Integrated Proactive and Reactive Scheduling for Refinery Front-End Crude Movement with Consideration of Unit Maintenance

Abstract

A typical front-end crude supply process of refineries encompasses many units, such as vessels, port-side storage tanks, long-distance pipeline, refinery-side charging tanks, and crude distillation units. In reality, the performance of these units will inevitably decay; thus, unit maintenance plans should be well integrated with normal crude movement schedules. In this paper, a novel methodology has been proposed to dynamically and simultaneously determine the optimal schedule of crude movement and unit maintenance operations. Specifically, both proactive scheduling for multiple preventive maintenance tasks and reactive scheduling for emergent corrective maintenance tasks are systematically integrated. Accordingly, the developed mixed-integer nonlinear programming (MINLP) model will optimize the operation schedule for the front-end crude supply process via minimizing the total operating cost during the scheduling time horizon. Meanwhile, it can also determine the optimal unit maintenance schedule, which will identify the best opportunities of not only isolating units from process system to perform maintenance but also resuming their services after maintenance. The model can simultaneously handle multiple unit maintenance tasks while satisfying all strict constraints for crude transferring, storing, blending, and processing operations. The efficacy of the developed methodology and the MINLP model has been demonstrated through various case studies.

Simultaneous Production and Maintenance Scheduling for Refinery Front-End Process with Considerations of Risk Management and Resource Availability

“…In these two terms, T v s and T v f denote the starting and ending times of the crude unloading operation for vessel v, respectively, and T v arr represents the arrival time of vessel v. The third term in eq 33 sums up all the costs for setting up crude transfer operations during the scheduling time horizon, which include the cost for transferring crudes from the vessels to the storage tanks, from the storage tanks to the charging tanks, and from the charging tanks to the CDUs. The last term of eq 33 represents the total inventory cost for all STs and CTs throughout the scheduling time horizon, which is referenced from Qu et al 17 . Note that the maintenance cost is not included in the objective function of eq 33 because it does not belong to routine operating costs.…”

“…It is an M-MINLP model. In which eqs 1 through 25 plus eq 33 are based on the previous study, 17 while eqs 26 through 32 and eq 34 are newly developed. To solve this multiobjective problem, the solving strategy employed in this study is to convert the multiobjective problem to a single-objective problem by transforming the objective function in eq 34 into the riskallowance constraint formulated in eq 35, where the TR value will be constrained by an upper bound, Risk u .…”

“…Thus, the risk-allowance constraint is actually relaxed, but the maintenance budget constraints are particularly concerned in the scheduling model. Its solution result will be compared with the previous one from Qu et al, 17 where both maintenance budget constraints and maintenance dynamic risk constraints are excluded in the scheduling model. On the basis of the same given data of case study 1, case study 2 explores the Pareto frontier of the developed full M-MINLP model.…”

“…Case study 1 is developed with the upper bound of the total maintenance risk score set as 315, which is a very high value for the studied FCTP system. Details of the studied FCTP system are given by Table 2 which is based on Qu et al 17 Note that the number of time events might affect the optimization results, especially when the number of time events is insufficient. Thus, case study 1 is actually solved multiple times with different time event numbers, so as to identify the best selection for the time event number.…”

Research on block storage yard scheduling according to grid method

“…Jahan et al 19 develop an efficient approach to process GTS (Group Trip Scheduling) queries and variants for the Euclidean space and road networks and propose a dynamic programming technique to eliminate the trip combinations that cannot be part of the optimal query answer. Qu et al 20 propose a novel methodology that dynamically and simultaneously determine the optimal schedule of crude movement and unit maintenance operations, and it can also determine the optimal unit maintenance schedule, which will identify the best opportunities of not only isolating units from process system to perform maintenance but also resuming their services after maintenance. Kisialiou et al 21 present a novel methodology for reliable supply vessel planning and scheduling, enabling planners to construct delivery schedules having a low expected total cost.…”