Chapter 15 - Dynamic Multi-objective Optimization of Integrated Waste Management Using Genetic Algorithms

Abstract

The inefficient management practices of the rapidly growing waste quantities impose serious environmental risks and overlook the resource value of the discarded materials. Waste-to-energy (WTE) systems have emerged aiming to mitigate such negative impacts and seize potential opportunities. However, decision makers often face difficulties to determine the optimum setting of WTE within integrated solid waste management (ISWM) strategies that are tailored to meet their specific needs and constraints. Recently, soft computing techniques have proven their functionality in combating such challenges and solving ill-defined multiparameter problems. This chapter presents a multi-objective dynamic framework for the optimization of waste allocation to various treatment, recovery, and disposal facilities by means of genetic algorithms (GAs). The waste processing byproducts were also optimized within the proposed framework. The optimum ISWM strategy was identified on the basis of three main objectives: maximum economic gains, minimum environmental footprint, and maximum energy recovery. The weighted importance of each objective was incorporated in the model to account for the trade-offs between incompatible objectives. The proposed optimization framework was validated from various sustainability perspectives on a case study over a 20-year assessment period. The obtained solution mostly involved the allocation of readily biodegradables to anaerobic digesters, recyclables to material recovery facilities (MRFs), and non-recyclables to incinerators. The optimized strategy suggested the diversion of the by-produced digestate, MRF rejects, and ash to the market, landfill, and incinerator, respectively. Such optimum hybrid ISWM strategy would save around 2.8 billion USD, emit approximately 13,357 Gg CO2e, and generate more than 109 TWh over 20 years. Overall, the GA-based solution method was found to effectively determine the optimal dynamic allocation of primary and secondary waste streams, satisfying multiple objectives.