Identification plants

Andersen, 0.M. and Francis, G.W., Techniques of pigment identification. Plant Pigments and their Manipulation, Davies, K., Ed., Blackwell Publishing, London, 2004, Chap. 10. 293. 

Finally, preliminary diagnostic evaluation criteria, based on preventive identification of critical areas of interest on the monitored item, spatial concentration of localized AE events as compared with average AE event density and evolution of local event concentration vs time and/or plant parameters, have been worked out and submitted to extensive testing under real operation conditions. Work on this very critical issue is still to be consohdated. 

A technique called probabiUstic safety assessment (PSA) has been developed to analy2e complex systems and to aid in assuring safe nuclear power plant operation. PSA, which had its origin in a project sponsored by the U.S. Atomic Energy Commission, is a formali2ed identification of potential events and consequences lea ding to an estimate of risk of accident. Discovery of weaknesses in the plant allows for corrective action. 

Plant-fiber identification is described in TAPPI T8 and TIO. In order to identify synthetic fibers, it usually is necessary to conduct solubihty and physical properties tests in addition to light microscopy observations. Systematic sampling is required to obtain quantitative information on sample composition. Because different types of pulps contain varying numbers of fibers per unit weight, it is necessary to multiply the total number of each kind of fiber by a relative weight factor, thereby the weight percentage that each fiber type contributes to the sample can be deterrnined. 

Ha2ard identification of the contents of in-plant bulk storage tanks, warehouses, etc, may be achieved by a system developed by the NFPA (48). The system makes use of three diamond-shaped areas, which are marked with numbers 0, 1, 2, 3, or 4 indicating increasing ha2ards of toxicity, flammabHity, and reactivity, respectively. 

Identification of Piping Systems, American National Standards Institute, New York, A13.1,1981 Chemical Plant and Refiney Piping, B31.3,1990 Precautionay Eabeling of Ha rdous Industrial Chemicals, Z129.1,1988. 

Hazardous Air Pollutants. Tide 3 of the CAAA of 1990 addresses the release of hazardous air poUutants (HAPs) by requiring both the identification of major stationary sources and area source categories for 189 toxic chemicals and the promulgation of control standards. Major sources of air toxics, also referred to as HAPs, include any stationary source or group of sources emitting 10 or more tons/yr of any single Hsted toxic chemical or 25 tons/yr of a combination of any Hsted toxic. Area sources of HAPs include smaller plants that emit less than the 10 or 20 tons/yr thresholds. The major sources of HAPs are typically industrial faciHties. However, Tide 3 requites the EPA to study potential health affects associated with emissions of HAPs from electric UtiHty boilers (11). 

The earliest references to cinnamic acid, cinnamaldehyde, and cinnamyl alcohol are associated with thek isolation and identification as odor-producing constituents in a variety of botanical extracts. It is now generally accepted that the aromatic amino acid L-phenylalanine [63-91-2] a primary end product of the Shikimic Acid Pathway, is the precursor for the biosynthesis of these phenylpropanoids in higher plants (1,2). 

Implementation Issues A critical factor in the successful application of any model-based technique is the availability of a suitaole dynamic model. In typical MPC applications, an empirical model is identified from data acquired during extensive plant tests. The experiments generally consist of a series of bump tests in the manipulated variables. Typically, the manipulated variables are adjusted one at a time and the plant tests require a period of one to three weeks. The step or impulse response coefficients are then calculated using linear-regression techniques such as least-sqiiares methods. However, details concerning the procedures utihzed in the plant tests and subsequent model identification are considered to be proprietary information. The scaling and conditioning of plant data for use in model identification and control calculations can be key factors in the success of the apphcation. 

Fire and Explosion Index (Ffrom fires and explosions. frequency The rate at which observed or predicted events occur. HAZOP HAZOP stands for hazard and operabihty studies. This is a set of formal hazard identification and ehmination procedures designed to identify hazards to people, process plants, and the environment. See subsequent sections for a more complete description. 

Extended Plant-Performance Triangle The historical representation of plant-performance analysis in Fig. 30-1 misses one of the principal a ects identification. Identification establishes troubleshooting hypotheses and measurements that will support the level of confidence required in the resultant model (i.e., which measurements will be most beneficial). Unfortunately, the relative impact of the measurements on the desired end use of the analysis is frequently overlooked. The most important technical step in the analysis procedures is to identify which measurements should be made. This is one of the roles of the plant-performance engineer. Figure 30-3 includes identification in the plant-performance triangle. 

Plant Operation The purpose is to maintain and improve performance (i.e., product quality, rate, efficiency, safety, and profits). Examples include identification of plant conditions that limit performance (troubleshooting, debottlenecking) and exploration of new operating regions. 

Measurement Selection The identification of which measurements to make is an often overlooked aspect of plant-performance analysis. The end use of the data interpretation must be understood (i.e., the purpose for which the data, the parameters, or the resultant model will be used). For example, building a mathematical model of the process to explore other regions of operation is an end use. Another is to use the data to troubleshoot an operating problem. The level of data accuracy, the amount of data, and the sophistication of the interpretation depends upon the accuracy with which the result of the analysis needs to oe known. Daily measurements to a great extent and special plant measurements to a lesser extent are rarelv planned with the end use in mind. The result is typically too little data of too low accuracy or an inordinate amount with the resultant misuse in resources. 

If the problem were accurately known, identification of which measurements should be taken would be exact. When the problem is initially not accurately known, the identification, measurement, and analysis procedure is iterative. Famiharity with the plant will help in identifying the measurements most likely to provide insight. 

Therefore, the identification of appropriate tests and measurements most important to understanding the unit operation is a critical step in the successful analysis of plant performance. 

Overview Interpretation is the process for using the raw or adjusted unit measurements to troubleshoot, estimate parameters, detect faults, or develop a plant model. The interpretation of plant performance is defined as a discreet step but is often done simultaneously with the identification of hypotheses and suitable measurements and the treatment of those measurements. It is isolated here as a separate process for convenience of discussion. 

The current and past operation should be compared so that the timing of the observed problems is estabhshed. The possible causes (hypotheses) can be compared against the measurements found on the log sheets. The number of possible causes can then be reduced. When the quantity or quahty or measurements is insufficient to further reduce the set of causes, additional measurements are required. These may require special instruments (e.g., gamma-ray scanning) not routinely usedin the plant. Alternative operating conmtions may also be required to further reduce the number of causes. As part of the problem identification, it is alwavs important to look for measurements that are inconsistent with the proposed explanation. They will be more informative than the ones justifying the hypothesized cause. Ultimately, with appropriate additional measurements, the cause can be identified. This is not an exact science and, as stated above, relies heavily upon the communication, technical, and investigative skills of analysts. 

Nepeta (Lamiaceae) is a genus of perennial or annual herbs found in Asia, Europe and North Africa. About 250 species of Nepeta are reported of which, 67 species are present in Iran. Some species of this genus are important medicinal plants and their extracts have been used for medicinal purposes. Aerial parts of Nepeta sintenisii Bornm. was subjected to hydrodistillation and the chemical composition of isolated essential oil has been analyzed by GC/MS method for first time. Identification of components of the volatile oil was based on retention indices relative to n-alkanes and computer matching with the Wiley275.L library, as well as by comparison of the fragmentation patterns of the mass spectra with those reported in the literature. 

The hazard identification step of the QRA typically requires the greatest involvement of plant personnel. For an existing process, only plant personnel know the status of process equipment and the current operating and maintenance practices. Excluding those personnel from the hazard identification step increases the chance of overlooking important potential hazards. For accurate results, the QRA team must have access to this information. 

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