Contribution to the development of more sustainable process industries under uncertainty

Identificador:  TDX:2655

Handle: https://hdl.handle.net/20.500.11797/TDX2655

Autores:  Sabio Arteaga, Nagore

Resumen:
Over the past decades, the challenges originated as a result of high energy prices and the growing pressure to reduce greenhouse gas emissions have fuelled a large interest in energy and process systems related research. On the one hand, process industries are faced with the need to cover the increasing demand for energy as developing nations grow and developed countries continue to progress in an increasingly uncertain marketplace, and on the other hand, the resources that have traditionally supported this continued progress begin to show environmental impacts that could threaten the sustainable development of species in the world. As a consequence, the present situation could be described as driven along three main edges: energy, sustainability and uncertainty. Of particular relevance for these problems is research on computer-aided systems technology to develop strategies for investigating the impact of process industries on both, the system efficiency and its life cycle environmental impact in the presence of uncertainty. In this sense, the general goal of this thesis is to explicitly address these challenges by first making a step towards closing the gap between science-based and systems-based research in Process Systems Engineering. The problem is addressed by devising a set of advanced multi-objective mathematical programming tools able to deal with environmental and uncertainty concerns in the design and planning of more sustainable process industries. In particular, multiple life cycle assessment and risk management stochastic metrics are appended to the optimization MILP and MINLP problems as additional criteria to be optimized, and Principal Components Analysis is applied for identifying redundant life cycle metrics and reduce the problem dimensionality. These models presented here are thus able to deal with single-site and multi-site process systems are capable of addressing, in a holistic manner, the three major sources of uncertainty: parameter, model and methodological.