**Process simulation** is used for the design, development, analysis, and optimization of technical processes such as: chemical plants, chemical processes, environmental systems, power stations, complex manufacturing operations, biological processes, and similar technical functions.

Process simulation is a **model**-based representation of chemical, physical, biological, and other technical processes and unit operations in software. Basic prerequisites for the model are chemical and physical properties^{ [1] } of pure components and mixtures, of reactions, and of mathematical models which, in combination, allow the calculation of process properties by the software.

Process simulation software describes processes in flow diagrams where unit operations are positioned and connected by product or educt streams. The software solves the mass and energy balance to find a stable operating point on specified parameters. The goal of a process simulation is to find optimal conditions for a process. This is essentially an optimization problem which has to be solved in an iterative process.

In the example above the feed stream to the column is defined in terms of its chemical and physical properties. This includes the composition of individual molecular species in the stream; the overall mass flowrate; the streams pressure and temperature. For hydrocarbon systems the Vapor-Liquid Equilibrium Ratios (K-Values) or models that are used to define them are specified by the user. The properties of the column are defined such as the inlet pressure and the number of theoretical plates. The duty of the reboiler and overhead condenser are calculated by the model to achieve a specified composition or other parameter of the bottom and/or top product. The simulation calculates the chemical and physical properties of the product streams, each is assigned a unique number which is used in the mass and energy diagram.

Process simulation uses models which introduce approximations and assumptions but allow the description of a property over a wide range of temperatures and pressures which might not be covered by available real data. Models also allow interpolation and extrapolation - within certain limits - and enable the search for conditions outside the range of known properties.

The development of models^{ [2] } for a better representation of real processes is the core of the further development of the simulation software. Model development is done through the principles of chemical engineering but also control engineering and for the improvement of mathematical simulation techniques. Process simulation is therefore a field where practitioners from chemistry, physics, computer science, mathematics, and engineering work together.

Efforts are made to develop new and improved models for the calculation of properties. This includes for example the description of

- thermophysical properties like vapor pressures, viscosities, caloric data, etc. of pure components and mixtures
- properties of different apparatus like reactors, distillation columns, pumps, etc.
- chemical reactions and kinetics
- environmental and safety-related data

There are two main types of models:

- Simple equations and correlations where parameters are fitted to experimental data.
- Predictive methods where properties are estimated.

The equations and correlations are normally preferred because they describe the property (almost) exactly. To obtain reliable parameters it is necessary to have experimental data which are usually obtained from factual data banks^{ [3] }^{ [4] } or, if no data are publicly available, from measurements.

Using predictive methods is more cost effective than experimental work and also than data from data banks. Despite this advantage predicted properties are normally only used in early stages of the process development to find first approximate solutions and to exclude false pathways because these estimation methods normally introduce higher errors than correlations obtained from real data.

Process simulation has encouraged the development of mathematical models in the fields of numerics and the solving of complex problems.^{ [5] }^{ [6] }

The history of process simulation is related to the development of the computer science and of computer hardware and programming languages. Early implementations of partial aspects of chemical processes were introduced in the 1970s when suitable hardware and software (here mainly the programming languages FORTRAN and C) became available. The modelling of chemical properties began much earlier, notably the cubic equation of states and the Antoine equation were precursory developments of the 19th century.

Initially process simulation was used to simulate steady state processes. Steady-state models perform a mass and energy balance of a steady state process (a process in an equilibrium state) independent of time.

Dynamic simulation is an extension of steady-state process simulation whereby time-dependence is built into the models via derivative terms i.e. accumulation of mass and energy. The advent of dynamic simulation means that the time-dependent description, prediction and control of real processes in real time has become possible. This includes the description of starting up and shutting down a plant, changes of conditions during a reaction, holdups, thermal changes and more.

Dynamic simulations require increased calculation time and are mathematically more complex than a steady state simulation. It can be seen as a multiple repeated steady state simulation (based on a fixed time step) with constantly changing parameters.

Dynamic simulation can be used in both an online and offline fashion. The online case being model predictive control, where the real-time simulation results are used to predict the changes that would occur for a control input change, and the control parameters are optimised based on the results. Offline process simulation can be used in the design, troubleshooting and optimisation of process plant as well as the conduction of case studies to assess the impacts of process modifications. Dynamic simulation is also used for operator training.

**Computational chemistry** is a branch of chemistry that uses computer simulation to assist in solving chemical problems. It uses methods of theoretical chemistry, incorporated into efficient computer programs, to calculate the structures and properties of molecules and solids. It is necessary because, apart from relatively recent results concerning the hydrogen molecular ion, the quantum many-body problem cannot be solved analytically, much less in closed form. While computational results normally complement the information obtained by chemical experiments, it can in some cases predict hitherto unobserved chemical phenomena. It is widely used in the design of new drugs and materials.

A **mathematical model** is a description of a system using mathematical concepts and language. The process of developing a mathematical model is termed **mathematical modeling**. Mathematical models are used in the natural sciences and engineering disciplines, as well as in the social sciences.

**Computer simulation** is the process of mathematical modelling, performed on a computer, which is designed to predict the behaviour of or the outcome of a real-world or physical system. Since they allow to check the reliability of chosen mathematical models, computer simulations have become a useful tool for the mathematical modeling of many natural systems in physics, astrophysics, climatology, chemistry, biology and manufacturing, as well as human systems in economics, psychology, social science, health care and engineering. Simulation of a system is represented as the running of the system's model. It can be used to explore and gain new insights into new technology and to estimate the performance of systems too complex for analytical solutions.

**VisSim** is a visual block diagram program for simulation of dynamical systems and model based design of embedded systems, with its own visual language. It is developed by Visual Solutions of Westford, Massachusetts. Visual Solutions was acquired by Altair in August 2014 and its products have been rebranded as Altair Embed as a part of Altair's Model Based Development Suite. With Embed, you can develop virtual prototypes of dynamic systems. Models are built by sliding blocks into the work area and wiring them together with the mouse. Embed automatically converts the control diagrams into C-code ready to be downloaded to the target hardware.

**Chemical process modeling** is a computer modeling technique used in chemical engineering process design. It typically involves using purpose-built software to define a system of interconnected components, which are then solved so that the steady-state or dynamic behavior of the system can be predicted. The system components and connections are represented as a process flow diagram. Simulations can be as simple as the mixing of two substances in a tank, or as complex as an entire alumina refinery.

**Reservoir simulation** is an area of reservoir engineering in which computer models are used to predict the flow of fluids through porous media.

The **Dortmund Data Bank** is a factual data bank for thermodynamic and thermophysical data. Its main usage is the data supply for process simulation where experimental data are the basis for the design, analysis, synthesis, and optimization of chemical processes. The DDB is used for fitting parameters for thermodynamic models like NRTL or UNIQUAC and for many different equations describing pure component properties, e.g., the Antoine equation for vapor pressures. The DDB is also used for the development and revision of predictive methods like UNIFAC and PSRK.

**CFD-ACE+** is a commercial computational fluid dynamics solver developed by ESI Group. It solves the conservation equations of mass, momentum, energy, chemical species and other scalar transport equations using the finite volume method. These equations enable coupled simulations of fluid, thermal, chemical, biological, electrical and mechanical phenomena.

**Dynamic simulation** is the use of a computer program to model the time-varying behavior of a dynamical system. The systems are typically described by ordinary differential equations or partial differential equations. A simulation run solves the state-equation system to find the behavior of the state variables over a specified period of time. The equation is solved through numerical integration methods to produce the transient behavior of the state variables. Simulation of dynamic systems predicts the values of model-system state variables, as they are determined the past state values. This relationship is found by creating a model of the system.

**CALPHAD** stands for *CALculation of PHAse Diagrams*, a methodology introduced in the 1960s by Larry Kaufman. An equilibrium phase diagram is usually a diagram with axes for temperature and composition of a chemical system. It shows the regions where substances or solutions are stable and regions where two or more of them coexist. Phase diagrams are a very powerful tool for predicting the state of a system under different conditions and were initially a graphical method to rationalize experimental information on states of equilibrium. In complex systems, computational methods such as CALPHAD are employed to model thermodynamic properties for each phase and simulate multicomponent phase behavior. The CALPHAD approach is based on the fact that a phase diagram is a manifestation of the equilibrium thermodynamic properties of the system, which are the sum of the properties of the individual phases. It is thus possible to calculate a phase diagram by first assessing the thermodynamic properties of all the phases in a system.

**Advanced process monitor (APMonitor)** is a modeling language for differential algebraic (DAE) equations. It is a free web-service or local server for solving representations of physical systems in the form of implicit DAE models. APMonitor is suited for large-scale problems and solves linear programming, integer programming, nonlinear programming, nonlinear mixed integer programming, dynamic simulation, moving horizon estimation, and nonlinear model predictive control. APMonitor does not solve the problems directly, but calls nonlinear programming solvers such as APOPT, BPOPT, IPOPT, MINOS, and SNOPT. The APMonitor API provides exact first and second derivatives of continuous functions to the solvers through automatic differentiation and in sparse matrix form.

A **hydrologic model** is a simplification of a real-world system that aids in understanding, predicting, and managing water resources. Both the flow and quality of water are commonly studied using hydrologic models.

**Process flowsheeting** is the use of computer aids to perform steady-state heat and mass balancing, sizing and costing calculations for a chemical process. It is an essential and core component of process design.

Creating a **cellular model** has been a particularly challenging task of systems biology and mathematical biology. It involves developing efficient algorithms, data structures, visualization and communication tools to orchestrate the integration of large quantities of biological data with the goal of computer modeling.

**Combustion models for CFD** refers to combustion models for computational fluid dynamics. Combustion is defined as a chemical reaction in which a hydrocarbon fuel reacts with an oxidant to form products, accompanied with the release of energy in the form of heat. Being the integral part of various engineering applications like: internal combustion engines, aircraft engines, rocket engines, furnaces, and power station combustors, combustion manifests itself as a wide domain during the design, analysis and performance characteristics stages of the above-mentioned applications. With the added complexity of chemical kinetics and achieving reacting flow mixture environment, proper modeling physics has to be incorporated during computational fluid dynamic (CFD) simulations of combustion. Hence the following discussion presents a general outline of the various adequate models incorporated with the Computational fluid dynamic code to model the process of combustion.

Template:No context Aspen Plus, Aspen HYSYS, ChemCad and MATLAB, PRO are the commonly used process simulators for modeling, simulation and optimization of a distillation process in the chemical industries. Distillation is the technique of preferential separation of the more volatile component (s) from the less volatile ones in a feed followed by condensation.The vapor produced is richer in the more volatile components(s). The distribution of the component in the two phase is governed by the vapour-liquid equilibrium relationship. In practice, distillation may be carried out by either two principal methods. The first method is based on the production of vapor boiling the liquid mixture to be separated and condensing the vapors without allowing any liquid to return to the still. There is no reflux. The second method is based on the return of part of the condensate to still under such conditions that this returning liquid is brought into intimate contact with the vapors on their way to condenser.

**Predictive engineering analytics (PEA)** is a development approach for the manufacturing industry that helps with the design of complex products. It concerns the introduction of new software tools, the integration between those, and a refinement of simulation and testing processes to improve collaboration between analysis teams that handle different applications. This is combined with intelligent reporting and data analytics. The objective is to let simulation drive the design, to predict product behavior rather than to react on issues which may arise, and to install a process that lets design continue after product delivery.

The **GEKKO** Python package solves large-scale mixed-integer and differential algebraic equations with nonlinear programming solvers. Modes of operation include machine learning, data reconciliation, real-time optimization, dynamic simulation, and nonlinear model predictive control. In addition, the package solves Linear programming (LP), Quadratic programming (QP), Quadratically constrained quadratic program (QCQP), Nonlinear programming (NLP), Mixed integer programming (MIP), and Mixed integer linear programming (MILP). GEKKO is available in Python and installed with pip from PyPI of the Python Software Foundation.

- ↑ Rhodes C.L., “The Process Simulation Revolution: Thermophysical Property Needs and Concerns”, J.Chem.Eng.Data, 41, 947-950, 1996
- ↑ Gani R., Pistikopoulos E.N., “Property Modelling and Simulation for Product and Process Design″, Fluid Phase Equilib., 194-197, 43-59, 2002
- ↑ Marsh K., Satyro M.A., “Integration of Databases and their Impact on Process Simulation and Design”, Conference, Lake Tahoe, USA, 1994, 1-14, 1994
- ↑ Wadsley M.W., “Thermochemical and Thermophysical Property Databases for Computational Chemical Process Simulation”, Conference, Korea, Seoul, August 30 - September 2, 1998, 253-256, 1998
- ↑ Saeger R.B., Bishnoi P.R., “A Modified 'Inside-Out' Algorithm for Simulation of Multistage Multicomponent Separation Processes Using the UNIFAC Group-Contribution Method”, Can.J.Chem.Eng., 64, 759-767, 1986
- ↑ Mallya J.U., Zitney S.E., Choudhary S., Stadtherr M.A., “Parallel Frontal Solver for Large-Scale Process Simulation and Optimization″, AIChE J., 43(4), 1032-1040, 1997
- ↑ "ASL: Physical Vapor Deposition Simulation".

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