| dc.description.abstract | The rising energy demands and the burgeoning population combined with concerns about the risks of climate change mandate a cost-conscious transition towards low-carbon or carbon-neutral energy, that will not limit the economic growth. Such transition introduces major challenges, and thus requires holistic strategies and systematic approaches during its execution. In this work, process and energy systems engineering thinking along with mathematical optimization and machine learning are utilized to address some of the outstanding issues related to the energy transition and the circular economy (CE) implementation.
First, a novel forecasting framework to calculate the average as well as the market (spot) price of energy in the United States is presented. The complex energy landscape is thoroughly analyzed to accurately determine the two key factors of this framework: the total demand of the energy products directed to the end-use sectors, and the corresponding price of each product in the form of either a monthly or a spot price. Spot prices are available to date, while data for the demand and the monthly price of energy products lag several months. This issue is overcome with the introduction of state-of-the-art forecasting methodologies that allow accurate forecasting for the demand and the prices of the energy products up to 48 and 12 months respectively. The forecasting capabilities of the framework are rigorously tested over a long period of 184 months, while its effectiveness is demonstrated by addressing four policy questions of significant public interest.
Then, a literature review listing Process Systems Engineering approaches that have been developed and can be used to facilitate the transition towards CE has been conducted. Thereafter, a novel CE system engineering framework for the modeling and optimization of food supply chains is introduced, demonstrating efficient ways for the re-utilization of products and materials along with the extensive usage of renewable energy sources. Due to the conflicting objectives involved, a multi-objective optimization strategy for trade-off analysis capturing different demand scenarios and uncertainty factors is also presented. Finally, a micro-level CE assessment framework with sector-specific indicators as well as overall and category-based metrics is proposed, allowing the robust and holistic assessment of multi-scale, multi-faceted, and interconnected CE supply chains. | |
