Monitoring variables

To address this situation, a data interpretation system was constructed to monitor and detect changes in the second stage that will significantly affect the product quality. It is here that critical properties are imparted to the process material. Intuitively, if the second stage can be monitored to anticipate shifts in normal process operation or to detect equipment failure, then corrective action can be taken to minimize these effects on the final product. One of the limitations of this approach is that disturbances that may affect the final product may not manifest themselves in the variables used to develop the reference model. The converse is also true—that disturbances in the monitored variables may not affect the final product. However, faced with few choices, the use of a reference model using the process data is a rational approach to monitor and to detect unusual process behavior, to improve process understanding, and to maintain continuous operation. 

As an illustration, let us take a look at a bioreactor (Fig. 1.1). To find out if the bioreactor is operating properly, we monitor variables such as temperature, pH, dissolved oxygen, liquid level, feed flow rate, and the rotation speed of the impeller. In some operations, we may also measure the biomass and the concentration of a specific chemical component in the liquid or the composition of the gas effluent. In addition, we may need to monitor the foam head and make sure it does not become too high. 

It s been suggested that this pattern can form a general design policy, one that is enforced by some research programming languages that all variables and parameters be declared as types and never as classes. The monitors variable in LineCircuit could not be declared to be a list of UsageMonitors you would be forced to invent the supertype. 

Finally, the use of PAT should not be limited to existing processes and products but is especially attractive in the R D and scale-up of new processes and products. PAT is especially effective in scale-up. As PAT involves consideration of all monitored variables and not only an empirical selection of some of those variables, and since in-process monitoring techniques are normally multiparametric (e.g., near-infrared spectra of a whole sample), they will be more suited to capture scale effects present in the sample s matrix that show up clearly in a consolidated multivariate analysis of quality and operating variables, thus helping the skillful engineer or scientist to pinpoint and solve scale-up problems thus resulting in a much faster process prototyping and scale-up. 

As concerns the former, statistical tests on the measured data are usually adopted to detect any abnormal behavior. In other words, an industrial process is considered as a stochastic system and the measured data are considered as different realizations of the stochastic process. The distribution of the observations in normal operating conditions is different from those related to the faulty process. Early statistical approaches are based on univariate statistical techniques, i.e., the distribution of a monitored variable is taken into account. For instance, if the monitored variable follows a normal distribution, the parameters of interest are the mean and standard deviation that, in faulty conditions, may deviate from their nominal values. Therefore, fault diagnosis can be reformulated as the problem of detecting changes in the parameters of a stochastic variable [3, 30],... 

In the so-called predictive model illustrated in Table 4, progressively better correlations with PCDD concentrations in flue gas exiting the combustor are obtained as monitoring variables comprising the concentrations of CO, NOx and water and the furnace temperature are successively combined into a single overall control model. When all four monitoring variables are combined, excellent prediction of PCDD concentrations is obtained. However, when control variables such as waste moisture content, rear wall air flow, total overfire air flow, and underfire air flow are correlated with PCDD emission concentrations, the overall fit is much less effective (see Table 5). A similar trend was observed... 

Countries that have undertaken environmental studies in the Antarctic can contribute data already gathered. Work previously done should be reviewed and shared by all countries so that past experiences with the difficulties of working in this continent can be used in planning future work. Such an approach should lead to the development of a set of agreed protocols for measurement of particular variables. The Antarctic Environmental Officers Network (AEON) has now focused on such a protocol manual with the assistance of SCAR and has provided an initial set of monitoring variables related specifically to station impacts. 

Selecting variables is difficult and yet is crucial to the success of the monitoring exercise. One way is to define valued ecosystem components and construct a matrix of interactions between causes and effects. These interactions are then scored for magnitude and relative importance. Those which show the greatest effects are then considered as monitoring variables. This system is used effectively at present for environmental impact assessment in North America and Europe. At present it can be criticised on the grounds of subjective assessment of importance and magnitude, but there are no methods in which environmental quality determinations such as these can be made more independent. 

The method classifies All correctly when the process nears steady-state (Figure 7.16). There is a brief period of misclassification during the transient between All and AIII due to the similar responses of the two monitored variables. With more process information, the misclassification can be possibly avoided. As in (a), there is a temporary misclassification between All and AIV. 

For monitoring variables. Use filters to reduce the noise at frequencies higher than the effects being observed. Recall that averaging is a filter that is often performed by the DCS historian features. 

The most common means of monitoring variable data is with X the R and control chart combination. It is assum that the observations collected from the process are independent emd normally distributed. The X chart is utilized to monitor the process meem, and the R chart is used to monitor the process variation. Without exception, the X and R charts should rdways be used together. The normality assumption embeds the fact that two peuameters, the mean and variance, completely characterize the process therefore, both control charts are necesstuy to monitor the process completely. 

Interlocks are commonly used safety devices. The function of an interlock is to prevent the occurrence of an event in the piesraice of certain conditions. Some interlocks prevent action or motion, others send signals to other devices that prevent the action or motion. They automatically reconfigure or interrupt final control devices if monitored variables deviate significantly from specifications. Typical process variables monitored are flow, pressure, level, and temperature. Typical machine variables monitored are coolant level and temperature, lubricant level and temperature, vibration, speed, etc. Interlocks allow equipment to start and operate only when monitored variables are within designed specifications. Interlocks inhibit unanticipated actuation of equipment and ensure correct startup/shutdown sequences are followed. A permissive interlock will not allow a process or equipment to startup unless certain conditions are met. There ate two types of interlocks—safety and process interlocks. Each serves a different function. 

Criterion 13 - Instrumentation and control. Instrumentation shall be provided to monitor variables and systems over their anticipated ranges for normal operation, for anticipated operational occurrences, and for accident conditions as appropriate to assure adequate safety, including those variables and systems that can affect the fission process, the integrity of the reactor core, the reactor coolant pressure boundary, and the containment and its associated systems. Appropriate controls shall be provided to maintain these variables and systems within prescribed operating ranges. 

ABSTRACT Bayesian Belief Networks are probabilistic models that are particularly suited to applications where new evidence can be introduced on the variables. By means of Bayes theorem, the probability associated to events can be updated following observations and new information available. This feature has advantages in system fault diagnostic processes where sensors on the system provide evidence for the state of a system. Where sensors indicate that the behavior of the monitored variable deviates from that expected, the information is used to find the possible causes of a fault. 

First, the system is divided into sections, each section has the capabUity to effect a system process variable and contains a sensor that monitors the functioning of the variable of interest. The possible trends of the monitored variable are smdied and these are correlated to the states of the section. In this way, specific patterns are identified for each possible section failed state. Non-coherent Fault Trees (FTs) are then built to represent the causality relations between the failed state of the sections and the component failures (Hurdle et al., 2005 and 2007). The FTs are converted into BNs and these are finally coimected together in a unique network that represents aU system scenarios. The trends observed in the sensors are also included in the strucmre of the BN so that evidence can he introduced in the networks when the sensor are observed. Posterior probability is calculated for the component failure events in all scenarios and the list of component failures whose posterior prohahihty has increased with respect to their prior prohahUity is derived. This gives the lists of possible causes for all system scenarios. 

This module, called Production Xpert, graphically monitors variables specific to the injection-molding process and can automatically determine the quality control limits. The key advantage of the Production Xpert is its ability to automatically detect process variations and drift and it can either suggest how to correct the process or make the necessary changes itself... 

As stated above, during the cure, at each level of conversion, a thermosetting material could be assimilated to a completely new material with specific thermomechanical properties. Then, the possibility of following with a monitoring variable the level of conversion reached by the system during the manufacturing process represents an important issue. 

Corrosion monitoring can be used to provide operational infonnation. If corrosion can be controlled by maintaining a single variable (e.g., temperature, pH, chemical treatment, etc.) within limits previously determined, then that variable can be used to predict changes in corrosion patterns as the limits are exceeded in both a positive and negative direction. An extension of this technique is to use a monitored variable to control chemical addition directly through automatic feed systems. 

The additional relations that should be established and maintained by the computer based system between monitored variables (process variables, operator signals) and controlled variables (output to actuators and indicators) when it operates in this environment. 

An alternative method of obtaining a linear movement across the chart is to use a potential difference or potentio-metric system (Fig. 47). This provides a better accuracy than the moving-coil recorder, but in general can be used only to record signals whose frequency is below 2 Hz. It is very satisfactory for monitoring variables (e g., transducer outputs) in a process plant and forms the basis of marty X-Y plotters, an arrangement in which two poterrtiomet-ric systems are used, one to provide the X deflection and the other the Y deflection. 

The nuclear power plant shall be designed to operate safely within defined ranges of parameters such that the radiological risk to the public and the environment is within the regulatory limits (Ref. [1], para. 5.24). The plant state should change in response to initiating events, but the plant may approach a state that is outside the envelope of safe operation. Certain systems important to safety actuate to effect the necessary actions to return the plant to a safe state. These systems actuate when a monitored variable reaches a predetermined set point. 

For a given monitored variable (e.g. primary circuit pressure, containment pressure) or calculated variable (e.g. reactor power, critical heat flux ratio), a safety limit is established on the basis of safety criteria. This limit should be that value of the variable beyond which unacceptable safety consequences are expected to occur (see Fig. 2). 

In some cases the monitored variable is not identical with the variable used to specify a safety Umit. Examples of such cases are ... 

In selecting the range of measurement for each monitored variable, the accuracy, the speed of response and the amount of overrange necessary for the particular function and any necessary post-accident monitoring capability should be taken into account. If more than one sensor is necessary to cover the entire range of the monitored variable adequately, a reasonable amount of overlap from one sensor to another should be provided at each transition point to ensure that saturation or foldover effects do not prevent the required protective function from being performed. 

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