Computer-Aided Chemical Engineering - Chap 1: Computer Simulation in Process Engineering

Nội dung text: Computer-Aided Chemical Engineering - Chap 1: Computer Simulation in Process Engineering

1. 1/25/2014 Computer-Aided Chemical Engineering An Introduction to Process Simulation C1. Computer Simulation in Process Engineering Computer Simulation in Process Engineering 1. Computer Simulation in Process Engineering 2. An Historical View 3. Approach of A Simulation Problem 3.1. Definition 3.2. Input 3.3. Execution 3.4. Results 3. 5. Analysis 4. Architecture of Flowsheeting Software 4.1 Computation Strategy 4.2 Sequential-Modular Approach 4.3 Equation-Oriented Approach 4.4 Simultaneous-Modular Approach 5. Integrated systems (Aspen Technology; Hyprotech; Simulation Sciences) 6. Selection of A Simulation Software (Functional Analysis; Computer Science Analysis; Commercial Analysis) 7. Summary 1
2. 1/25/2014 1. Computer Simulation in Process Engineering Figure 1: The new paradigm of Process Engineering: Simulation as core activity in Research & Development, Design and Operation 1. Computer Simulation in Process Engineering Table 1: Process Simulation applications in Chemical Process Industries Chemical Process Industries Applications Oil & Gas Offshore exploration, Surface treatment, Pipeline transport, Underground storage, Gas processing Refining Gasoline and fuels Petrochemicals Hydrocarbon based chemicals, Methanol, Monomers Basic Organic Chemicals Intermediates, Solvents, Detergents, Dyes Inorganic Chemicals Ammonia, Sulphuric Acid, Fertilisers Fine Chemicals Pharmaceuticals, Cosmetics Biotechnology Food and bio products Metallurgy Steel, Aluminium, Copper, etc. Polymers Polyethylene, PVC, Polystyrene, fibres, etc. Paper & Wood Paper pulp Energy Power plants, Coal gasification Nuclear industry Waste treatment, Safety Environment Water cleaning, Biomass valorisation 3
3. 1/25/2014 2. An Historical View Personal Computer arrived in 1982. Although the power of PC's was weak for flowsheeting, the idea of a 'personal' tool was strong enough to incite enthusiasts (development of ChemCAD, ChemStations and Hysys, Hyprotech). The challenge of leaving the elitist environment of mainframes was launched. At the beginning of 1990's the domination of PC products was a fact. The relative stabilisation in operating systems, dominated nowadays by UNIX and Windows, enabled the development of new generation of simulation software. The Graphical User Interface became a central part in the software development. The power of the former super-computers was available on desktops. 3. Approach of A Simulation Problem Figure 3: Methodological levels in steady-state simulation 5
4. 1/25/2014 3. Approach of A Simulation Problem 3.4. Results A simulation delivers a large amount of results. The most important are: - Stream report (material and heat balance), including flowsheet convergence report. - Unit report, including material and heat balance, as well as unit convergence report. - Rating performances of units. - Tables and graphs of physical properties. The graphical presentation of results may take various forms. Generally, advanced software provides their own analysis tools, but the exchange of data with all-purpose spreadsheets is usually available. Detailed results, as internal flows or tables of properties, may be exported to specialised design packages. 3. Approach of A Simulation Problem 3. 5. Analysis Flowsheeting analysis tools enable to get more value from the simulation results. The most used is the sensitivity analysis. This consists usually of recording the variation of some 'sampled variables' as function of 'manipulated variables'. The interpretation of results can be exploited directly, as trends, correlation or pre-optimisation. Case studies can be employed to investigate combinations (scenarios) of several flowsheet variables. Finally, the simulation work may be refined by multi-variable optimisation. A more advanced use of flowsheeting capabilities is the controllability analysis of standalone units, or the study of a plantwide control strategy. 7
5. 1/25/2014 4. Architecture of Flowsheeting Software B. In Equation-Oriented (EO) approach all the modelling equations are assembled in a large sparse system producing Non-linear Algebraic Equations (NAE) in steady state simulation, and stiff Differential Algebraic Equations (DAE) in dynamic simulation. Thus, the solution is obtained by solving simultaneously all the modelling equations. Among the advantages of the equation-solving architecture we may mention: - Flexible environment for specifications, which may be inputs, outputs, or internal unit (block) variables. - Better treatment of recycles, and no need for tear streams. - Note that an object oriented modelling approach is well suited for the EO architecture. However, there are also substantial drawbacks, as: - More programming effort. - Need of substantial computing resources, but this is less and less a problem. - Difficulties in handling large DAE systems. - Difficult convergence follow-up and debugging. 4. Architecture of Flowsheeting Software C. In Simultaneous-Modular approach the solution strategy is a combination of Sequential-Modular and Equation-Oriented approaches. Rigorous models are used at units' level, which are solved sequentially, while linear models are used at flowsheet level, solved globally. The linear models are updated based on results obtained with rigorous models. This architecture has been experimented in some academic products. It may be concluded that Sequential-Modular approach keeps a dominant position in steady state simulation. The Equation-Oriented approach has proved its potential in dynamic simulation, and real time optimisation. The solution for the future generations of flowsheeting software seems to be a fusion of these strategies. The release 11.1 of Aspen Plus (2002) incorporates for the first time EO features in the environment of a SM simulator. 9
6. 1/25/2014 4. Architecture of Flowsheeting Software Note that the system (1) has a strong non-linear character, particularly due to the interdependence between physical properties and state variables. It is important to keep in mind that physical properties may consume up to 90% from the computation time. The above system should be seen as completed with equations for constraints. The difference between the total number of non-redundant variables in the system (1) and the number of independent algebraic equations gives the degrees of freedom. These are usually specifications that a user must supply to run a simulation. In SM approach each simulation unit (block) is treated by the rule: output variables = function {input variables, unit variables, unit parameters} The functional relation is specific for each unit, as flash, pump, reactor, distillation column, etc. Because of a large variety of physical situations, it is rational to incorporate a part of the algorithm in the routine that solves the unit. From programming point of view it is said that the approach is procedural. 4. Architecture of Flowsheeting Software The architecture of software is a matter of computer science. However, as with every complex system, the user should be aware about the main elements. Figure 5 presents a generic architecture of a Sequential-Modular simulator. The heart of the system is the Executive Program. Its function is to manage both computation and data exchange tasks, as for example calculation sequence, retrieval of parameters for physical properties, routines for unit operations, convergence follow-up, and management of the data file system. Other essential components are: - Databases with physical parameters for pure components and mixtures. - Librarian for computing physical properties of components and mixtures. - Librarian for physical and chemical equilibrium calculations. - Librarian for unit operations and reactors. - Librarian with mathematical Solvers. - Graphical User Interface (GUI). 11
7. 1/25/2014 4. Architecture of Flowsheeting Software 4.3 Equation-Oriented approach In Equation-Oriented (EO) approach the software architecture is close to a solver of equations. EO is more suited for dynamic simulation since this can be modelled by a system of differential-algebraic equations (DAE) of the form: dx = f (u,x,d,p) (2) dt The steady state solution is obtained by setting the derivatives to zero. The overall DAE system (2) is sparse and stiff, its size varying between 103 and 105 equations. Dynamic simulation is more demanding as its steady state counterpart. Firstly, it needs much more sizing elements. Then, the pressure variation cannot be neglected or lumped in the specification of simulation unit. However, in general the specification of variables is more flexible. Any flowsheet variable could be set as input or output streams, or internal unit variables. 4. Architecture of Flowsheeting Software The software architecture built with an EO approach is presented in Fig. 6. The input of the simulation problem can be formulated by means of a meta- language, or be supported by an intelligent GUI. In Aspen Dynamics, the problem definition starts at steady state in Aspen Plus in an SM environment. Adding accumulation terms to the equations of units generates the DAE system. In an EO simulator the algorithmic treatment includes not only the mathematical solution, but also problem debugging, compilation/linking, as well as correction and addition of equations. An important feature is the post-processing of results, as timerecordings and plots of different variables. 13
8. 1/25/2014 5. Integrated Systems 5.2 Hyprotech The special feature of the flowsheeting system proposed by Hyprotech is that steady state and dynamic simulation are available in the graphical environment. Other products have been developed as stand-alone applications for engineering or operation purposes. The system is designed for complete customisation. The main components are: - Hysys.Concept: conceptual design package for design and retrofit applications, with two components:  DISTIL: distillation column sequences,  HX: heat integration projects by Pinch analysis. - Hysys.Process: steady state flowsheeting for optimal new designs and modelling of existing plants, evaluate retrofits and improve the process. - Hysys.Plant: steady state and dynamic simulation to evaluate designs of existing plants, and analyse safety and control problems. - Hysys.Operator Training: start-up, shutdown or emergency conditions, consisting of an instructor station with DCS (Distributed Control System) interface, and combined with Hysys.Plant as calculation engine. - Hysys.RTO+: real-time multivariable optimisation; on-line models may be used offline to aid maintenance, scheduling and operations decision-making. - Hysys.Refinery: rigorously modelling of complete refining processes, integrating crude oil database and a set of rigorous refinery reactor models. - Hysys.Ammonia: full plant modelling and optimisation of ammonia plants. 5. Integrated Systems 5.3 Simulation Sciences The integrated system proposed by Simsci is built around a database environment (PROVISION), and can be in principle interfaced with third- party components. The system is oriented to applications in oil & gas industries, as described below. Process Engineering: tools for process engineering design and operational analysis. - Pro/II: general-purpose process flowsheeting and optimisation. - Hextran: Pinch analysis and design of heat-transfer equipment. - Datacon: plant gross error detection and data reconciliation. - Inplant: multiphase, fluid flow simulation for plant piping networks. - Visual Flow: design and modelling of safety systems and pressure relief networks. Upstream Optimisation: decision-support tools designed for oil and gas production. - Pipephase: multiphase fluid flow simulator for pipelines and networks. - Tacite: multiphase simulator for complex transient flow phenomena. - Netopt: optimisation of oil and gas production operations. On-line Performance: Advanced Process Control (APC) and on-line optimisation. - ROMeo: on-line plant modelling and optimisation, off-line analysis tool. - Connoisseur: APC multivariable controls several via the plant's DCS (Distributed Control System). 15
9. 1/25/2014 6. Selection of A Simulation Software Each question is quoted by a mark and weighted by a factor. Consequently, this procedure will produce a list of two or three good candidates. The final choice will imply a finer assessment of the above aspects. More information may be asked by experts, as benchmarks, customised demonstrations, programming samples, quality assurance documents, etc. In this respect it is important to test the product on problems close to the user application area. Despite similar functionalities among generalist suppliers, every system has capabilities where it performs better than others. This could have historical reasons, or could come from the profile of clients. The reliability of physical properties and of parameters in thermodynamic models is an essential feature in design. That is why the quality of thermodynamics is a peculiar feature in selecting a simulation system for process design purposes. Other important features are customisation of units, programming capabilities, transmission of information and control structures, as well as debugging convergence problems with recycles. A system in which the user has the control on all the aspects of the modelling background should be preferred. As mentioned, the process simulation market has known severe transformations in the 1985-1995 decade. Relatively few systems have survived. Table 2 presents a sample of the main commercial software at the end of 2001. 6. Selection of A Simulation Software Table 2: Process Design & Simulation commercial software Supplier Software Applications 1 Aspen Tech Aspen Plus Flowsheeting, sizing, costing Cambridge-MA/USA Aspen Dynamics Dynamic Simulation, Real time systems Advent Energy Integration Split Non-ideal Distillation Systems Bijac Heat exchanger design Polymer Plus Polymer processes Batchfrac Batch and semi-continuous processes 2 Chemstations ChemCad Flowsheeting, sizing, costing Houston-USA CC-ReACS Batch reactor simulator 3 Hyprotech Hysys Combined steady state and dynamic simulation Calgary-Canada Concept Non-ideal Distillation Systems Hyprop Thermodynamics 4 Prosim ProSim Flowsheeting Toulouse-France 5 Simulation Science Pro II Flowsheeting, sizing Los Angeles-USA Provision Graphical environment Hextran Energy Integration Datacon Data reconciliation ROMeo Rigorous on-line modelling 6 WinSim Design II Flowsheeting, sizing Houston-USA 7 Imperial College g_PROMS Dynamic Simulation London-UK 8 Bryan Research & Prosim Flowsheeting Engineering Tsweet Gas purification 9 KBC/Linnhoff March Supertarget Energy integration 10 Intelligen, Scotch Plains, BatchPro Designer Scheduling and Design of batch processes NJ-USA 17