Handbook of Reliability, Availability, Maintainability and Safety in Engineering Design - Part 53 ppt

4.4 Application Modelling of Availability and Maintainability in Engineering Design 503Table 4.14 Preliminary design data for simulation model sector 1 Assembly Code Flow vol.. Logical f

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4.4 Application Modelling of Availability and Maintainability in Engineering Design 503

Table 4.14 Preliminary design data for simulation model sector 1

Assembly Code Flow vol Mass flow Liq Solids

Transfer conveyor 1 C015141 575 1,508 106 1,403

Transfer conveyor 2 C015241 575 1,508 106 1,403

Rev.shuttle conveyor 1 C024111M 575 1,508 106 1,403

Rev.shuttle conveyor 2 C024211M 575 1,508 106 1,403

a) Evaluation of Simulation Model Sector 1

A major characteristic of the process flow diagram (PFD) of sector 1 is that it depicts

material flow and indicates how inputs are generated and then transformed by each system (or assembly) into outputs that, in turn, become the inputs to the next system (or assembly), as depicted in the preliminary design data given in Table 4.14 These are specifically mass-flow volumes of solids The PFD is systematically examined

to analyse deviations in process flow and system performance and, in this case,

to determine mass-flow balance through the integrated assemblies Each assembly

is graphically represented in the simulation model by a virtual prototype process equipment model (PEM).

Each of the assemblies of the PFD depicted in Fig 4.51 (i e the feeder, three storage bins, three chute conveyors, and three transfer conveyors) is a process equip-ment model

Each PEM contains selected model components that are linked together with

logical flows A model component is a modular design entity with a complete

spec-ification describing how it is connected to other model components in a model con-figuration Model configurations are created when two or more model components are connected to each other via their interfaces Each model component has

connec-tors that are the interface points of the component (or block) Connections are lines used to specify the logical flow between the connectors from one block’s output to another’s input Thus, a process system or assembly can be represented either as

a single model component or as a configuration of several components

Logical flow initiation—the random number generator The model components

are selected, configured and assembled in such a way that the design specifications

of each system are met through the component’s attributes, and the linked logical flows Thus, the feeder assembly PEM, for example, has its own specific model

con-figuration, in contrast to that of the storage bins, as depicted in the design details of

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504 4 Availability and Maintainability in Engineering Design

Fig 4.51 Process flow diagram for simulation model sector 1

Figs 4.52 and 4.53 However, all simulation models, especially Monte Carlo simu-lation, have random number generators for ‘seed’ values initiation of the simulation model’s input flow variable(s) that constitute the initial flow of the linked logical flows thereafter

The model component’s attributes depicted in Fig 4.52 generate random num-bers according to a statistical probability distribution, convert the outputs of a con-version function by modifying the component’s inputs through a selection of statis-tical functions, as well as calculating the mean, variance and standard deviation of the component’s inputs

Logical flow storage—the process equipment models (PEMs) Logical flow in

the context of process systems simulation modelling represents upstream material feed that, in effect, causes the initiation of the process equipment model (PEM).

Logical flow storage PEMs are process simulation models in which the model

con-figuration incorporates a model component attribute of an output conversion func-tion that modifies the component’s inputs through a selecfunc-tion of statistical funcfunc-tions,

and statistical probability distributions As previously indicated, the PEMs incor-porate all the essential process analysis preliminaries for preliminary engineering designs of large integrated process systems

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Fig 4.52 Design details for simulation model sector 1: logical flow initiation

The application of dynamic systems simulation modelling incorporating the PEMs is primarily to determine the effect of logic flow in complex integrations

of systems in large engineered installations The model component’s attributes de-picted in Fig 4.53 incorporate probability distribution modifiers of the logic flow within each PEM

Output performance results The Extendc Performance Modelling program

pro-vides a powerfully flexible graphical output presentation through dynamic plotters These plotters can be placed anywhere in the modelled system configuration, and connected between any of the PEM input/output interface connectors, or within each PEM between model component connectors

Figure 4.54 illustrates a typical output document showing performance results of the storage bin assembly These performance variables relate to system or assembly contents, input and output flow quantities, as well as flow surges The flow surge gives an indication of material flow balancing in the process, subject to upstream material feed The storage bin PEM illustrated in Fig 4.54 has a plotter connected

to the output model components of the PEM The plotted graph in the figure shows the trend of material flow through the storage bin from start-up to steady state

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Fig 4.53 Design details for simulation model sector 1: logical flow storage PEMs

b) Conclusion of Simulation Model Sector 1 Evaluation

Table 4.15 below indicates the values of a comparative analysis of preliminary de-sign data and simulation output data for simulation model sector 1 Column 2 of the table gives the specified preliminary design flow volumes, and column 3 gives the mean of the simulation model’s output data On first scrutiny, these two values are identical with an expectation of a 100% correlation, resulting in the conclusion that the model’s output is a perfect match to the specified preliminary design flow volumes of the listed assemblies in simulation model sector 1

The evaluation of simulation model output data is, however, not that simple, as other factors must be included such as requirements for meeting the full design

spec-ification inclusive of allowable tolerances, and determining whether the minimum

and maximum values, i e the range of output variances for each simulation run of the model’s output data, fall within the expected confidence intervals of the design specification The test of whether the simulation model’s output variances fall within the allowable design tolerances is set at a 99% level of confidence The allowable design tolerance for throughput flow volumes is set at±2.5% of the mean.

Figure 4.55 indicates the simulation model’s output for simulation model sec-tor 1, including operational flow throughput (OPS), maximum and minimum flow (MAX) and (MIN), and mean flow output (MEAN) However, an acceptable lower

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Fig 4.54 Design details for simulation model sector 1: output performance results

Table 4.15 Comparative analysis of preliminary design data and simulation output data for

simu-lation model sector 1

Assembly Design flow Model flow Model min Model max.

vol vol flow vol flow vol.

tolerance limit (LL) and an upper tolerance limit (UL), against which the minimum and maximum values of the simulation model’s output data can be compared, need

to be established to determine whether the range of variances of the model’s output data falls within these tolerance limits

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Fig 4.55 Simulation output for simulation model sector 1

Table 4.16 Acceptance criteria of simulation output data, with preliminary design data for

simu-lation model sector 1

Assembly Design min Design max Model min Model max Yes/no

vol 2.5% tol vol 2.5% tol vol. vol at 99%

Validation of the simulation model’s output data is thus not confined to a mere correlation of the mean values, whereby problems of autocorrelation can be sig-nificant, and the simulation model runs are not large enough to justify statistical spectral analysis of the output data (especially with very large, complex dynamic

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4.4 Application Modelling of Availability and Maintainability in Engineering Design 509 simulation models), but the range or variance of the model’s output data is com-pared to acceptable lower and upper confidence limits within a specified exact prob-ability The design specification is thus used as the mean, and the allowable design tolerance of±2.5% of the mean is used as the square root of the variance, or

stan-dard deviation in the statistical t-distribution, to determine a confidence range or interval with lower tolerance limit (LL) and an upper tolerance limit (UL) at a 99% level of confidence for ten simulation runs The minimum and maximum values

of the simulation model’s output data are then compared against this confidence range or interval The last column of Table 4.16 indicates whether the model’s output is acceptable in meeting the design criteria within a 99% level of confi-dence

c) Evaluation of Simulation Model Sector 2

A major characteristic of the process flow diagram (PFD) of sector 2 is that it

de-picts the conversion of solids to a solids and liquid slurry flow (through the action

of the mills), and indicates how inputs are transformed into logical flow outputs that become modified inputs to the following assemblies (through the action of the

Table 4.17 Preliminary design data for simulation model sector 2

Assembly Code Flow vol Mass flow Liq Solids

Mill discharge tank 1 T024141 1,119 2,102 943 1,159

Mill discharge tank 4 T024441

Classifier feed pump 1/1 P024151 560 1,051 472 580

Classifier feed pump (S) P024451

Classifier feed pump (S) P024452

Screen feed pot 1 V024161 1,152 2,142 982 1,159

Screen feed pot 2 V024261 1,152 2,142 982 1,159

Screen feed pot 3 V024361 1,152 2,142 982 1,159

Screen feed pot 4 V024461

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Fig 4.56 Process flow diagram for simulation model sector 2

pumps), as depicted in the preliminary design data given in Table 4.17 The PFD

is systematically examined to analyse deviations in process flow and system perfor-mance and, in this case, to determine solids to fluids mass-flow balance through the integrated assemblies

Each assembly is graphically represented in the simulation model by a virtual prototype process equipment model (PEM) Each of the assemblies of the PFD de-picted in Fig 4.56 (i e the four mill feeder chutes, eight mills, eight pumps, four mixer chutes, four multi-bin feeders) is a process equipment model

Process design specifications Each PEM contains model components that are

con-figured in such a way that the design specifications of each system or assembly are

met through the component’s attributes The model component’s attributes for the mill input feeder chute and the mill output mixer chute connect three input values to

a single output, and two input values to a single output respectively The attributes for the bin feeder convert the output by modifying the component’s multi-ple inputs through a selection of statistical functions The attributes for the mill pump also convert the pump’s output by modifying the component’s inputs through

a selection of statistical functions representing typical pump delivery character-istics

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Fig 4.57 Design details for simulation model sector 2: holding tank process design specifications

Figure 4.57 illustrates the model component’s attributes of the rod mill, specifi-cally the holding tank process characteristics such as operating contents, maximum and minimum contents, initial flow, final flow, initial contents, final contents, as well

as initial and final flow surge

Output performance results Performance variables relate to system or assembly

contents, input and output flow quantities, as well as flow surges The flow surge gives an indication of mass-flow balancing in the process The output document is particular to each PEM and can be opened at any time, anywhere, in the dynamic systems simulation to determine the status of the process flow The Extendc

Perfor-mance Modelling program plotters can be placed anywhere in the modelled system configuration, and connected between any of the PEM input/output interface con-nectors, or within each PEM between model component connectors The different process equipment models illustrated in Fig 4.58 have plotters connected to the model components of each PEM’s model configuration

Figure 4.58 illustrates a typical output document showing performance results of the second-stage mill assembly The plotted graph shows the trend of flow through the mill from start-up to steady state

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Fig 4.58 Design details for simulation model sector 2: output performance results

d) Conclusion of Simulation Model Sector 2 Evaluation

Table 4.18 gives the values of a comparative analysis of preliminary design data and simulation output data for simulation model sector 2

Figure 4.59 shows the simulation model’s output for simulation model sector 2

As with simulation model sector 1, the range or variance of the model’s output data

is compared to acceptable lower and upper confidence limits within a specified exact probability The design specification is again used as the mean, and the allowable design tolerance of ±2.5% of the mean is used as the standard deviation in the

t-distribution, to determine a confidence range or interval with lower tolerance limit (LL) and an upper tolerance limit (UL) at a 99% level of confidence for ten simula-tion runs The minimum and maximum values of the simulasimula-tion model’s output data are similarly compared against this confidence range or interval The last column of Table 4.19 indicates whether the model’s output is acceptable in meeting the design criteria within a 99% level of confidence As can be seen, the mills and classifier feed pumps have a flow volume variance that is not acceptable within the 99% con-fidence interval as set by the design criteria, whereas the ball mills partially comply with the design criteria in that the simulated minimum flow is within the acceptable lower limit (LL)

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