Abstract:
A method for removing calcium, iron, other metals, and amines from crude oil in a refinery desalting process includes the steps of adding a wash water to the crude oil; adding the wash water to the crude oil to create an emulsion; adding to the wash water, the crude oil or the emulsion an acid additive consisting of at least one of the following: oxalic acid, citric acid, water-soluble hydroxyacid selected from the group consisting of glycolic acid, gluconic acid, C.sub.2-C.sub.4 alpha-hydroxy acids, malic acid, lactic acid, poly-hydroxy carboxylic acids, thioglycolic acid, chloroacetic acid, polymeric forms of the above hydroxyacids, poly-glycolic esters, glycolate ethers, and ammonium salt and alkali metal salts of these hydroxyacids, and mixtures thereof; heating at least one of the crude oil, the wash water or the emulsion to a desired temperature; resolving the emulsion containing the acid additive into a hydrocarbon phase and an aqueous phase using electrostatic coalescence, the metals and amines being transferred to the aqueous phase; measuring at least one desalting process characteristic at at least one process point; performing a statistical calculation of the desalting process performance based upon the measuring; and adjusting a control setting of the desalting process as a function of the statistical calculation. Other methods and devices are also provided.

Description:
FIELD OF THE INVENTION 
       [0001]    The present invention relates to a method and device for removing metals, amines, and other contaminants from crude oil and, more particularly, to automated control of the device and method for use in an oil refinery and to an oil refinery for employing such methods. 
       BACKGROUND INFORMATION 
       [0002]    U.S. Pat. No. 4,853,109 discloses a method for removing metal contaminants, particularly iron and non-porphyrin, organically-bound iron components from crude petroleum. This process comprises mixing crude oil with an aqueous solution of hydroxo-carboxylic acids or salts thereof, preferably citric acid, and separating the aqueous solution and metals from the crude. 
         [0003]    U.S. Pat. 5,078,858 discloses a method for extracting iron species from crude oil by directly adding oxalic or citric acid to the crude oil feedstock, mixing the acid and oil, then adding wash water to form a water in oil emulsion. The emulsion is resolved separating the aqueous solution and metals from the crude. 
         [0004]    U.S. Pat. No. 7,497,943 discloses a method for transferring metals and/or amines from a hydrocarbon phase to a water phase in an oil refinery desalting process. The method consists of adding to a wash water an effective amount of a composition comprising certain water-soluble hydroxyacids to transfer metals and/or amines from a hydrocarbon phase to a water phase. The water-soluble hydroxyacid is, selected from the group consisting of glycolic acid, gluconic acid, C.sub.2-C.sub.4 alpha-hydroxyacids, malic acid, lactic acid, poly-hydroxy carboxylic acids, thioglycolic acid, chloroacetic acid, polymeric forms of the above hydroxyacids, poly-glycolic esters, glycolate ethers, ammonium salt and alkali metal salts of these hydroxyacids, and mixtures thereof. The pH of the wash water is lowered to 6 or below, before, during and/or after adding the composition and the wash water is added to crude oil to create an emulsion. Finally, the emulsion is resolved into the hydrocarbon phase and an aqueous phase using electrostatic coalescence, where at least a portion of the metals and/or amines are transferred to the aqueous phase. 
         [0005]      Optimum Temperature in the Electrostatic Desalting of Maya Crude Oil  by Pruneda et al published in the 2005 Journal of the Mexican Chemical Society discloses a simulation model which suggests that there is an optimum temperature to maximize economic benefit when desalting heavy crude oil. As indicated in the art, an increase in process temperature has two effects to be considered. First, as temperature is increased, there is a corresponding decrease in oil density and viscosity which implies a significant increase in the settling rate of water droplets within the oil phase thus allowing a greater amount of oil to be processed resulting in an increase in profit from performing oil desalting. However, crude oil conductivity increases exponentially with temperature which implies a higher rate of electrical power consumption during electrostatic coalescence which increases processing expense. 
         [0006]      Basic Statistics  by Kiemele et al published in 1991 discloses statistical process control techniques whereby data is collected on a manufacturing or industrial process, statistics are computed on the data, and human operators interpret the results. In the process outlined by Kiemele et al, all decision-making and process change operations are made by a human operator. 
         [0007]    U.S. Pat. No. 4,853,109, U.S. Pat. No. 5,078,858, U.S. Pat. No. 7,497,943,  Optimum Temperature in the Electrostatic Desalting of Maya Crude Oil  by Pruneda et al, and  Basic Statistics  by Kiemele et al are hereby incorporated by reference herein. 
       SUMMARY OF THE INVENTION 
       [0008]    The present invention provides a method for removing calcium, iron, other metals, and amines from crude oil in a refinery desalting process comprising the steps of: adding a wash water to the crude oil; adding the wash water to the crude oil to create an emulsion; adding to the wash water, the crude oil or the emulsion an acid additive consisting of at least one of the following: oxalic acid, citric acid, water-soluble hydroxyacid selected from the group consisting of glycolic acid, gluconic acid, C.sub.2-C.sub.4 alpha-hydroxy acids, malic acid, lactic acid, poly-hydroxy carboxylic acids, thioglycolic acid, chloroacetic acid, polymeric forms of the above hydroxyacids, poly-glycolic esters, glycolate ethers, and ammonium salt and alkali metal salts of these hydroxyacids, and mixtures thereof; heating at least one of the crude oil, the wash water or the emulsion to a desired temperature; resolving the emulsion containing the acid additive into a hydrocarbon phase and an aqueous phase using electrostatic coalescence, the metals and amines being transferred to the aqueous phase; measuring at least one desalting process characteristic at at least one process point; performing a statistical calculation of the desalting process performance based upon the measuring; and adjusting a control setting of the desalting process as a function of the statistical calculation. 
         [0009]    The present invention also provides a method for controlling a refinery desalting process comprising the steps of: measuring at least one desalting process characteristic at at least one process point; performing a statistical calculation of the desalting process performance based upon the measuring; and adjusting a control setting of the desalting process as a function of the statistical calculation. 
         [0010]    The present invention also provides a crude oil refinery operating the present methods, as well as an electrostatic desalter comprising: a crude oil supply for storing crude oil; a wash water supply for supplying wash water to the crude oil to form an emulsion; an acid additive supply for supplying an acid additive to the wash water, the emulsion, or the crude oil; a heater for changing the temperature of the crude oil, the wash water or the emulsion; pumps and valves for controlling fluid flow in the desalting process; and a controller monitoring a desalter process characteristic and updating a statistical calculation of the desalter performance based upon the monitoring. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]      FIG. 1  shows a block diagram of a typical single stage crude oil electrostatic desalting mechanism according to one embodiment of the present invention; 
           [0012]      FIG. 2  shows a block diagram of a typical first stage dehydration followed by a second stage electrostatic desalting mechanism according to one embodiment of the present invention; 
           [0013]      FIG. 3  shows a block diagram of a typical two stage electrostatic desalting mechanism according to one embodiment of the present invention; 
           [0014]      FIG. 4  shows an algorithm diagram of one embodiment of the control methods of the present invention for a typical crude oil electrostatic desalting operation. 
           [0015]      FIG. 5  shows a diagram of one embodiment of the method of the present invention for a typical crude oil desalting operation. 
       
    
    
     DETAILED DESCRIPTION 
       [0016]      FIG. 1  shows a diagram of a single stage crude oil electrostatic desalting mechanism  1000  of the present invention. 
         [0017]    The desalting mechanism  1000  of the present invention includes a crude oil supply  10  for storing crude oil. The crude oil supply  10  is connected to a controllable pump  70  which is connected to an optional controllable fluid mixer  80 . The optional controllable fluid mixer  80  allows an emulsion of crude oil  10 , wash water  20 , and acid additive  30  to be created prior to heating based upon the specific characteristics of the crude oil supply  10  to be desalted. The optional controllable fluid mixer  80 , if necessary to process the crude oil supply  10 , is controlled by the controller  110  to create and maintain the proper emulsion mix of crude oil  10 , wash water  20 , and acid additive  30 . 
         [0018]    Following either the controllable pump  70  or the optional controllable fluid mixer  80  is a controllable flow control valve (FCV)  120 . The controllable flow control valve  120  and the controllable pump  70  work in conjunction under command of the controller  110  to control and maintain the crude oil feed rate and pressure. The crude oil  10  or crude oil emulsion created via optional controllable fluid mixer  80  is then heated to a desired processing temperature by the heater  130  which is controlled by controller  110 . 
         [0019]    The desalting mechanism  1000  of the present invention also includes a wash water supply  20  and an acid additive supply  30 . In the embodiment of  FIG. 1 , as is preferred, the acid additive  30  is mixed with the wash water  20  by the controllable fluid mixer  40  before the crude oil/wash water emulsion is formed. Alternatively, the acid additive  30  could be mixed with the wash water  20  and crude oil  10  during the emulsion creation or after water-oil emulsion creation or with the crude oil  10  itself. The fluid mixer  40  is controlled by the controller  110  to create and maintain the proper solution mixture of acid additive  30  and wash water  20 . The acid additive  30  can be selected from the group consisting of oxalic acid, citric acid, glycolic acid, gluconic acid, C.sub.2-C.sub.4 alpha-hydroxy acids, malic acid, lactic acid, poly-hydroxy carboxylic acids, thioglycolic acid, chloroacetic acid, polymeric forms of the above hydroxyacids, poly-glycolic esters, glycolate ethers, and ammonium salt and alkali metal salts of these hydroxyacids, and mixtures thereof. 
         [0020]    After mixing the solution of acid additive  30  and wash water  20  with the controllable fluid mixer  40 , the resulting solution is input to a controllable flow control valve  90  which is used to allow samples of the mixed acid additive  30  and wash water  20  solution to be measured at a measurement station  200 . Measurements made on the solution samples would include but not be limited to solution pH, solution impurity levels, and percentage of acid additive  30  to wash water  20 . This information is sent to the controller  110 . 
         [0021]    After mixing the solution of acid additive  30  and wash water  20  with the controllable fluid mixer  40 , the resulting solution is also input to a controllable pump  50  whose output is connected to a controllable flow control valve  60 . The controllable pump  50  and the flow control valve  60  work in conjunction under the command of the controller  110  to control and maintain the wash water/acid solution feed rate and pressure. In the embodiment of  FIG. 1 , the controllable flow control valve  60  is shown to be a three-way valve to allow for emulsion creation with the crude oil supply  10  via the optional controllable fluid mixer  80 , the optional controllable fluid mixer  140 , or both. Like the optional controllable fluid mixer  80 , the optional controllable fluid mixer  140 , if necessary to process the crude oil supply  10 , is controlled by the controller  110  to create and maintain the proper emulsion mix of crude oil  10 , wash water  20 , and acid additive  30 . The controllable flow control valve  60  also allows for the acid additive  30  and wash water  20  solution to be presented to the optional controllable fluid mixer  80  and optional controllable fluid mixer  140  at the same or different flow rates when both mixer devices are used in the desalting process. 
         [0022]    Following the optional controllable fluid mixer  140 , the emulsion passes through a pressure control valve  160  before entering the electrostatic desalter  170 . The electrostatic desalter  170  includes a liquid level sensor (LS)  210  used to measure the aqueous level in the electrostatic desalter  170 . In the embodiment of  FIG. 1 , the measurement output of the liquid level sensor  210  is routed to the controller  110 . The controller  110  uses the liquid level measurement data to control the controllable flow control valve  220  to drain the effluent from the electrostatic desalter  170  and control the aqueous layer and emulsion layer within the electrostatic desalter  170 . Alternatively, the liquid level sensor  210  output may be directly connected to a level control valve instead of the controllable flow control valve  220  to drain the effluent. The controllable flow control valve  220  is also configured to allow samples of the effluent solution to be measured at a measurement station  200 . Measurements made on the solution samples would include but not be limited to solution pH, solution impurity levels, temperature, and amount of residual oil present in the effluent. This information is sent to the controller  110 . 
         [0023]    The electrical power supply  150  provides the voltage necessary to create the electric field necessary for electrostatic coalescence in the electrostatic desalter  170 . The controller  110  controls the electrical power supply  150  output. The electrical power supply  150  output may be static (i.e. constant voltage with a current limit) or, preferably, able to change key parameters to enhance the desalting operation. The electrical power supply  150  under the control of the controller  110  would preferably be able to alter its&#39; output to include but not be limited to changes in the voltage level applied to the electrostatic desalter  170 , the voltage waveform applied to the electrostatic desalter  170 , current limits (if any) on the electrical power supply  150 , or any combination thereof. 
         [0024]    The desalted crude output of the electrostatic desalter  170  passes through a pressure control valve  180  and a controllable flow control valve  190 . The controllable flow control valve  190  has two outputs to direct the desalted crude oil. Under control of the controller  110 , the controllable flow control valve  190  controls and maintains the flow rate of desalted crude oil to the remaining refinery operations. Additionally, under control of the controller  110 , the controllable flow control valve  190  can also direct samples of the desalted crude to the measurement station  200 . Measurements made on the solution samples would include but not be limited to impurity levels, temperature, residual acid additive  30  and wash water  20  solution, etc. This information is sent to the controller  110 . 
         [0025]    In the embodiment of  FIG. 1 , the controller  110  collects measurements from process points including but not limited to the various points described herein to evaluate the efficiency of the desalting mechanism  1000 . The measurement points can be divided into two data types: intra-process data and product data. Intra-process data include factors that have an impact in the results of the final product, but are not measurements of the final product. Product data are measurements of the final product. 
         [0026]    In the embodiment of  FIG. 1 , the controller  110  collects measurements from a number of intra-process data points including but not limited to the following: 
         [0027]    The crude oil supply  10  feed rate through the flow control valve  120   
         [0028]    The temperature, viscosity, and density of the crude oil supply  10  or, optionally, the emulsion created by mixing the crude oil supply with a solution comprising the acid additive  30  or acid additive  30  and wash water  20  through the controllable heater  130 . 
         [0029]    The acid additive  30  and wash water  20  feed rate through the controllable flow control valve  60 . 
         [0030]    The characteristics of the solution mixture of acid additive  30  and wash water  20  through the controllable flow control valve  90  and measurement station  200 . Specific measurements may include but not be limited to the percentage of acid additive in the solution, other impurities present either in the wash water  20  or acid additive  30 , etc. 
         [0031]    The electrostatic desalter  170  water level and emulsion layer through the liquid level sensor  210 . 
         [0032]    The characteristics of the electrostatic desalter  170  effluent through the controllable flow control valve  220  and the measurement station  200 . 
         [0033]    The characteristics of the electrostatic desalter  170  electric field through the electrical power supply  150 . Specific measurements may include but not be limited to peak voltage, peak current, RMS current, RMS voltage, current limit, etc. 
         [0034]    The intra-process measurements made under the control of the controller  110  are made at random or, preferably, regular intervals while processing the crude supply  10 . If the desalting process includes a customer specification for any intra-process measurement, each applicable measurement is compared to the specification to determine if the sample is within the customer required limits. If the specification is not met, immediate corrective action is taken as per the customers&#39; requirement. 
         [0035]    The intra-process data measurements made under the control of the controller  110  are used under the present invention to compute statistical quantifiers for use by the controller  110  to signal potential problems with and control the electrostatic desalter mechanism  1000 . The data measurements are classified as either binary or continuous. Examples of a binary data set include a pass or fail criteria. Continuous data includes measurements such as temperature, pressure, voltage, etc. 
         [0036]    In the embodiment of  FIG. 1 , the controller  110  collects measurements of the desalted crude oil output product through the controllable flow control valve  190  and the measurement station  200 . The product measurements made under the control of the controller  110  are made at random or, preferably, regular intervals. If the desalting process includes a customer specification for any measurement either individually or in combination with others, each applicable measurement is compared to the specification to determine if the sample is within the customer required limits. If the specification is not met, immediate corrective action is taken per the customers&#39; requirements. The product data measurements made under the control of the controller  110  are also used under the present invention to compute statistical quantifiers for use by the controller  110  to signal potential problems with and control the electrostatic desalter mechanism  1000 . The product data measurements are classified as either binary or continuous in the same manner as the intra-process data measurements. 
         [0037]    Based upon the type of crude oil being processed, the controller  110  can adjust various factors of the desalting operation including but not limited to the following: 
         [0038]    The crude oil supply  10  feed rate through the controllable pump  70  and controllable flow control valve  120   
         [0039]    The temperature of the crude oil supply  10  or, optionally, the emulsion created by mixing the crude oil supply  10  with a solution comprising the acid additive  30  and/or wash water  20  through the controllable heater  130 . 
         [0040]    The solution mixture of acid additive  30  and wash water  20  through the controllable fluid mixer  40 . 
         [0041]    The flow rate of the solution mixture of acid additive  30  and wash water  20  through the controllable pump  50  and controllable flow control valve  60 . 
         [0042]    The emulsion formation through optional controllable fluid mixer  80  and/or optional controllable fluid mixer  140 . 
         [0043]    The electrostatic desalter  170  electric field through the controllable electrical power supply  150 . 
         [0044]    Control of the electrostatic desalter water level and emulsion layers through the liquid level sensor  210 , the controllable flow control valve  220 , and the controllable flow control valve  190 . 
         [0045]    As different crude oils are processed by the desalting mechanism  1000 , the characteristics necessary to efficiently desalt the crude oil will require some adjustment. Additionally, differences in electrostatic desalter  170  characteristics, wash water supply  20  purity, etc. between different desalting mechanisms  1000  require the storage of different control settings. The memory/data storage  100  function of the desalting mechanism  1000  allows the controller to access and update, if required, the control settings required to efficiently process various types of crude oil supplies  10 . 
         [0046]      FIG. 2  shows a diagram of a typical first stage dehydration followed by a second stage electrostatic desalting mechanism  2000  of the present invention. 
         [0047]    The desalting mechanism  2000  of the present invention includes a crude oil supply  2010  for storing crude oil. The crude oil supply  2010  is connected to a controllable pump  2070  whose output is connected to a controllable flow control valve (FCV)  2120 . The controllable flow control valve  2120  and the controllable pump  2070  work in conjunction under command of the controller  2110  to control and maintain the crude oil feed rate and pressure. The crude oil  2010  is then heated to a desired processing temperature by the heater  2130  which is controlled by controller  2110 . In the embodiment of  FIG. 2 , the heated crude oil passes through a pressure control valve  2160  before entering the dehydration mechanism  2310 . The dehydration mechanism  2310  is designed to remove high salinity water from the crude oil supply  2010 . The dehydration process relies on establishing a varying high voltage electric field in the oil phase of the dehydration mechanism  2310 . Due to the action of the imposed electric field, the droplets are agitated causing the drops to coalesce into droplets of sufficient size to migrate via gravity to the lower water phase of the dehydration mechanism  2310 . The dehydration mechanism  2310  includes a liquid level sensor (LS)  2340  used to measure the water level in the dehydration mechanism  2310 . In the embodiment of  FIG. 2 , the measurement output of the liquid level sensor  2340  is routed to the controller  2110 . The controller  2110  uses the liquid level measurement data to control the controllable flow control valve  2330  to drain the waste water from the dehydration mechanism  2310  and control the water layer and oil layer within the dehydration mechanism  2310 . Alternatively, the liquid level sensor  2340  output may be directly connected to a level control valve instead of the controllable flow control valve  2330  to drain the waste water. The controllable flow control valve  2330  is also configured to allow samples of the effluent solution to be measured at a measurement station  2200 . Measurements made on the solution samples would include but not be limited to solution pH, solution impurity levels, temperature, and amount of residual oil present in the waste water. This information is sent to the controller  2110 . 
         [0048]    The electrical power supply  2300  provides the voltage necessary to create the electric field necessary for water coalescence in the dehydration mechanism  2310 . The controller  2110  controls the electrical power supply  2300  output. The electrical power supply  2300  output may be static (i.e. constant voltage with a current limit) or, preferably, able to change key parameters to enhance the dehydration operation. The electrical power supply  2300  under the control of the controller  2110  would preferably be able to alter its&#39; output to include but not be limited to changes in the voltage level applied to the dehydration mechanism  2310 , the voltage waveform applied to the dehydrator, current limits (if any) on the electrical power supply  2300 , or any combination thereof. 
         [0049]    The crude output of the dehydration mechanism  2310  passes through a pressure control valve  2320  on its way to the controllable fluid mixer  2350 . The controllable fluid mixer  2350  allows an emulsion of crude oil  2010 , wash water  2020 , and acid additive  2030  to be created based upon the specific characteristics of the crude oil supply  2010  to be desalted. The controllable fluid mixer  2350  is controlled by the controller  2110  to create and maintain the proper emulsion mix of crude oil  2010 , wash water  2020 , and acid additive  2030 . 
         [0050]    The desalting mechanism  2000  of the present invention also includes a wash water supply  2020  and a acid additive supply  2030 . In the embodiment of  FIG. 2 , as is preferred, the acid additive  2030  is mixed with the wash water  2020  by the controllable fluid mixer  2040  before the crude oil/wash water emulsion is formed. Alternatively, the acid additive  2030  could be mixed with the wash water  2020  and crude oil  2010  during the emulsion creation or after emulsion creation or with the crude oil  2010  itself. The fluid mixer  2040  is controlled by the controller  2110  to create and maintain the proper solution mixture of acid additive  2030  and wash water  2020 . The acid additive  2030  can be selected from the group consisting of oxalic acid, citric acid, glycolic acid, gluconic acid, C.sub.2-C.sub.4 alpha-hydroxy acids, malic acid, lactic acid, poly-hydroxy carboxylic acids, thioglycolic acid, chloroacetic acid, polymeric forms of the above hydroxyacids, poly-glycolic esters, glycolate ethers, and ammonium salt and alkali metal salts of these hydroxyacids, and mixtures thereof. 
         [0051]    After mixing the solution of acid additive  2030  and wash water  2020  with the controllable fluid mixer  2040 , the resulting solution is input to a controllable flow control valve  2090  which is used to allow samples of the mixed acid additive  2030  and wash water  2020  solution to be measured at a measurement station  2200 . Measurements made on the solution samples would include but not be limited to solution pH, solution impurity levels, and percentage of acid additive  2030  to wash water  2020 . This information is sent to the controller  2110 . 
         [0052]    After mixing the solution of acid additive  2030  and wash water  2020  with the controllable fluid mixer  2040 , the resulting solution is also input to a controllable pump  2050  whose output is connected to a controllable flow control valve  2060 . The controllable pump  2050  and the flow control valve  2060  work in conjunction under the command of the controller  2110  to control and maintain the wash water/acid solution feed rate and pressure. The output of the flow control valve  2060  is an input to the controllable fluid mixer  2350  where the emulsion of crude oil  2010 , wash water  2020 , and acid additive  2030  is formed. 
         [0053]    After mixing the crude oil  2010 , acid additive  2030 , and wash water  2020  in the controllable fluid mixer  2350 , the resulting emulsion passes through a controllable flow control valve  2360  before entering the electrostatic desalter  2170 . The controllable flow control valve  2360 , under command of the controller  2110 , controls the flow rate of the crude oil emulsion into the electrostatic desalter  2170  as well as allowing samples of the emulsion to be directed to the measurement station  2200 . Measurements made on the solution samples would include but not be limited to impurity levels, temperature, amount of acid additive  2030  and wash water  2020  solution, etc. This information is sent to the controller  2110 . 
         [0054]    The electrostatic desalter  2170  includes a liquid level sensor (LS)  2210  used to measure the aqueous level in the electrostatic desalter  2170 . In the embodiment of  FIG. 2 , the measurement output of the liquid level sensor  2210  is routed to the controller  2110 . The controller  2110  uses the liquid level measurement data to control the controllable flow control valve  2220  to drain the effluent from the electrostatic desalter  2170  and control the aqueous layer and emulsion layer within the electrostatic desalter  2170 . Alternatively, the liquid level sensor  2210  output may be directly connected to a level control valve instead of the controllable flow control valve  2220  to drain the effluent. The controllable flow control valve  2220  is also configured to allow samples of the effluent solution to be measured at a measurement station  2200 . Measurements made on the solution samples would include but not be limited to solution pH, solution impurity levels, temperature, and amount of residual oil present in the effluent. This information is sent to the controller  2110 . 
         [0055]    The electrical power supply  2150  provides the voltage necessary to create the electric field necessary for electrostatic coalescence in the electrostatic desalter  2170 . The controller  2110  controls the electrical power supply  2150  output. The electrical power supply  2150  output may be static (i.e. constant voltage with a current limit) or, preferably, able to change key parameters to enhance the desalting operation. The electrical power supply  2150  under the control of the controller  2110  would preferably be able to alter its&#39; output to include but not be limited to changes in the voltage level applied to the electrostatic desalter  2170 , the voltage waveform applied to the electrostatic desalter  2170 , current limits (if any) on the electrical power supply  2150 , or any combination thereof. 
         [0056]    The desalted crude output of the electrostatic desalter  2170  passes through a pressure control valve  2180  and a controllable flow control valve  2190 . The controllable flow control valve  2190  has two outputs to direct the desalted crude oil. Under control of the controller  2110 , the controllable flow control valve  2190  controls and maintains the flow rate of desalted crude oil to the remaining refinery operations. Additionally, under control of the controller  2110 , the controllable flow control valve  2190  can also direct samples of the desalted crude to the measurement station  2200 . Measurements made on the solution samples would include but not be limited to impurity levels, temperature, residual acid additive  2030  and wash water  2020  solution, etc. This information is sent to the controller  2110 . 
         [0057]    In the embodiment of  FIG. 2 , the controller  2110  takes measurements including but not limited to the various points described herein to evaluate the efficiency of the desalting mechanism  2000 . The measurement points can be divided into two data types: intra-process data and product data. Intra-process data include factors that have an impact in the results of the final product, but are not measurements of the final product. Product data are measurements of the final product. 
         [0058]    In the embodiment of  FIG. 2 , the controller  2110  collects measurements from a number of intra-process data points including but not limited to the following: 
         [0059]    The crude oil supply  2010  feed rate through the flow control valve  2120 . 
         [0060]    The temperature, viscosity, and density of the crude oil supply  2010  through the controllable heater  2130 . 
         [0061]    The dehydration mechanism  2310  water level and emulsion layer through the liquid level sensor  2340 . 
         [0062]    The characteristics of the dehydration mechanism  2310  waste water through the controllable flow control valve  2330  and the measurement station  2200 . 
         [0063]    The characteristics of the dehydration mechanism  2310  electric field through the electrical power supply  2300 . 
         [0064]    The acid additive  2030  and wash water  2020  feed rate through the controllable flow control valve  2060 . 
         [0065]    The characteristics of the solution mixture of acid additive  2030  and wash water  2020  through the controllable flow control valve  2090  and measurement station  2200 . 
         [0066]    The characteristics of the wash water  2020 /acid additive  2030 /crude oil  2010  emulsion through the controllable flow control valve  2360  and measurement station  2200 . 
         [0067]    The electrostatic desalter  2170  water level and emulsion layer through the liquid level sensor  2210 . 
         [0068]    The characteristics of the electrostatic desalter  2170  effluent through the controllable flow control valve  2220  and the measurement station  2200 . 
         [0069]    The characteristics of the electrostatic desalter  2170  electric field through the electrical power supply  2150 . 
         [0070]    The intra-process measurements made under the control of the controller  2110  are made at random or, preferably, regular intervals while processing the crude supply  2010 . If the desalting process includes a customer specification for any intra-process measurement, each applicable measurement is compared to the specification to determine if the sample is within the customer required limits. If the specification is not met, immediate corrective action is taken as per the customers&#39; requirement. 
         [0071]    The intra-process data measurements made under the control of the controller  2110  are used under the present invention to compute statistical quantifiers for use by the controller  2110  to signal potential problems with and control the electrostatic desalter mechanism  2000 . The data measurements are classified as either binary or continuous. Examples of a binary data set include a pass or fail criteria. Continuous data includes measurements such as temperature, pressure, voltage, etc. 
         [0072]    In the embodiment of  FIG. 2 , the controller  2110  collects measurements of the desalted crude oil output product through the controllable flow control valve  2190  and the measurement station  2200 . The product measurements made under the control of the controller  2110  are made at random or, preferably, regular intervals. If the desalting process includes a customer specification for any measurement either individually or in combination with others, each applicable measurement is compared to the specification to determine if the sample is within the customer required limits. If the specification is not met, immediate corrective action is taken per the customers&#39; requirements. The product data measurements made under the control of the controller  2110  are also used under the present invention to compute statistical quantifiers for use by the controller  2110  to signal potential problems with and control the electrostatic desalter mechanism  2000 . The product data measurements are classified as either binary or continuous in the same manner as the intra-process data measurements. 
         [0073]    Based upon the type of crude oil being processed, the controller  2110  can adjust various factors of the desalting operation including but not limited to the following: 
         [0074]    The crude oil supply  2010  feed rate through the controllable pump  2070  and controllable flow control valve  2120   
         [0075]    The temperature of the crude oil supply  2010  through the controllable heater  2130 . 
         [0076]    Control of the dehydration mechanism  2310  water level and oil layers through the liquid level sensor  2340 , the controllable flow control valve  2330 , and the controllable flow control valve  2360 . 
         [0077]    The dehydration mechanism  2310  electric field through the controllable power supply  2300 . 
         [0078]    The solution mixture of acid additive  2030  and wash water  2020  through the controllable fluid mixer  2040 . 
         [0079]    The flow rate of the solution mixture of acid additive  2030  and wash water  2020  through the controllable pump  2050  and controllable flow control valve  2060 . 
         [0080]    The emulsion formation through controllable fluid mixer  2350 . 
         [0081]    The electrostatic desalter  2170  electric field through the controllable electrical power supply  2150 . 
         [0082]    Control of the electrostatic desalter  2170  water level and emulsion layers through the liquid level sensor  2210 , the controllable flow control valve  2220 , and the controllable flow control valve  2190 . 
         [0083]    As different crude oils are processed by the desalting mechanism  2000 , the characteristics necessary to efficiently desalt the crude oil will require some adjustment. Additionally, differences in dehydration mechanism  2310  characteristics, electrostatic desalter  2170  characteristics, wash water supply  2020  purity, etc between different desalting mechanisms  2000  require the storage of different control settings. The memory/data storage  2100  function of the desalting mechanism  2000  allows the controller to access and update, if required, the control settings required to efficiently process various types of crude oil supplies  2010 . 
         [0084]      FIG. 3  shows a diagram of a typical two stage electrostatic desalting mechanism  3000  of the present invention. 
         [0085]    The desalting mechanism  3000  of the present invention includes a crude oil supply  3010  for storing crude oil. The crude oil supply  3010  is connected to a controllable pump  3070  whose output is connected to a controllable flow control valve (FCV)  3120 . The controllable flow control valve  3120  and the controllable pump  3070  work in conjunction under command of the controller  3110  to control and maintain the crude oil feed rate and pressure. The crude oil  3010  is heated to a desired processing temperature by the heater  3130  which is controlled by controller  3110 . In the embodiment of  FIG. 3 , the heated crude oil is mixed with recycled effluent from the electrostatic desalter  3170  to create an emulsion mix of the crude oil supply  3010  and recycled effluent from the electrostatic desalter  3170  via the controllable fluid mixer  3380 . Use of an effluent recycle as indicated in  FIG. 3  is well-known in the art. The crude oil/effluent recycle emulsion passes through a pressure control valve  3160  before entering the electrostatic desalter  3310 . The electrostatic desalter  3310  includes a liquid level sensor (LS)  3340  used to measure the aqueous level in the electrostatic desalter  3310 . In the embodiment of  FIG. 3 , the measurement output of the liquid level sensor  3340  is routed to the controller  3110 . The controller  3110  uses the liquid level measurement data to control the controllable flow control valve  3330  to drain the waste effluent from the electrostatic desalter  3310  and control the aqueous layer and emulsion layer within the electrostatic desalter  3310 . Alternatively, the liquid level sensor  3340  output may be directly connected to a level control valve instead of the controllable flow control valve  3330  to drain the waste effluent. The controllable flow control valve  3330  is also configured to allow samples of the waste effluent solution to be measured at a measurement station  3200 . Measurements made on the solution samples would include but not be limited to solution pH, solution impurity levels, temperature, and amount of residual oil present in the waste effluent. This information is sent to the controller  3110 . 
         [0086]    The electrical power supply  3300  provides the voltage necessary to create the electric field necessary for electrostatic coalescence in the electrostatic desalter  3310 . The controller  3110  controls the electrical power supply  3300  output. The electrical power supply  3300  output may be static (i.e. constant voltage with a current limit) or, preferably, able to change key parameters to enhance the electrostatic coalescence operation. The electrical power supply  3300  under the control of the controller  3110  would preferably be able to alter its&#39; output to include but not be limited to changes in the voltage level applied to the electrostatic desalter  3310 , the voltage waveform applied to the desalter, current limits (if any) on the electrical power supply  3300 , or any combination thereof. 
         [0087]    The crude output of the electrostatic desalter  3310  passes through a pressure control valve  3320  on its way to the controllable fluid mixer  3350 . The controllable fluid mixer  3350  allows a second emulsion of electrostatic desalter  3310  output, wash water  3020 , and acid additive  3030  to be created based upon the specific characteristics of the crude oil supply  3010  to be desalted. The controllable fluid mixer  3350  is controlled by the controller  3110  to create and maintain the proper emulsion mix of crude oil  3010 , wash water  3020 , and acid additive  3030 . 
         [0088]    The desalting mechanism  3000  of the present invention also includes a wash water supply  3020  and an acid additive supply  3030 . In the embodiment of  FIG. 3 , as is preferred, the acid additive  3030  is mixed with the wash water  3020  by the controllable fluid mixer  3040  before the crude oil/wash water emulsion is formed. Alternatively, the acid additive  3030  could be mixed with the wash water  3020  and crude oil  3010  during the emulsion creation or after emulsion creation or with the crude oil  3010  itself. The fluid mixer  3040  is controlled by the controller  3110  to create and maintain the proper solution mixture of acid additive  3030  and wash water  3020 . The acid additive  3030  can be selected from the group consisting of oxalic acid, citric acid, glycolic acid, gluconic acid, C.sub.2-C.sub.4 alpha-hydroxy acids, malic acid, lactic acid, poly-hydroxy carboxylic acids, thioglycolic acid, chloroacetic acid, polymeric forms of the above hydroxyacids, poly-glycolic esters, glycolate ethers, and ammonium salt and alkali metal salts of these hydroxyacids, and mixtures thereof. 
         [0089]    After mixing the solution of acid additive  3030  and wash water  3020  with the controllable fluid mixer  3040 , the resulting solution is input to a controllable flow control valve  3090  which is used to allow samples of the mixed acid additive  3030  and wash water  3020  solution to be measured at a measurement station  3200 . Measurements made on the solution samples would include but not be limited to solution pH, solution impurity levels, and percentage of acid additive  3030  to wash water  3020 . This information is sent to the controller  3110 . 
         [0090]    After mixing the solution of acid additive  3030  and wash water  3020  with the controllable fluid mixer  3040 , the resulting solution is also input to a controllable pump  3050  whose output is connected to a controllable flow control valve  3060 . The controllable pump  3050  and the flow control valve  3060  work in conjunction under the command of the controller  3110  to control and maintain the wash water/acid solution feed rate and pressure. The output of the flow control valve  3060  is an input to the controllable fluid mixer  3350  where the second emulsion of electrostatic desalter  3310  output, wash water  3020 , and acid additive  3030  is formed. 
         [0091]    After mixing the second emulsion in the controllable fluid mixer  3350 , the second emulsion passes through a controllable flow control valve  3360  before entering the electrostatic desalter  3170 . The controllable flow control valve  3360 , under command of the controller  3110 , controls the flow rate of the second emulsion into the electrostatic desalter  3170  as well as allowing samples of the emulsion to be directed to the measurement station  3200 . Measurements made on the solution samples would include but not be limited to impurity levels, temperature, amount of acid additive  3030  and wash water  3020  solution, etc. This information is sent to the controller  3110 . 
         [0092]    The electrostatic desalter  3170  includes a liquid level sensor (LS)  3210  used to measure the aqueous level in the electrostatic desalter  3170 . In the embodiment of  FIG. 3 , the measurement output of the liquid level sensor  3210  is routed to the controller  3110 . The controller  3110  uses the liquid level measurement data to control the controllable flow control valve  3220  to recycle the effluent from the electrostatic desalter  3170  and control the aqueous layer and emulsion layer within the electrostatic desalter  3170 . Alternatively, the liquid level sensor  3210  output may be directly connected to a level control valve instead of the controllable flow control valve  3220  to recycle the effluent. The controllable flow control valve  3220  along with the controllable pump  3370 , under command of the controller  3110 , control and maintain the recycled effluent flow rate and pressure to the controllable mixer  3380 . The controllable flow control valve  3220  is also configured to allow samples of the effluent solution to be measured at a measurement station  3200 . Measurements made on the solution samples would include but not be limited to solution pH, solution impurity levels, temperature, and amount of residual oil present in the effluent. This information is sent to the controller  3110 . 
         [0093]    The electrical power supply  3150  provides the voltage necessary to create the electric field necessary for electrostatic coalescence in the electrostatic desalter  3170 . The controller  3110  controls the electrical power supply  3150  output. The electrical power supply  3150  output may be static (i.e. constant voltage with a current limit) or, preferably, able to change key parameters to enhance the desalting operation. The electrical power supply  3150  under the control of the controller  3110  would preferably be able to alter its&#39; output to include but not be limited to changes in the voltage level applied to the electrostatic desalter  3170 , the voltage waveform applied to the electrostatic desalter  3170 , current limits (if any) on the electrical power supply  3150 , or any combination thereof. 
         [0094]    The desalted crude output of the electrostatic desalter  3170  passes through a pressure control valve  3180  and a controllable flow control valve  3190 . The controllable flow control valve  3190  has two outputs to direct the desalted crude oil. Under control of the controller  3110 , the controllable flow control valve  3190  controls and maintains the flow rate of desalted crude oil to the remaining refinery operations. Additionally, under control of the controller  3110 , the controllable flow control valve  3190  can also direct samples of the desalted crude to the measurement station  3200 . Measurements made on the solution samples would include but not be limited to impurity levels, temperature, residual acid additive  3030  and wash water  3020  solution, etc. This information is sent to the controller  3110 . 
         [0095]    In the embodiment of  FIG. 3 , the controller  3110  takes measurements including but not limited to the various points described herein to evaluate the efficiency of the desalting mechanism  3000 . The measurement points can be divided into two data types: intra-process data and product data. Intra-process data include factors that have an impact in the results of the final product, but are not measurements of the final product. Product data are measurements of the final product. 
         [0096]    In the embodiment of  FIG. 3 , the controller  3110  collects measurements from a number of intra-process data points including but not limited to the following: 
         [0097]    The crude oil supply  3010  feed rate through the flow control valve  3120 . 
         [0098]    The temperature, viscosity, and density of the crude oil supply  3010  through the controllable heater  3130 . 
         [0099]    The electrostatic desalter  3310  water level and emulsion layer through the liquid level sensor  3340 . 
         [0100]    The characteristics of the electrostatic desalter  3310  effluent through the controllable flow control valve  3330  and the measurement station  3200 . 
         [0101]    The characteristics of the electrostatic desalter  3310  electric field through the electrical power supply  3300 . 
         [0102]    The acid additive  3030  and wash water  3020  feed rate through the controllable flow control valve  3060 . 
         [0103]    The characteristics of the solution mixture of acid additive  3030  and wash water  3020  through the controllable flow control valve  3090  and measurement station  3200 . 
         [0104]    The characteristics of the wash water  3020 /acid additive  3030 /crude oil  3010  emulsion through the controllable flow control valve  3360  and measurement station  3200 . 
         [0105]    The electrostatic desalter  3170  water level and emulsion layer through the liquid level sensor  3210 . 
         [0106]    The characteristics of the electrostatic desalter  3170  effluent through the controllable flow control valve  3220  and the measurement station  3200 . 
         [0107]    The characteristics of the electrostatic desalter  3170  electric field through the electrical power supply  3150 . 
         [0108]    The intra-process measurements made under the control of the controller  3110  are made at random or, preferably, regular intervals while processing the crude supply  3010 . If the desalting process includes a customer specification for any intra-process measurement, each applicable measurement is compared to the specification to determine if the sample is within the customer required limits. If the specification is not met, immediate corrective action is taken as per the customers&#39; requirement. 
         [0109]    The intra-process data measurements made under the control of the controller  3110  are used under the present invention to compute statistical quantifiers for use by the controller  3110  to signal potential problems with and control the electrostatic desalter mechanism  3000 . The data measurements are classified as either binary or continuous. Examples of a binary data set include a pass or fail criteria. Continuous data includes measurements such as temperature, pressure, voltage, etc. 
         [0110]    In the embodiment of  FIG. 3 , the controller  3110  collects measurements of the desalted crude oil output product through the controllable flow control valve  3190  and the measurement station  3200 . The product measurements made under the control of the controller  3110  are made at random or, preferably, regular intervals. If the desalting process includes a customer specification for any measurement either individually or in combination with others, each applicable measurement is compared to the specification to determine if the sample is within the customer required limits. If the specification is not met, immediate corrective action is taken per the customers&#39; requirements. The product data measurements made under the control of the controller  3110  are also used under the present invention to compute statistical quantifiers for use by the controller  3110  to signal potential problems with and control the electrostatic desalter mechanism  3000 . The product data measurements are classified as either binary or continuous in the same manner as the intra-process data measurements. 
         [0111]    Based upon the type of crude oil being processed, the controller  3110  can adjust various factors of the desalting operation including but not limited to the following: 
         [0112]    The crude oil supply  3010  feed rate through the controllable pump  3070  and controllable flow control valve  3120   
         [0113]    The temperature of the crude oil supply  3010  through the controllable heater  3130 . 
         [0114]    Control of the electrostatic desalter  3310  aqueous level and emulsion layers through the liquid level sensor  3340 , the controllable flow control valve  3330 , and the controllable flow control valve  3360 . 
         [0115]    The electrostatic desalter  3310  electric field through the controllable power supply  3300 . 
         [0116]    The solution mixture of acid additive  3030  and wash water  3020  through the controllable fluid mixer  3040 . 
         [0117]    The flow rate of the solution mixture of acid additive  3030  and wash water  3020  through the controllable pump  3050  and controllable flow control valve  3060 . 
         [0118]    The first emulsion formation of crude oil supply  3010  and recycled effluent from electrostatic desalter  3170  through controllable flow control valve  3220 , controllable pump  3370 , and controllable fluid mixer  3380 . 
         [0119]    The second emulsion formation through controllable fluid mixer  3350 . 
         [0120]    The electrostatic desalter  3170  electric field through the controllable electrical power supply  3150 . 
         [0121]    Control of the electrostatic desalter  3170  water level and emulsion layers through the liquid level sensor  3210 , the controllable flow control valve  3220 , and the controllable flow control valve  3190 . 
         [0122]    As different crude oils are processed by the desalting mechanism  3000 , the characteristics necessary to efficiently desalt the crude oil will require some adjustment. Additionally, differences in electrostatic desalter  3310  and  3170  characteristics, wash water supply  3020  purity, etc between different desalting mechanisms  3000  require the storage of different control settings. The memory/data storage  3100  function of the desalting mechanism  3000  allows the controller to access and update, if required, the control settings required to efficiently process various types of crude oil supplies  3010 . Preferably, the control settings are determined based upon maximizing the economic benefit for the desalting the crude oil supply  3010 . 
         [0123]      FIG. 4  shows a process flow diagram of one embodiment of the control algorithm  4000  of the present invention for a typical crude oil desalting operation. The intra-process data is measured in step  4010 . The intra-process data may include but not be limited to the crude oil feed rate, the crude oil temperature, viscosity, and density, the characteristics of the electrostatic desalter effluent, the electrostatic desalter water level and emulsion layer, the characteristics of the electrostatic desalter electric field, the acid additive feed rate, the wash water feed rate, the characteristics of the wash water/acid additive/crude oil emulsion, et al. During step  4010 , the intra-process data may be measured at random or, preferably, regular intervals such that the measurements cover the frequency of potential sources of variation. As the intra-process data is collected, it is compared to any applicable specification or customer requirement in step  4020 . Decision step  4030  determines whether or not the applicable intra-process data met the customer requirements. If not, a human operator is notified in step  4040 . In step  4050 , the repair/reaction plan is implemented to repair the failed sensor, piece of equipment, etc. that caused the intra-process data measurement  4010  to fail. After the repair/reaction plan of step  4050  has been executed, the intra-process data is measured again in step  4010 . If the decision step  4030  results in all applicable intra-process data measurements meeting the customer requirements, then the intra-process data is separated into their applicable measurement subsets to form a subgroup and the number of data points in each subset subgroup is compared to the appropriate subgroup size, n, in step  4060 . As an example, if we assume that the crude oil flow rate, crude oil temperature, and electrostatic desalter water level are all measured in step  4010 , then there will be a crude oil flow rate data subset, a crude oil temperature data subset, and an electrostatic desalter water level data subset. Step  4060  compares the number of data samples in each subset subgroup to the required number of samples, n, needed for each subset subgroup. Each subset subgroup may have a different requirement for the number of samples, n. If, in step  4060 , the number of data points in a subset subgroup is less than the number, n, that is required, then the intra-process data is measured again in step  4010 . If, in step  4060 , the number of subset subgroup data points equals the required number of samples, n, then the subgroup statistics are computed in step  4070 . The statistics computed in step  4070  are made on continuous data and depend upon the number of samples in the subset subgroup. Preferably, the intra-process measurements collected in step  4010  are continuous. If the number of subset subgroup samples is one, there is no calculation to be made in step  4070 . If the number of subset subgroup samples is greater than one, there are three calculations that may be made. The subset subgroup sample mean is computed as 
         [0000]    
       
         
           
             
               M 
               A 
             
             = 
             
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   n 
                 
                  
                 
                     
                 
                  
                 
                   x 
                   i 
                 
               
               n 
             
           
         
       
     
         [0124]    where M A  is the sample mean of subset subgroup number A, x i  represents the individual subset subgroup measurement values, and n is the number of samples that make up the subset subgroup. The subset subgroup sample standard deviation is computed as 
         [0000]    
       
         
           
             
               σ 
               A 
             
             = 
             
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                       
                   
                    
                   
                     
                       ( 
                       
                         
                           x 
                           i 
                         
                         - 
                         
                           M 
                           A 
                         
                       
                       ) 
                     
                     2 
                   
                 
                 
                   n 
                   - 
                   1 
                 
               
             
           
         
       
     
         [0125]    where σ A  is the sample standard deviation of subset subgroup A, M A  is the sample mean of subset subgroup number A, x i  represents the individual subset subgroup measurement values, and n is the number of samples that make up the subset subgroup. The subset subgroup range is computed as 
         [0000]      R A =Max Value({ x   i })−Min Value({ x   i })
 
         [0126]    where R A  is the range of the subset subgroup A and {x i } represents the set of subset subgroup measurement values. 
         [0127]    Decision step  4080  compares the number of subset subgroups to the required number of subgroups, N, needed for each subset. If, in step  4080 , the number of subset subgroups is less than the number, N, that is required, then the intra-process data is measured again in step  4010  for a new subset subgroup. If, in step  4080 , the number of subset subgroups equals the required number of subgroups, N, then the subset statistics are computed in step  4090 . The computations made in step  4090  depend upon whether or not the intra-process measurement data collected in step  4010  is binary or continuous and the number of subset subgroup samples taken. 
         [0128]    If, the data measured in step  4010  is binary (i.e. pass/fail, above or below a threshold value, et al), the subset computations to be made in step  4090  can be based upon either the number of discrete events (i.e. number of values above a threshold per subset subgroup) or the fraction of discrete events (i.e. number of values above a threshold divided by the number of subset subgroup samples). For tracking and controlling the number of discrete events, the average threshold crossing count (ATC) for the subset is computed as 
         [0000]    
       
         
           
             ATC 
             = 
             
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   N 
                 
                  
                 
                     
                 
                  
                 
                   TC 
                   i 
                 
               
               N 
             
           
         
       
     
         [0129]    where ATC is the average threshold crossing count for the subset, TC i  is the threshold crossing count for each subset subgroup, and N is the number of subset subgroups. The average subgroup threshold crossing ratio is computed as 
         [0000]    
       
         
           
             ASTC 
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 N 
               
                
               
                   
               
                
               
                 
                   TC 
                   i 
                 
                 
                   n 
                   i 
                 
               
             
           
         
       
     
         [0130]    where ASTC is the average subgroup threshold crossing ratio, TC i  is the threshold crossing count for each subset subgroup, and n i  is the number of samples that make up the particular subset subgroup. The upper and lower control limits are computed as 
         [0000]      UL ATC =ATC+3×√{square root over (ATC×(1−ASTC))}
 
         [0000]      LL ATC =ATC−3×√{square root over (ATC×(1−ASTC))}
 
         [0131]    where UL ATC  is the average threshold crossing count upper limit, LL ATC  is the average threshold crossing count lower limit, ATC is the average threshold crossing count for the subset, and ASTC is the average subgroup threshold crossing ratio. 
         [0132]    For tracking and controlling the fraction of discrete events, the average subgroup threshold crossing ratio (ASTC) is computed as above along with the upper and lower control limits as 
         [0000]      UL ASTC =ASTC+3×√{square root over (ASTC×(1−ASTC))}
 
         [0000]      LL ASTC =ASTC−3×√{square root over (ASTC×(1−ASTC))}
 
         [0133]    where UL ASTC  is the average subgroup threshold crossing ratio upper limit, LL ASTC  is the average subgroup threshold crossing ratio lower limit, and ASTC is the average subgroup threshold crossing ratio. An advantage of using ASTC over ATC for binary data tracking and control is that ASTC allows for a variable sample number, n, for each subgroup while ATC assumes a constant number of samples. 
         [0134]    If, the intra-process data measured in step  4010  is continuous, the subset computations to be made in step  4090  may vary based upon the number of subset subgroup data samples, n. If the number of subset subgroup data samples is one (1), then the subset average value is computed as 
         [0000]    
       
         
           
             
               AM 
               1 
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 N 
               
                
               
                   
               
                
               
                 x 
                 i 
               
             
           
         
       
     
         [0135]    where AM 1  is the one sample subset average, x i  represents the individual subset subgroup measurement values, and N is the number of subset subgroups. The one sample subgroup range is computed as 
         [0000]      SR i   =|x   i   −x   i−1 | for  i= 2 to  N    
         [0136]    where SR i  is the range between the i and (i−1) data value and x i  represents the individual subset subgroup measurement values. The subset average range is computed as 
         [0000]    
       
         
           
             
               AR 
               1 
             
             = 
             
               
                 
                   ∑ 
                   
                     i 
                     = 
                     2 
                   
                   N 
                 
                  
                 
                     
                 
                  
                 
                   SR 
                   i 
                 
               
               
                 N 
                 - 
                 1 
               
             
           
         
       
     
         [0137]    where AR 1  is one sample subset average range, SR i  represents the subset subgroup ranges, and N is the number of subset subgroups. The upper and lower control limits for the one sample subset average (AM 1 ) and the one sample subset average range (AR 1 ) are computed as 
         [0000]      UL AM1 =AM 1   +E   2 ×AR 1  
 
         [0000]      LL AM1 =AM 1   −E   2 ×AR 1  
 
         [0000]      UL AR1   =D   4 ×AR 1  
 
         [0000]      LL AR1   =D 3×AR 1  
 
         [0138]    where AR 1  is the one sample subset average range, UL AM1  is the one sample subset average upper limit, LL AM1  is the one sample subset average lower limit, UL AR1  is the one sample subset average range upper limit, LL AR1  is the one sample subset average range lower limit, the constant D 3  is zero, and the values E 2  and D 4  computed as 2.660 and 3.267, respectively, based upon the theoretical distribution of range values between two samples. If the range, SR i , were computed using more individual sample values and/or the intra-process data measurements collected in step  4010  are not normally distributed, then the values of D 3 , E 2 , and D 4  may be different. 
         [0139]    If, the data measured in step  4010  is continuous and the number of subset subgroup data samples, n, is greater than one (1), then the subset average value is computed as 
         [0000]    
       
         
           
             AM 
             = 
             
               
                 
                   ∑ 
                   
                     A 
                     = 
                     1 
                   
                   N 
                 
                  
                 
                     
                 
                  
                 
                   M 
                   A 
                 
               
               N 
             
           
         
       
     
         [0140]    where AM is the subset average, M A  is the sample mean of subset subgroup number A computed in step  4070 , and N is the number of subset subgroups. The subset average range is computed as 
         [0000]    
       
         
           
             AR 
             = 
             
               
                 
                   ∑ 
                   
                     A 
                     = 
                     1 
                   
                   N 
                 
                  
                 
                     
                 
                  
                 
                   R 
                   A 
                 
               
               N 
             
           
         
       
     
         [0141]    where AR is the subset average range, R A  is the range of the subset subgroup number A computed in step  4070 , and N is the number of subset subgroups. The subset average standard deviation is computed as 
         [0000]    
       
         
           
             
               A 
                
               
                   
               
                
               σ 
             
             = 
             
               
                 
                   ∑ 
                   
                     A 
                     = 
                     1 
                   
                   N 
                 
                  
                 
                     
                 
                  
                 
                   σ 
                   A 
                 
               
               N 
             
           
         
       
     
         [0142]    where Aσ is the subset average standard deviation, σ A  is the sample standard deviation of subset subgroup number A computed in step  4070 , and N is the number of subset subgroups. The upper and lower control limits for the subset average (AM) and the subset average range (AR) when the subset average range is used for tracking and control are computed as 
         [0000]      UL AM =AM+ A   2 ×AR
 
         [0000]      LL AM =AM− A   2 ×AR
 
         [0000]      UL AR   =D   4 ×AR
 
         [0000]      LL AR   =D   3 ×AR
 
         [0143]    where AM is the subset average, AR is the subset average range, UL AM  is the subset average upper limit, LL AM  is the subset average lower limit, UL AR  is the subset average range upper limit, LL AR  is the subset average range lower limit, and A 2 , D 3 , and D 4  are constants that vary based upon the number of subset subgroup samples, n. Table I, below, provides values for A 2 , D 3 , and D 4  for subset subgroup sample sizes from 2 to 25 assuming that the intra-process data measurements collected in step  4010  are normally distributed. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE I 
               
             
             
               
                   
               
               
                 Control Limit Values for A 2 , D 3 , and D 4   
               
             
          
           
               
                   
                 Subset Subgroup 
                   
                   
                   
               
               
                   
                 Sample Size, n 
                 A 2   
                 D 3   
                 D 4   
               
               
                   
                   
               
             
          
           
               
                   
                 2 
                 1.880 
                 0.000 
                 3.267 
               
               
                   
                 3 
                 1.023 
                 0.000 
                 2.574 
               
               
                   
                 4 
                 0.729 
                 0.000 
                 2.282 
               
               
                   
                 5 
                 0.577 
                 0.000 
                 2.114 
               
               
                   
                 6 
                 0.483 
                 0.000 
                 2.004 
               
               
                   
                 7 
                 0.419 
                 0.076 
                 1.924 
               
               
                   
                 8 
                 0.373 
                 0.136 
                 1.864 
               
               
                   
                 9 
                 0.337 
                 0.184 
                 1.816 
               
               
                   
                 10 
                 0.308 
                 0.223 
                 1.777 
               
               
                   
                 11 
                 0.285 
                 0.256 
                 1.744 
               
               
                   
                 12 
                 0.266 
                 0.283 
                 1.717 
               
               
                   
                 13 
                 0.249 
                 0.307 
                 1.693 
               
               
                   
                 14 
                 0.235 
                 0.328 
                 1.672 
               
               
                   
                 15 
                 0.223 
                 0.347 
                 1.653 
               
               
                   
                 16 
                 0.212 
                 0.363 
                 1.637 
               
               
                   
                 17 
                 0.203 
                 0.378 
                 1.622 
               
               
                   
                 18 
                 0.194 
                 0.391 
                 1.608 
               
               
                   
                 19 
                 0.187 
                 0.403 
                 1.597 
               
               
                   
                 20 
                 0.180 
                 0.415 
                 1.585 
               
               
                   
                 21 
                 0.173 
                 0.425 
                 1.575 
               
               
                   
                 22 
                 0.167 
                 0.434 
                 1.566 
               
               
                   
                 23 
                 0.162 
                 0.443 
                 1.557 
               
               
                   
                 24 
                 0.157 
                 0.451 
                 1.548 
               
               
                   
                 25 
                 0.153 
                 0.459 
                 1.541 
               
               
                   
                   
               
             
          
         
       
     
         [0144]    The upper and lower control limits for the subset average (AM) and the subset average standard deviation (Aσ) when the subset average standard deviation is used for tracking and control are computed as 
         [0000]      UL AMS =AM+ A   3   ×A   σ   
         [0000]      LL AMS =AM− A   3   ×A   σ 
 
         [0000]      UL Aσ   =B   4   ×Aσ   
         [0000]      LL Aσ   =B   3   ×Aσ   
         [0145]    where AM is the subset average, Aσ is the subset average standard deviation, UL AMS  is the subset average upper limit, LL AMS  is the subset average lower limit, UL Aσ  is the subset average standard deviation upper limit, LL Aσ  is the subset average standard deviation lower limit, and A 3 , B 3 , and B 4  are constants that vary based upon the number of subset subgroup samples, n. Table II, below, provides values for A 3 , B 3 , and B 4  for subset subgroup sample sizes from 2 to 25 assuming that the intra-process data measurements collected in step  4010  are normally distributed. 
         [0000]    
       
         
               
             
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                 TABLE II 
               
             
             
               
                   
               
               
                 Control Limit Values for A 3 , B 3 , and B 4   
               
             
          
           
               
                   
                 Subset Subgroup 
                   
                   
                   
               
               
                   
                 Sample Size, n 
                 A 3   
                 B 3   
                 B 4   
               
               
                   
                   
               
             
          
           
               
                   
                 2 
                 2.659 
                 0.000 
                 3.267 
               
               
                   
                 3 
                 1.954 
                 0.000 
                 2.568 
               
               
                   
                 4 
                 1.628 
                 0.000 
                 2.266 
               
               
                   
                 5 
                 1.427 
                 0.000 
                 2.089 
               
               
                   
                 6 
                 1.287 
                 0.030 
                 1.970 
               
               
                   
                 7 
                 1.182 
                 0.118 
                 1.882 
               
               
                   
                 8 
                 1.099 
                 0.185 
                 1.815 
               
               
                   
                 9 
                 1.032 
                 0.239 
                 1.761 
               
               
                   
                 10 
                 0.975 
                 0.284 
                 1.716 
               
               
                   
                 11 
                 0.927 
                 0.321 
                 1.679 
               
               
                   
                 12 
                 0.886 
                 0.354 
                 1.646 
               
               
                   
                 13 
                 0.850 
                 0.382 
                 1.618 
               
               
                   
                 14 
                 0.817 
                 0.406 
                 1.594 
               
               
                   
                 15 
                 0.789 
                 0.428 
                 1.572 
               
               
                   
                 16 
                 0.763 
                 0.448 
                 1.552 
               
               
                   
                 17 
                 0.739 
                 0.466 
                 1.534 
               
               
                   
                 18 
                 0.718 
                 0.482 
                 1.518 
               
               
                   
                 19 
                 0.698 
                 0.497 
                 1.503 
               
               
                   
                 20 
                 0.680 
                 0.510 
                 1.490 
               
               
                   
                 21 
                 0.663 
                 0.523 
                 1.477 
               
               
                   
                 22 
                 0.647 
                 0.534 
                 1.466 
               
               
                   
                 23 
                 0.633 
                 0.545 
                 1.455 
               
               
                   
                 24 
                 0.619 
                 0.555 
                 1.445 
               
               
                   
                 25 
                 0.606 
                 0.565 
                 1.435 
               
               
                   
                   
               
             
          
         
       
     
         [0146]    After the subset statistics are computed in step  4090 , the subset subgroup statistics computed in step  4070  and the subset statistics computed in step  4090  are compared to a set of criteria in step  4100  that are designed to provide evidence of statistical instability in the intra-process data measurements collected in step  4010 . Preferably, the indicators of statistical instability provide warnings of adverse process conditions before the process produces intra-process measurements or product that fails the customer specifications. 
         [0147]    If the intra-process data measured in step  4010  for a given subset is binary, the calculations made in steps  4070  and  4090  for the subset subgroup threshold crossing counts (TC i ), subset average threshold crossing count (ATC), the subset average threshold crossing count upper limit (UL ATC ), and the subset average threshold crossing count lower limit (LL ATC ) and/or the subset subgroup fractional threshold crossing counts 
         [0000]    
       
         
           
             
               ( 
               
                 
                   TC 
                   i 
                 
                 
                   n 
                   i 
                 
               
               ) 
             
             , 
           
         
       
     
         [0000]    the average subgroup threshold crossing ratio (ASTC), the average subgroup threshold crossing ratio upper limit (UL ASTC ), and the average subgroup threshold crossing ratio lower limit (LL ASTC ) are compared in step  4100 . 
         [0148]    If the intra-process data measured in step  4010  for a given subset is continuous and the number of subset subgroup data samples, n, is one (1), then the intra-process data measurements, x i , made in step  4010  and the calculations made in step  4090  for the one sample subset average (AM 1 ), the one sample subset average upper limit (UL AM1 ), and the one sample subset average lower limit (LL AM1 ) are compared in step  4100 . Additionally, the calculations made in step  4090  for the one sample subgroup range (SR i ), the one sample subset average range (AR 1 ), the one sample subset average range upper limit (UL AR1 ), and the one sample subset average range lower limit (LL AR1 ) are compared in step  4100 . 
         [0149]    If the intra-process data measured in step  4010  for a given subset is continuous and the number of subset subgroup data samples, n, is greater than one, then either the calculations made using range in step  4070  and step  4090  or the calculations made using the standard deviation in step  4070  and step  4090  are used for comparison in step  4100 . The choice of which calculation set (range or standard deviation) can be made based upon the number of subset subgroup samples, n. Preferably, the standard deviation calculation set will be used if the number of subset subgroup samples is greater than or equal to 10. If the range calculations are used in step  4100 , then the calculations made in step  4070  and step  4090  for the subset subgroup sample means (M A ), the subset average (AM), the subset average upper limit (UL AM ), and the subset average lower limit (LL AM ) are compared. Additionally, the calculations made in step  4070  and step  4090  for the subset subgroup ranges (R A ), the subset average range (AR), the subset average range upper limit (UL AR ), and the subset average range lower limit (LL AR ) are compared in step  4100 . 
         [0150]    If the standard deviation calculations are used in step  4100 , then the calculations made in step  4070  and step  4090  for the subset subgroup sample means (M A ), the subset average (AM), the subset average upper limit (UL AMS ), and the subset average lower limit (LL AMS ) are compared. Additionally, the calculations made in step  4070  and step  4090  for the subset subgroup sample standard deviation (σ A ), the subset average standard deviation (Aσ), the subset average standard deviation upper limit (LL Aσ ), and the subset average standard deviation lower limit (UL Aσ ) are compared in step  4100 . 
         [0151]    For each calculation set (such as M A , AM, UL AM , and LL AM ), the subset subgroup computations (such as M A ) are compared in order (i.e. 1 thru N sequentially) in step  4100  with the corresponding subset average, upper limit, and lower limit. Intra-process data measurements collected in step  4010  that are binary have only one calculation set (either threshold crossing counts or threshold crossing count ratios) while continuous data have two sets (either mean and range or mean and standard deviation) for criteria comparison. The criteria to be applied in step  4100  to provide evidence of potential instability may include, but not be limited to, one or more of the following: 
         [0152]    One or more subset subgroup computations are greater than the corresponding subset average upper limit or less than the corresponding subset average lower limit. For instance, if any M A  value is greater than UL AM  or less than LL AM . 
         [0153]    N B  consecutive subset subgroup computations exist between the mean and the upper limit or the mean and the lower limit. For instance, if N B  was chosen to be 8, this criteria would be breached if there were 8 or more consecutive M A  values greater than AM and less than UL AM  or less than AM and greater than LL AM . N B  must be less than or equal to the number of subgroups, N. 
         [0154]    N T  consecutive subset subgroup computations continually increase or continually decrease. For instance, if N T  was chosen to be 5, this criteria would be breached if M N     T−5   &lt;M N     T−4   &lt;M N     T−3   &lt;M N     T−2   &lt;M N     T−1   &lt;M N     T    or M N     T−5   &gt;M N     T−4   &gt;M N     T−3   &gt;M N     T−2   &gt;M N     T−1   &gt;M N     T   . N T  must be less than or equal to the number of subgroups, N. 
         [0155]    N A  consecutive subset subgroup computations alternate in direction; increasing then decreasing. For instance, if N A  was chosen to be 10, this criteria would be breached if; 
         [0000]      M N     A−9   &gt;M N     A−8   &lt;M N     A−7   &gt;M N     A−6   &lt;M N     A−5   &gt;M N     A−4   &lt;M N     A−3   &gt;M N     A−2   &lt;M N     A−1   &gt;M N     A      
         [0000]      or, if 
         [0000]      M N     A−9   &lt;M N     A−8   &gt;M N     A−7   &lt;M N     A−6   &gt;M N     A−5   &lt;M N     A−4   &gt;M N     A−3   &lt;M N     A−2   &gt;M N     A−1   &lt;M N     A      
         [0156]    N A  must be less than or equal to the number of subgroups, N. 
         [0157]    X out of Y consecutive subset subgroup computations lie in a band between the upper limit and a statistically improbable point below the upper limit or a band between the lower limit and a statistically improbably point above the lower limit such that all of the X points lie in the same band. For instance, if X was chosen to be 2, Y was chosen to be 3, and the band limits were from the limit boundaries and halfway between the limit boundaries and the subset mean, this criteria would be breached if either of the following conditions were true; 
         [0000]    
       
         
           
             
               UL 
               AM 
             
             ≥ 
             
               M 
               A 
             
             ≥ 
             
               
                 [ 
                 
                   AM 
                   + 
                   
                     
                       ( 
                       
                         
                           UL 
                           AM 
                         
                         - 
                         AM 
                       
                       ) 
                     
                     2 
                   
                 
                 ] 
               
                
               for 
                
               
                   
               
                
               2 
                
               
                   
               
                
               out 
                
               
                   
               
                
               of 
                
               
                   
               
                
               3 
                
               
                   
               
                
               consecutive 
                
               
                   
               
                
               
                 M 
                 A 
               
                
               
                   
               
                
               values 
             
           
         
       
       
         
           
             
               LL 
               AM 
             
             ≤ 
             
               M 
               A 
             
             ≤ 
             
               
                 [ 
                 
                   AM 
                   + 
                   
                     
                       ( 
                       
                         
                           LL 
                           AM 
                         
                         - 
                         AM 
                       
                       ) 
                     
                     2 
                   
                 
                 ] 
               
                
               for 
                
               
                   
               
                
               2 
                
               
                   
               
                
               out 
                
               
                   
               
                
               of 
                
               
                   
               
                
               3 
                
               
                   
               
                
               consecutive 
                
               
                   
               
                
               
                 M 
                 A 
               
                
               
                   
               
                
               values 
             
           
         
       
     
         [0158]    Y must be less than or equal to the number of subgroups, N. 
         [0159]    Other criteria may also be applied to the subset subgroup and subset computations in step  4100  to provide statistical indicators of process instability. 
         [0160]    Decision step  4110  determines whether or not the subset subgroup and subset statistics indicate any potential problems with the intra-process functions of the crude oil desalter. If any of the subset comparisons conducted in step  4100  do not meet the chosen criteria, the subset(s) failing the comparisons indicate there may be a problem with one or more elements of the oil desalting process even though the product being produced and/or the intra-process data measurements are within spec. If any of the subset comparisons conducted in step  4100  do not meet the chosen criteria, decision step  4110  directs that a human operator is notified indicating a potential problem with one or more elements of the oil desalting process. In step  4120 , the human operator is provided with the data and information indicating the possible problem and a new set of subset subgroup and subset data is collected. If all subset comparisons conducted in step  4100  meet the chosen criteria, there is no indicated problem and a new set of subset subgroup and subset data is collected. 
         [0161]    The oil desalting process product data is measured in step  4170 . The product data may include but not be limited to the desalted crude oil salt content, basic sediments and water content of the desalted crude oil, the temperature of the desalted crude oil, the density of the desalted crude oil, the viscosity of the desalted crude oil, et al. During step  4170 , the product data may be measured at random or, preferably, regular intervals such that the measurements cover the frequency of potential sources of variation. As the product data is collected, it is compared to any application specification or customer requirement in step  4180 . Decision step  4190  determines whether or not the measured product data met the customer requirements. If not, a human operator is notified in step  4150 . In step  4160 , the failed product plan is implemented per the customers requirements based upon the failed measurements. The output of step  4160  is input to step  4140  along with the failed data points for further analysis and action. Step  4140  will compute adjustments to one or more controllable parameters of the oil desalter as well as implement automated reaction functions based upon the product failure which may include a quarantine of the suspect product produced since the last known good product, implementation of on-line repair procedures, statistical analysis to estimate the failure root cause, or, in case of catastrophic failure, automated process shutdown. 
         [0162]    If the decision step  4190  results in all applicable product data measurements meeting the customer requirements, then the product data is separated into their applicable product measurement subsets to form a product subgroup and the number of data points in each product subset subgroup is compared to the appropriate product subgroup size, n, in step  4200 . As an example, if we assume that the salt content in the desalted crude oil, desalted crude oil temperature, and basic sediments and water content of the desalted crude oil are all measured in step  4170 , then there will be a salt content in the desalted crude oil data subset, a desalted crude oil temperature data subset, and a basic sediments and water content data subset. Step  4200  compares the number of data samples in each product subset subgroup to the required number of samples, n, needed for each product subset subgroup. Each product subset subgroup may have a different requirement for the number of samples, n. If, in step  4200 , the number of data points in a product subset subgroup is less than the number, n, that is required, then the product data is measured again in step  4170 . If, in step  4200 , the number of product subset subgroup data points equals the required number of samples, n, then the subgroup statistics are computed in step  4210 . The statistics computed in step  4210  are made on continuous data and depend upon the number of samples in the product subset subgroup. Preferably, the product measurements collected in step  4170  are continuous. If the number of product subset subgroup samples is one, there is no calculation to be made in step  4210 . If the number of product subset subgroup samples is greater than one, there are three calculations that may be made. The product subset subgroup sample mean is computed as 
         [0000]    
       
         
           
             
               PM 
               A 
             
             = 
             
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   n 
                 
                  
                 
                     
                 
                  
                 
                   y 
                   i 
                 
               
               n 
             
           
         
       
     
         [0163]    where PM A  is the sample mean of product subset subgroup number A, y i  represents the individual product subset subgroup measurement values, and n is the number of samples that make up the product subset subgroup. The product subset subgroup sample standard deviation is computed as 
         [0000]    
       
         
           
             
               P 
                
               
                   
               
                
               
                 σ 
                 A 
               
             
             = 
             
               
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     n 
                   
                    
                   
                       
                   
                    
                   
                     
                       ( 
                       
                         
                           y 
                           i 
                         
                         - 
                         
                           PM 
                           A 
                         
                       
                       ) 
                     
                     2 
                   
                 
                 
                   n 
                   - 
                   1 
                 
               
             
           
         
       
     
         [0164]    where Pσ A  is the sample standard deviation of product subset subgroup A, PM A  is the sample mean of product subset subgroup number A, y i  represents the individual product subset subgroup measurement values, and n is the number of samples that make up the product subset subgroup. The product subset subgroup range is computed as 
         [0000]      PR A =Max Value({ y   i })−Min Value({ y   i })
 
         [0165]    where PR A  is the range of the product subset subgroup A and {y i } represents the set of product subset subgroup measurement values. 
         [0166]    Decision step  4220  compares the number of product subset subgroups to the required number of subgroups, N, needed for each product subset. If, in step  4220 , the number of product subset subgroups is less than the number, N, that is required, then the product data is measured again in step  4170  for a new subset subgroup. If, in step  4220 , the number of product subset, subgroups equals the required number of subgroups, N, then the product subset statistics are computed in step  4230 . The computations made in step  4230  depend upon whether or not the product measurement data collected in step  4170  is binary or continuous and the number of subset subgroup samples taken. 
         [0167]    If, the data measured in step  4170  is binary (i.e. pass/fail, above or below a threshold value, et al), the subset computations to be made in step  4230  can be based upon either the number of discrete events (i.e. number of values above a threshold per subset subgroup) or the fraction of discrete events (i.e. number of values above a threshold divided by the number of subset subgroup samples). For tracking and controlling the number of discrete events, the average threshold crossing count (PATC) for the product subset is computed as 
         [0000]    
       
         
           
             PATC 
             = 
             
               
                 
                   ∑ 
                   
                     i 
                     = 
                     1 
                   
                   n 
                 
                  
                 
                   PTC 
                   i 
                 
               
               N 
             
           
         
       
     
         [0168]    where PATC is the average threshold crossing count for the product subset, PTC i  is the threshold crossing count for each product subset subgroup, and N is the number of product subset subgroups. The average product subgroup threshold crossing ratio is computed as 
         [0000]    
       
         
           
             PASTC 
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 N 
               
                
               
                 
                   PTC 
                   i 
                 
                 
                   n 
                   i 
                 
               
             
           
         
       
     
         [0169]    where PASTC is the average product subgroup threshold crossing ratio, PTC i  is the threshold crossing count for each product subset subgroup, and n i  is the number of samples that make up the particular product subset subgroup. The upper and lower control limits are computed as 
         [0000]      UL PATC =PATC+3×√{square root over (PATC×(1−PATC))}
 
         [0000]      LL PATC =PATC−3×√{square root over (PATC×(1−PATC))}
 
         [0170]    where UL PATC  is the average product threshold crossing count upper limit, LL PATC  is the average product threshold crossing count lower limit, PATC is the average threshold crossing count for the product subset, and PASTC is the average product subgroup threshold crossing ratio. 
         [0171]    For tracking and controlling the fraction of discrete events, the average product subgroup threshold crossing ratio (PASTC) is computed as above along with the upper and lower control limits as 
         [0000]      UL PASTC =PASTC+3×√{square root over (PASTC×(1−PASTC))}
 
         [0000]      LL PASTC =PASTC−3×√{square root over (PASTC×(1−PASTC))}
 
         [0172]    where UL PASTC  is the average product subgroup threshold crossing ratio upper limit, LL PASTC  is the average product subgroup threshold crossing ratio lower limit, and PASTC is the average product subgroup threshold crossing ratio. An advantage of using PASTC over PATC for binary data tracking and control is that PASTC allows for a variable sample number, n, for each subgroup while PATC assumes a constant number of samples. 
         [0173]    If, the product data measured in step  4170  is continuous, the product subset computations to be made in step  4230  may vary based upon the number of product subset subgroup data samples, n. If the number of product subset subgroup data samples is one (1), then the product subset average value is computed as 
         [0000]    
       
         
           
             
               PAM 
               1 
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 N 
               
                
               
                 y 
                 i 
               
             
           
         
       
     
         [0174]    where PAM 1  is the one sample product subset average, y i  represents the individual product subset subgroup measurement values, and N is the number of product subset subgroups. The one sample product subgroup range is computed as 
         [0000]      PSR i   =|y   i   −y   i−1 | for  i= 2 to  N    
         [0175]    where PSR i  is the range between the i and (i−1) data value and y i  represents the individual product subset subgroup measurement values. The product subset average range is computed as 
         [0000]    
       
         
           
             
               PAR 
               1 
             
             = 
             
               
                 
                   ∑ 
                   
                     i 
                     = 
                     2 
                   
                   N 
                 
                  
                 
                   PSR 
                   i 
                 
               
               
                 N 
                 - 
                 1 
               
             
           
         
       
     
         [0176]    where PAR 1  is one sample product subset average range, PSR i  represents the product subset subgroup ranges, and N is the number of product subset subgroups. The upper and lower control limits for the one sample product subset average (PAM 1 ) and the one sample product subset average range (PAR 1 ) are computed as 
         [0000]      UL PAM1 =PAM 1   +E   2 ×PAR 1  
 
         [0000]      LL PAM1 =PAM 1   −E   2 ×PAR 1  
 
         [0000]      UL PAR1   =D   4 ×PAR 1  
 
         [0000]      LL PAR1   =D 3×PAR 1  
 
         [0177]    where PAR 1  is the one sample product subset average range, UL PAM1  is the one sample product subset average upper limit, LL PAM1  is the one sample product subset average lower limit, UL PAR1  is the one sample product subset average range upper limit, LL PAR1  is the one sample product subset average range lower limit, the constant D 3  is zero, and the values E 2  and D 4  are computed as 2.660 and 3.267, respectively, based upon the theoretical distribution of range values between two samples. If the range, PSR i , were computed using more individual sample values and/or the product data measurements collected in step  4170  are not normally distributed, then the values of D 3 , E 2 , and D 4  may be different. 
         [0178]    If, the product data measured in step  4170  is continuous and the number of subset subgroup data samples, n, is greater than one (1), then the product subset average value is computed as 
         [0000]    
       
         
           
             PAM 
             = 
             
               
                 
                   ∑ 
                   
                     A 
                     = 
                     1 
                   
                   N 
                 
                  
                 
                   PM 
                   A 
                 
               
               N 
             
           
         
       
     
         [0179]    where PAM is the product subset average, PM A  is the sample mean of product subset subgroup number A computed in step  4210 , and N is the number of product subset subgroups. The product subset average range is computed as 
         [0000]    
       
         
           
             PAR 
             = 
             
               
                 
                   ∑ 
                   
                     A 
                     = 
                     1 
                   
                   N 
                 
                  
                 
                   PR 
                   A 
                 
               
               N 
             
           
         
       
     
         [0180]    where PAR is the product subset average range, PR A  is the range of the product subset subgroup number A computed in step  4210 , and N is the number of product subset subgroups. The product subset average standard deviation is computed as 
         [0000]    
       
         
           
             
               PA 
                
               
                   
               
                
               σ 
             
             = 
             
               
                 
                   ∑ 
                   
                     A 
                     = 
                     1 
                   
                   N 
                 
                  
                 
                   P 
                    
                   
                       
                   
                    
                   
                     σ 
                     A 
                   
                 
               
               N 
             
           
         
       
     
         [0181]    where PAσ is the product subset average standard deviation, Pσ A  is the sample standard deviation of product subset subgroup number A computed in step  4210 , and N is the number of product subset subgroups. The upper and lower control limits for the product subset average (PAM) and the product subset average range (PAR) when the product subset average range is used for tracking and control are computed as 
         [0000]      UL PAM =PAM+ A   2 ×PAR
 
         [0000]      LL PAM =PAM− A   2 ×PAR
 
         [0000]      UL PAR   =D   4 ×PAR
 
         [0000]      LL PAR   =D   3 ×PAR
 
         [0182]    where PAM is the product subset average, PAR is the product subset average range, UL PAM  is the product subset average upper limit, LL PAM  is the product subset average lower limit, UL PAR  is the product subset average range upper limit, LL PAR  is the product subset average range lower limit, and A 2 , D 3 , and D 4  are constants that vary based upon the number of product subset subgroup samples, n. Table I provides values for A 2 , D 3 , and D 4  for product subset subgroup sample sizes from 2 to 25 assuming that the product data measurements collected in step  4170  are normally distributed. 
         [0183]    The upper and lower control limits for the product subset average (PAM) and the product subset average standard deviation (PAσ) when the product subset average standard deviation is used for tracking and control are computed as 
         [0000]      UL PAMS =PAM+ A   3 ×PAσ
 
         [0000]      LL PAMS =PAM− A   3 ×PAσ
 
         [0000]      UL PAσ   =B   4 ×PAσ
 
         [0000]      LL PAσ   =B   3 ×PAσ
 
         [0184]    where PAM is the product subset average, PAσ is the product subset average standard deviation, UL PAMS  is the product subset average upper limit, LL PAMS  is the product subset average lower limit, UL PAσ  is the product subset average standard deviation upper limit, LL PAσ  is the product subset average standard deviation lower limit, and A 3 , B 3 , and B 4  are constants that vary based upon the number of subset subgroup samples, n. Table II provides values for A 3 , B 3 , and B 4  for product subset subgroup sample sizes from 2 to 25 assuming that the product data measurements collected in step  4170  are normally distributed. 
         [0185]    After the product subset statistics are computed in step  4230 , the product subset subgroup statistics computed in step  4210  and the product subset statistics computed in step  4230  are compared to a set of criteria in step  4240  that are designed to provide evidence of statistical instability in the product data measurements collected in step  4170 . Preferably, the indicators of statistical instability provide warnings of adverse process conditions before the process produces product that fails the customer specifications. 
         [0186]    If the product data measured in step  4170  for a given product subset is binary, the calculations made in steps  4210  and  4230  for the product subset subgroup threshold crossing counts (PTC i ), product subset average threshold crossing count (PATC), the product subset average threshold crossing count upper limit (UL PATC ), and the product subset average threshold crossing count lower limit (LL PATC ) and/or the product subset subgroup fractional threshold crossing counts 
         [0000]    
       
         
           
             
               ( 
               
                 
                   PTC 
                   i 
                 
                 
                   n 
                   i 
                 
               
               ) 
             
             , 
           
         
       
     
         [0000]    the average product subgroup threshold crossing ratio (PASTC), the average product subgroup threshold crossing ratio upper limit (UL PASTC ), and the average subgroup threshold crossing ratio lower limit (LL PASTC ) are compared in step  4240 . 
         [0187]    If the product data measured in step  4170  for a given product subset is continuous and the number of product subset subgroup data samples, n, is one (1), then the product data measurements, y i , made in step  4170  and the calculations made in step  4230  for the one sample product subset average (PAM 1 ), the one sample product subset average upper limit (UL PAM1 ), and the one sample product subset average lower limit (LL PAM1 ) are compared in step  4240 . Additionally, the calculations made in step  4230  for the one sample product subgroup range (PSR i ), the one sample product subset average range (PAR 1 ), the one sample product subset average range upper limit (UL PAR1 ), and the one sample product subset average range lower limit (LL PAR1 ) are compared in step  4240 . 
         [0188]    If the product data measured in step  4170  for a given product subset is continuous and the number of product subset subgroup data samples, n, is greater than one, then either the calculations made using range in step  4210  and step  4230  or the calculations made using the standard deviation in step  4210  and step  4230  are used for comparison in step  4240 . The choice of which calculation set (range or standard deviation) can be made based upon the number of product subset subgroup samples, n. Preferably, the standard deviation calculation set will be used if the number of product subset subgroup samples is greater than or equal to 10. If the range calculations are used in step  4240 , then the calculations made in step  4210  and step  4230  for the product subset subgroup sample means (PM A ), the product subset average (PAM), the product subset average upper limit (UL PAM ), and the product subset average lower limit (LL PAM ) are compared. Additionally, the calculations made in step  4210  and step  4230  for the product subset subgroup ranges (PR A ), the product subset average range (PAR), the product subset average range upper limit (UL PAR ), and the product subset average range lower limit (LL PAR ) are compared in step  4240 . 
         [0189]    If the standard deviation calculations are used in step  4240 , then the calculations made in step  4210  and step  4230  for the for the product subset subgroup sample means (PM A ), the product subset average (PAM), the product subset average upper limit (UL PAMS ), and the product subset average lower limit (LL PAMS ) are compared. Additionally, the calculations made in step  4210  and step  4230  for the product subset subgroup sample standard deviation (Pσ A ), the product subset average standard deviation (PAσ), the product subset average standard deviation upper limit (LL PAσ ), and the product subset average standard deviation lower limit (UL PAσ ) are compared in step  4240 . 
         [0190]    For each calculation set (such as PM A , PAM, UL PAM , and LL PAM ), the product subset subgroup computations (such as PM A ) are compared in order (i.e. 1 thru N sequentially) in step  4240  with the corresponding product subset average, upper limit, and lower limit. Product data measurements collected in step  4170  that are binary have only one calculation set (either threshold crossing counts or threshold crossing count ratios) while continuous data have two sets (either mean and range or mean and standard deviation) for criteria comparison. The criteria to be applied in step  4240  to provide evidence of potential instability may include, but not be limited to, the same or similar criteria identified for step  4100 . 
         [0191]    Decision step  4250  determines whether or not the product subset subgroup and product subset statistics indicate any potential problems with the product output of the crude oil desalter. If any of the product subset comparisons conducted in step  4240  do not meet the chosen criteria, the product subset(s) failing the comparisons indicate there may be a problem with one or more elements of the oil desalting process even though the product being produced and/or the intra-process data measurements are still within specification. If any of the product subset comparisons conducted in step  4240  do not meet the chosen criteria, decision step  4250  will direct the control flow to step  4140  to compute the necessary variable adjustments necessary to bring the product measurements back within statistical stability. 
         [0192]    If the all of the product subset comparisons conducted in step  4240 , meet the criteria chosen, decision step  4250  will direct the control flow step  4260  to compute the process capability. Process capability is computed in step  4260  on product subsets that are continuous, the product subset subgroups contain more than one (1) sample, and the underlying product subset subgroup measurements collected in step  4170  for each product subset have a customer specification (upper limit, lower limit, or both). For each product subset meeting these criteria that also used the range calculation set in step  4230 , the product subset process standard deviation estimate is computed as 
         [0000]    
       
         
           
             
               P 
                
               
                   
               
                
               
                 σ 
                 pr 
               
             
             = 
             
               PAR 
               
                 d 
                 2 
               
             
           
         
       
     
         [0193]    where PAR is the product subset average range computed in step  4230 , Pσ pr  is the product subset process standard deviation estimate based upon product subset range, and d 2  is a constant that varies based upon the number of product subset subgroup samples, n. If the product subset used the standard deviation calculation set in step  4230 , the product subset process standard deviation estimate is computed as 
         [0000]    
       
         
           
             
               P 
                
               
                   
               
                
               
                 σ 
                 
                   p 
                    
                   
                       
                   
                    
                   σ 
                 
               
             
             = 
             
               
                 PA 
                  
                 
                     
                 
                  
                 σ 
               
               
                 c 
                 4 
               
             
           
         
       
     
         [0194]    where PAσ is the product subset average standard deviation computed in step  4230 , Pσ pσ  is the product subset process standard deviation estimate based upon product subset standard deviation, and c 4  is a constant that varies based upon the number of product subset subgroup samples, n. Table III, below, provides values for d 2  and c 4  for product subset subgroup sample sizes from 2 to 25 assuming that the product data measurements collected in step  4170  are normally distributed. 
         [0000]    
       
         
               
             
               
               
               
             
               
               
               
             
           
               
                 TABLE III 
               
             
             
               
                   
               
               
                 Control Limit Values for d 2  and c 4   
               
             
          
           
               
                 Product subset subgroup 
                   
                   
               
               
                 sample size, n 
                 d 2   
                 c 4   
               
               
                   
               
             
          
           
               
                 2 
                 1.128 
                 0.7979 
               
               
                 3 
                 1.693 
                 0.8862 
               
               
                 4 
                 2.059 
                 0.9213 
               
               
                 5 
                 2.326 
                 0.9400 
               
               
                 6 
                 2.534 
                 0.9515 
               
               
                 7 
                 2.704 
                 0.9594 
               
               
                 8 
                 2.847 
                 0.9650 
               
               
                 9 
                 2.970 
                 0.9693 
               
               
                 10 
                 3.078 
                 0.9727 
               
               
                 11 
                 3.173 
                 0.9754 
               
               
                 12 
                 3.258 
                 0.9776 
               
               
                 13 
                 3.336 
                 0.9794 
               
               
                 14 
                 3.407 
                 0.9810 
               
               
                 15 
                 3.472 
                 0.9823 
               
               
                 16 
                 3.532 
                 0.9835 
               
               
                 17 
                 3.588 
                 0.9845 
               
               
                 18 
                 3.640 
                 0.9854 
               
               
                 19 
                 3.689 
                 0.9862 
               
               
                 20 
                 3.735 
                 0.9869 
               
               
                 21 
                 3.778 
                 0.9876 
               
               
                 22 
                 3.819 
                 0.9882 
               
               
                 23 
                 3.858 
                 0.9887 
               
               
                 24 
                 3.895 
                 0.9892 
               
               
                 25 
                 3.931 
                 0.9896 
               
               
                   
               
             
          
         
       
     
         [0195]    After the product subset process standard deviation estimates are computed in step  4260 , the process capacity is estimated in step  4260  based upon computing a Z value for the product subset relative to the specification limits. For a unilateral or one-sided specification limit, the product subset Z value for normally distributed product data is computed as 
         [0000]    
       
         
           
             
               Z 
               SSSL 
             
             = 
             
                
               
                 
                   SL 
                   - 
                   PAM 
                 
                 
                   P 
                    
                   
                       
                   
                    
                   σ 
                 
               
                
             
           
         
       
     
         [0196]    where Z SSSL  is the product subset Z value for a single-sided specification limit, SL represents the product subset specification limit, PAM is the product subset average computed in step  4230 , and Pσ is the product subset process standard deviation estimate based either on range or standard deviation computed in step  4260 . For a bilateral or two-sided specification limit, the product subset Z values for normally distributed product data are computed as 
         [0000]    
       
         
           
             
               Z 
               USL 
             
             = 
             
                
               
                 
                   USL 
                   - 
                   PAM 
                 
                 
                   P 
                    
                   
                       
                   
                    
                   σ 
                 
               
                
             
           
         
       
       
         
           
             
               Z 
               LSL 
             
             = 
             
                
               
                 
                   LSL 
                   - 
                   PAM 
                 
                 
                   P 
                    
                   
                       
                   
                    
                   σ 
                 
               
                
             
           
         
       
     
         [0197]    where Z USL  is the product subset Z value for the upper specification limit, Z LSL  is the product subset Z value for the lower specification limit, USL represents the product subset upper specification limit, LSL presents the product subset lower specification limit, PAM is the product subset average computed in step  4230 , and Pσ is the product subset process standard deviation estimate based either on range or standard deviation computed in step  4260 . 
         [0198]    After the Z values are computed for each product subset in step  4260 , an estimate for probability of producing in specification product can be computed through an iterative calculation using the Z values or saving a Z value table in memory. The use of Z value statistics for estimating probability is well known in the art. 
         [0199]    Step  4140  computes the adjustments to be made to the controllable parameters of the oil desalter. The adjustments include, but are not limited to, the crude oil feed rate, the crude oil temperature, the wash water feed rate, the wash water and additive solution mix, the temperature of the oil/wash water emulsion, the electrostatic desalter water level, the addition rate of acid additive, and the electric field generated within the electrostatic desalter either individually or in combination. In one embodiment of the present invention, the computations in step  4140  consider the intra-process subset subgroup statistics computed in step  4070 , the intra-process subset statistics computed in step  4090 , the comparison results of the intra-process subset subgroup and subset statistics in step  4100 , the product subset subgroup statistics computed in step  4210 , the product subset statistics computed in step  4230 , and the comparison results of the product subset subgroup and subset statistics in step  4240 . If the product subset and product subset subgroup comparison of step  4240  was determined to be within the control criteria in decision step  4250 , the process capability computations of step  4260  are also considered in step  4140 . In the embodiment of the present invention, the variables may be adjusted to maintain process control (for instance, an adjustment to a parameter to bring an intra-process measurement within statistical stability) or to maximize the oil desalter&#39;s economic benefit. As an example, if the process capability for the oil desalter was estimated to produce within specification product 99.9999% of the time given a set of parameter settings, it may make economic sense to increase the crude oil flow rate such that the oil desalter process capability is reduced to producing within specification product 99.99% while simultaneously increasing the product output for a given period of time. 
         [0200]    Step  4130  receives the adjustments computed in step  4140  to the controllable parameters of the oil desalter and implements these adjustments. Following the implementation of the adjustments in step  4130 , a new set of product subset subgroup and product subset data is measured in step  4170 . 
         [0201]      FIG. 5  shows a process diagram  5000  of one embodiment of the method of the present invention for a typical crude oil electrostatic desalting operation implementing automated control. The desalting process is set to an initial state in step  5100  based upon the characteristics of the configuration of the desalting operation and the characteristics of the crude oil to be desalted. An acid additive is mixed with wash water or directly with the crude oil in step  5200 . An emulsion of crude oil, wash water, and acid additive is created in step  5300 . The emulsion is resolved into a hydrocarbon or oil phase and an aqueous or water phase in step  5400 . The intra-process characteristics of the desalting operation that may or may not be dependent upon desalting configuration are measured along with characteristics of the desalted crude oil in step  5500 . Tracking and control statistics are computed in step  5600  on the intra-process characteristics and desalted crude oil characteristics measured in step  5500 . An optional estimate of the probability of producing desalted crude oil that is within customer specification is computed in step  5700 . Characteristics of the desalting operation to include but are not limited to one or more of the following may be varied in step  5800  to maximize the economic benefit of the desalting operation based upon the measurements in step  5500 , the statistics computed in step  5600 , and the optional probability estimate in step  5700 : the crude oil feed rate, the crude oil temperature, the electric field characteristics of the dehydration/desalter mechanisms, the wash water flow rate, the crude oil emulsion formation, control of the dehydration/desalter water levels and emulsion layers, the acid additive type and addition rate, and the effluent recycle (as appropriate). 
         [0202]    The above embodiments are merely preferred and the scope of the invention defined by the claims below.