Process deviation qualitative

Hazard and Operability Study (HAZOP) A systematic qualitative technique to identify process hazards and potential operating problems using a series of guide words to study process deviations. 

Each event, such as equipment failure, process deviation, control function, or administrative control, is considered in turn by asking a simple yes/no question. Each is then illustrated by a node where the tree branches into parallel paths. Each relevant event is addressed on each parallel path until all combinations are exhausted. This can result in a number of paths that lead to no adverse consequences and some that lead to the incident as the consequence. The investigator then needs to determine which path represents the actual scenario. Generally, a qualitative event tree is developed when used for incident investigation purposes. 

Because of Eq. (A.36), Onsager s theory is usually viewed as being limited to close to equilibrium irreversible processes. Actually, however, Onsager s theory is qualitatively valid for all nearly reversible irreversible processes for which throughout the process the true and parabolic forms for 5(Tp A) never deviate qualitatively. 

LDG qualitatively tries to find cause and effect relationships between process deviations generated by HAZOP guide words. LDG HAZOP is a web-based multiclient expert system for HAZOP developed in Java language [4]. The main subunits connected to the user interface are the DOC and LDG modules. The DOC module is mainly used for the word processing unit. DOC modules consist of (1) result/report generation subsystems, (2) a document management subsystem, and (3) a hint generator. 

Deviations from Raonlt s law in solution behavior have been attributed to many charac teristics such as molecular size and shape, but the strongest deviations appear to be due to hydrogen bonding and electron donor-acceptor interac tions. Robbins [Chem. Eng. Prog., 76(10), 58 (1980)] presented a table of these interactions. Table 15-4, that provides a qualitative guide to solvent selection for hqnid-hqnid extraction, extractive distillation, azeotropic distillation, or even solvent crystallization. The ac tivity coefficient in the liquid phase is common to all these separation processes. 

Process technology information will be a part of the process safety information package and should include employer-established criteria for maximum inventory levels for process chemicals limits beyond which would be considered upset conditions and a qualitative estimate of the consequences or results of deviation that could occur if operating beyond the established process limits. Employers are encouraged to use diagrams that will help users understand the process. 

Process technology information includes diagrams such as Figure 3.1.3-1, and criteria for maximum inventoiy levels for process chemical limits beyond which the process is considered to be upset. Also included is a qualitative estimate of the consequences that could result from deviating from the limits. 

These Br nsted-type plots often seem to be scatter diagrams until the points are collated into groups related by specific structural features. Thus, p-nitrophenyl acetate gives four separate, but parallel, lines for reactions with pyridines, anilines, imidazoles, and oxygen nucleophiles.Figure 7-4 shows such a plot for the reaction of trans-cmmm c anhydride with primary and secondary aliphatic amines to give substituted cinnamamides.All of the primary amines without substituents on the a carbon (R-CHi-NHi) fall on a line of slope 0.62 cyclopentylamine also lies on this line. If this line is characteristic of normal behavior, most of the deviations become qualitatively explicable. The line drawn through the secondary amines (slope 1.98) connects amines with the structure R-CHi-NH-CHi-R. The different steric requirements in the acylation reaction and in the model process... 

Both qualitative observations and quantitative measurements cannot be reproduced with absolute reliability. By reason of inevitable deviations, measured results vary within certain intervals and observations, mostly in form of decision tests, may fail. The reliability of analytical tests depends on the sample or the process to be controlled and the amount of the analyte, as well as on the analytical method applied and on the economical expenditure available. 

We discussed reticulated foams earlier in this book. They appear to have many desirable properties of ideal scaffolds. Depending on the feedstock, the manufacturers can produce a wide variety of pore sizes. Foams made specifically for reticulation have very narrow pore size distributions. If we compare the reported cell size distribution with that of Zeltringer, we can illustrate the precision of the reticulated foam process in the context of scaffolds for cell growth. Caution is advised in reviewing the Figure 7.6 plot. It is qualitative and assumes a normal distribution for both systems. It estimates the Zeltringer data based on the published standard deviation. 

The main limitation of this model [6,14] is that it assumes that the measured response at a given sensor is due entirely to the constituents considered in the calibration step, whose spectra are included in the matrix of sensitivities, S. Hence, in the prediction step, the response of the unknown sample is decomposed only in the contributions that are found in S. If the response of the unknown contains some contributions from constituents that have not been included in S (in addition to background problems and baseline effects), biased predicted concentrations may be obtained, since the system will try to assign this signal to the components in S. For this reason, this model can only be used for systems of known qualitative composition (e.g. gas-phase spectroscopy, some process monitoring or pharmaceutical samples), in which the signal of all the pure constituents giving rise to a response can be known. For the same reason, CLS is not useful for mixtures where interaction between constituents or deviations from the Lambert-Beer law (nonlinear calibration curves) occur. 

The deviation scenarios found in the previous step of the risk analysis must be assessed in terms of risk, which consists of assigning a level of severity and probability of occurrence to each scenario. This assessment is qualitative or semi-quantitative, but rarely quantitative, since a quantitative assessment requires a statistical database on failure frequency, which is difficult to obtain for the fine chemicals industry with such a huge diversity of processes. The severity is clearly linked to the consequences of the scenario or to the extent of possible damage. It may be assessed using different points of view, such as the impact on humans, the environment, property, the business continuity, or the company s reputation. Table 1.4 gives an example of such a set of criteria. In order to allow for a correct assessment, it is essential to describe the scenarios with all their consequences. This is often a demanding task for the team, which must interpret the available data in order to work out the consequences of a scenario, together with its chain of events. 

Besides this quantitative problem (which may seem a luxury problem to many other industries) the very idea of focusing on just the negative outcomes of process control deviations neglects the valuable lessons to be learned on the basis of positive outcomes. Every time an operator, manager, procedure, or piece of equipment behaves in an unexpected way and thereby prevents a likely breakdown of the production system (e.g. as in reduced product quality, environmental releases, etc.) or restores the required levels of safety and reliability, these positive deviations could be detected, reported and analysed in order to improve the qualitative insight into system functioning on the whole. 

HAZOP and What-If reviews are two of the most common petrochemical industry qualitative methods used to conduct process hazard analyses. Up to 80% of a company s process hazard analyses may consist of HAZOP and What-If reviews with the remainder 20% from Checklist, Fault Tree Analysis, Event Tree, Failure Mode and Effects Analysis, etc. An experienced review team can use the analysis to generate possible deviations from design, construction, modification, and operating intent that define potential consequences. These consequences can then be prevented or mitigated by the application of the appropriate safeguards. 

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