Waste treatment is a complex procedure that comprises multidimensional aspects. The challenges posed by the energy transition and international protocols emphasize the role of waste in its material and energy dimension. With an increasing population and living standards across the globe, waste generation is expected to outpace population growth by a factor of at least two. Therefore, the sustainable handling of waste is imperative.

From an environmental perspective, waste management is directly responsible for 5% of all Greenhouse gas emissions. However, waste conversion and valorization affect a broad range of sectors. In order to analyze the vast number of different waste treatment options, their impact on other components of the energy system and potential synergies that can emerge from efficient treatment, holistic decision-making approaches are required.

This thesis addresses an array of open questions in the broad context of waste, by proposing methods, solutions, and recommendations. It is divided into four chapters. Besides methodological developments, each chapter includes one or more examples of applications.
The first chapter addresses the mathematical foundation of this thesis, dealing with generating models and superstructures. In the first part, multi-objective optimization is used to generate solutions. A case study explores alternative wastewater uses, with configurations achieving cost and emission reductions of up to 73% compared to normal operation. In the second part, an adaptive strategy to build surrogate models is explored. Artificial Neural Networks, Random forests, and standard Kriging models outperform other, more simplistic surrogates in all assessed criteria.

Chapter 2 tackles the mineralization of CO2, the largest waste produced worldwide and thus one of the most urgent to deal with. Integrating waste heat and leveraging existing infrastructure in industrial sectors proved critical to mineralization deployment. The European cement and steel industries, together with waste incineration, were used to illustrate the method. By tackling these sectors, a 35% reduction in overall European emissions could be achieved.

Chapter 3 deals with creating and choosing robust solutions. A bi-level method is proposed to integrate uncertainty into solution generation, evaluation, and ranking. The combination of different market values guarantees that the bias introduced by the contingent character of current values is reduced - ensuring that less likely configurations are also generated. An evaluation stage is developed that enables ranking solutions. A case study on biowaste illustrates the method, promoting the use of CO2 as a sound way to co-produce fuels and chemicals.

Chapter 4 focuses on logistics and supply chains. It leverages and expands the contribution of the previous chapters by including transportation between network nodes. Its application to a plastic waste case proves the importance of considering logistics at an early stage - adopting a system perspective, rather than a pure process analysis. It highlights waste duality, placing recycling and incineration as allies in a sustainable energy system. The extrapolation of designs to EU countries could reduce up to 210 Mton of CO2 - equivalent to 7% of all European emissions.
Broadly, this thesis provides a base to understand and promote the use and integration of waste in all its dimensions, alongside its contribution to the present and future energy system.