Equipment simulation modules

Comparison Selections Datasheet name Pressure (kPa) Feed cone, [vol frac) Cake resistance (m/kg) Cake cone. (v7v) Filtration Consolidation Consolidation coeff. fm S/sl 

Density Viscosity Surface tension Slurry cone. Solute cone. Solute diffusivity Particle size Vacuum 

Drum diameter Drum width Drum submergence Area fraction - washing Area fraction - gas deliquor 0-2 Phase not used Rotational speed  

The remainder of the information required for simulation is typed by the user in the Simulation Data box towards the top right hand comer of the display. Each tab corresponds to a phase in the filter cycle or provides facility to enter data specific to the filter or the feed solids, liquid and solute. 

The results of a simulation are shown towards the bottom half of the display. In the Schematic Mass Balance box, a graphical display of the mass 

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The two equipment simulation modules provide calculation sequences for more than 20 types of vacuum and pressure filters, potentially involving combinations of cake formation, compression, gas deliquoring and washing. Batch filters include single and multi-element leaf filters, filter presses and diaphragm and tube presses while continuous filters include the horizontal belt, drum, disc, table and tilting pan filters. The user is able to define filter... 

The simulation module simulates the basic operation(s) which are processed by a combination of a vessel and a station using a discrete event simulator. All necessary data (basic operation(s), equipment parameters, recipe scaling percentage, etc.) is provided by the scheduling-module. The simulator calculates the processing times and the state changes of the contents of the vessels (mass, temperature, concentrations, etc.) that are relevant for logistic considerations. 

Crew Station/equipment characteristics The crew station design module and library is a critical component in the MIDAS operation. Descriptions of discrete and continuous control operation of the equipment simulations are provided at several levels of functiontil deteiil. The system can provide discrete equipment operation in a stimulus-response (blackbox) format, a time-scripted/ event driven format, or a full discrete-space model of the transition among equipment states. Similarly, the simulated operator s knowledge of the system can be at the same varied levels of representation or can be systematically modified to simulate various states of misunderstanding the equipment function. 

Figure 5.6 The role of computer software in the selection, sizing, simulation and optimisation of solid/liquid separation equipment. FDS modules (1) equipment selection (2) and (4) data analysis (3) scale-up data generation (5) equipment simulation.

The data analysis module facilitates interactive analysis of leaf filtration, jar sedimentation and piston press test data. Calculations are performed in a hierarchical manner using the available information if some data are not measured then FDS performs the best possible analysis using approximations. The results of an analysis can be used to refine (shorten) a list of selected equipment and/or provide scale-up information for equipment simulation. 

Figure 5.8 Initial display screen of FDS showing access to the Equipment Selection, Data Analysis and two Simulation modules for vacuum (partially hidden) and pressure filters. Images used with permission from Amafilter, Andritz, atech innovations, Axsia Mozley, Broadbent, Dorr-Oliver Eimco, Filtration Services, Larox, Leiblein, Lenntech, Mavag and Sernagiotto.

In the first column, the equipment numbers on the toluene hydrodeallg lation PFD (Figure 1.51 are given. It should be noted that there is not a one-to-one correspondence between the actual equipment and the simulation modules. For example, three splitters and six mixers are required in the simulation, but these are not identified in the PFD. In addition, several pieces of equipment associated with the benzene purification tower are simulated by a single simulation unit. The numbering of the streams in Figure 13.6... 

A library of equipment performance sub-routines (modules) which simulate the equipment and enable the output streams to be calculated from information on the inlet streams. 

Another way to calculate the partial derivatives is possible. Figure 15.12 represents a typical module. If a module is simulated individually rather than in sequence after each unknown input variable is perturbed by a small amount, to calculate the Jacobian matrix, (C + 2)nci + ndi + 1 simulations will be required for the ith module, where nci = number of interconnecting streams to module i and ndi = number of unspecified equipment parameters for module /. This method of calculation of the Jacobian matrix is usually referred to as full-block perturbation. 

The approach of Motz et al. [55, 56] can be seen as a combination of the approach of Ginski et al. [48] (compendial dissolution equipment) and the approach of Kobayashi et al. [50] (open dissolution module). The most dominant advantage may be seen in the application of complete dosage forms and the application flow rates resulting in physiologically relevant concentrations. Furthermore, the apparatus appears to be robust being equipped with compendial dissolution equipment. However, the apparatus is still lacking a pH simulation unit. 

The computational architecture is a sequential modular approach with advanced features. To model a process, each equipment module is simulated by a program module. The overall process is simulated by connecting the models together in the same way as the equipment in the flow sheet. When the input streams are known then the outputs can be calculated. The entire flowsheet can be calculated "sequentially" in this manner. Advanced features are discussed below in connection with an example. 

Figure 3 shows the flowsheet of Figure 2 expressed in terms of the five modules. Lower case letters are used to distinguish modules from plant sections which are denoted by capitals. Some modules are identical with equipment units, e.g. filters and thickeners are distribution modules. In other cases, one piece of equipment is simulated by two or more modules. For example, absorber A is represented by a, c, d and b modules. 

The second classification is the physical model. Examples are the rigorous modules found in chemical-process simulators. In sequential modular simulators, distillation and kinetic reactors are two important examples. Compared to relational models, physical models purport to represent the actual material, energy, equilibrium, and rate processes present in the unit. They rarely, however, include any equipment constraints as part of the model. Despite their complexity, adjustable parameters bearing some relation to theory (e.g., tray efficiency) are required such that the output is properly related to the input and specifications. These models provide more accurate predictions of output based on input and specifications. However, the interactions between the model parameters and database parameters compromise the relationships between input and output. The nonlinearities of equipment performance are not included and, consequently, significant extrapolations result in large errors. Despite their greater complexity, they should be considered to be approximate as well. 

In this article, we have presented a series of LD and MD simulations for ice Ih using a variety of water potentials and the results were compared with INS measured DOS. Neutron measurements were shown to provide unique information on the fundamental intramolecular and intermolecular modes, some of which cannot be obtained from the standard IR and Raman techniques. A full knowledge of the intermolecular vibrations as modulated by the molecule s environment in the lattice systems is necessary for a complete analysis of the dynamics of these ice structures. Equipped with the precise knowledge of the structural information obtained by the diffraction measurements [81,82], one can model the system rigorously with suitable force fields or potential functions. The extensive simulation results show that classic pair-wise potentials were unsuccessful in reproducing the measured DOS for ice Ih. 

Particle energy loss via Cerenkov radiation is only a negligible fraction of the total one and the number of Cerenkov photons emitted by a charged relativistic particle in water is roughly 300 per cm of track4. Simulations show that an underwater detector having an instrumented volume of about 1 km3 equipped with 5000 optical modules can achieve an affective area of 1 km2 and an angular resolution of 0.1° for E > 10 TeV muons [36],... 

For assistance in the use of the Adjust and Set objects, the reader is referred to the module HYSYS -> Principles of Flowsheet Simulation Getting Started in HYSYS -> Convergence of Simulation on the multimedia CD-ROM that accompanies this text. As was discussed in the subsection on bidirectional information flow, for all of its subroutines, HYSYS.Plant provides a bidirectional information flow, that is, when product stream variables are specified, the subroutines calculate most of the unknown inlet-stream variables. In CHEMCAD, a control unit, with one inlet stream and one outlet stream (which may be identical to the inlet stream), is placed into the simulation flowsheet using the CONT subroutine. The parameters of the control unit are specified so as to achieve the desired value of a stream variable (or an expression involving stream variables) or an equipment parameter (or an expression involving equipment parameters) by manipulating an equipment parameter or a stream variable. This is the feed-backward mode, which requires that the control unit be placed downstream of the units being simulated. The CONT subroutine also has afeed-forward mode. 

After the subroutine (or model, or block) computations are completed, all of the stream variables and equipment parameters may be displayed or printed, as illustrated in the report files for ASPEN PLUS in the multimedia CD-ROM under the module ASPEN Principles of Flowsheet Simulation — Interpretation of Input and Output Program Output. 

Process simulation units (that is, blocks, modules, or subroutines) are mapped into more descriptive models of process equipment (e.g., mapping a HEATX simulation unit into a floating-head, shell-and-tube heat exchanger mapping a RADFRAC simulation unit into a tray tower, complete with reboiler, condenser, reflux accumulator, etc.) and associated plant bulks, which include installation items, such as piping, instrumentation, insulation, paint, etc. 

After the parameters for estimating equipment sizes and the utility parameters are adjusted, and a new steam utility is defined, the simulation units (blocks, modules, or subroutines) are mapped into Aspen IPE. In this case, there is only one distillation unit, Dl, to be mapped. The default mapping results in (1) a tray tower, (2) a shell-and-tube heat exchanger with a fixed tube sheet for the condenser, (3) a horizontal drum for the reflux accumulator, (4) a centrifugal reflux pump, and (5) a kettle reboiler with U tubes. 

The following details FDS, Windows software for the selection and simulation of solid/liquid separation equipment as well as the analysis of test data. FDS has been developed in collaboration with multi-national companies spanning a wide range of industrial sectors, the aim being to provide a comprehensive calculation, education and training tool that maintains a balance between ease of use, level of knowledge conveyed and comprehensibility. FDS is a sequence of interlinked modules that can be used independently from one another as necessary Figure 5.8 shows the Start Menu display. 

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