Plant model

The three vertices are the operating plant, the plant data, and the plant model. The plant produces a product. The data and their uncertainties provide the histoiy of plant operation. The model along with values of the model parameters can be used for troubleshooting, fault detection, design, and/or plant control. 

The vertices are connected with hues indicating information flow. Measurements from the plant flow to plant data, where raw measurements are converted to typical engineering units. The plant data information flows via reconciliation, rec tification, and interpretation to the plant model. The results of the model (i.e., troubleshooting, model building, or parameter estimation) are then used to improve plant operation through remedial action, control, and design. 

Overview Interpretation is the process for using the raw or adjusted unit measurements to troubleshoot, estimate parameters, detect faults, or develop a plant model. The interpretation of plant performance is defined as a discreet step but is often done simultaneously with the identification of hypotheses and suitable measurements and the treatment of those measurements. It is isolated here as a separate process for convenience of discussion. 

Mixing or splitting of flows in the plant model is handled by having flows (columns) add or subtract material in given ratios from special tanks (rows) set... 

Robust stability provides a minimum requirement in an environment where there is plant model uneertainty. For a eontrol system to have robust performanee it should be eapable of minimizing the error for the worst plant (i.e. the one giving the largest error) in the family G(jtu) [Pg.308]

Error back-propagation through plant model... 

Internal Model Control was diseussed in relation to robust eontrol in seetion 9.6.3 and Figure 9.19. The IMC strueture is also applieable to neural network eontrol. The plant model GmC) in Figure 9.19 is replaeed by a neural network model and the eontroller C(.v) by an inverse neural network plant model as shown in Figure 10.30. 

FIAZOP is a formally struetured method of systematieally investigating eaeh element of a system for all ways where important parameters ean deviate from the intended design eonditions to ereate hazards and operability problems. The HAZOP problems are typieally determined by a study of the piping and instrument diagrams (or plant model) by a team of personnel who eritieally analyze eflfeets of potential problems arising in eaeh pipeline and eaeh vessel of the operation. 

Either method, properly performed, provides PSA plant models that are accepted by the... 

PRISIM embodies the IREP model of Arkansas 1. It includes extensive grapitivs of. simplified flow diagrams and relevant operating history from LERs (Licensee Event Reports required by Regulatory Guide 1.16) The plant model consists of 500 cutsets truncated by probabilities determined from normal operation. 

Of course, such generic data only give an idea of the importance of the problem. Actual plant-specific data must be developed, using plant models. 

This section reflects on the limitations of the PSA process and draws extensively from NUREG-1050. These subjects are discussed as plant modeling and evaluation, data, human errors, accident processes, containment, fission product transport, consequence analysis, external events, and a perspective on the meaning of risk. 

Accident Sequence Quantification estimates the IE frequency. Specifically, the plant model built in the Step 2 is quantified by data from Step 3 according to Boolean algebra. Quantification may be a point-value calculation in which all parameters are delermimsiic, or as uncertain values known by their distribution function. 

Johnstone, R.E. and Thring, M.W., 1957. Pilot Plants, Models and Scale-up Methods in Chemical Engineering. New York McGraw Hill. 

Plant model costs vary depending upon the degree of detail included. Considerable decision making information can be obtained from a set-up of block layout only, and these costs would be extremely small. For a reasonably complete scale piping detail model the costs are reported as O.I to 0.6 percent of the cost of the plant. The large plants over 20 million cost in the lower 0.1 percent range while small plant models cost in the 0.6 to 1.0... 

Paton [15] reports total model costs of 0.4 to 1.0 percent of erected plant costs for a 1 million plant. These are actual costs and do not reflect profits. Material costs are less than 10 percent of total model costs, and usually less than 5 percent. For a. 30 million plant model costs run as low as 0.1 percent. These are for models which include plant layout, piping layout, and piping details. If simpler models are used the costs should be less. 

Some years ago, comparative investigations into the biodegradation of secondary alkanesulfonates using 14C-labeled preparations [106] and radiometric studies of the biodegradation of secondary alkanesulfonates in a sewage plant model [107] are published (Fig. 42). 

The identification of plant models has traditionally been done in the open-loop mode. The desire to minimize the production of the off-spec product during an open-loop identification test and to avoid the unstable open-loop dynamics of certain systems has increased the need to develop methodologies suitable for the system identification. Open-loop identification techniques are not directly applicable to closed-loop data due to correlation between process input (i.e., controller output) and unmeasured disturbances. Based on Prediction Error Method (PEM), several closed-loop identification methods have been presented Direct, Indirect, Joint Input-Output, and Two-Step Methods. 

Large-scale models, to a scale of at least 1 30, are normally made for major projects. These models are used for piping design and to decide the detailed arrangement of small items of equipment, such as valves, instruments and sample points. Piping isometric diagrams are taken from the finished models. The models are also useful on the construction site, and for operator training. Proprietary kits of parts are available for the construction of plant models. 

Several plant parameters are important to the design of ET landfill covers. Among the most important are parameters describing rooting depth, leaf-area-index (LAI), temperature requirements, time to maturity, and water requirements. Models that are suitable for use in design of ET covers will utilize these parameters. The quality of the plant model controls the quality of AET estimates. 

The next task is to seek a model for the observer. We stay with a single-input single-output system, but the concept can be extended to multiple outputs. The estimate should embody the dynamics of the plant (process). Thus one probable model, as shown in Fig. 9.4, is to assume that the state estimator has the same structure as the plant model, as in Eqs. (9-13) and (9-14), or Fig. 9.1. The estimator also has the identical plant matrices A and B. However, one major difference is the addition of the estimation error, y - y, in the computation of the estimated state x. 

Artola-Garicano et al. [27] compared their measured removals of AHTN and HHCB [24] to the predicted removal of these compounds by the wastewater treatment plant model Simple Treat 3.0. Simple Treat is a fugacity-based, nine-box model that breaks the treatment plant process into influent, primary settler, primary sludge, aeration tank, solid/liquid separator, effluent, and waste sludge and is a steady-state, nonequilibrium model [27]. The model inputs include information on the emission scenario of the FM, FM physical-chemical properties, and FM biodegradation rate in activated sludge. 

Simulation of plant models (equations, modules, or both) of varying degrees of detail. 

Commercial process simulators mainly use a form of SQP. To use LP, you must balance the nonlinearity of the plant model (constraints) and the objective function with the error in approximation of the plant by linear models. Infeasible path, sequential modular SQP has proven particularly effective. 

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