way of identifying patterns in data and using them to automatically make predictions or decisions. For regression, you will learn how to measure the correlation between two variables and compute a best-fit line for making predictions when the underlying relationship is linear.

1. Experimental Setup

Components Required

The Bolt Wifi module
3 female to male wire
Temperature Sensor: LM35 sensor

Connecting the LM35 Sensor to the Bolt

(This section is completely given in the Training, module number 03)

Step 1: Hold the sensor in a manner such that you can read LM35 written on it.

Step 2: In this position, identify the pins of the sensor as VCC, Output and Gnd from your left to right.

Connecting jumpers

In the above image, VCC is connected to the red wire, Output is connected to the orange wire and Gnd is connected to the brown wire.Step 3: Using male to female wire connect the 3 pins of the LM35 to the Bolt Wifi Module as follows:

VCC pin of the LM35 connects to 5v of the Bolt Wifi module.
Output pin of the LM35 connects to A0 (Analog input pin) of the Bolt Wifi module.
Gnd pin of the LM35 connects to the Gnd.

Connection to the module

The final circuit should look like the image below:

Circuit Diagram

Predicting the Temperature

Step 1: Make the same connections as 'Hardware connections for temperature monitor' screen, in the 'Interfacing sensor over VPS' topic of the 'Cloud, API and Alerts' module.

Step 2: Power up the circuit and let it connect to the Bolt Cloud. (The Green LED of the Bolt should be on)

Step 3: Go to cloud.boltiot.com and create a new product. While creating the product, choose product type as Output Device and interface type as GPIO. After creating the product, select the recently created product and then click on configure icon.

Step 4: In the hardware tab, select the radio button next to the A0 pin. Give the pin the name 'temp' and save the configuration using the 'Save' icon.

Bolt IoT Could - Setup

Step 5: Move to the code tab, give the product code the name 'predict', and select the code type as js.

Java Script Code

Step 6: Write the following code to plot the temperature data and run the polynomial regression algorithm on the data, and save the product configurations.

setChartLibrary('google-chart');setChartTitle('Polynomial Regression');setChartType('predictionGraph');setAxisName('time_stamp','temp');mul(0.0977);plotChart('time_stamp','temp');

Step 7: In the products tab, select the product created and then click on the link icon. Select your Bolt device in the popup and then click the 'Done' button.

Step 8: Click on 'deploy configuration' button and then the 'view this device' icon to view the page that you have designed. Below is the screenshot of the final output.

Polynomial Regression

Step 9: Wait for about 2 hours for the device to upload enough data point to the Cloud. You can then click on the predict button to view the prediction graph based on polynomial regression algorithm.

Polynomial Regression

Final Prediction Looks Like This

Polynomial Regression

Code

from boltiot import Bolt, Sms #Import Sms and Bolt class from boltiot library  
import json, time,requests
SID="The SID in the Twilio account"
AUTH_TOKEN="Auth_token from twilio account"
TO_NUMBER="Phone number to which you want to send the message"
FROM_NUMBER="Number provided by Twilio"
Channel_ID="Telegram ChannelID"
Tele_BotID="Bot API key"
SMS=Sms(SID,AUTH_TOKEN,TO_NUMBER,FROM_NUMBER)                          
def Twilio_message(message):
        try:
                print('Sending message through twilio')
                response=SMS.send_sms(message)
                print('The status of twilio messsage is:',str(response.status))
        except Exception as e:
                print("Error occured in Twilio message",e)
                print(e)
                return False
def Tele_message(message):
        url='https://api.telegram.org/'+Tele_BotID+ '/sendMessage'
        data={'chat_id':Channel_ID,'text':message}
        try:
                response=requests.request("GET",url,params=data)
                print('This is the Telegram response')
                print(response.text)
                telegram_data=json.loads(response.text)
                return telegram_data["ok"]
        except Exception as e:
                print("Error occured in sending message via Telegram")
                print(e)
                return False
Bolt_ID="your bolt id"
API_Key='bolt api key"
garbage_full_limit = 7 # the distance between device and  garbage in dustbin
mybolt = Bolt(API_Key,Bolt_ID)#This is to access the bolt device and send commands
response = mybolt.serialRead('10')#fetch data from arduino
message="Hello Brother, the trash can is full"
while True:
        response = mybolt.serialRead('10')  #Fetching the value from Arduino 
        data = json.loads(response)
        garbage_limit = data['value'].rstrip()
        print("Garbage level is", garbage_limit)
        if int(garbage_limit)<garbage_full_limit:
                Twilio_message(message)
                Tele_message(message)
        time.sleep(10)

Credits

Yeshwanth Patel

1 project • 0 followers

Smart and Sustainable Waste Management System

Things used in this project

Hardware components

Software apps and online services

Story

Connecting the LM35 Sensor to the Bolt

Predicting the Temperature

Final Prediction Looks Like This

Schematics

DRY WASTE MANAGEMENT - CIRCUIT DIAGRAM

Code

PYTHON CODE

ARDUINO CODE

PREDICTION MODEL

Credits

Yeshwanth Patel

Comments

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Smart and Sustainable Waste Management System

Smart and Sustainable Waste Management System

Things used in this project

Hardware components

Software apps and online services

Story

Connecting the LM35 Sensor to the Bolt

Predicting the Temperature

Final Prediction Looks Like This

Schematics

DRY WASTE MANAGEMENT - CIRCUIT DIAGRAM

Code

PYTHON CODE

ARDUINO CODE

PREDICTION MODEL

Credits

Yeshwanth Patel

Comments

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