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

Advanced and novel modeling techniques for simulation, optimization and monitoring chemical engineering tasks with refinery and petrochemical unit applications

Engineers predict, optimize, and monitor processes to improve safety and profitability. Models automate these tasks and determine precise solutions. This research studies and applies advanced and novel modeling techniques to automate and aid engineering decision-making. Advancements in computational ability have improved modeling software’s ability to mimic industrial problems. Simulations are increasingly used to explore new operating regimes and design new processes. In this work, we present a methodology for creating structured mathematical models, useful tips to simplify models, and a novel repair method to improve convergence by populating quality initial conditions for the simulation’s solver. A crude oil refinery application is presented including simulation, simplification tips, and the repair strategy implementation. A crude oil scheduling problem is also presented which can be integrated with production unit models. Recently, stochastic global optimization (SGO) has shown to have success of finding global optima to complex nonlinear processes. When performing SGO on simulations, model convergence can become an issue. The computational load can be decreased by 1) simplifying the model and 2) finding a synergy between the model solver repair strategy and optimization routine by using the initial conditions formulated as points to perturb the neighborhood being searched. Here, a simplifying technique to merging the crude oil scheduling problem and the vertically integrated online refinery production optimization is demonstrated. To optimize the refinery production a stochastic global optimization technique is employed. Process monitoring has been vastly enhanced through a data-driven modeling technique Principle Component Analysis. As opposed to first-principle models, which make assumptions about the structure of the model describing the process, data-driven techniques make no assumptions about the underlying relationships. Data-driven techniques search for a projection that displays data into a space easier to analyze. Feature extraction techniques, commonly dimensionality reduction techniques, have been explored fervidly to better capture nonlinear relationships. These techniques can extend data-driven modeling’s process-monitoring use to nonlinear processes. Here, we employ a novel nonlinear process-monitoring scheme, which utilizes Self-Organizing Maps. The novel techniques and implementation methodology are applied and implemented to a publically studied Tennessee Eastman Process and an industrial polymerization unit

Optimization of crude oil operations scheduling by applying a two-stage stochastic programming approach with risk management

Producción CientíficaThis paper focuses on the problem of crude oil operations scheduling carried out in a system composed of a refinery and a marine terminal, considering uncertainty in the arrival date of the ships that supply the crudes. To tackle this problem, we develop a two-stage stochastic mixed-integer nonlinear programming (MINLP) model based on continuous-time representation. Furthermore, we extend the proposed model to include risk management by considering the Conditional Value-at-Risk (CVaR) measure as the objective function, and we analyze the solutions obtained for different risk levels. Finally, to evaluate the solution obtained, we calculate the Expected Value of Perfect Information (EVPI) and the Value of the Stochastic Solution (VSS) to assess whether two-stage stochastic programming model offers any advantage over simpler deterministic approaches.Gobierno de España - proyects a-CIDiT (PID2021-123654OB-C31) and InCo4In (PGC 2018-099312-B-C31)Junta de Castilla y León - EU-FEDER (CLU 2017-09, CL-EI-2021-07, UIC 233

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

Scheduling of crude oil and product blending and distribution operations in a refinery

Ph.DDOCTOR OF PHILOSOPH

Modelling & Predictive Control of a Crude Distillation Unit

The purpose of this project is to implement Model Predictive Control strategy to a Crude Distillation Unit model and to compare it to PI controllers in terms of controller performance. The motivation of this project comes to the fact that there is a need to reduce CO2 emission and at the same time to reduce energy consumption within the unit. The author has developed the CDU model using HYSYS and also in state-space representation using MATLAB, the latter was being used to design MPC controllers. From this project, it can be seen that the success of MPC implementation depends on the accuracy of the plant model to represent actual process. The MPC controller proved to be more effective in regulating the percent liquid level of the condenser but not so effective for the other two variables being studied

Process design Optimisation, heat integration, and techno-economic analysis of oil refinery: A case study

This paper outlines a comprehensive analysis of the optimal design and simulation of a crude oil distillation system within a refinery process, including pre-treatment and blending of two crude oils to increase the refinery’s annual profit. This distillation process is currently in operation, and the desired amount of feedstock is obtained from Iraqi Basra light-2015 and Kirkuk-2011 crude oil. To improve the energy efficiency of the utilization rate of crude oil, an atmospheric distillation process unit in this refinery with a capacity of 150,000 barrels per day (bpd) is considered. Aspen HYSYS simulation is used to optimize the distillation unit configuration and its operating performance. This paper also deals with three scenarios by comparing the feedstock compositions to the distillation process and the produced product compositions to minimize utility consumption. A heat integration approach was applied to the 3rd scenario by recycling hot outlet streams to the heat exchangers to increase the temperature of the inlet stream of the distillation column. Results indicated that about £2.29 million per year (Mpy) could be saved from the heat integration systems. Economic analysis and cut yields were carried out for each scenario to investigate the cost-effective and economically viable. Based on the economic analysis, scenario three showed better performance with a comparatively high cumulative cash flow of £31,886 M

Planning and optimising of petroleum industry supply chain and logistics under uncertainty

Petroleum industry has a major share in the world energy and industrial markets. In the recent years, petroleum industry has grown increasingly complex as a result of tighter competition, stricter environmental regulations and lower-margin profits. It is facing a challenging task to remain competitive in a globalised market, the fluctuating demand for petroleum products and the current situation of fluctuating high petroleum crude oil prices is a demonstration that markets and industries throughout the world are impacted by the uncertainty and volatility of the petroleum industry. These factors and others forced petroleum companies for a greater need in the strategic planning and optimisation in order to make decisions that satisfy conflicting multi-objective goals of maximising expected profit while simultaneously minimising risk. These decisions have to take into account uncertainties and constraints in factors such as the source and availability of raw material, production and distribution costs and expected market demand. The main aim of this research is the development of a strategic planning and optimising model suitable for use within the petroleum industry supply chain under different types of uncertainty. The petroleum supply chain consists of all those activities related to the petroleum industry, from the recovery of raw materials to the distribution of the finished product. This network of activities forms the basis of the proposed mathematical and simulation models. Mathematical model of two-stage stochastic linear programming taking into consideration the effect of uncertainty in market demand is developed to address the strategic planning and optimisation of petroleum supply chain. GAMS software is used to solve the proposed mathematical models for this research. Arena simulation Software is utilised to develop a model for the proposed petroleum supply chain starting from crude oil supply to the system, going through three stages of separation processes and finally reaching the distillation stage. The model took into account the following factors: Input Rate, Oil Quality, Distillation Capacity and Number of Failed Separators which are analysed against the performance measures: Total Products and Equipment Utilisation. The results obtained from the experiment are analysed using SPSS Programme