Advanced Numerical Modeling Techniques For Modern Waste Management Systems

Abstract

This thesis presents original results to the advancement of numerical modelling of a modern waste management system with respect to generation, collection, and disposal of non-hazardous solid waste. The first part of the thesis specifically look at lag times relating to variables that attempt to predict municipal yard waste generation using machine learning approaches. Weekly averaged climatic and socio-economic variables are screened through correlation analysis and the significant variables are then used to develop yard waste models. These models then utilize artificial neural networks where the variables are time lagged a different number of weeks. Optimal lag times for each model varied from 1-11 weeks. The best model used both the ambient air temperature and population variables, in a model with 3 layers, 11 neurons in the hidden layer, and an optimal lag time of 1 week. A mean absolute percentage error of 18.72% was obtained at testing stage. One model saw a 55.4% decrease in the mean squared error at training, showing the value of lag time on the accuracy of weekly yard waste prediction models. The second part of the thesis focuses on geospatial modelling of a dual phase waste collection. A model integrating the handcart pre-collection phase and truck collection phase was proposed. Temporary 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. A total of 30 scenarios were considered in order to investigate 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 suggest a single temporary collection point in the study is able to serve about 2,590 people in an area of 0.11 km2. It is found that the number and distribution of temporary collection points greatly affected the cost effectiveness in both pre-collection and collection phases. In the third part of thesis, landfill gas data was collected at semi-arid landfills, and curve fitting was carried out to find optimal k and L0 or DOC values using LandGEM, Afvalzorg Simple, and IPCC first order decay models. Model parameters at each landfill were estimated and compared using default values. Methane generation rates were substantially overestimated using default values (with percentage errors from 55 to 135%). The mean percentage errors for the optimized k and L0 or DOC values ranged from 11.60% to 19.93% at the Regina landfill, and 1.65% to 10.83% at the Saskatoon landfill. Finally, the effect of different iterative methods on the curve fitting process was examined. The residual sum of squares for each model and iterative approaches were similar, with the exception of iterative method 1 for the IPCC model. The default values in these models fail to represent landfills located in cold semi-arid climates. The fourth part of the thesis focuses on the development of a systematic approach for modelling of WMS. ANN time series was first applied to forecast the amounts of recyclables and garbage in the year 2023 at the target study area. MAPE of 10.92% to 16.51% were obtained for the forecast. Both the amount of recyclables and garbage appeared to decrease with time. Truck travel distance of the optimized routes were found sensitive to the composition and density of the materials. The use of dual-compartment trucks reduced total travel distances by 10.30% to 16.00%. However, single-stream trucks were likely to be more efficient in terms of total collection time.