A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Development of expeditious process integration methods for retrofit of non-energy-intensive industries
Industry is expected to play a prominent role in decarbonising the energy system towards reaching the EU climate targets. This requires retrofitting existing process plants with the goal of reducing their energy use. Process integration methods proved to be highly effective in analysing the energy utilisation of industrial facilities and identifying possible actions for increasing their energy efficiency. However, they are far from constituting the industrial practice. A major barrier to their use is the large time and resources required for performing the analysis. This issue is especially felt in non-energy-intensive industries, which are deemed to hide a large potential for energy savings. In fact, the low cost savings deriving from energy-efficiency projects in individual plants do not justify lengthy and expensive investigations. In this way, a large potential for energy saving which lays in non-obvious solutions is missed. This thesis aims at lowering this barrier by developing expeditious process integration retrofit methods. The most time-consuming activities of available methods were identified, and two novel methods were proposed, named “Required Data Reduction Analysis” (RDRA) and “Energy-Saving Decomposition” (ESD) method. They respectively aim at reducing the time consumption of the “data acquisition” and of the “design” phases of process integration retrofit projects. Their performance was tested and validated by applying them to nine case studies belonging to three different industrial sectors. This allowed to investigate their major merits and limitations and propose future development activities. The RDRA bases on the idea that measurements are performed to increase our knowledge, and this knowledge is quantifiable in terms of uncertainty. It employs uncertainty analysis, sensitivity analysis, and mathematical optimisation techniques in a systematic setting, to identify (i) a limited number of process parameters to measure, and (ii) the maximum acceptable uncertainty in their measurement. The ...
Development of expeditious process integration methods for retrofit of non-energy-intensive industries
Industry is expected to play a prominent role in decarbonising the energy system towards reaching the EU climate targets. This requires retrofitting existing process plants with the goal of reducing their energy use. Process integration methods proved to be highly effective in analysing the energy utilisation of industrial facilities and identifying possible actions for increasing their energy efficiency. However, they are far from constituting the industrial practice. A major barrier to their use is the large time and resources required for performing the analysis. This issue is especially felt in non-energy-intensive industries, which are deemed to hide a large potential for energy savings. In fact, the low cost savings deriving from energy-efficiency projects in individual plants do not justify lengthy and expensive investigations. In this way, a large potential for energy saving which lays in non-obvious solutions is missed. This thesis aims at lowering this barrier by developing expeditious process integration retrofit methods. The most time-consuming activities of available methods were identified, and two novel methods were proposed, named “Required Data Reduction Analysis” (RDRA) and “Energy-Saving Decomposition” (ESD) method. They respectively aim at reducing the time consumption of the “data acquisition” and of the “design” phases of process integration retrofit projects. Their performance was tested and validated by applying them to nine case studies belonging to three different industrial sectors. This allowed to investigate their major merits and limitations and propose future development activities. The RDRA bases on the idea that measurements are performed to increase our knowledge, and this knowledge is quantifiable in terms of uncertainty. It employs uncertainty analysis, sensitivity analysis, and mathematical optimisation techniques in a systematic setting, to identify (i) a limited number of process parameters to measure, and (ii) the maximum acceptable uncertainty in their measurement. The ...
Development of expeditious process integration methods for retrofit of non-energy-intensive industries
Bergamini, Riccardo (author)
2020-01-01
Bergamini , R 2020 , Development of expeditious process integration methods for retrofit of non-energy-intensive industries . DCAMM Special Report , no. S257 , Technical University of Denmark , Kgs. Lyngby .
Book
Electronic Resource
English
DDC:
690
Key Best Practices for Process Energy Use in Four Energy Intensive Industries
| British Library Conference Proceedings | 2005