Advances in optimal design and retrofit of chemical processes with uncertain parameters - Applications in design of heat exchanger networks

Abstract: There is widespread consensus that the omnipresent climate crisis demands humanity to rapidly reduce global greenhouse gas (GHG) emissions. To allow for such a rapid reduction, the industrial sector as a main contributor to GHG emissions needs to take immediate actions. To mitigate GHG emissions from the industrial sector, increasing energy efficiency as well as fuel and feedstock switching, such as increased use of biomass and (green) electricity, are the options which can have most impact in the short- and medium-term. Such mitigation options usually create a need for design of new or redesign of existing processes such as the plant energy systems. The design and operation of industrial plants and processes are usually subject to uncertainty, especially in the process industry. This uncertainty can have different origins, e.g., process parameters such as flow rates or transfer coefficients may vary (uncontrolled) or may not be known exactly. This thesis proposes theoretical and methodological developments for designing and/or redesigning chemical processes which are subject to uncertain operating conditions, with a special focus on heat recovery systems such as heat exchanger networks. In this context, this thesis contributes with theoretical development in the field of deterministic flexibility analysis. More specifically, new approaches are presented to enhance the modelling of the expected uncertainty space, i.e., the space in which the uncertain parameters are expected to vary. Additionally, an approach is presented to perform (deterministic) flexibility analysis in situations when uncertain long-term development such as a switch in feedstocks interferes with operational short-term disturbances. In this context, the thesis presents an industrial case study to i) show the need for such a theoretical development, and ii) illustrate the applicability. Aside of advances in deterministic flexibility analysis, this thesis also explores the possibility to combine valuable designer input (e.g. non-quantifiable knowledge) with the efficiency of mathematical programming when addressing a design under uncertainty problem. More specifically, this thesis proposes to divide the design under uncertainty problem into a design synthesis step which allows direct input from the designer, and several subsequent steps which are summarized in a framework presented in this thesis. The proposed framework combines different approaches from the literature with the theoretical development presented in this thesis, and aims to identify the optimal design specifications which also guarantee that the the final design can operate at all expected operating conditions. The design synthesis step and the framework are decoupled from each other which allows the approach to be applied to large and complex industrial case studies with acceptable computational effort. Usage of the proposed framework is illustrated by means of an industrial case study which presents a design under uncertainty problem.