Out-of-control situation

There are several rules appHed to control charts to spot a lack of randomness. The most obvious is a point outside the control limits. A trend such as a mn, where at least seven consecutive data points are either above or below the average line, or a trend of seven consecutive points either increasing or decreasing in value, also indicates an out of control situation (29). A lack of randomness is also apparent from a pattern in which there is a repeated sequence of points cycling between rising then falling, or when points tend to cluster around the center line or near the control limits. 

Stop production in the event of an out-of-control situation developing. 

Scale-up can also have a significant effect on the basic process control system and safety systems in a reactive process. In particular, a larger process will likely require more temperature sensors at different locations in the process to be able to rapidly detect the onset of out-of-control situations. Consideration should be given to the impact of higher-temperature gradients in plant-scale equipment compared to a laboratory or pilot plant reactor (Hendershot 2002). 

Dump Systems For an inhibitor injection or quench system, the inhibitor or quenching medium is transferred from an external supply to the reactive material in a dump system, the reactive material is transferred from the storage/handling facility to a safer location that is the same size or, more commonly, larger than the normal capacity of the facility. This allows depressurizing and deinventory of the reacting mass from the facility in an out-of-control situation, such as an incipient runaway reaction. 

Records of action taken to remedy out-of-control situations to be provided at the same time with control charts. 

Although it is not very unlikely that a single value is outside the range defined by the wanting limits, the probabihty that two out of three sueeessive values are outside this range is very low. Hence this should be treated as an out-of-control situation as well. 

There is no normal dstribution ofthe values fom the control sample due to persisting out of control situations (e.g. blank values)... 

This shde contains hnks to EXCEL-sheets from ExcelKontrol 2.1. This sheets can be nsed to mn control charts with checks for out-of-control situations and with graphical displays. In the electronic material you will find the whole zipped program, which also allows target control charts. 

The control charts discussed earlier are very useful in the diagnostic aspects of quality process improvement. They can be used to stabilize a process by identifying out-of-control situations. After the process is stabilized and brought in control, further improvement of the process can be achieved by using some special control charts such as the cumulative sum (CUSUM) control chart and the exponentially weighted moving average (EWMA) control chart. These control charts can be used when small shifts in a process are of interest. 

Instant feedback to the technical staff on acceptable runs, drift, and out-of-control situations. 

The major objective in SPC is to use process data and statistical techniques to determine whether the process operation is normal or abnormal. The SPC methodology is based on the fundamental assumption that normal process operation can be characterized by random variations around a mean value. The random variability is caused by the cumulative effects of a number of largely unavoidable phenomena such as electrical measurement noise, turbulence, and random fluctuations in feedstock or catalyst preparation. If this situation exists, the process is said to be in a state of statistical control (or in control), and the control chart measurements tend to be normally distributed about the mean value. By contrast, frequent control chart violations would indicate abnormal process behavior or an out-of-control situation. Then a search would be initiated to attempt to identify the assignable cause or the. special cause of the abnormal behavior... 

Flawed process validation that is, omission of one or more control parameters or conditions so that an out-of-control situation would go unnoticed and still impact batch quality... 

Establishment of corrective measures designed to correct any out of control situation at each and every CCP. 

Endothermic Reactions An endothermic reaction process is generally easier to bring to a safe state if an out-of-control situation is detected. Discontinuing the heat input is usually the primary line of defense to stop the operation. In this regard, the endothermic reaction is inherently safer than an exothermic reaction. 

When the measurement process is under control, that is when any variation in counts is basically random, the counts obtained with the RMs, in 95% of the cases, fall between the lower and upper warning limits and in 99.1% of cases between the lower and upper action limits. When the variation in the counts does not conform to the pattern that might reasonably be produced by chance variation, then it is concluded that the process is out of control. It means that one or more systematic errors have been introduced into the system. Several tests for detecting out of control situations have been developed. The purpose of each test is to detect a particular non-random pattern in the points plotted on the control chart. These tests have been evaluated by Nelson [42,43]. The following criteria are used for interpreting the microhiological control charts [38,42] ... 

There are a number of statistical run rules which are used to indicate out-of-control situations. Some laboratories use only one rule of a data point beyond 3 sigmas. This in itself may not be adequate, since it will ignore several short term and more importantly long term indications of system instability. 

In precision charts (the range chart or R-chart), the data from duplicates are plotted with the vertical scale (ordinate) in units such as percent, and the horizontal scale (abscissa) in units of batch number or time. Usually the mean of the duplicates is reported and the difference between the duplicates, or range, is examined for acceptability. The mean and standard deviation are calculated from the data. It is common practice in analytical laboratories to run duplicate analyses at frequent intervals as a means of monitoring the precision of analyses and detecting out-of-control situations. This is often done for analyses for which there are no suitable control samples or reference materials available. 

A variable control chart can provide a lot of statistical information. First, if all of the data points lie within the control limits, the process may be in statistical control. There are some other rules that have to be followed that could indicate an out-of-control situation These rules are covered in SPC texts where chart interpretation is discussed. Once you have a stable process, several types of analyses can be done. The one that 1 have found to be most useful is to use a z-statistic and analyze the data. This way the amount of out-of-specification material that is produced can be calculated. Once... 

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