abstract
- © 2017 Walter de Gruyter GmbH, Berlin/Boston. Efficient energy recovery is one of the key technological strategies for coping with reduction and emission of green-house gases. Commonly, such gases are produced during combustion of fossil fuel-based processes. A way to contribute to reduce the emission of green-house gases has to do with energy recovery from low-temperature energy sources, commonly available in industrial processing systems, deploying both mixtures of organic fluids and power Rankine cycles. However, feasible optimal design calls for taking into account uncertainty both in model and process parameters. In this work we propose a robust optimal design approach, based on nonlinear programming, for addressing efficient energy recovery from low-temperature sources which takes advantage of the presence of uncertainty to render optimal processing conditions realizable in practice. The proposed robust optimal approach is particularly suitable when no probability distributions of uncertainty are available and only upper and lower bounds on uncertainty are known. Three case studies are presented to stress the importance of considering the impact and presence of uncertainty on low-temperature Rankine cycles.