Plant Parameters

Operating ranges for instrumentation and control equipment High and low values within which production conditions should be maintained Required accuracy, integrity, and/or redundancy for safety and product critical parameters Plant and Instrumentation Drawing reference... 

A number of key parameters important to ensure sufficient safety margins in analysis are presented in Section 6. These parameters include core property parameters, plant initial conditions, system performance characteristics and assumptions about the unavailability of systems. Suggestions are... 

Computer simulation of plant operation may also be made offline real time using computer models of the complete installation, which are capable of simulating dynamic plant response to changes in operating parameters, plant upsets, etc. Such systems may be used for off line optimization studies and for operator training in handling emegencies, start-up- and shut-down situations, etc. Without risk to plant or personnel. Simulators are described in [751-758, 923]. 

In describing reactor performance, selectivity is usually a more meaningful parameter than reactor yield. Reactor yield is based on the reactant fed to the reactor rather than on that which is consumed. Clearly, part of the reactant fed might be material that has been recycled rather than fresh feed. Because of this, reactor yield takes no account of the ability to separate and recycle unconverted raw materials. Reactor yield is only a meaningful parameter when it is not possible for one reason or another to recycle unconverted raw material to the reactor inlet. By constrast, the yield of the overall process is an extremely important parameter when describing the performance of the overall plant, as will be discussed later. 

Data Logger it acquires the external plant parameter signals (e.g. load steam flow, temperature and pressure etc.) required for correlation with the AE activity. 

Off-line analysis of stored data review of the stored data, organize data in different presentation windows, plot AE and plant parameters data so as to enable comparison and coirelation with the possibility to present data (histogram of AE events vs position, plant parameters and/or AE parameters vs time) conditioned in terms of time interval (initial time, final time) and/or position interval (defined portion of the component = initial coordinate, final coordinate) and/or plant parameters intervals (one or more plant parameters = initial value, final value). 

Fig.6 Example of trend vs time parameter representation the view is Epical of a paper recorder, with time in Y coordinate decreasing from the top to the bottom. The eight top buttons represent the different choice of plant parameters...

Finally, preliminary diagnostic evaluation criteria, based on preventive identification of critical areas of interest on the monitored item, spatial concentration of localized AE events as compared with average AE event density and evolution of local event concentration vs time and/or plant parameters, have been worked out and submitted to extensive testing under real operation conditions. Work on this very critical issue is still to be consohdated. 

Different plant operating conditions (steady load, load variations, startups / shutdowns) have been encountered during the monitoring period. Electrical load, steam pressure and steam temperature values vs time have been acquired and stored during the entire period. At the same time, the RMS values of the acoustical background noise were have been continuously checked and stored, thus providing a quick check of proper instrumentation condition and a correlation between variations of plant parameters and the acoustical behaviour of the components. 

Figure 5 provides an example of AE monitoring data from 22.01.1997 to 03,03.1997, in terms of time history of the main plant parameters (fig.Sa), as well as of the AE RMS values (fig. 5b). Normally, very little or no events were recorded, with the exception of the above period, in which an AE activity, very much concentrated in time and space, could be observed a sharp step in cumulative AE events takes place in a short, well defined time interval. A smaller sharp step in EA events had been observed a few days earlier, in the same position. 

The same general comments hold as for Unit 3. Figure 7 provides an example of the AE monitoring data collected from 19.06.97 to 16.07.97, in terms of the main plant parameters vs time (fig. 7a), as well as of the AE RMS values (fig. 7b). 

Fig.7 600 MW ENEL power plant, unit 3. Monitoring period from 22-01-1997 to 03-03-1997. Fig.7a Main plant parameters and cumulative AE events vs time Fig.7b AE RMS values vs time...

However, it is expected that this situation will change, since a number of novel "non-invasive NDT techniques are now becoming available. With some of these techniques, the time required for a shutdown can be reduced. Other techniques make it possible to perform inspections whilst the installation is in full service. It is obvious that the availability of such techniques could support the knowledge already available on operational parameters and degradation mechanisms, in order to base shutdown intervals on actual plant condition. 

In plant maintenance, the availability of quantitative and non-invasive screening NDT methods will reduce the time needed for shutdowns and increase the intervals between them. Modem NDT methods will become just as important a tool for Risk Based Inspection approaches and maintenance planning as operational parameters and degradation mechanisms already are. 

Determination of separation efficiencies from pilot-plant data is also affected by axial dispersion. Neglecting it yields high or values. Literature data for this parameter have usually not been corrected for this effect. 

Apart from determinating the optimum size of equipment, the degree of flexibiHty is another key plant design parameter. FlexibiHty means cost, thus only as much flexibiHty as required by the processes should be buHt. Excessive flexibiHty is counterproductive (2). 

Due to thek similarity to a-c furnaces, d-c furnaces can be substituted for neatly any a-c furnace including the open-arc, submerged-arc, and arc-resistance furnaces, provided that design criteria, particularly electrical parameters, are properly chosen. Currently, steel and ferrochrome is being made commercially in d-c furnaces and a siUcon metal pilot plant is being built. 

Operating parameters of this German plant, on the basis of one cubic meter of raw gas, iaclude 0.139 m O2, 0.9 kg briquettes, 1.15 kg steam, 1.10 kg feed water, 0.016 kWh, and 1.30 kg gas Hquor produced. Gasifier output is 1850 m /h and gas yield is 1465 m /t dry, ash-free coal. The coal briquettes have a 19% moisture content, 7.8% ash content (dry basis), and ash melting poiat of 1270°C. Thermal efficiency of the gas production process is about 60%, limited by the quaHty and ash melting characteristics of the coal. Overall efficiency from raw coal to finished products is less than 50%. 

The four process control parameters are temperature, pressure, flow, and level. Modem process level detection systems are varied and ubiquitous in modem chemical plants there are thousands of processes requiring Hquid level indication and Hquid level control. From accumulators to wet wells, the need for level devices is based on the need for plant efficiency, safety, quaUty control, and data logging. Unfortunately, no single level measurement technology works rehably on all chemical plant appHcations. This fact has spawned a broad selection of level indication and control device technologies, each of which operates successfully on specific appHcations. 

Parameter Conventional steam power plant MHD- Eady -Steam power plant Advanced... 

The resulting overall energy balance for the plant at nominal load conditions is shown in Table 3. The primary combustor operates at 760 kPa (7.5 atm) pressure the equivalence ratio is 0.9 the heat loss is about 3.5%. The channel operates in the subsonic mode, in a peak magnetic field of 6 T. AH critical electrical and gas dynamic operating parameters of the channel are within prescribed constraints the magnetic field and electrical loading are tailored to limit the maximum axial electrical field to 2 kV/m, the transverse current density to 0.9 A/cm , and the Hall parameter to 4. The diffuser pressure recovery factor is 0.6. 

Fig. 11. Changes ia mixing parameters on scale-up to 125 times the volume of the pilot plant.

Operational Constraints and Problems. Synthetic ammonia manufacture is a mature technology and all fundamental technical problems have been solved. However, extensive know-how in the constmction and operation of the faciUties is required. Although apparendy simple in concept, these facihties are complex in practice. Some of the myriad operational parameters, such as feedstock source or quaUty, change frequendy and the plant operator has to adjust accordingly. Most modem facihties rely on computers to monitor and optimize performance on a continual basis. This situation can produce problems where industrial expertise is lacking. 

Sewer Disposal. Photoprocessing and printing wastes tend to be aqueous solutions that ate combined with other plant effluents and sent to the local sewer plant for treatment. The parameters of concern include silver, pH, and biological oxygen demand (BOD). BOD is a measure of how well a waste material degrades in the environment. Lower values ate preferred. Silver-bearing waste streams ate typically treated on-site, and the treated effluent is released to the drain. The printer usually receives a small cash credit for silver recovered. 

Adaptive Control. An adaptive control strategy is one in which the controller characteristics, ie, the algorithm or the control parameters within it, are automatically adjusted for changes in the dynamic characteristics of the process itself (34). The incentives for an adaptive control strategy generally arise from two factors common in many process plants (/) the process and portions thereof are really nonlinear and (2) the process state, environment, and equipment s performance all vary over time. Because of these factors, the process gain and process time constants vary with process conditions, eg, flow rates and temperatures, and over time. Often such variations do not cause an unacceptable problem. In some instances, however, these variations do cause deterioration in control performance, and the controllers need to be retuned for the different conditions. 

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