Chapter 1 Modeling, Simulation, and Optimization in the Chemical
1.3 Book Organization
In this book, we present the use of different software to address problems in a broad spectrum of the bachelor curriculum of chemical engineering. In particu-lar, we present examples based on problems whose solution by hand represents a tedious process. We start with general software (Excel®, MATLAB®, Mathcad®) and go on to process simulators (CHEMCAD®, ASPEN®) and specialized software such as Computational Fluid Dynamics (CFD) (COMSOL®), Discrete Element Method (DEM) (EDEM®), gPROMS®, AIMMS®, and GAMS®. We use the capa-bilities of such packages to solve mass and energy balances, fluid flow, heat, and mass transfer. We continue to process analysis and synthesis and equipment design and, finally, we include basic notions of optimization from process operation and synthesis to plant location and operation. Table 1.1 presents a summary of the examples studied in this book and the software used. We must highlight that, in general, most software is capable of solving most of the problems in one way or another. The aim of this book is not to provide a thorough manual for the use of each of the packages; more complete books and user guides that are referenced can be used for a deeper study of the software. The idea is to introduce the main fea-tures of the software to undergraduates in the chemical engineering field to iden-tify the capabilities and help them get started in the use of such packages to solve problems. In this sense, it is expected to serve as an initialization to independent usage of the software based on typical examples that are familiar to the chemical engineer undergraduate student. Finally, we should note that we have not included in this book software for discrete-event simulation as this topic is still rather spe-cialized for chemical engineers.
REFERENCES
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3. Freshwater, D.C., Yates, B. (1989) The development of process engineering at the United Kingdom, Working Party on Chemical Engineering Education, EFCE, Loughborough, U.K.
4. AIChE. (2008) 30 Authors and their groundbreaking chemical engineering books, CEP, August 2008, pp. 62–63.
5. Peppas, N.A. (ed.) (1999) One Hundred Years of Chemical Engineering, Kluwer Academic Publishers, Dordrecht, the Netherlands.
6. Furter, W.F. (ed.) (1980) History of Chemical Engineering, Advances in Chemistry Series 190, American Chemical Society, Washington, DC.
7. Kantor, J.C., Edgar, T.F. (1996) Computer skills in the chemical engineering curriculum.
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8. Rudd, D., Powers, G., Siirola, J. (1973) Process Synthesis, Prentice-Hall, Englewood Cliffs, NJ.
9. Douglas, J.M. (1988) Conceptual Design of Chemical Processes, McGraw-Hill, New York.
10. Biegler, L.T., Grossmann, I.E., Westerberg, A. W. (1997) Systematic Methods of Chemical Process Design, Prentice Hall, Englewood Cliffs, NJ.
11. Wintermantel, K. (1999) Process and product engineering: Achievements, present and future changes, Trans. IChemE A, 77, 175–188.
12. Cussler, E.L., Wagner, Q., Maarchal-Heusler, L. (2010) Designing chemical products requires more knowledge of perception, AICHE J., 56(2), 283–288.
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Prog., 12, 76–79.
11
2 Modeling, Simulation, and Optimization
in the Process and
Commodities Industries
Iiro Harjunkoski and Mariano Martín Martín
2.1 INTRODUCTION
With the recent rapid development of information technologies and the increasing level of automation in production systems, it is clear that more and more tasks and responsibilities will be taken over by software solutions. A model is a mathemati-cal representation of a system that is useful in a number of levels. Apart from the fact that it can help clarify the behavior of the system, models are currently used to reduce production and design costs, reduce time to market, evaluate alternatives, or train operators, among the most common [1–4]. The final aim of any simula-tion is to improve the operasimula-tion and, eventually, to determine the optimal solusimula-tion.
Optimization is a field where even a very simple PC can easily outperform any personnel—not owing to the knowledge it possesses but owing to the capability of systematically and reliably testing thousands of alternatives within seconds.
CONTENTS
2.1 Introduction ... 11 2.2 Industrial Production System Landscape ... 12 2.3 Modeling and Simulation in Industry ... 13 2.4 Optimization Problems in Industry ... 14 2.5 Industrial Example Cases ... 16 2.5.1 Dairy Industry ... 17 2.5.2 Long-Term Planning and Scheduling of a Refinery ... 17 2.5.3 P&P Industry ... 17 2.5.4 Crude-Oil Blend Scheduling Optimization ... 18 2.5.5 Equipment Design and Operation... 18 2.5.6 Process Design ... 19 2.6 Conclusions ... 19 References ...20
One of the main questions is how to use this opportunity to create added value.
Global competition has become very present all over the world and laying off people or moving a production site to another country with more advantageous cost struc-ture is common under the pressure to produce cheaper, faster, and more flexibly than ever. In many industries, the amount of various product alternatives has exploded through individually tailored products and shorter lead times, which also emphasizes the cost and risk of higher inventories. The consequence of this is that smaller batch sizes and shorter campaigns calls for more agile solutions able to adapt to frequently changing situations. As a response to these challenges, new business strategies, require-ments, and models have appeared, driven by the multilevel needs of a processing plant also taking into account economic, environmental, and legislative factors, which makes traditional approaches and production philosophies not anymore fully functional.
In managing and optimization of a production process, energy also plays a more important role than ever, partly because of the increasing and volatile costs triggered by more uncertain availability of electricity, for example, due to the increasing share of renewable energy sources (spot-market electricity prices can vary by a factor of more than 20 depending on the point in time). Also, the less predictable prices of conventional fuels, for example, crude oil, and the overall larger focus on total costs and emissions motivate efforts toward better energy efficiency. Thus, there is a large demand and opportunity to improve optimization of industrial processes. Before going into details, it is worth putting some attention into the landscape that consti-tutes a production environment.
2.2 INDUSTRIAL PRODUCTION SYSTEM LANDSCAPE
One of the main issues that must be considered in any industrial application of simu-lation and optimization technologies is the system architecture. Figure 2.1 shows a layout of a typical processing landscape from the process layer including controllers, sensors, and actuators (hardware) up to the business layer that mainly comprises software solutions managing and integrating the overall process. One of the most important practical challenges is how to realize an efficient information exchange—
both between internal and external system components. The main challenge is not to be able to transmit the data as bytes across the system (in intelligent systems, almost any device can be connected with each other) but to exchange information in a cor-rect format.
Why should the integration aspect in the first place be mentioned within the con-text of simulation and optimization? There are of course several possible answers but the most generic is: Even the most brilliant optimization solution can never solve an industrial problem in an industrial environment, unless it has been built into as an integrated part of a production system. Some of the most critical aspects are as follows:
• Data input and output should be automatic and not require any manual support.
• Current production situation should always be considered—every optimi-zation builds on top of a starting situation.
• Solution algorithms should be configurable in order to take into account normal daily and possibly frequently changing requirements.
• Optimization results should be returned sufficiently quickly (milliseconds to minutes).
• Results from the optimization should be allowed to be manually tuned and adapted by the operator, if feasible.
• Ease of use is critical for the acceptance of any solution.
Some of these aspects are easily lost if the professional communities creating the industrial production systems and those working on optimization algorithms are not connected and their main interests are not well aligned. Therefore, the more indi-viduals there are with insights to both “worlds,” the higher success probability there is to achieve enhanced and increased amount of productized optimization solutions.
There are industrial standards for data exchange, for example, ISA-88 [6] and ISA-95 [7], that define how to structure the necessary data for exchange, which may be of significant help in projects related to optimization as they allow to focus the exist-ing resources on more challengexist-ing topics. More information about industry-specific requirements for manufacturing execution systems (MES) can be found in [8].
2.3 MODELING AND SIMULATION IN INDUSTRY
For many years, developments and improvements in the chemical industry relied on a trial-and-error approach. However, the expensive experimental trials at pilot and industrial scale in terms of time and money and the current simulation capacities have placed a lot of pressure on modeling and simulation of equipment and processes as the tools for systematic process analysis, design, and optimization. Frank Popoff, former
PAC
FIGURE 2.1 Logical view of a production system. (From ARC Advisory Group, The Collaborative Process Automation System for the 21st Century—CPAS 2.0, Three Allied Drive, Dedham, MA, 2010. With permission.)
CEO at Dow Chemical, said in 1996 that “Process modeling is the single technology that has had the biggest impact on our business in the last decade.” The modeling task is based on determining the physical, chemical, and biological principles that govern any operation such as mass and energy balances, momentum, heat and mass transfer, and chemical equilibria and kinetics to develop a reliable mathematical representation of the operation of the equipment or process so that we can evaluate its performance in a cheaper, quicker way. One important issue is determining the level of detail so as to be able to capture the features of the process without formulating an overwhelm-ing model. The next step consists of solvoverwhelm-ing such a model. As the power of computers increased, their capabilities together with software development (process simulators such as Aspen®, CHEMCAD®, gPROMS®; computational fluid dynamics [CFD] and multiphysics, i.e., ANSYS®, COMSOL®; particle technology, i.e., EDEM®) have pro-vided the tools to solve complex phenomena more realistically. Finally, and before we can use the model as a decision-making tool, validation is required. Once validated, the model can be used to evaluate designs and operating conditions; reduce design time and production costs; improve productivity and efficiency; evaluate risks, in essence, making informed decisions; and train personnel [3,4]. As a note, BASF believes that their net benefits from the broad use of process simulation, in a comprehensive way, have been between 10% and 30% of the installed capital cost of the projects [3].
2.4 OPTIMIZATION PROBLEMS IN INDUSTRY
An industrial production process is always a chain of tasks and processes through which raw and intermediate material is fed and which finally results in various end products. A successful production process requires thus a seamless collaboration between many various components, of which many in fact comprise some optimiza-tion capability. Figure 2.2 (based on [9]) shows the decision layers of a typical batch process. Optimization is critical for each of these layers and an important aspect is that they should not work against each other in a competitive manner such that the total production targets are met.
According to [9], the planning layer sets the production targets. The scheduling layer transforms this plan into batches, assigns them to equipment, and sequences the batches. The recipe control system governs the execution of the batches making use of production recipes, and the recipe execution triggers the phases of the recipes and provides the set points for the process parameters in the phases. The continuous optimization layer optimizes the trajectories during the phases of the batch (RTO for continuous processes). The advanced control layer implements the optimal tra-jectory, typically applying linear model-predictive control (MPC), and provides reference values to the low-level controllers. Any changes or disturbances in the process or any of these optimization layers need to be communicated properly across the other layers.
Nevertheless, the previously mentioned example illustrates that optimization is present at all layers of a production process, be it manual or automated. The fact that a single unconnected optimization function is incapable of contributing to a running production process should be emphasized. In [10], many aspects of collaborative process automation systems are described in more detail. Apart from
the process-centric optimization problems, recent focus has been put into supply-chain optimization and enterprise-wide optimization (EWO) [11] problems, where the optimization focus is clearly expanded to larger logistic problems, which makes it possible to ensure that the overall strategy of an enterprise with possibly several production units is also considered in the short-term decision making. Another cur-rent trend is to identify optimal ways to deal with uncertainties both in the control (e.g., process disturbances) and scheduling (e.g., uncertainties in production orders) levels. In the scheduling literature, approaches vary from robust [12] and stochastic optimization [13] and multiparametric programming [14] to rescheduling concepts, that is, initiate a new schedule as soon as something changes. However, these more recent research activities still need some years to be established as an integral part of industrial solutions.
Technologies that have established themselves also within the industrial applica-tions are, for instance, linear MPC and especially owing to the good developments of commercial solvers (e.g., CPLEX®, Gurobi®, XPRESS-MP®) and commercial modeling environments (e.g., MATLAB®, GAMS®, AIMMS®, AMPL®, MOSEK®);
many mathematical programming approaches have been widely implemented to tackle problems within production planning and optimization problems. Looking
Planning
Scheduling
Recipe execution
Continuous optimization
Advanced control
Low-level control
Process Demands, costs
Production targets Produced amounts
Batch sizes, assignments, start times Progress, equipment availability
Set-points, constraints End times, yields, quality parameters
Targets Measured and estimated variables
References Control variables, measured data
Manipulated variables Measurements, binary feedback Raw materials
Utilities
Products Waste
FIGURE 2.2 Various decision layers of batch production.
back in the history, it is not unusual that from the first concept introduction, it takes 20–40 years before a solution technology becomes a commodity in industrial prac-tice. Thus, it is valid to expect that many of the novel approaches today will be estab-lished as widely accepted industrial solutions after 2020.
2.5 INDUSTRIAL EXAMPLE CASES
Knowing that a number of theoretical breakthroughs have taken place in the last decades, the existence of simulation and optimization software—both commercial and open-source—and the fact that data integration is becoming more smooth owing to standards, in this section some practical achievements in process simulation and optimization will be highlighted. On the simulation part, process simulators, CFD, or discrete element method (DEM) modeling have had an important impact in crude-oil industry, pharmaceuticals, and consumer goods industry. From the optimization point of view, one of the early adopters of mathematical optimization has been the refin-ing industry. Here, linear programmrefin-ing (LP) and advanced process control (MPC) methods with strong optimization emphasis have started to establish already some decades ago. The oil and gas industry is still today one of the main drivers for novel approaches, for example, supply-chain logistics for transportation and optimization under uncertainty. Examples of other industries where the optimization potential has been early identified are the so-called heavy industries (integrated pulp and paper [P&P] mills, metals plants) and power generation and distribution. In P&P, apart from process control optimization applications, emphasis has been put on production planning and scheduling and solving the cutting stock problem in order to minimize material losses. Within the metals industry, especially in the 1980s, several expert sys-tems were generated to improve the optimality of earlier manual decision making. The demand for a better optimization support is growing due to more complex problem instances that are, for instance, triggered by significant changes in the energy markets.
However, in general, it is difficult to prove the goodness of an optimization solu-tion, which also decreases the openness of companies to do significant investments into optimization solutions. Some of these complicating factors are as follows:
• The objective function, that is, the target of the optimization activity, is always a simplification of reality.
• Optimization problem constraints cannot comprise all relevant production-related aspects.
• A direct comparison between an optimized and nonoptimized production situation is difficult due to the unforeseeable dynamics of production.
• Most processes aim at continuous improvement, which makes it more dif-ficult to separate the optimization benefits from other independent process improvements.
• In most processes, many decisions are still made manually and an optimi-zation solution may only serve as a guideline.
There are of course a vast number of academic contributions to the topic of opti-mization as well as industrial sales oriented documents aimed at promoting the
commercial application of optimization technologies but also a relatively small number of publications that also highlight industrial needs in a more analytical and technical manner. Recent industrial perspectives in the area of process control are given by [15], and in the area of production scheduling, a more industrially oriented review is provided by [16].
The following subsections discuss some short examples of reported success sto-ries within the field of optimization and production manufacturing systems.
2.5.1 dairy indUstry
The dairy industry follows typical batch processing with critical time constraints, due to the fact that the shelf life of intermediates is limited. Also, cleaning policies must be fully respected due to the regulations. A benchmarking study on schedul-ing of a dairy industry process has been reported in [17], which discusses a more complex ice-cream production problem (one processing line feeding eight packaging lines). The logistics of this problem is challenging due to the fact that the optimal sequence based on sequence-dependent setup times is the opposite for the process and the packaging lines. The study tests a number of scheduling software packages and using the most successful one resulted in a throughput increase of around 30%.
2.5.2 lOng-tErm planningand schEdUlingOfa rEfinEry
The example of a refinery discussed in [18] also highlights the value of integration.
In this case, the MES plays an important role as multiple products are produced and material planning modules must be integrated by product line changes. The model-based solution will jointly analyze the feasibility and economics of alternative product strategies performing the monthly planning, which also sets targets for the optimized production and manages inventory levels and product distribution.
The refinery’s LP model is used for scheduling and blending optimization and to calculate the APC parameters. The comprehensive decision support results in esti-mated savings of 3 million EUR on raw material costs. Apart from this, the blending units can be utilized optimally and the computer-based workflows are precise and simplify the handling of the model structures.
2.5.3 p&p indUstry
In the pulp and paper industry, the collaboration of various optimization compo-nents is key [8]. Most production facilities in the paper industry produce paper and board from large quantities of groundwood, pulp, and recycled paper together with water, chemicals, and additives. There are also multiple products ranging from light-weight tissue to heavy cardboard, each of which has high-quality requirements, for instance, on the basis of weight, caliper, and brightness. After paper production, the paper is further processed in a converting mill, and therefore the controlling and visualization of material flows as well as continuous and comprehensive quality
In the pulp and paper industry, the collaboration of various optimization compo-nents is key [8]. Most production facilities in the paper industry produce paper and board from large quantities of groundwood, pulp, and recycled paper together with water, chemicals, and additives. There are also multiple products ranging from light-weight tissue to heavy cardboard, each of which has high-quality requirements, for instance, on the basis of weight, caliper, and brightness. After paper production, the paper is further processed in a converting mill, and therefore the controlling and visualization of material flows as well as continuous and comprehensive quality