Forecasting Solid Waste Generation in Trivandrum - A System Dynamic Approach

Prepared and Presented By Sudharma V K in 26th Kerala Science Congress,  Wayanad

Waste Generation means the volume of materials and products that enter the waste stream before recycling, composting, landfilling, or combustion. The main reasons are ,changing life styles, food habits, fast economic developments, growing tourism, rapid urbanization, increasing population etc.

Trivandrum city is an emerging metropolitan city in the southernmost part of India. When we look into the waste generation, it can be noted that the huge volume of waste is depositing daily at Vilappil Sala, the dumping yard of Thiruvananthapuram Corporation. Waste disposal from the capital city of Kerala is in a deadlock today.  If the waste generation continues to rise like this, it will exceed the assimilative capacity of our environment and management. So there is an urgent necessity of improved planning and implementation of efficient Municipal Solid Waste Management System. By modelling waste generation we will get future quantities of waste generation.  And the data on quantity and composition of waste generated provide information on the magnitude of the problems faced in the management of solid waste

System Dynamics

Developed by an electrical engineer Jay W. Forrester in the1950s . It is an investigation of the information-feedback characteristics of [managed] systems and the use of models for the design of improved organizational form and guiding policy

• A way of thinking - a paradigm - that looks at the whole, the parts, and the interconnections between these elements. 
• Computer aided approach to policy analysis and design

Justification for System Dynamics

1. Traditional methods (like regression analysis, geometry average method, Least Squares Regression Method, curve extension method, etc.) need high quantity and quality data, which is lacking in waste generation. – trend extension 
2. When the database is not sufficient to support traditional statistical forecasting analyses – SD provide results with reasonable accuracy.
3. Policy making - Design better strategies
4. Cost advantage

System Dynamic Steps 

1. Define the problem boundary

2. Identify the most important stocks and flows that change         these stock levels

3. Identify the main feedback loops, draw a causal loop diagram that links the stocks, flows and sources of information

4. Write the equations that determine the flows

5. Estimate the parameters and initial conditions. These can be estimated using statistical methods, expert opinion, market research data or other relevant sources of information.

6. Simulate the model and analyse results

Define the problem boundary 

• Taking Trivandrum city only
• Assuming 
• There is no recycling program mandated by Govt.
• No waste regulation policies by Govt.
• 60% of the people has awareness about the consequences of MSW accumulation.
• GDDP of TVM district is taken as the GDDP of TVM city

Indicators of waste generation

• Population
• Number of persons per household
• GDDP
• Historical amount of waste generated
• Income

Awareness Multiplier (Am)

Am =  Social awareness + Community participation (that is, some people may take initiative and they started composting in small scale like, pipe composting etc.)

Causal Loop Diagram

Stock and flow diagram

The simulation software Vensim is used for drawing stock and flow diagram

1. In this work we have developed a System Dynamics Model to predict solid waste generation in Trivandrum city
2. Comparison of existing data with the model prediction shows excellent agreement 
3. Since the waste generation indicators are identified, using  the developed model we can study and plan the behaviour the system if it varies 
4.   This will be a valuable tool in developing suitable waste management strategy