Two methods of data reconciliation for pipeline networks

Isom, Joshua D.; Stamps, Andrew T.; Esmaili, Ali; Mancilla, Camilo

doi:10.1016/j.compchemeng.2018.05.008

Cited by 12 publications

(8 citation statements)

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“…To investigate the performance of algorithms for crude oil transportation problems similar to those proposed in previous studies, [62][63][64][65][66] we present a physically and economically realistic pipeline network model and test case that includes auction market parameters drawn from prevailing price and quantity data in January of 2020.…”

Section: Seaway Test Casementioning

confidence: 99%

Optimal economic operation of liquid petroleum products pipeline systems

et al. 2021

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The majority of overland transport needs for crude petroleum and refined petroleum products are met using pipelines. Numerous studies have developed optimization methods for design of these systems in order to minimize construction costs while meeting capacity requirements. Here, we formulate problems to optimize the operations of existing single liquid commodity pipeline systems subject to physical flow and pump engineering constraints. The objectives are to maximize the economic value created for users of the system and to minimize operating costs. We present a general computational method for this class of continuous, non‐convex nonlinear programs, and examine the use of pump operating settings and flow allocations as decision variables. The approach is applied to compute optimal operating regimes and perform engineering economic sensitivity analyses for a case study of a crude oil pipeline developed using publicly available data.

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Section: Seaway Test Casementioning

confidence: 99%

Optimal economic operation of liquid petroleum products pipeline systems

et al. 2021

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“…the non-linearised model is solved with initial values to provide more precise data for t j and subsequently v j Weight calculation Calculate t k j ∀j ∈ J and then weights v k j according to eq (12) 6: Solve 2 Solve model with eq(9) and calculated weights v k j and w j and starting values from Solve 1 7: until (∃j ∈ J :…”

Section: Pseudocodementioning

confidence: 99%

“…First, the gross errors are detected using their prior probabilities and second, the data reconciliation is performed. The paper [12] compares the two common methods of data reconciliation -Kalman lter and quadratic program employed in the pipeline network. On the other hand, in [13], the data of natural gas processing were reconciled to satisfy mass balance equations.…”

Section: Introductionmentioning

confidence: 99%

Statistical Evaluation of Large-Scale Data Logistics System

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Šmejkalová

et al. 2018

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Data recording is struggling with the occurrence of errors, which worsen the accuracy of follow-up calculations. Achievement of satisfactory results requires the data processing to eliminate the influence of errors. This paper applies a data reconciliation technique for mining of data from ecording movement vehicles. The database collects information about the start and end point of the route (GPS coordinates) and total duration.The presented methodology smooths available data and allows to obtain an estimation of transportation time through individual parts of the entire recorded route. This process allows obtaining valuable information which can be used for further transportation planning. First, the proposed mathematical model is tested on simplifled example. The real data application requires necessary preprocessing within which anticipated routes are designed. Thus, the database is supplemented with information on the probable speed of the vehicle. The mathematical model is based on weighted least squares data reconciliation which is organized iteratively. Due to the time-consuming calculation, the linearised model is computed to initialize the values for a complex model. The attention is also paid to the weight setting. The weighing system is designed to reflect the quality of specific data and the dependence on the frequency of trafic. In this respect, the model is not strict, which leaves the possibility to adapt to the current data. The case study focuses on the GPS data of shipping vehicles in the particular city in the Czech Republic with several types of roads.

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“…NG trade over long distances has increased rapidly in the last few decades and many countries now have long term plans to increase its share in their energy supply mix. Several models have been developed to forecast future NG demand for countries in which NG currently is or will be one of their main energy resources according to these plans (Hartono et al, 2017;Shaikh and Ji 2016;Azadeh et al, 2010;Melikoglu 2013;Honore 2014;Taspinar et al, 2013;Boran, 2015), and a number of them studied the technical issues of NG development like network expansion problems (Da Silva et al, 2016;Zavala, 2014;Isom et al, 2018;Odetayo et al, 2018). Subsidizing or keeping NG prices low are among those plans that can encourage NG usage as a replacement to more polluting fossil fuels.…”

Section: Introductionmentioning

confidence: 99%