Process control of an industrial plant

Abstract

A system ( 10 ) for controlling an industrial plant ( 12 ) comprises automatic control equipment ( 14 ) comprising a plurality of measurement sensors ( 16 ) for sensing predetermined variables associated with components of the industrial plant ( 12 ). The sensors ( 16 ) generate measured data relating to operation of the components of the industrial plant ( 12 ). A database ( 20 ) contains operational data, including observational data, regarding the industrial plant ( 12 ). A processor ( 18 ) is in communication with the automatic control equipment ( 14 ) and the database ( 20 ) for receiving the measured data from the sensors ( 16 ) of the automatic control equipment ( 14 ) and the operational data from the database ( 20 ). The processor ( 18 ) manipulates the measured and operational data to provide an evolving description of a process condition of each component over time, along with output information relating to operational control of the industrial plant ( 12 ) and for updating the database ( 20 ).

1 . A system for controlling an industrial plant, the system comprising:
 automatic control equipment comprising a plurality of measurement sensors for sensing predetermined variables associated with components of the industrial plant, the sensors generating measured data relating to operation of the components of the industrial plant;   a database containing operational data, including observational data, regarding the industrial plant; and   a processor in communication with the automatic control equipment and the database for receiving the measured data from the sensors of the automatic control equipment and the operational data from the database, the processor manipulating the measured and the operational data to provide an evolving description of a process condition of each component over time, along with output information relating to operational control of the industrial plant and for updating the database.   
     
     
         2 . The system of  claim 1  in which the automatic control equipment constitutes a first system level, the processor and database constitute a second system level with the system including a third system level, being a management level. 
     
     
         3 . The system of  claim 2  in which the management level uses the information output from the processor for effecting control of the industrial plant. 
     
     
         4 . The system of  claim 2  in which the levels are configured to achieve an improvement in a number of operating variables of the plant. 
     
     
         5 . The system of  claim 4  which is operable within a range for each variable as determined by variability within the process and which acts to reduce variation within each variable and other key process variables through identifying abnormal or systemic, damaging patterns of variation which can be related to a single dominant cause. 
     
     
         6 . The system of  claim 1  which includes a classifier module in communication with the processor for classifying variations of operating variables of the plant into one of a predetermined number of classes of variations. 
     
     
         7 . The system of  claim 6  in which the classifier module classifies variations in a process variable into one of three classes being: common cause or natural variation, special cause variation or structural variation. 
     
     
         8 . The system of  claim 1  which is operable to take into account information about the total process condition including process variable trajectory over a preceding period of time. 
     
     
         9 . The system of  claim 1  in which, when the plant is an aluminium smelting plant, the automatic control equipment includes bath superheat sensors, bath resistivity sensors, sensors for monitoring and noting electrical current variation and characteristic frequencies, cell off-gas temperature and flow rate sensors, and other control inputs. 
     
     
         10 . The system of  claim 9  in which the observational data relates to the operational state of the individual cells, the operational state being formally monitored and integrated into individual cell process conditions and including:
 anode condition including red carbon, airburnt anodes, red stubs, spikes, cracked anodes;   bath condition including carbon dust, gap between bath and crust, bubble generation and location of evolution of the bubbles in the cell, bath level;   metal level and the projected metal tap history;   cover condition (remaining thickness and height on the anode connectors/stubs), crust damage, fume escape from superstructure;   alumina and bath spillage on electrical conductors (rods, beams, bus bars);   control action history over previous weeks including aluminium fluoride addition, alumina addition, extra voltage, excessive, unplanned anode beam movements, metals and bath transfers, etc;   cathode condition including cathode voltage drop (CVD) history, collector bar current density, instability history, anode changing observations, anode effect frequency, etc;   shell condition, including red plates, shell deformation and excessive heat rising to the catwalk from a certain shell location;   hooding condition—gaps, damage, fitment, door and quarter shield sealing;   bus bar and flexible damage, collector bars cut;   lack of duct gas suction as observed through fume escape into the pot room;   feeder operation, feeder chutes, feeder holes blocked, alumina not entering feeder holes;   side wall ledge condition, silicon carbide mass loss, history of silicon level in metal;   excessive liquid bath output from cells or from a pot room, indicating a change in heat balance causing melting of ledge, crust or dissolution of bottom sludge;   iron level in metal which is an indicator of bath level and anode condition;   trace elements in the metal which is indicative of trends in current efficiency over time;   flame colour, including blue flames, lazy yellow flames (sludge), bright yellow (sodium) shooting flames which may indicate some anode to metal direct contact in a cell; and   general housekeeping around each cell.   
     
     
         11 . The system of  claim 10  in which each operational state is monitored automatically by the sensors, using regular cell observations or both by the sensors and by observation, information obtained from the monitoring process being integrated with state variable measurements to build a description of the process condition of each individual cell and its evolution over time. 
     
     
         12 . The system of  claim 11  in which the processor and the database are operable to check the process condition for each cell individually with the database being updated periodically. 
     
     
         13 . The system of  claim 9  in which the processor includes a causal framework for relating identified problems and cell process conditions to specific causes. 
     
     
         14 . The system of  claim 13  in which the causal framework forms part of a learning algorithm of the processor which is improved and periodically updated over time using data from the database. 
     
     
         15 . The system of  claim 14  in which the management level employs causal trees containing the learning algorithm to provide a growing framework of decision support and, in the case of a smelting operation, cell diagnosis over time. 
     
     
         16 . The system of  claim 13  in which the processor further uses a complexity measure to assess predictability of the process outcomes and the overall operation of the plant. 
     
     
         17 . A method of controlling an industrial plant, the method comprising:
 monitoring operation of the industrial plant by a plurality of sensors forming part of automatic control equipment;   transferring measured data from the sensors and observational data relating to operation of the industrial plant to a processor;   accessing a database containing operational data including data from the sensors and the observational data relating to operation of the industrial plant, as periodically updated by the processor; and   generating evolving process condition descriptions of each monitored component of the industrial plant and output information relating to operation of the industrial plant.   
     
     
         18 . The method of  claim 17  which includes forming three system levels, the automatic control equipment constituting a first system level, the processor and database constituting a second system level and a third system level being a management level. 
     
     
         19 . The method of  claim 18  which includes using the information output from the processor in the management level for effecting control of the industrial plant. 
     
     
         20 . The method of  claim 18  which includes configuring the levels to achieve an improvement in a number of operating variables of the plant rather than acting only to maintain the operating variables at arbitrary target levels. 
     
     
         21 . The method of  claim 20  in which the plant is an aluminium smelting plant and in which the method includes configuring the levels to achieve improvements in a number of operational aspects of the plant. 
     
     
         22 . The method of  claim 21  in which the operational aspects may include feed control to achieve desired alumina dissolution; feed control to reduce, and, if possible, eliminate, periods of sludge accumulation; compositional control to maintain the mass of aluminium fluoride at an approximately constant level in a bath in each cell and reduce compositional and temperature variation over time; energy balance control to maintain both sufficient superheat and actual bath temperature for alumina dissolution; energy balance control to inhibit periods of excessive superheat over time; statistical and causal analysis to continuously reduce variations across pot lines; and enterprise level management to assess actual pot line capabilities cell by cell to organise and prioritise improvement actions to improve capability over time and to optimise the production of metals with specifications matching sales orders. 
     
     
         23 . The method of  claim 21  which includes operating the plant within a range for each variable as determined by variability within the process and which acts to reduce variation within each variable and other key process variables through identifying abnormal or systemic, damaging patterns of variation which can be related to a single dominant cause. 
     
     
         24 . The method of  claim 23  which includes correcting or minimising identified causes as appropriate, reducing the range of each process variable and improving process capability over time. 
     
     
         25 . The method of  claim 21  which includes classifying variations of operating variables of the plant into one of a predetermined number of classes of variations. 
     
     
         26 . The method of  claim 25  which includes classifying variations in a process variable into one of three classes being: common cause or natural variation, special cause variation or structural variation. 
     
     
         27 . The method of  claim 21  which includes taking into account information about the total process condition including process variable trajectory over a preceding period of time. 
     
     
         28 . The method of  claim 21  in which the observational data relates to the operational state of the individual cells, the method including formally monitoring and integrating the operational state into individual cell process conditions and the operational states including:
 anode condition including red carbon, airburnt anodes, red stubs, spikes, cracked anodes;   bath condition including carbon dust, gap between bath and crust, bubble generation and location of evolution of the bubbles in the cell, bath level;   metal level and the projected metal tap history;   cover condition (remaining thickness and height on the anode connectors/stubs), crust damage, fume escape from superstructure;   alumina and bath spillage on electrical conductors (rods, beams, bus bars);   control action history over previous weeks including aluminium fluoride addition, alumina addition, extra voltage, excessive, unplanned anode beam movements, metals and bath transfers, etc;   cathode condition including cathode voltage drop (CVD) history, collector bar current density, instability history, anode changing observations, anode effect frequency, etc;   shell condition, including red plates, shell deformation and excessive heat rising to the catwalk from a certain shell location;   hooding condition—gaps, damage, fitment, door and quarter shield sealing;   bus bar and flexible damage, collector bars cut;   lack of duct gas suction as observed through fume escape into the pot room;   feeder operation, feeder chutes, feeder holes blocked, alumina not entering feeder holes;   side wall ledge condition, silicon carbide mass loss, history of silicon level in metal;   excessive liquid bath output from cells or from a pot room, indicating a change in heat balance causing melting of ledge, crust or dissolution of bottom sludge;   iron level in metal which is an indicator of bath level and anode condition;   trace elements in the metal which is indicative of trends in current efficiency over time;   flame colour, including blue flames, lazy yellow flames (sludge), bright yellow (sodium) shooting flames which may indicate some anode to metal direct contact in a cell; and   general housekeeping around each cell.   
     
     
         29 . The method of  claim 28  which includes monitoring each operational state automatically by the sensors, using regular cell observations or both by the sensors and by observation, information obtained from the monitoring process being integrated with state variable measurements to build a description of the cell process condition of each individual cell and its evolution over time. 
     
     
         30 . The method of  claim 29  which includes operating the processor and the database to check the process condition for each cell individually and updating the database periodically. 
     
     
         31 . The method of  claim 21  which includes using a causal framework to relate identified problems and cell process conditions to specific causes. 
     
     
         32 . The method of  claim 31  which includes integrating the causal framework into a learning algorithm of the processor which is improved and updated over time using data from the database. 
     
     
         33 . The method of  claim 32  which includes employing causal trees containing the learning algorithm to provide a growing framework of decision support and, in the case of a smelting operation, cell diagnosis over time. 
     
     
         34 . The method of  claim 21  which includes using a complexity measure to assess predictability of the process outcomes and the overall operation of the plant. 
     
     
         35 . A system for controlling an industrial plant, the system comprising:
 automatic control equipment comprising a plurality of measurement sensors for sensing predetermined variables associated with components of the industrial plant;   a database containing operational data, including observational data, regarding the industrial plant; and   a processor in communication with the automatic control equipment and the database for receiving data from the sensors of the automatic control equipment and from the database, the processor using causal tree analysis comprising at least one continually updated learning algorithm to provide a framework of decision support and plant component diagnosis over time.   
     
     
         36 . A method of controlling an industrial plant, the method comprising:
 monitoring operation of the industrial plant by a plurality of sensors forming part of automatic control equipment;   transferring data from the sensors and observational data relating to operation of the industrial plant to a processor;   accessing a database containing operational data, including the data from the sensors and the observational data relating to operation of the industrial plant, as periodically updated by the processor; and   using causal tree analysis comprising at least one continually updated learning algorithm to provide a framework of decision support and plant component diagnosis over time.   
     
     
         37 . Automatic control equipment for a system for controlling an industrial plant, the system comprising:
 a plurality of measurement sensors for sensing predetermined variables associated with components of the industrial plant;   a signal processing module responsive to the sensors and control input data; and   a classifier module in communication with the signal processing module for classifying variations of operating variables of the plant, as detected by the sensors, into one of a predetermined number of classes of variations.   
     
     
         38 . A method of operating an industrial plant, the method comprising:
 monitoring operation of the industrial plant by a plurality of sensors;   processing data from the sensors and other control inputs; and   classifying variations of operating variables of the plant, as detected by the sensors, into one of a predetermined number of classes of variations.   
     
     
         39 . A method of operating an industrial plant, the method comprising
 monitoring operation of the industrial plant by a plurality of sensors forming part of automatic control equipment;   transferring measured data from the sensors and observational data relating to operation of the industrial plant to a processor;   accessing a database containing operational data, including the data from the sensors and the observational data relating to operation of the industrial plant, as periodically updated by the processor, to provide mechanisms to assist in identification and removal of causes of variations in the measured data; and   combining automatic control as carried out by the automatic control equipment with said mechanisms to provide continuous improvement in the operation of the plant.   
     
     
         40 . A system for controlling an industrial plant, the system comprising:
 automatic control equipment comprising a plurality of measurement sensors for sensing predetermined variables associated with components of the industrial plant;   a database containing operational data, including observational data, regarding the industrial plant; and   a processor in communication with the automatic control equipment and the database for receiving data from the sensors of the automatic control equipment and from the database, the processor using a complexity measure to assess predictability of the plant.   
     
     
         41 . A method of controlling an industrial plant, the method comprising:
 monitoring operation of the industrial plant by a plurality of sensors forming part of automatic control equipment;   transferring data from the sensors and observational data relating to operation of the industrial plant to a processor;   accessing a database containing operational data, including the data from the sensors and the observational data relating to operation of the industrial plant, as periodically updated by the processor; and   using a complexity measure to assess predictability of the plant.