Optimising Integrated Municipal Solid Waste Management System under Multiple Uncertainties

Research summary:

To define a holistic and systematic approach to municipal waste management, an integrated municipal solid waste management (IMSWM) system is proposed. This system includes functional elements of waste generation, source handling and processing, waste collection, waste processing at facilities, transfer, and disposal. Multi-objective optimisation algorithms are used to develop an optimum IMSWM that can satisfy all main pillars of sustainable development, aiming to minimise the total cost of the system (economic) and minimise the total greenhouse gas emissions (environmental) while maximising the total social suitability of the system (social). For the social objective, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to identify the main parameters that affect the social suitability of the system. Identifying the best set of technologies to be used in the system, requires complete knowledge of the technologies, their requirements, compatibility, and feasibility based on the characteristics of the locations they will be used.

This research focuses on developing an optimised holistic model that considers all four main components of a modern IMSWM: transfer, recycling, treatment, and disposal. The model is formulated as a mixed-integer linear programming (MILP) problem. To include all three objectives of the system, the epsilon constraint handling method is adopted. A metaheuristic method is developed using non dominated sorting genetic algorithm (NSGA) to deal with larger problems. A solution repair function is developed to handle several equality constraints included in the proposed IMSWM model.

To evaluate the robustness of the proposed system, sensitivity analyses are conducted to identify the effect of changes in parameters on the objective functions. The proposed metaheuristic algorithm based on NSGA-II performed better than other algorithms based on the results. The interval-parameter programming (IPP) methods are used to consider various uncertainties in the system. The model is applied to the Australian capital territory case study (ACT). The data is gathered from several resources, including Australian national waste reports and ACT government transport Canberra and city services (TCCS). Several feasible scenarios are recommended based on the waste characteristic and city map.

Project date: Started in July 2019, submitted thesis in March 2022.

Achievement:     

Ansari, M, Abbasi, A., and Chakrabortty, R.K. An Integrated Municipal Waste Management System to identify the optimum technologies for waste composition using MILP and evolutionary algorithms. Waste Management – under review