Process manufacturers have been using Process Simulation & Optimization (PSO) tools for several decades. End customer requirements are constantly changing, however, as they try to capture market opportunities in volatile economic times. Advances are being made on the business and technology front, increasing manufacturers’ needs for open computing, a single plant representation, collaborative manufacturing, and enterprise integration. As a result, one of the most pressing challenges manufacturers face today involves efficiently deploying assets and resources to be able to respond effectively to new opportunities without compromising product quality or profitability.
Meanwhile, the escalating complexity of technology and advances in computer hardware, operating systems, networking technology, programming tools and languages, and database technology challenge suppliers to devel-op products that satisfy the changing requirements of end users. In addition, the Internet and cloud computing redefine how these tools are used and deployed, thus influencing how PSO suppliers conduct business. Balancing these dynamic market forces in the market creates uncertainty among suppliers and users alike. The uncertainty is not about whether there is a need for process simulation & optimization, but rather the appropriate strategic direction for new and existing software tools.
ARC defines process simulation & optimization software as steady-state or dynamic (time-based) simulation software for improved process design and operational analysis. Typical uses include rigorous heat integration and ma-terial balance calculations for a wide range of chemical processes. Typical assets include process vessels, piping, reactors, distillation columns, and pumps. PSO software typically includes chemical and physical properties components, mixtures, reactions, and mathematical models that allow a process model to be calculated in computers.
PSO software enables engineers to create steady-state and dynamic models for plant design, performance monitoring, troubleshooting, operational improvement, business planning, and asset management. Process simulation software typically is visualized in flow diagrams supported by computational data and worksheets.
Process Simulation & Optimization Key Product Classifications
In recent years, intensified global competition has placed severe pressure on companies to shift their focus from purely growth to agile operations and sustainable long-term profitability. To reduce costs and operate more efficiently, manufacturers often use PSO solutions. In fact, virtually all process industry segments use PSO applications to some degree. Single Evolving Model Basis
Manufacturing companies today are looking for ways to accelerate process and plant design to reduce costs and move products to market faster. In addition, companies are looking for ways to maximize and leverage their investments in models developed in the conceptual and design phase of a project. The model evolution approach is an important strategic develop-ment that facilitates model reuse between various simulation and optimization applications throughout the entire lifecycle of a plant or pro-cess. A model evolution methodology allows models to scale through the plant lifecycle – be it during the research, conceptual design, process de-sign, plant design, construction, commissioning, op-erations, or revamp stages. Model evolution maximizes modeling efforts by reducing duplication and ensures that decisions during different lifecycle phases are based on a consistent representation of the plant.
Process simulation & optimization software suppliers come from a wide range of commercial origins. Several represent the commercialization of technologies developed at universities. A few represent commercial spinoffs from user industry consortiums. Many engineering firms that focus on the application of advanced technology to the process industries have developed design, simulation, and optimization tools to help deliver their application knowledge. EPC and automation suppliers looking for growth opportunities embrace the design, process simulation and optimization software markets through in-house develop-ment efforts, by acquiring established suppliers, or in some instances, both. Operating companies with in-house expertise have developed their own software, but generally do not offer their solutions for external sale.
Simulation is the process of using computer-based modeling of a system to understand its behavior and predict the effect of changes. Simulation rep-resents a powerful method for analyzing, designing, and operating complex systems. It provides a proven, cost-effective way to explore new processes and designs, without having to resort to expensive pilot pro-grams or prototypes. The level of understanding that can be developed through simulation is seldom achievable by any other means.
Process modelers are primarily interested in representing the behavior of a real-world physical process in a replicable, mathematical form. They may imitate the process of interest by configuring pre-built objects and blocks, as is the case when building a model in a sequential modular flowsheeting environment. They can also provide their own understanding of the through an equation-oriented package.
Since material objects or streams interact with unit operation modules to change their physical or chemical state, high-fidelity simulation environ-ments can include comprehensive physical and thermodynamic property databases. These allow engineers to identify the physical and chemical characteristics of the process material from feedstock through each phase of the process.
Two types of simulation software are used for engineering and design pur-poses: steady state and dynamic. Both are intended to accurately represent the process that they are simulating. Steady-state simulators, as the name implies, will provide details of a manufacturing process under one specific set of conditions. These simulation models can be run multiple times to develop cases for different operating ranges.
Dynamic simulation, in contrast, predicts how process variables change with time when moving from one steady state condition to another. In oth-er words, during a transient upset, dynamic simulation would track the values of variables over the transition period; while steady-state simulation would only provide the values of the variables at the beginning and end state.
Traditionally, steady-state simulation has been the workhorse for performing process analysis and de-sign to determine the requirements of a system. It offers a safe, reliable, and inexpensive means to test the capabilities of a system and perform “what if analysis” for configuring processes and evaluating new equipment. Steady-state simulation also helps users understand how their plant operates to aid in process improvements, de-bottlenecking, troubleshooting, and performance monitoring. Using steady-state simula-tion, companies have drastically improved operating efficiencies.
One of the most important contributions that dynamic simulation makes is to enable controllability analysis of process designs. Here, the objective is to find not only an effective control strategy, but to design a process that is easy to control. In addition, dynamic simulation is used to test or checkout
control systems prior to implementation. Prior to operations, dynamic simulation provides an effective tool for planning a successful startup pro-cedure. Furthermore, as the basis for an operator training system, dynamic simulation enables the operations staff to gain a better understanding of their operations through simulated training exercises.
Empirical techniques may also be used to develop an accurate mathemati-cal representation of an actual process operation. Models based on actual data are generally useful for design and engineering tasks that relate to a process that is already in operation. Furthermore, models developed from actual process conditions are not generally used for engineering design in regions outside the known operating ranges of the process.
Off-line Optimization Software
Optimization software differs from simulation software in that some of the degrees of freedom are left unspecified. The values of the corresponding variables are determined by an algorithm that will minimize an objective func-tion specified by the developer while respecting physical, operational, safety, product quality, con-tractual, or any other constraints. The objective function is usually an economic variable such as net operating cost.
Many off-line optimization applications take advantage of various tools available for engineering design. Dynamic simulation is often used in con-junction with steady-state simulation to optimize the operation of the operating plant after it has been optimized in the design stage.
Other offline optimization tools, such as neural nets, are finding much wid-er use. Using actual plant data, accurate models of the process are created to optimize production by identifying important interactions in the process and by conducting “what-if” analysis.