Modeling Tool For Planning The Operation Of Refineries

CATEGORY: REFINERY PLANNING
PATENT
Modeling Tool For Planning The Operation Of Refineries
United States Patent Application 20120083914
Inventors:
Kocis, Gary R. (Vienna, VA, US)
Warrick, Philip H. (Oakton, VA, US)
Depaola, Victor P. (Vienna, VA, US)
Publication Date:  04/05/2012
Assignee:  EXXONMOBIL RESEARCH AND ENGINEERING COMPANY (Annandale, NJ, US)
Abstract:
A modeling tool for determining the operation of a production facility. A variety of different activities can be modeled by the present invention, including (a) feed material selection, including quantity and timing, (b) product sales, including quantity and timing, (c) process operations, including process conditions and timing, (d) blending operations, including process conditions and timing, and/or (e) inventory management. The modeling tool may represent time using continuous-time, discrete-time, asynchronous time periods, synchronous time periods, and combinations of these various approaches.
TECHNICAL FIELD
The present invention relates to methods for optimizing the operation of refineries and the related supply chains.
BACKGROUND
Planning tools are widely used in the petrochemical industry to assist in the planning of activities in a refinery. Among the planning tools widely used in the petrochemical industry are PIMS by AspenTech, RPMS by Honeywell, and GRTMPS by Haverly Systems. The models used in these tools are typically composed of:
raw material (e.g. crude oil) supply data, including quantities and prices,
raw material characterization data,
product (e.g. gasoline, diesel) demand data, including quantities and prices,
process models which represent the production facilities,
product blending models which represent the blending facilities and specifications, and
other constraints.

These planning tools can be used as decision support aids for raw material purchases, refinery operations, product sales, or other planning decisions. Traditionally, raw material purchase and product sales decisions are made on the basis of a planning optimization model that represents the time horizon as one period with average conditions being assumed throughout each period (i.e. period-average models). The task of developing the raw material delivery schedule and the production schedule are predominantly separate activities which are finalized after the period average plan for raw material purchases and production has been developed. Given these purchases and sales decisions, more detailed production planning and/or production scheduling are performed as subsequent steps. Ideally, a feasible production schedule would be developed that is consistent and equivalent to the planning model result. In practice, however, the process operation is not uniform over the time horizon and a planning model which assumes this to be true often leads to a planning result which cannot be converted into an equivalent schedule.
A simulation model may be used to support the development of a production schedule. The production schedule may include changes in various process operations which are scheduled to start and end at certain times during the time horizon. Raw material deliveries and product shipments are to be scheduled such that these are consistent and feasible with the production schedule. Often, transportation costs are significant and the minimization of transportation costs is important. Inventory dynamics within each period are not considered in the production planning activity since the plan is performed with a period average view of the production. However, inventory and the fluctuation of material inventory levels within each period can be important in the production schedule. Typically, the production scheduling activity seeks to find a feasible schedule which matches the production plan, where feasibility includes keeping inventory levels within the allowed range (minimum and maximum levels) at all times during the time horizon. An additional objective is to minimize the inventory holding costs (i.e. minimize capital costs).
Scheduling tools may be used to obtain a feasible schedule for raw material delivery, production, and product shipments. Examples of such production scheduling tools are ORION by AspenTech, Production Scheduler by Honeywell, H/SCHED by Haverly Systems, and SIMTO by M3 Technology. It is desirable for the planning models and the scheduling models to be consistent. Even so, there is no guarantee that the production plan can be converted into an equivalent production schedule. One weakness of this approach is that production planning and production scheduling are performed as two separate sequential steps. The optimization model used for raw material valuation and selection decisions does not reflect scheduling considerations. This can limit the quality of the solution obtained from this optimization model. When the scheduling considerations have a significant impact on the purchase and sales decisions, the absence of these factors in the optimization model can lead to a non-optimal overall solution. Thus, it is desirable to optimize a combination of several or all of these activities as one unified activity.
Notable publications on the topic of developing models for scheduling problems include the following: (1) R. Karuppiah, K. C. Furman, and I. E. Grossmann, “Global Optimization for Scheduling Refinery Crude Oil Operations,” Computers &Chemical Engineering 32, 2745-2766 (2008); (2) S. Mouret, I. E. Grossmann, and P. Pestiaux, “A Novel Priority-Slot Based Continuous-Time Formulation for Crude-Oil Scheduling Problems,” Ind. Eng. Chem. Res. 48, 8515−8528 (2009). In both of these publications, the vessel schedule (or the transportation schedule) and the raw material purchase decisions are given as input. Thus, these approaches do not apply to the simultaneous solution of raw material selection, transportation scheduling, and production scheduling.
SUMMARY
The present invention provides a method for planning (including scheduling) the operations of one or more production facilities. The production facilities can include one or more of the following; petroleum refineries, petrochemical refineries, chemical plants, or other manufacturing plants, or any combination of these, along with their related supply chains. The method uses a modeling approach that represents the operation of one or more production facilities, which can include transportation activities and inventory. The modeling approach handles situations where different production entities may operate on asynchronous schedules. The method can be used for various applications, including optimization or simulation of the production facilities.
A variety of planning activities can be modeled and solved by the present invention, including (a) feed material selection, including quantity and timing, (b) product sales, including quantity and timing, (c) process operations, including process conditions and timing, (d) blending operations, including process conditions and timing, (e) transportation to and/or from facilities, including transportation modes, quantities, and timing, and/or (f) inventory management, including inventory movements and timing, inventory limits, and inventory quantities over time. In one embodiment, the method can be used to optimize: (i) feed material selection, (ii) transportation scheduling, and (iii) production planning (including production scheduling), as a combined problem.
In one aspect, the present invention provides a method for determining the operation of a production facility. The method uses a computer-based mathematical model of the production facility, wherein the mathematical model includes a representation of two or more non-tank production entities and one or more tank production entities (i.e. tank entities) that are associated with (e.g. connected to, directly or indirectly; or upstream and/or downstream of) the non-tank production entities. For example, a tank entity may be downstream of a non-tank production entity by a connection that is direct or indirect (via an intermediate equipment); and may be upstream of another non-tank production entity by a connection that is direct or indirect (via an intermediate equipment).
Each non-tank production entity is represented by a corresponding submodel comprising a set of mathematical relationships (given as equations) that model the behavior of the non-tank production entity. The submodel represents the non-tank production entity in each operational time interval of the non-tank production entity. In one embodiment, the number of equations contained in a submodel of the non-tank production entity is related to (i.e. having a mathematical relationship to, such as proportional or equal to) the number of operational time intervals of that non-tank production entity.
The number of operational time intervals for the each non-tank production entity may be received as input data (e.g. directly by the user, or from a database, table, or spreadsheet) or calculated by the modeling tool using other inputted data. The submodel for each non-tank production entity may include decision variables relating to the operation of the non-tank production entity. These decision variables may include decision variables relating to the duration of the operational time intervals.
The tank entity is represented by a corresponding submodel comprising a set of mathematical relationships (given as equations) that model the behavior of the tank entity. The submodel for a tank entity represents the tank entity in each operational time interval of the tank entity. In one embodiment, the number of equations contained in a submodel of the tank entity is related to (i.e. having a mathematical relationship to, such as proportional or equal to) the number of operational time intervals of that tank entity. The number of operational time intervals for the tank entity is calculated using the number of operational time intervals of the non-tank production entities which are associated with (e.g. connected to) the tank entity. This calculation can be performed in any suitable way to reflect how the behavior and/or operation of the tank entity is affected by its relationship with the associated non-tank production entities. In some cases, the calculation may yield the maximum number of operational time intervals that would be needed to represent the tank entities and the operational time intervals of the associated non-tank production entities, as further described below.
The approach described above is used to model the production facility(s). One advantage of the approach for handling operational time intervals is that it can reduce or minimize the number of time intervals that are considered while allowing for optimization of the operations with time. An objective function is defined based on one or more performance metrics for the production facilities. The resulting mathematical model is solved using one or more computers to obtain a solution. The solution results are used to determine an operational plan for the production facility, including the operating schedule for the production entities. In some embodiments, the method further comprises operating the production facility according to the operational plan (including operating the production entities, and optionally, the associated supply chain).
In certain embodiments, the mathematical model may be formulated as an optimization model, which comprises one or more objective functions for a performance metric of the production facility. The objective function(s) may include terms from the submodels, such as quantities (e.g. feed material consumption, production, etc.) and composition (properties, qualities, etc.) that contribute to the performance metric. The mathematical model is solved for maximizing or minimizing the performance metric. For calculating the performance metric, the mathematical model may comprise various cost-related parameters, including parameters relating to the cost of feed materials and parameters relating to the cost of holding inventory. For making decisions based on the performance metric, the mathematical model may comprise various decision variables, including those related to the selection of feed materials. In some cases, the mathematical model further comprises parameters relating to the cost of transportation options and decision variables relating to transportation scheduling. The model may comprise decision variables related to quantities (e.g. quantity of each feed to purchase), properties (e.g. the physical properties of a product stream), rates (e.g. flowrate of a feed stream), and/or temporal decisions (e.g. the delivery schedule for feed materials).
In some embodiments, the modeling methodology of the present invention handles situations where the operational time intervals for two or more of the non-tank production entities are asynchronous. One of the roles of the tank entities is to handle the asynchronous operational time intervals of non-tank production entities. For example, the tank entities may have time intervals which align with the time intervals of the associated non-tank production entities. To define how the time intervals of the tank entity align with the time intervals of the non-tank production entities, the mathematical model may further comprise binary variables, continuous variables, and/or mathematical relationships representing the relationships between the operational time intervals of the tank entities with the operational time intervals of the non-tank production entities.
The present invention may also be embodied as a computer-readable storage medium having executable instructions for performing the various processes as described herein. The storage medium may be any type of computer-readable medium (i.e., one capable of being read by a computer), including non-transitory storage mediums such as magnetic or optical tape or disks (e.g., hard disk or CD-ROM), solid state volatile or non-volatile memory, including random access memory (RAM), read-only memory (ROM), electronically programmable memory (EPROM or EEPROM), or flash memory. The term “non-transitory computer-readable storage medium” encompasses all computer-readable storage media, with the sole exception being a transitory, propagating signal.
The present invention may also be embodied as a computer system that is programmed to perform the various processes described herein. The computer system may include various components for performing these processes, including processors, memory, input devices, and/or displays. The computer system may be any suitable computing device, including general purpose computers, embedded computer systems, network devices, or mobile devices, such as handheld computers, laptop computers, notebook computers, tablet computers, and the like. The computer system may be a standalone computer or may operate in a networked environment.
Free Full Text Source: http://www.freepatentsonline.com/y2012/0083914.html