The Table Filter

The filtration area of large table filters is more than 200 m and having few moving parts can rotate at a cycle time of 1.5 minutes. These machines can handle thick cakes and may be operated at high vacuum levels. The major subassemblies of the machine include  

The cycle of a table filter that includes three countercurrent washing stages consists of the following zones  

With cells under vacuum during filtration  

Cloudy Port Recycle or Sedimentation Pool (before applying vacuum). Cake Formation. 

Cake discharged dry and conveyed by the screw to the cake hopper. Cloth wash and sluicing for the removal of the heel. 

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Figure 1.24 Photograph of a partially assembled horizontal rotary table filter (Dorr-Oliver Eimco). (1) Individual segments on whieh the cloth is mounted (2) feed trough (3) wrash liquor delivery or additional feed points (4) screwr conveyor for cake discharge. A schematic of the table filter cycle is shown in Figure 7.4.

The tipping (tilting) pan filter obviously, and the rotary table filter somewhat less obviously, are developments of the simple batch vacuum filter described in the previous chapter. The single lipping pan is a batch filter, just like the Nutsche, but other versions, including the table filter, are intended to allow more or less continuous operation. 

An important variation of this filter is based on replacing the rigid outer waU necessary for containing the feed and the cake on the rotating table by an endless mbber belt. The belt is held under tension and rotates with the table. It is in contact with the table rim except for the sector where the discharge screw is positioned, and where the belt is deflected away from the table to aUow the soHds to be pushed off the table. The cloth can also be washed in this section by high pressure water sprays. This filter, recendy developed in Belgium, is avaUable in sizes up to 250 m, operated at speeds of 2 minutes per revolution, and cake thicknesses up to 200 mm. 

The anaerobic filter, UASB, and fluidized bed reac tors have all been used for anaerobic treatment of industrial wastes, as each is especially suited for use in anaerobic treatment. Table 25-44 presents results from these applications. 

Design and (Operation Important design and operation considerations for deep-well injection are related to (1) well-site selection, (2) pretreatment, (3) installation of an injec tion well, and (4) monitoring. Important factors related to these design and operation considerations are reported in Table 25-76. As noted in the table, wastes are usually treated prior to injec tion to prevent clogging of the formation and damage to equipment. Particles greater than about 1 to 5 Im must be removed. Typically, treated wastes must be filtered prior to... 

Table 12-4 is a summary of liquid fuel speeifieations set by manufaeturers for effieient maehine operations. The water and sediment limit is set at 1% by maximum volume to prevent fouling of the fuel system and obstruetion of the fuel filters. Viseosity is limited to 20 eentistokes at the fuel nozzles to prevent elogging of the fuel lines. Also, it is advisable that the pour point be 20 °F (11 °C) below the minimum ambient temperature. Failure to meet this speeifieation ean be eorreeted by heating the fuel lines. Carbon residue should be less than 1% by weight based on 100% of the sample. The hydrogen eontent is related to the smoking tendeney of a fuel. Lower... 

Intensive soluble recovery or removal of contaminants from the cake as accomplished by countercurrent washing operations. This is especially the case with horizontal belt, tilting pan and table filters, which are described later in this subseetion,... 

Depending on the test method and test result, particle filters are classified as coarse, fine, HEPA, and ULPA filters (Table 9.2). Electrofilters are usually included in the fine filter group. Chemical filters are used for gases. 

The table shows that energy costs account for 80% of the total cost during the plant s period of operation. The actual costs of the filter, investment, and maintenance correspond to about 20%, while the costs of dumping amount to only 0.5%. The calculation is based on filtering outdoor air, and filters in industrial applications can have quite different figures. 

This type of bag-cleaning method is a fundamental characteristic of this type of collector. Terminology in the fabric filter field is not totally consistent or comprehensive. Table 13.2 presents acceptable definitions for common fabric filter terminology. 

Figures 4-65, 4-66, and 4-67 show several units of the bag. The bags may be of cotton, wool, synthetic fiber, and glass or asbestos with temperature limits on such use as 180°F, 200°F, 275°F, 650°F respectively, except for unusual rnaterials. (See Table 4-12A and B.) These units are used exclusively on dry solid particles in a gas stream, not being suitable for wet or moist applications. The gases pass through the woven filter cloth, depositing the dust on the surface. At intervals the unit is subject to a de-dust-ing action such as mechanical scraping, shaking or back-flow of clean air or gas to remove the dust from the cloth. The dust settles to the lower section of the unit and is removed. The separation efficiency may be 99%-i-, but is dependent upon the system and nature of the particles. For extremely fine particles a precoat of dry dust similar to that used in some wet filtrations may be required before re-establishing the pi ocess gas-dust flow.

One form of comparative analysis is direct comparison of the acquired data to industrial standards or reference values. The International Standards Organization (ISO) established the vibration severity standards presented in Table 43.2. These data are applicable for comparison with filtered narrowband data taken from machine-trains with true running speeds between 600 and 12,000 rpm. The values from the table include all vibration energy between a lower limit of 0.3x true running speed and an upper limit of 3.0X. For example, an 1800-rpm machine would have a filtered narrowband between 540 (1800 x 0.3) and 5400rpm (1800 x 3.0). A 3600-rpm machine would have a filtered narrowband between 1,080 (3600 x 0.3) and 10,800rpm (3600 x 3.0). 

Various extraction methods for phenolic compounds in plant material have been published (Ayres and Loike, 1990 Arts and Hollman, 1998 Andreasen et ah, 2000 Fernandez et al., 2000). In this case phenolic compounds were an important part of the plant material and all the published methods were optimised to remove those analytes from the matrix. Our interest was to find the solvents to modily the taste, but not to extract the phenolic compounds of interest. In each test the technical treatment of the sample was similar. Extraction was carried out at room temperature (approximately 23 °C) for 30 minutes in a horizontal shaker with 200 rpm. Samples were weighed into extraction vials and solvent was added. The vials were closed with caps to minimise the evaporation of the extraction solvent. After 30 minutes the samples were filtered to separate the solvent from the solid. Filter papers were placed on aluminium foil and, after the solvent evaporahon, were removed. Extracted samples were dried at 100°C for 30 minutes to evaporate all the solvent traces. The solvents tested were chloroform, ethanol, diethylether, butanol, ethylacetate, heptane, n-hexane and cyclohexane and they were tested with different solvent/solid ratios. Methanol (MeOH) and acetonitrile (ACN) were not considered because of the high solubility of catechins and lignans to MeOH and ACN. The extracted phloem samples were tasted in the same way as the heated ones. Detailed results from each extraction experiment are presented in Table 14.2. 

Equations (41.15) and (41.19) for the extrapolation and update of system states form the so-called state-space model. The solution of the state-space model has been derived by Kalman and is known as the Kalman filter. Assumptions are that the measurement noise v(j) and the system noise w(/) are random and independent, normally distributed, white and uncorrelated. This leads to the general formulation of a Kalman filter given in Table 41.10. Equations (41.15) and (41.19) account for the time dependence of the system. Eq. (41.15) is the system equation which tells us how the system behaves in time (here in j units). Equation (41.16) expresses how the uncertainty in the system state grows as a function of time (here in j units) if no observations would be made. Q(j - 1) is the variance-covariance matrix of the system noise which contains the variance of w. 

Equation (41.11) represents the (deterministic) system equation which describes how the concentrations vary in time. In order to estimate the concentrations of the two compounds as a function of time during the reaction, the absorbance of the mixture is measured as a function of wavelength and time. Let us suppose that the pure spectra (absorptivities) of the compounds A and B are known and that at a time t the spectrometer is set at a wavelength giving the absorptivities h (0- The system and measurement equations can now be solved by the Kalman filter given in Table 41.10. By way of illustration we work out a simplified example of a reaction with a true reaction rate constant equal to A , = 0.1 min and an initial concentration a , (0) = 1. The concentrations are spectrophotometrically measured every 5 minutes and at the start of the reaction after 1 minute. Each time a new measurement is performed, the last estimate of the concentration A is updated. By substituting that concentration in the system equation xff) = JC (0)exp(-A i/) we obtain an update of the reaction rate k. With this new value the concentration of A is extrapolated to the point in time that a new measurement is made. The results for three cycles of the Kalman filter are given in Table 41.11 and in Fig. 41.7. The... 

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