Analisis Usulan Perencanaan Kapasitas Tangki Crude Oil Berdasarkan Tingkat Keekonomisan Refinery Unit X PT Y

Crude Oil merupakan bahan utama dalam pembuatan Bahan Bakar Minyak (BBM) dan non BBM. Crude Oil bahan baku produksi diperoleh dari berbagai macam lokasi eksplorasi di Indonesia harus ditampung terlebih dahulu dalam tangki penampungan. Penerimaan Crude Oil melalui beberapa macam jalur, yaitu kapal tongkang dan pipa. Penerimaan melalui jalur pipa diterima selama 24 jam sehari. Hal ini mengakibatkan perlunya perencanaan kapasitas tangki penampungan Crude Oil agar dapat cukup menampung Crude Oil yang diterima oleh refinery unit. Penelitian menggunakan metode deskrispsi sistem yang dapat digunakan untuk mendeskripsikan permasalahan yang dihadapi dengan lebih baik. Perencanaan kapasitas menggunakan model matematis yang telah digunakan Perusahaan untuk dapat memperoleh solusi optimal. Hasil analisis mengungkapkan bahwa kapasitas tangki yang dibutuhkan sebanyak 1.165,79  Mega Barel (MB) per hari dengan total cost sebesar USD  668.051,55. Hasil ini dapat digunakan untuk membantu pengambilan keputusan dalam simulasi untuk memperoleh kapasitas tangki paling optimal

Self-optimizing control methodology for mixed integer programming problems: a case study of refinery production scheduling

Problem formulation as mixed integer nonlinear programming (MINLP) is one of the most challenging task in refinery scheduling optimization. In most of the work reported in refinery scheduling, uncertainties from design point of view predominate. However, there is also a need to consider operational uncertainties (disturbances) as they affect the accuracy and robustness of the overall schedule. This study proposed a novel approach under self- optimizing control (SOC) framework to deal with multi-period refinery scheduling problems under uncertain conditions. The goal is to maintain global optimum by controlling the gradient of the cost function at zero via approximating necessary conditions of optimality (NCO) over the whole uncertain parameter space. A regression model for the plant expected revenue (profit) as a function of independent variables using optimal operation data was obtained and a feedback input (manipulated variable) was derived. The performance of the proposed approach was tested using case studies. The first case assumed a system with no disturbance with the base case model giving an optimal profit of $56,696,407 while the proposed approach yields$50,523,054, translating to 10.888 % loss. The percentage loss for the second, third and fourth cases with disturbances are 5.807 %, 4.409% and 7.898% respectively. The results obtained have shown that the idea presented was able to effectively deal with the situation at hand with percentage loss within a reasonable degreeKeywords: Refinery scheduling scheduling, MINLP formulation, Operational uncertainty (disturbances), Necessary condition of optimality, Feedback contro

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Department of Technology and Innovation ManagementMany machine learning applications are being employed to forecast weather conditions. In this paper, we focus more on small-scale weather forecasts with limited meteorological observation data. When oil refinery companies in non-oil-producing countries import crude oil by VLCCs (Very Large Crude Carriers), VLCCs unload crude oil to onshore storage tanks using SPM (Single Point Buoy Mooring System). Weather conditions in the offshore area where loading buoys are anchored are critical in determining whether unloading process is possible. The current practice of such decision making relies mostly on human experiences, and the predictive accuracy of the current practice is reported as about 75%. We tested machine learning methods to see if these methods can increase predictive accuracy in this problem of classification, the possibility of unloading given weather conditions such as wave heights, wind speeds, and wind directions. The results of our analysis showed that random forest and XGBoost have much better performance (more than 90%) than support vector machines and logistic regression in predicting unloading conditions in the time range from one hour to three days.clos

Petroleum refinery scheduling with consideration for uncertainty

Scheduling refinery operation promises a big cut in logistics cost, maximizes efficiency, organizes allocation of material and resources, and ensures that production meets targets set by planning team. Obtaining accurate and reliable schedules for execution in refinery plants under different scenarios has been a serious challenge. This research was undertaken with the aim to develop robust methodologies and solution procedures to address refinery scheduling problems with uncertainties in process parameters. The research goal was achieved by first developing a methodology for short-term crude oil unloading and transfer, as an extension to a scheduling model reported by Lee et al. (1996). The extended model considers real life technical issues not captured in the original model and has shown to be more reliable through case studies. Uncertainties due to disruptive events and low inventory at the end of scheduling horizon were addressed. With the extended model, crude oil scheduling problem was formulated under receding horizon control framework to address demand uncertainty. This work proposed a strategy called fixed end horizon whose efficiency in terms of performance was investigated and found out to be better in comparison with an existing approach. In the main refinery production area, a novel scheduling model was developed. A large scale refinery problem was used as a case study to test the model with scheduling horizon discretized into a number of time periods of variable length. An equivalent formulation with equal interval lengths was also presented and compared with the variable length formulation. The results obtained clearly show the advantage of using variable timing. A methodology under self-optimizing control (SOC) framework was then developed to address uncertainty in problems involving mixed integer formulation. Through case study and scenarios, the approach has proven to be efficient in dealing with uncertainty in crude oil composition

Modeling the Crude Oil Scheduling Problem with Integration with Lower Level Production Optimization and Uncertainty

This research is focused on the modeling and optimization of the crude oil scheduling problem in order to generate the most appropriate schedule for the unloading, charging, blending, and movement of crude oil in a refinery, which means obtaining the schedule that generates the lowest costs. Uncertainty, which is often present in these types of optimization problems, is also analyzed and taken into account for the resolution of crude oil scheduling problem. A comprehensive novel model is proposed to describe the upper level crude oil scheduling problem, generate an optimal solution for the mentioned problem, and allow integration with the lower level production optimization problem of a refinery. This integration is possible due to the consideration of total flows of the different types of crude oil instead of flows of a particular key component in the crude oil to linearize the upper level problem and generate a less complex model. The proposed approach incorporates all the logistical costs including the sea waiting, unloading and inventory costs together with the costs associated with the transfer of crude oil from one to another entity. Moreover, this model also offers the possibility of considering multiple tank types including storage and blending tanks throughout the supply chain and the incorporation of the capability of storing more than one crude oil type in the storage tanks during the schedule horizon. A comparative analysis is performed against other models proposed and preliminary results of integration with a lower operational level are provided. In order to take into account the possibility of uncertainty or fuzziness in the scheduling problem, for the first time an approach is proposed to face the resolution of this problem in order to obtain a more realistic scheduling of the allocations of crude oil. Fuzzy linear programming theory is used here to represent this uncertainty in order to find an optimal solution that takes into account the lack of precise information on the part of the decision maker without losing the linearity of the original system. Uncertainty in the minimum demand to be satisfied in the distillation unit according to the necessities of the market and the lack of precise information about certain costs involved in the operations throughout the supply chain are separately considered. Among the different approaches utilized in fuzzy linear programming, the flexible programming or Zimmermann method and its extension to fuzziness in objective functions are implemented. A comparison between the two cases studied and the crisp model is performed with the aim of determining the effect of these uncertainties in the schedule of the crude oils movements between the different entities in the supply chain and the total cost generated