Original Research ARTICLE
Simplifi□cation of data acquisition in Process Integration retrofi□t studies based on uncertainty and sensitivity analysis
- 1Technical University of Denmark, Denmark
Process integration methodologies proved to be effective tools in identifying energy saving opportunities in the industrial sector and suggesting actions to enable their exploitation. However, they extensively rely on large amounts of process data, resulting in often overlooked uncertainties and a significant time-consumption. This might discourage their application, especially in non-energy intensive industries, for which the savings potential does not justify tedious and expensive analysis.
Hereby a method aimed at the simplification of the data acquisition step in process integration retrofit analysis is presented. Four steps are employed. They are based on Monte Carlo techniques for uncertainties estimation and three methods for sensitivity analysis: Multivariate linear regression, Morris screening, and Variance decomposition-based techniques. Starting from rough process data, it identifies: (i) non-influencing parameters, and (ii) the maximum acceptable uncertainty in the influencing ones, in order to reach reliable energy targets. The detailed data acquisition can be performed, then, on a subset of the total required parameters and with a known uncertainty requirement.
The proposed method was shown to be capable of narrowing the focus of the analysis to only the most influencing data, ultimately reducing the excessive time consumption in the collection of unimportant data. A case study showed that out of 205 parameters required by acknowledged process integration methods, only 28 needed precise measurements in order to obtain a standard deviation on the energy targets below 15 % and 25 % of their nominal values, for the hot utility and cold utility respectively.
Keywords: Data Acquisition (DA), Monte Carlo (MC), Process integration (PI), retrofit, Sensitivity analyis, simplification, Uncertainty analysis (UA)
Received: 30 Nov 2018;
Accepted: 20 Sep 2019.
Copyright: © 2019 Bergamini, Nguyen and Elmegaard. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mr. Riccardo Bergamini, Technical University of Denmark, Kongens Lyngby, Denmark, email@example.com