Developing the next generation of Real Time Optimization Technologies (Blend Optimization)
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Transcript of Developing the next generation of Real Time Optimization Technologies (Blend Optimization)
Real-‐Time Blend Optimization Industrial Modeling Framework (RTBO-‐IMF)
i n d u s t r IAL g o r i t h m s LLC. (IAL)
www.industrialgorithms.com June 2013
Introduction to Real-‐Time Blend Optimization, UOPSS and QLQP Presented in this short document is a description of what is typically known as on-‐line or real-‐time "multi-‐process", "multi-‐pool", "multi-‐product", "multi-‐property" and "multi-‐period" blend optimization. This kind of processing is found in all petroleum refineries where the blending process mixes diverse refinery rundown streams or components into various types and grades of gasoline, jet fuel, diesel and heating oil. Figure 1 depicts these four types of blended products with shared components resources including their inventory such as cracked naphtha, kerosene, etc. configured in our unit-‐operation-‐port-‐state superstructure (UOPSS) (Kelly, 2004b, 2005, and Zyngier and Kelly, 2012).
Figure 1. Gasoline, Jet Fuel, Diesel & Heating Oil Blending Flowsheet Example.
The CTank's and PTank's (triangle shapes) in Figure 1 represent component and product tanks or pools where the small circle shapes define what we call inlet and outlet (with "x") ports and are only found in our UOPSS. The Blender's (rectangle shapes with "x") are controlled mixers in the sense that component flows into the blenders can be regulated and are sometimes referred to as pools with no inventory and maybe either in continuous or semi-‐continuous operation. The diamond shapes are called perimeters and are the usual source and sink nodes found in other types of network flow representations. On-‐line analyzers or instruments are usually available to measure the intensive property specifications of the material such as octane, cetane, sulfur, viscosity, density, vapor pressure, distillation temperature, flash point, cloud point, etc. just to name a few. The other type of continuous process configured is a Hydrotreater which reacts hydrogen with the virgin diesel stream (VDiesel) in the presence of a catalyst at high pressure to reduce its sulfur content i.e., HDiesel will have a very low sulfur concentration. The "severity" (i.e., its process/operating condition) of the hydrotreater is also modeled in order to be able to manipulate or optimize the degree or extent to which the virgin diesel is desulfurized. The quantity (flows and inventories) and quality (properties and conditions) aspects of the problem as well as its logic attributes (Kelly, 2006) define what we call the quantity-‐logic-‐quality phenomena (QLQP) where more details around the blending process modeling and its planning and scheduling can also be found in Kelly (2004a) and Castillo, Kelly and Mahalec (2013). Another important issue is the handling feedback especially when controlling flows, inventories and properties in real-‐time or closed-‐loop. This is addressed using our state-‐of-‐the-‐art dynamic and nonlinear data reconciliation and regression technology (Kelly, 1998 and 2004c) implemented inside a "moving horizon estimation" (MHE) framework (Kelly and Zyngier, 2008). What makes this blending configuration interesting is the modeling of all four products together into a single blending optimization problem. Due to the sharing of rundown components between one or more blenders at different times, there is tremendous opportunity to produce on-‐specification product using the lowest cost and most available components. Existing blend control and optimization software only manage one blender at a time with no other pools such as tanks included, and they look out no further than the current blend (mono-‐period). In our formulation we look out over multiple blends of product over multiple blenders considering multiple periods or time-‐intervals into the future where these time-‐periods can be either of equal or unequal duration. In addition and unique to our formulation, we also allow the integration of other types of processes (not only hydrotreaters) such as crude distillation units, catalytic reformers, fluidized catalytic converters, hydrocrackers and alkylation units. This allows for upstream manipulations of process/operating conditions to produce more appropriate component rundown properties before they even enter the blending area. This alleviates possible quantity and/or quality bottlenecks (long and shorts of material) that may arise during the blending operation avoiding off-‐specification events as well as minimizing over and under-‐use of high-‐octane, high-‐cetane, low-‐sulfur and/or low-‐viscosity component rundowns. Benefits for such a RTBO application can be in the millions of dollars and are comparable to the benefits defined by Kelly and Mann (2003) for crude-‐oil blend optimization. More specifically, a similar installation of this technology and its approach installed at a major oil company's refinery in Europe quoted a payback period of only two-‐weeks! Industrial Modeling Framework (IMF), IMPRESS and SIIMPLE
To implement the mathematical formulation of this and other systems, IAL offers a unique approach and is incorporated into our Industrial Modeling and Pre-‐Solving System we call IMPRESS. IMPRESS has its own modeling language called IML (short for Industrial Modeling Language) which is a flat or text-‐file interface as well as a set of API's which can be called from any computer programming language such as C, C++, Fortran, Java (SWIG), C# or Python (CTYPES) called IPL (short for Industrial Programming Language) to both build the model and to view the solution. Models can be a mix of linear, mixed-‐integer and nonlinear variables and constraints and are solved using a combination of LP, QP, MILP and NLP solvers such as COINMP, GLPK, LPSOLVE, SCIP, CPLEX, GUROBI, LINDO, XPRESS, CONOPT, IPOPT and KNITRO as well as our own implementation of SLP called SLPQPE (successive linear & quadratic programming engine) which is a very competitive alternative to the other nonlinear solvers and embeds all available LP and QP solvers. The underlying system architecture of IMPRESS is called SIIMPLE (we hope literally) which is short for Server, Interacter (IPL), Interfacer (IML), Modeler, Presolver Libraries and Executable. The Server, Presolver and Executable are primarily model or problem-‐independent whereas the Interacter, Interfacer and Modeler are typically domain-‐specific i.e., model or problem-‐dependent. Fortunately, for most industrial planning, scheduling, optimization, control and monitoring problems found in the process industries, IMPRESS's standard Interacter, Interfacer and Modeler are well-‐suited and comprehensive to model the most difficult of production and process complexities allowing for the formulations of straightforward coefficient equations, ubiquitous conservation laws, rigorous constitutive relations, empirical correlative expressions and other necessary side constraints. User, custom, adhoc or external constraints can be augmented or appended to IMPRESS when necessary in several ways. For MILP or logistics problems we offer user-‐defined constraints configurable from the IML file or the IPL code where the variables and constraints are referenced using unit-‐operation-‐port-‐state names and the quantity-‐logic variable types. It is also possible to import a foreign LP file (row-‐based MPS file) which can be generated by any algebraic modeling language or matrix generator. This file is read just prior to generating the matrix and before exporting to the LP, QP or MILP solver. For NLP or quality problems we offer user-‐defined formula configuration in the IML file and single-‐value and multi-‐value function blocks writable in C, C++ or Fortran. The nonlinear formulas may include intrinsic functions such as EXP, LN, LOG, SIN, COS, TAN, MIN, MAX, IF, LE, GE and KIP, LIP, SIP (constant, linear and monotonic spline interpolation) as well as user-‐written extrinsic functions. Industrial modeling frameworks or IMF's are intended to provide a jump-‐start to an industrial project implementation i.e., a pre-‐project if you will, whereby pre-‐configured IML files and/or IPL code are available specific to your problem at hand. The IML files and/or IPL code can be easily enhanced, extended, customized, modified, etc. to meet the diverse needs of your project and as it evolves over time and use. IMF's also provide graphical user interface prototypes for drawing the flowsheet as in Figure 1 and typical Gantt charts and trend plots to view the solution of quantity, logic and quality time-‐profiles. Current developments use Python 2.3 and 2.7 integrated with open-‐source Dia and Matplotlib modules respectively but other prototypes embedded within Microsoft Excel/VBA for example can be created in a straightforward manner. However, the primary purpose of the IMF's is to provide a timely, cost-‐effective, manageable and maintainable deployment of IMPRESS to formulate and optimize complex industrial manufacturing systems in either off-‐line or on-‐line environments. Using IMPRESS alone would be somewhat similar (but not as bad) to learning the syntax and semantics of an AML as well as having to code all
of the necessary mathematical representations of the problem including the details of digitizing your data into time-‐points and periods, demarcating past, present and future time-‐horizons, defining sets, index-‐sets, compound-‐sets to traverse the network or topology, calculating independent and dependent parameters to be used as coefficients and bounds and finally creating all of the necessary variables and constraints to model the complex details of logistics and quality industrial optimization problems. Instead, IMF's and IMPRESS provide, in our opinion, a more elegant and structured approach to industrial modeling and solving so that you can capture the benefits of advanced decision-‐making faster, better and cheaper. References Kelly, J.D., "A regularization approach to the reconciliation of constrained data sets", Computers & Chemical Engineering, 1771, (1998). Kelly, J.D., Mann, J.M., "Crude-‐oil blend scheduling optimization: an application with multi-‐million dollar benefits", Hydrocarbon Processing, June, 47, July, 72, (2003). Kelly, J.D., "Formulating production planning models", Chemical Engineering Progress, January, 43, (2004a). Kelly, J.D., "Production modeling for multimodal operations", Chemical Engineering Progress, February, 44, (2004b). Kelly, J.D., "Techniques for solving industrial nonlinear data reconciliation problems", Computers & Chemical Engineering, 2837, (2004c). Kelly, J.D., "The unit-‐operation-‐stock superstructure (UOSS) and the quantity-‐logic-‐quality paradigm (QLQP) for production scheduling in the process industries", In: MISTA 2005 Conference Proceedings, 327, (2005). Kelly, J.D., "Logistics: the missing link in blend scheduling optimization", Hydrocarbon Processing, June, 45, (2006). Kelly, J.D., Zyngier, D., "Continuously improve planning and scheduling models with parameter feedback", FOCAPO 2008, July, (2008). Zyngier, D., Kelly, J.D., "UOPSS: a new paradigm for modeling production planning and scheduling systems", ESCAPE 22, June, (2012). Castillo, P.A., Kelly, J.D., Mahalec, V., "Inventory pinch analysis for gasoline blend planning", AIChE J., June, (2013).