Engineering Judgment

Engineering judgment is the principal tool for deciding what variables to control. 

A good understanding of the process leads in most cases to a logical choice of what needs to be controlled. Considerations of economics, safety, constraints, availability and reliability of sensors, etc. must be factored into this decision. 

For example, in a distillation column we are usually interested in control- 

Levels and pressures must be controlled. In most cases these choices are fairly obvious. 

It should be remembered that controlled variables need not be simple directly measured variables. They can also be computed from a number of sensor inputs. Common examples are heat removal rates, mass flow rates, ratios of flow rates, etc. 

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This recommended practice is intended to apply to faciUties that (/) handle or store flammable or explosive substances in such a manner that a release of ca 5 t of gas or vapor could occur in a few minutes and (2) handle toxic substances. The threshold quantity for the toxic materials would be determined using engineering judgment and dispersion modeling, based on a potential for serious danger as a result of exposures of <1 h. 

Parameter estimation is a procedure for taking the unit measurements and reducing them to a set of parameters for a physical (or, in some cases, relational) mathematical model of the unit. Statistical interpretation tempered with engineering judgment is required to arrive at realistic parameter estimates. Parameter estimation can be an integral part of fault detection and model discrimination. 

This minimization can be unweighted as above, or it can be weighted using the statistical uncertainty with respect to the measurements or engineering judgment. 

As with troubleshooting, parameter estimation is not an exact science. The facade of statistical and mathematical routines coupled with sophisticated simulation models masks the underlying uncertainties in the measurements and the models. It must be understood that the resultant parameter values embody all of the uncertainties in the measurements, underlying database, and the model. The impact of these uncertainties can be minimized by exercising sound engineering judgment founded upon a famiharity with unit operation and engineering fundamentals. 

It is intended that the use of the tables should be combined with sound engineering judgment and consideration of all relevant factors. Eurthermore, all the solutions presented may not be applicable to a given situation. It should also be recognized that the solutions presented could introduce potential hazards that were not originally present. Therefore, it is necessary to use the table in the context of the total design concept to insure that all hazards have been considered. 

Probably the least appreciated weakness of QRA is that the results are difficult to duplicate by independent experts. Even with the variety of sophisticated tools available for use, QRA is still largely dependent on good engineering judgment. The subtle assumptions of analysts performing QRA studies can often be the driving force behind the results. Many times these assumptions are at best arguable, and at worst arbitrary. 

If the monitoring data, mass balance, or omission factor used to estimate the release Is not specific to the toxic chemical being reported, the form should identify the estimate as based on engineering calculations or best engineering judgment. 

All data available at your facility must be utilized to calculate treatment efficiency and influent chemical concentration. You areDfll required to collect any new dataforthe purposes of this reporting requirement. If data are lacking, estimates must be made using best engineering judgment or other methods. 

O - Estimate is based on other approaches such as engineering calculations (e.g., estimating volatilization using published mathematical formulas) or best engineering judgment. This would Include applying an estimated removal efficiency to a wastestream, even if the composition of the stream before treatment was fully characterized by monitoring data. 

Unfortunately, maintenance reports do not always present all the information indicated in Table 4,3-2. Descriptions of component unavailability or work performed may be unclear, requiring engineering judgment regarding whether a component was made unavailable by maintenance or maintenance was required because of component failure. 

The most popular approach to solve these problems is to use experience and good engineering judgment. A quick experiment may be another solution. A computer simulation is a third option. All those approaches may eventually lead to success. This chapter presents various methods of computer simulation for industrial ventilation design. 

The user must have experience in thermal analysis and HVAC dimensioning and be familiar with the theoretical principles and details upon which such analysis is based in the program used. Engineering judgment will have to be used for tbe definition of the input parameters. 

The carrying out of visualization techniques or measurements is one approach to obtain answers to these questions. Computer simulation is another method that is now becoming a more exact science. A third, essential approach is to depend on experience and good engineering judgment. All the above methods may eventually lead to success however, the effort and cost of the work may differ considerably. This chapter describes the measurement and visualization techniques that can be applied in industrial ventilation problems. 

Expansion must always be kept in mind. The question of multiplying the number of units or increasing the size of the prevailing unit or units merits more study than can be given here. It suffices to say that one must exercise engineering Judgment. 

Once tlie system components and their failure modes have been identified, tlie acceptability of risks taken as a result of such failures must be determined. Tlie risk assessment process yields more comprehensive and better results when reliable statistical and probability data are available. In tlie absence of such data, tlie results are a strong function of tlie engineering judgment of tlie design team. The important issue is tliat both tlie severity and probability (frequency) of the accident must be taken into account. 

The future of utility planning is uncertain. Good engineering judgment and technological advancements, however, will prevail as future system requirements are defined. 

API standards are published to facilitate the broad availability of proven, sound engineering and operating practices. These standards are not intended to obviate the need for applying sound engineering judgment regarding when and where these standards should be utilized. The formulation and publication of API standards is not intended in any way to inhibit anyone from using any other practices. 

A special tearing procedure known as diakoptics (K10) was investigated by Brameller et al. (BIO) and by Gay and Middleton (Gl). According to Brameller et al. (BIO), this procedure lends itself very readily to the exercise of engineering judgment. However, computational performance of this method reported by Gay and co-workers (Gl, G2) has not been too impressive. The more recent work of Gay and Preece (G2) indicates that the nodal method of diakoptics is outperformed by an alternative method based on formulation D for problems up to 100 edges and 50 cycles. 

The methods proposed for evaluating the parameters in this case are based on engineering judgments further experimental studies will provide better means of evaluation. 

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