From the Bin, To the Bulb: Turning Household Waste into Local Clean Energy
By: Taylor Stanley
Tags: Predictive Analytics, Waste, Energy, Waste To Energy, Data Science

For my final capstone project in Flatiron School’s Immersive Data Science Program, I decided to test my newfound skills and continue furthering my personal investigations into the relationships that exist between data, waste, and energy. Recently, I have been learning more about the various ways that Municipal Solid Waste (MSW) can be transformed into energy. The most promising and efficient technology that I have come across to date is Plasma Arc Gasification. In my research, I discovered that understanding specific composition details about the MSW to be used as feedstock is one of many critical steps in designing a plasma gasification facility. What I set out to do for my capstone project, was to see if I could find some MSW collection datasets and perform a Feedstock Analysis with the intent of calculating specific Waste Type Compositions, Energy Density (kWh/kg), and Total Energy (kWh) for each sample.

Plasma Gasification

Before we get into the data analysis required for calculating proximate MSW energy values, lets talk a bit more about the paradigm-shifting technology, Plasma Gasification.

“The conversion of carbonaceous material into a gaseous product for the production of energy products and by-products in an oxygen starved environment.” Municipal Solid Waste to Energy Conversion Process”, Gary C. Young , 2010

The technical definition above is quite self-explanatory, though can be even further simplified by thinking of this process as being able to take almost any material (except metals and glass) on the planet and convert it into its basic molecular components. This product is called “Syngas” and is mostly comprised of Carbon Monoxide (CO) and Hydrogen (H2). Syngas can be further processed into many different valuable chemical products, though this depends on the facilities purpose and design. I am interested in understanding how to create electricity from MSW.

Proposals for gasification facilities require revenue projections just like most business undertakings. These projections depend on understanding the energy in the fuel that one uses as feedstock for the gasification process. This is where determining the energy density, through a feedstock analysis, comes into play. I want to build one model that can take in a specific waste sample, measured by weight from a set list of items, and predict its energy density.

There is another by-product of this process, either vitrified slag, or biochar, that are created as a result of the gasification process. Depending on the specific composition of MSW feedstock, there exist different markets for revenue from this product.

All in all, using this technology as part of the solution the growing MSW issues globally is a no-brainer. Scrubbing technology is installed throughout the entire gasification process to ensure that air emissions comply with local heath and safety regulations. When one compares this with the alternative long-term solution of outdoor anaerobic digestion, releasing countless tons of harmful methane into the atmosphere, plasma gasification becomes just a matter of business, and how to make this solution sustainable and profitable for all stakeholders.

Data Scrubbing & Modeling

Image by Author

This project’s Feedstock Analysis measures Energy Density (kWh/kg) and Total Household MSW Energy (kWh) in Municipal Solid Waste (MSW) sample data collected from Belize, Solomon Islands, and Vanuatu.

This data was collected by the Asia Pacific Water Consultants (APWC), commissioned by the Commonwealth Litter Programme (CLiP) with support from the Centre for Environment Fisheries and Aquaculture Science (CEFAS). The original purpose for the collection of this data was to support local governments in developing better ocean waste management strategies.

Each dataset per country had over 95,000 rows that needed to be cleaned and reformatted for my purposes. There were many repeating items in the datasets as each MSW sample was measured using 3 different methods: weight, count, and volume. To calculate the energy density, I just needed to look at the weight values (kg), and filtering for this substantially reduced the number of rows in each dataset. After removing all redundant and unnecessary values, and reshaping the dataset I was ready to model. The final shape of the dataset used for both top models was (438, 29), representing 438 different household MSW samples with a specific combination of 29 item features each. These data, combined from 3 different countries, were used to train and test my top performing model of the project.

I went through many different model types as part of my process. I started with some basic Linear Regression models using Statsmodels and Sci-kit Learn, both of which weren’t ideal as they overfit the data substantially. From there, I moved on to Decision Trees, Random Forests, and XGBoost models which all performed much better than their basic linear regression counterparts. Finally, I ended my modeling efforts with my favourite architecture, a Multilayer Perceptron (MLP), or known more recently as a Neural Network.

Fig 1. XGBoost Model Feature Importance — Total Household MSW Energy (kWh) Image by author

One noteworthy modeling feature I want to highlight is the Feature Importance method that comes standard with XGBoost models. I visually mapped this information (Fig 1) that represents the importance, or relative influence, that each specific feature had in the model.

As is clearly displayed above, we see the top 3 important features in this order:

Food Other Organics Nappies

So what does this mean? Well, since this model was trained and tested to predict the dependent variable representing total energy in kWh, we should expect to see displayed those items that contribute the most towards influencing the total energy value for each household. As it happens, these top 3 items are all organic in nature, meaning that they each contain a relatively high amount of carbon in them. Therefore, one can conclude that this model has done a good job at capturing and valuing, items with higher carbon levels rather than those with less.