A unified continuous-time model for integrated crude scheduling and refinery planning optimization

Integrating CDU, FCC and product blending models into refinery planning

The choice of a suitable time representation plays a major role in designing a computationally efficient planning/scheduling framework. For the time representation to be effective, it needs to capture the nuances of the actual process, while ensuring that the resultant model is tractable and computationally efficient. In this paper, a novel time representation scheme, titled hybrid time discretization, which combines both continuous- and discrete-time representations in a single framework, is proposed and used to solve a complex multiproduct maritime inventory routing problem (MIRP) with undedicated compartments. Several continuous- and discrete-time-based frameworks have been proposed to solve the MIRP over the years. While single-/multigrid discretized frameworks do efficiently capture the time-sensitive nuances of the problem (such as active/inactive jetty hours and hourly/daily variations in the demands), they struggle to solve larger problem instances to the required optimality gap in a reasonable time. This is primarily due to the large disparities among the timings of different task components in the model. On the other hand, continuous-time representation can significantly reduce the problem size and computational effort; however, modeling the entire problem using the continuous-time-based framework could have problems in representing some time-sensitive intricacies in the demands and jetty/port activities. To allay the above-mentioned drawbacks, in this paper, a novel hybrid time representation is proposed, which combines a continuous-time representation with a multigrid discrete-time framework. The proposed novel framework is cast in a rolling horizon strategy and tested on various scenarios generated for the above-mentioned maritime transportation problem, which are shown to be significantly more complex than those presented in the literature. The proposed MILP framework is able to solve most of the test cases to the required optimality in a reasonable time without the need for heuristic or meta-heuristic assists. The proposed hybrid time representation could find significant utility in other lateral applications such as batch scheduling and inventory routing problems.

Improved time representation model for the simultaneous energy supply and demand management in microgrids

Optimization of the production planning is crucial for the economic success of a petroleum refinery. Nevertheless, it is a difficult task, because of the large scale of the system and the complexity of the processes involved. Part 1 of this series of two papers addresses the formulation of process models for petroleum refinery planning. First, a generic formulation of nonlinear refinery planning model is adopted from the literature. Subsequently, the formulation of nonlinear empirical models for crude distillation units (CDUs) and a fluid catalytic cracking (FCC) unit is addressed. These empirical models were successfully validated using rigorous process simulators. Finally, the results from model validation showed that the accuracy of model predictions is as good as the current empirical process models reported in the literature, while the empirical process models proposed in this work overcome the limitations of both linear and nonlinear empirical models for CDUs and FCC units previously developed by other authors. Part 2 [Ind. Eng. Chem. Res.2011, DOI: 10.1021/ie200304v] involves the implementation of nonlinear empirical process models in the petroleum refinery planning.

Dynamics of quality improvement programs – Optimal investment policies

Order and inventory variance amplification: The equivalence of discrete and continuous time analyses

The objective of this paper is the development and solution of nonlinear and mixed-integer (MIP) optimization models for real-world planning and scheduling problems in petroleum refineries. Firstly, we present a nonlinear planning model that represents a general refinery topology and allows implementation of nonlinear process models as well as blending relations. The optimization model is able to define new operating points, thus increasing the production of the more valuable products and simultaneously satisfying all specification constraints. The second part addresses scheduling problems in oil refineries, which are formulated as MIP optimization models and rely on both continuous and discrete time representations. Three practical applications closely related to the current refinery scenario are presented. The first one addresses the problem of crude oil inventory management of a refinery that receives several types of crude oil delivered exclusively by a single oil pipeline. Subsequently, two optimization models intended to define the optimal production policy, inventory control and distribution are proposed and solved for the fuel oil and asphalt plant. Finally, the planning model of Moro et al. (1998) is extended in order to sequence decisions at the scheduling level in the liquefied petroleum gas (LPG) area for maximization of the production of petrochemical-grade propane and product delivery.

Crude distillation unit (CDU) generally exists severe corrosion. Risk-based inspection (RBI) technology can effectively assess risk of stationary equipment in petrochemical industry. Meanwhile, integrity operating window (IOW) can effectively control risk by establishing standard and critical limits. Limited of data integrated analysis and the complexity of interfaces connecting in different systems, current risk assessment of complete sets of equipment mainly adopted static method. API 584-2021describes the IOW parameters of the crude distillation unit, but there are few reports about how to apply the dynamic RBI and IOW in the field engineering to achieve real-time risk assessment and warning control of the safety operation boundary. Combining RBI and IOW technology, we recently developed a dynamic risk assessment and IOW warning system by interacting three types dynamic interface data, i.e., medium composition in laboratory information management system (LIMS), real-time process operation parameters in distributed control system (DCS) and equipment thickness in corrosion monitoring system (CMS). The developed system was further applied to the crude distillation unit. Taking three typical corrosion circuits of crude oil circuit before desalting, atmospheric tower overhead circuit and second-line circuit of vacuum tower as examples, the variation of dynamic indicators and risk were investigated. Firstly, the damage modes were analyzed according to API 581 and GB/T 30579. And then the monitoring points including medium compositions, process operation parameters and wall thickness were respectively established by analyzing the corresponding data in LIMS, DCS and CMS. Thus, the risk results will update dynamically with the variations of abovementioned data. Based on dynamic risk results, the critical IOW operation boundaries in typical circuits were also displayed. This research will facilitate grasping the equipment risk state in real-time with dynamic RBI technology while ensuring the equipment steady operation by IOW warning technology.

The scientific and reasonable maintenance strategy is critical to ensure the continuous operation of stationary equipment in the petrochemical industry; both risk-based inspection (RBI) and integrity operating windows (IOWs) are effective for stationary equipment maintenance. In traditional static RBI, the risk is assumed to remain constant in whole inspection period, and the latest variations of medium and process parameters are not fed back into risk calculation. Thus, risk value may be overestimated or underestimated, leading to unexpected failures or excessive inspection. Integrated application of dynamic risk-based inspection (DRBI) and IOWs is an advanced direction for this problem. However, due to the complexity of dynamic interaction mechanisms and risk assessment algorithms as well as the deficiency of powerful software, industrial applications of DRBI and IOWs are still limited. By proposing improved dynamic risk indexes and real-time monitoring process parameters as well as utilizing them in DRBI assessment, this article proposes an integrated DRBI-IOWs method and develops a corresponding system. Further, the method and system are applied in a crude distillation unit. The results show that DRBI-IOWs provide direct and real-time insights into the actual risk and give early warning to the process parameter controls, which is important to control the equipment risk of sudden failures.

Simultaneous re-design and scheduling of multiple effect evaporator systems

This brief explores the approximation properties of a unique basis expansion based on Pascal’s triangle, which realizes a sampled–data driven approach between a continuous–time signal and its discrete–time representation. The roles of certain parameters, such as sampling time interval or model order, and signal characteristics, i.e., its curvature, on the approximation are investigated. Approximate errors in one and multiple–step predictions are analyzed. Furthermore, time–variant approximations under the thresholds of signal curvature are employed to narrow errors and provide flexibilities.

Scheduling of loading and unloading of crude oil in a refinery using event-based discrete time formulation

During the last two decades, the problem of short-term scheduling of multiproduct and multipurpose batch plants has gained increasing attention in the academic, research, and manufacturing communities, predominantly because of the challenges and the high economic incentives. In the last 10 years, numerous formulations have been proposed in the literature based on continuous representations of time. The continuous-time formulations have proliferated because of their established advantages over discrete-time representations and in the quest to reduce the integrality gap and the resulting computational complexities. The various continuous-time models can be broadly classified into three distinct categories: slot-based, global event-based, and unit-specific event-based formulations. In this paper, we compare and evaluate the performance of six such models, based on our implementations using several benchmark example problems from the literature. Two different objective functions, maximization of profit and minimization of makespan, are considered, and the models are assessed with respect to different metrics such as the problem size (in terms of the number of binary variables, continuous variables, and constraints), computational times (on the same computer), and number of nodes needed to reach zero integrality gap. Two additional computational studies with resource constraints such as utility requirements are also considered.

The scheduling of multiple products through pipelines, to meet a demand over a considered time horizon, is a challenging problem that many researchers have been interested in. Although pipelines are considered mainly in the petroleum industry, they can also be used in other industries. This paper deals with the multiproduct pipeline network scheduling problem in the context of mining industry. The objective is to schedule a sequence of batches, to be transported through the pipeline network, to ensure all product demand, while respecting the different technical constraints. The proposed approach is a MILP model with a continuous representation of time and batch volume. It provides maximizes the volume of batches to satisfy all product demands. The model was tested using different instances over a short-term horizon.

Nowadays, manufacturing in process industries mostly takes place in branched supply networks consisting of several plants which are located at different sites and linked by complex logistics relations and material flows. Thus, a key issue of production planning and scheduling is the coordination of plant operations at different production stages considering the supply of intermediates and their transportation between various plants. This planning effort has to cope with the limited availability of resources as well as numerous storage and transportation constraints. Time-consuming and expensive setup and cleaning activities require production to be carried out in so-called campaigns. Identical production tasks are repeated in succession to reduce these unproductive times. In our paper, we propose an application-oriented approach for supply network planning in the chemical industry which is based on a novel aggregation scheme and a network flow formulation of the problem. The optimization model uses a continuous representation of time. Our approach generates a comprehensive schedule for coordinating the production activities at all plants in the network.