Abstract
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.
Highlights
The climate change threat is felt more and more as a concern, and countries world wide are nowadays starting to act in order to limit its destructive effects (United Nations, 2015)
They ground on two different philosophies of solving process integration problems: the former striving for the complete automation of the design procedure, the latter believing in the central role of the designer in the decisionmaking process
A novel method aiming at reducing the time consumption in the data acquisition step of process integration retrofit studies was presented
Summary
The climate change threat is felt more and more as a concern, and countries world wide are nowadays starting to act in order to limit its destructive effects (United Nations, 2015). Since the introduction of the first systematic tool by Linnhoff and Flower (1978) in 1978, known with the name of “Pinch analysis”, many developments have been achieved, (i) extending it to non-energy related targets (e.g., waste-water Wang and Smith, 1994 or emissions minimization and pressure drop optimization Polley et al, 1990), (ii) automating sections of the methodologies by means of computer-aided optimization procedures (Asante and Zhu, 1996; Nie and Zhu, 1999; Smith et al, 2010; Pan et al, 2013; Bütün et al, 2018), and (iii) simplifying the different procedures in order to promote their application in the established industrial practice (Polley and Amidpour, 2000; Dalsgård et al, 2002; Anastasovski, 2014; Pouransari et al, 2014; Chew et al, 2015; Bergamini et al, 2016) These three research lines have often been carried out separately, and in some cases even in a divergent way. The recent focus of the first one is in the definition of overarching super-structures for the robust design of heat exchanger networks, while the latter tries to develop heuristics able to indicate to the analyst the sub-problems to focus the attention on
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