# Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant
## 6 Conclusions  
The objective of LNG satellite plants is to continuously supply gas to remote geographical areas. Due to the small size of the tanks in these plants, usually 20, 30, or 60 m\({}^{3}\), emptying frequency is between two and four days in winter. For this reason, a key strategy for continuously maintaining the level of the plant is to implement predictive plans to refill tanks with LNG via trucks.  
First, this work addressed the problem of transforming aperiodic raw data based on events into periodic and validated/reconstructed data. For this, a data-validation methodology was proposed on the basis of a set of tests that analyze various temporal and spatial features of the data on the basis of time series and correlated models that, in turn, reconstruct the invalid data. This methodology was successfully applied to raw data on the LNG level in a tank (NVT) and gas consumption (integral of QIC) during 2018. As a result, tank-level data in a period of 78.5 to 79.5 days were invalidated. Using gas-consumption data, the invalidated data period was reconstructed to have 10 min of a full database.  
The second part of this work was devoted to applying two complementary methods for tank-state prognosis to generate predictive maintenance of truck refuelling of a company's LNG tanks. Both methods are data-driven, but one of them requires a plant model. The new algorithm developed is a nonlinear approach based on fuzzy inductive reasoning using historical data for model training. This method has been compared with a linear approach which is based on a double-aliasing filter. While the FIR method needs a qualitative model for each LNG satellite plant, the second method only one parameter must be tuned for each one. The advantage of the FIR method is that the RUL prediction is always monotonically decreasing differing from the second method in which RUL is not.  
Results illustrated that using both methods is possible to anticipate the emptying of the tank at more than 50% of cycle time, 10 days or more in summer, and 2 days or more in winter, which is enough time to refill the tanks. In the future, we aim to improve this methodology by integrating the proposed FIR method and the double-filter-aliasing method in one implemented algorithm, selecting one of the two proposed methods depending on available information (e.g., extra variables and data for learning) and finally, to apply it in real time to several company LNG tanks. The ultimate goal is to provide to the NEDGIA company a RUL prediction of all the LNG satellite plants which allows them to plan the loading of the numerous tanks available in different sites.  
In conclusion, the work presented here shows that diagnosis and prognostic techniques can contribute efficiently in planing, with more than four days in advance, the loading of tanks with LNG trucks. According to the literature review, these techniques had not been used in this kind of problems so far. In short, this is an industrial application success that will soon integrated into the supervision system of the LNG satellite plants.  
Conceptualization, J.Q., and A.M.; methodology, A.E. and T.E.; software, A.E. and J.Q.; validation, A.E., T.E. and J.Q.; formal analysis, J.Q. and A.M.; investigation, A.E.; resources, A.M.; data curation, A.E.; writing-original draft preparation, T.E.; writing-review and editing, A.E., T.E. and J.Q.; visualization, A.E.; supervision, J.Q.; project administration, J.Q. and A.M. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Spanish Ministry of Economy and Competitiveness (MINECO) and FEDER through projects SCAV (ref. DPI2017-88403-R), and by AGAUR ACCIO RIS3 CAT UTILITIES 4.0-P1 ACTIV 4.0. ref. COMRDI-16-1-0054-03 and SMART Project (ref no EFA153/16 Interreg Cooperation Program POCTEFA 2014- 2020).  
The authors thank the academic editor and reviewers for the valuable feedback and insightful comments.  
The authors declare no conflict of interest.