Plant statistics

By 31 December 1993, the main production data of PHENIX were as follows  

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Q. Is it true that in Leverkusen your plant statistics were prepared to cover the development of sales of all parts of Degesch, from the years 1938 to 1943 ... 

In the future it is anticipated that insurance rates will be set as a function of the safety of a plant. Illustrate the kinds of plant statistics that you would cite to reduce your insurance costs. 

In 1993, French consumption of these products was around 6 Mt and 2.5 Mt respectively for use in burners and in diesel engines. The latter figure appears in the statistics under the heading, marine bunker fuel . Its consumption been relatively stable for several years, whereas heavy industrial fuel use has diminished considerably owing to the development of nuclear energy. However, it seems that heavy fuel consumption has reached a bottom limit in areas where it is difficult to replace, e.g., cement plants. 

The control chart is set up to answer the question of whether the data are in statistical control, that is, whether the data may be retarded as random samples from a single population of data. Because of this feature of testing for randomness, the control chart may be useful in searching out systematic sources of error in laboratory research data as well as in evaluating plant-production or control-analysis data. ... 

The probabilistic nature of a confidence interval provides an opportunity to ask and answer questions comparing a sample s mean or variance to either the accepted values for its population or similar values obtained for other samples. For example, confidence intervals can be used to answer questions such as Does a newly developed method for the analysis of cholesterol in blood give results that are significantly different from those obtained when using a standard method or Is there a significant variation in the chemical composition of rainwater collected at different sites downwind from a coalburning utility plant In this section we introduce a general approach to the statistical analysis of data. Specific statistical methods of analysis are covered in Section 4F. 

Table 7 presents 1991 statistics on limestone and dolomite uses, and includes production from 2338 U.S. plants (16). Generally the growth markets ... 

It should be recognized that the total volume of wastewater as well as the chemical analyses iadicating the organic and inorganic components are requited, backed by statistical validity, before the conceptualizing of the overall treatment plant design can begia. The basic parameters ia wastewater characterization are summarized ia Table 2. 

For certain types of stochastic or random-variable problems, the sequence of events may be of particular importance. Statistical information about expected values or moments obtained from plant experimental data alone may not be sufficient to describe the process completely. In these cases, computet simulations with known statistical iaputs may be the only satisfactory way of providing the necessary information. These problems ate more likely to arise with discrete manufactuting systems or solids-handling systems rather than the continuous fluid-flow systems usually encountered ia chemical engineering studies. However, there ate numerous situations for such stochastic events or data ia process iadustries (7—10). 

Introduction Many types of statistical applications are characterized by enumeration data in the form of counts. Examples are the number of lost-time accidents in a plant, the number of defective items in a sample, and the number of items in a sample that fall within several specified categories. 

Rectification accounts for systematic measurement error. During rectification, measurements that are systematically in error are identified and discarded. Rectification can be done either cyclically or simultaneously with reconciliation, and either intuitively or algorithmically. Simple methods such as data validation and complicated methods using various statistical tests can be used to identify the presence of large systematic (gross) errors in the measurements. Coupled with successive elimination and addition, the measurements with the errors can be identified and discarded. No method is completely reliable. Plant-performance analysts must recognize that rectification is approximate, at best. Frequently, systematic errors go unnoticed, and some bias is likely in the adjusted measurements. 

Unknown Statistical Distributions Sixth, despite these problems, it is necessaiy that these data be used to control the plant and develop models to improve the operation. Sophisticated numerical and statistical methods have been developed to account for random... 

The above assumes that the measurement statistics are known. This is rarely the case. Typically a normal distribution is assumed for the plant and the measurements. Since these distributions are used in the analysis of the data, an incorrect assumption will lead to further bias in the resultant troubleshooting, model, and parameter estimation conclusions. 

Analysts should review the technical basis for uncertainties in the measurements. They should develop judgments for the uncertainties based on the plant experience and statistical interpretation of plant measurements. The most difficult aspect of establishing the measurement errors is estabhshing that the measurements are representative of what they purport to oe. Internal reactor CSTR conditions are rarely the same as the effluent flow. Thermocouples in catalyst beds may be representative of near-waU instead of bulk conditions. Heat leakage around thermowells results in lower than actual temperature measurements. 

Annual Reports of Gumulative System and Gomponent Reliability for Period from July 1, 1974, through December 31, 1982,serves as a source of engineering and failure statistics for the nuclear industry. It contains data for most components used in nuclear power plants. 

There are a variety of ways to express absolute QRA results. Absolute frequency results are estimates of the statistical likelihood of an accident occurring. Table 3 contains examples of typical statements of absolute frequency estimates. These estimates for complex system failures are usually synthesized using basic equipment failure and operator error data. Depending upon the availability, specificity, and quality of failure data, the estimates may have considerable statistical uncertainty (e.g., factors of 10 or more because of uncertainties in the input data alone). When reporting single-point estimates or best estimates of the expected frequency of rare events (i.e., events not expected to occur within the operating life of a plant), analysts sometimes provide a measure of the sensitivity of the results arising from data uncertainties. 

Another way of interpreting absolute risk estimates is through the use of benchmarks or goals. Consider a company that operates 50 chemical process facilities. It is determined (through other, purely qualitative means) that Plant A has exhibited acceptable safety performance over the years. A QRA is performed on Plant A, and the absolute estimates are established as calibration points, or benchmarks, for the rest of the firm s facilities. Over the years, QRAs are performed on other facilities to aid in making decisions about safety maintenance and improvement. As these studies are completed, the results are carefully scrutinized against the benchmark facility. The frequency/consequence estimates are not the only results compared—the lists of major risk contributors, the statistical risk importance of safety systems, and other types of QRA results are also compared. As more and more facility results are accumulated, resources are allocated to any plant areas that are out of line with respect to the benchmark facility. 

V. T. Covello, P. M. Sandman, and P. Slovic, Risk Communication, Risk Statistics, and Risk Comparisons A Manual for Plant Managers, Chemical Manufacturers Association, Washington, DC, 1988. 

By 1998, however, the Western European market had grown to over 90 000 t.p.a., that for the United States to about 140 000 t.p.a. and that for Japan to just over 60 000 t.p.a. There are also about a dozen USA and Western European manufacturers. Statistics on capacity are somewhat meaningless, as the polymer can be made using the same plant as employed for the manufacture of the much larger tonnage material PET. It is, however, quite clear that the market for injection moulded PBT is very much greater than that for injection moulded PET. 

Examine health reeords in your plant. Organize these by work areas and assess whether there are statistically higher ineidents that support respiratory ailments, including cold and flu statistics. If the data supports a particular work area as having a problem, can you identify the reasons If so, what are they and how would you go about better defining the problem and a solution ... 

This chapter provided a common basis for understanding the assigning of numerical values to "risk." in the context of probability as the behavior of an ensemble of plants. Predictions of short-term behavior are subject to statistical fluctuations and may be very misleading. Qualitative... 

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