Alumina ore slurry mill advanced process control system and method

Abstract

This invention relates to the field of grinding process control technology, specifically to an advanced process control system and method for alumina slurry grinding. The method includes the following steps: S1, a data acquisition module collects data during the alumina grinding process; S2, the grinding process is modeled to obtain the mill's output characteristics and response curve; S3, using soft instrumentation technology, the specific gravity of the slurry is calculated based on auxiliary variables using a soft instrumentation algorithm; S4, the dominant variable, α, is estimated online using a soft instrumentation model. K浆 Specific gravity of slurry ρ 浆 The value is combined with the mill quality controller to dynamically control the liquid-to-solid ratio data to achieve stable control of production indicators; S5, using a model predictive control algorithm, the mill's input parameters are adjusted in real time based on the predicted results and optimization objectives of the liquid-to-solid ratio parameters set by the quality controller. This invention can dynamically control the liquid-to-solid ratio data to achieve stable control of production indicators, enhancing the robustness and adaptability of the control system.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of ore grinding process control, and in particular to an advanced process control system for alumina ore slurry grinding. BACKGROUND

[0002] The alumina production process involves multiple links, and the raw ore grinding link is an important factor affecting the quality and cost of alumina. Figure 2 As shown in the figure, in the raw ore grinding process, the operating parameters of the mill, such as the ore quantity, the liquid flow rate, and the material transfer pump frequency, need to be adjusted in real time according to different material properties, environmental factors, and production requirements to ensure stable operation and efficient ore extraction of the mill.

[0003] For example, the patent with the Chinese authorized publication number CN213000524U discloses an alumina ore slurry grinding system. The alumina ore slurry grinding system comprises an alumina ore slurry storage device, a mill, an intermediate tank, a screen machine, and a qualified ore slurry tank. The mill is connected to the intermediate tank through the mill outlet. The intermediate tank is connected to the screen machine through the intermediate pump. The screen machine is connected to the mill through the screen surface. The screen surface is formed by welding stainless steel screen strips on the side plates. The adjacent screen strips form at least two levels of "eight" shaped variable-diameter screen gaps from top to bottom. Although this patent greatly improves the classification efficiency, the automation degree and production stability of the grinding process are not effectively solved.

[0004] However, due to the complexity, nonlinearity, and time-varying nature of the raw ore grinding process, traditional control methods often cannot meet the optimization requirements of production, resulting in high labor intensity of operators, unstable product indicators, and other problems. SUMMARY

[0005] The technical problem to be solved by the present application is to overcome the shortcomings of the prior art and provide an advanced process control system for alumina ore slurry grinding. The advanced control system and soft instrument technology are used to optimize the control system, effectively solve the control problem of the raw material mill, and improve the automation degree and production stability of the grinding process. The advanced control system is a control method that uses predictive model control technology to model, predict, and optimize the production process. The soft instrument technology is an engineering practical technology that estimates the value of the measured variable, i.e., the dominant variable, through online calculation of the soft measurement model.

[0006] The technical scheme of the present application is as follows:

[0007] An advanced process control method for alumina ore slurry grinding, comprising the following steps:

[0008] S1, a data acquisition module acquires data in the alumina ore grinding process;

[0009] S2, modeling the grinding process to obtain the output characteristics and response curve of the mill;

[0010] S3, calculating the value of the auxiliary variable according to the soft instrument algorithm by using the soft instrument technology and the pulp specific gravity ;

[0011] S4, on-line estimation of the leading variable by the soft measurement model , the pulp specific gravity and the quality controller, dynamic control of the liquid-solid ratio data to realize stable control of the production index;

[0012] S5, real-time adjustment of the input parameters of the mill according to the prediction results and optimization targets of the liquid-solid ratio parameters set by the quality controller by using the model predictive control algorithm.

[0013] Preferably, the data of S1 needs to be screened to remove obvious error data in the modeling process of step S2, and the data record under normal working conditions is intercepted for analysis and modeling to obtain the data model of the alumina ore pulp grinding system.

[0014] Preferably, the calculation method of the value of the pulp specific gravity is as follows:

[0015]

[0016]

[0017] Wherein:

[0018] : ore density, unit t / m 3 , reference value: 2.35;

[0019] : Al2O3 content, reference value: 49%;

[0020] : Al2O3 dissolution rate, reference value: 84%;

[0021] : liquid density, unit t / m 3 , reference value: 1.32;

[0022] : Al2O3 content, unit g / L, reference value: 111;

[0023] : caustic soda concentration, unit g / L, reference value 198;

[0024] : main mill ore amount, unit t / h;

[0025] ​: Main mill liquid distribution, unit t / h;

[0026] : Auxiliary mill ore quantity, unit t / h;

[0027] : Auxiliary mill liquid distribution, unit t / h.

[0028] Preferably, it further comprises a soft instrument correction algorithm:

[0029]

[0030] Wherein, y out is the output value of the soft instrument, Q is the above-mentioned soft instrument calculation formula, K i is the auxiliary variable correction coefficient, i represents the number of auxiliary variable calculation, q fi is the auxiliary variable deviation value, B 测 is the artificial measurement data correction deviation value, and f1 is the artificial correction coefficient.

[0031] Preferably, the quality controller can dynamically adjust the liquid-solid ratio in the production process according to the slurry specific gravity value provided by the soft instrument, so as to realize the prediction and stable control of the production index.

[0032] Preferably, the alumina slurry grinding system data model generates the distribution liquid flow, the buffer pump frequency output, and the belt scale rotation speed output, and predicts and optimally controls the distribution liquid flow, the slurry tank liquid level, and the liquid-solid ratio parameters in the production process.

[0033] Preferably, the basic principle of the quality controller is:

[0034] For the input-output system:

[0035]

[0036] x(k), x(k+1) respectively represent the state of the grinding process at the kth moment and the k+1th moment; y(k+1) represents the state of the system at the k+1th moment; u(k) represents the control input quantity of the system at the kth moment; A(k), B(k) respectively represent two corresponding parameter matrices of the grinding process at the kth moment; and C is a system matrix with appropriate dimensions.

[0037] The model can predict the future output state and output:

[0038]

[0039]

[0040] Feedback correction is performed on the estimated input:

[0041]

[0042] wherein the predicted value of the current output from the history input and state

[0043]

[0044] the predicted value of the jth output at future P j step is

[0045]

[0046] For single value prediction algorithm, the control time domain L=1, i.e. =u(k),i>0. Therefore

[0047]

[0048] wherein

[0049]

[0050] is the value of the jth output response at p j The optimal control can be obtained by the feedback correction algorithm for prediction:

[0051]

[0052] wherein

[0053]

[0054]

[0055]

[0056] An advanced process control system for alumina ore slurry grinding, applying the advanced process control method for alumina ore slurry grinding, the system comprising a DCS control subsystem, an OPC server, an APC server;

[0057] The DCS control subsystem comprises a data acquisition module, a central processing unit CPU, a network communicator, for collecting the mill process control data to the operation station and the OPC server, and executing command output and alarm signal;

[0058] The OPC server is a server for collecting DCS signals, for establishing linkage with the APC server, and transmitting all key signals to the APC server;

[0059] The APC server comprises a soft instrument, a mill system predictive controller and a quality controller, the soft instrument is used for generating soft instrument data according to a soft measurement model, the mill system predictive controller is used for generating a control signal according to an alumina ore slurry mill system data model to stabilize production states such as a proportioning liquid flow and an ore slurry tank liquid level, and the quality controller is used for guiding dynamic modification of a liquid-solid ratio to stabilize and optimize production indexes by using the soft instrument data.

[0060] Preferably, the DCS control subsystem is connected with field instrument electric signals through 4-20MA signals, and signals to be collected include ore quantity, proportioning liquid flow, material transfer pump frequency, buffer tank liquid level, ore slurry tank liquid level, conductivity and ore slurry temperature.

[0061] According to process characteristics of the production system, soft instrument technology is applied to obtain and a value of the ore slurry specific gravity are measured, and the whole mill production process is controlled through a multivariable optimization control model. The multivariable dynamic model predictive control can quickly and optimally coordinate each regulating loop of the PLC in real time, so that the production system is operated under optimal 'edge' conditions, the process is stabilized, and economic benefits are generated.

[0062] The mill circuit controller adopts a model-based robust multivariable predictive control technology, and realizes set value or area control of a series of related variables (controlled variables) through adjustment of a series of independent variables (manipulated variables). The mill circuit controller simultaneously solves all control problems of the mill circuit. The execution cycle of the mill circuit controller is 30 seconds.

[0063] The predictive control algorithm uses dynamic models between the manipulated variables and disturbance variables and the controlled variables. These models can be identified through step test data of the device, and are used by the controller to predict the process response and calculate the control action required to achieve the control objectives. The linear quadratic optimizer built in the controller can realize linear or quadratic optimization of the local controller through the target function set by the user. The main purpose of the mill circuit controller is to stabilize key product indexes and improve leaching rate under the premise of meeting various constraints.

[0064] Compared with the prior art, the present application has the following beneficial effects:

[0065] The soft measurement model is used to estimate the values of the dominant variables and the ore slurry specific gravity on line, and the quality controller is combined to dynamically control the liquid-solid ratio data to realize stable control of production indexes; the model predictive control algorithm is used to predict results and optimization objectives of the liquid-solid ratio parameters set by the quality controller, and input parameters of the mill, such as the proportioning liquid flow, the material transfer pump frequency and the ore belt speed, are adjusted in real time, so that the production process is more stable; and the robustness and adaptability of the control system are enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0066] In order to make the technical solutions in the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0067] Figure 1 is a structural schematic diagram of the present application.

[0068] Figure 2 is a grinding ore original process flow chart of the present application.

[0069] Figure 3 is a structural composition schematic block diagram of the present application.

[0070] Figure 4 is a liquid level control input effect diagram of the present application.

[0071] Figure 5 is a deployment liquid control input effect diagram of the present application.

[0072] Figure 6 is a ore output input effect diagram of the present application.

[0073] Figure 7 is a index improvement effect diagram of the present application. DETAILED DESCRIPTION

[0074] In order to make the technical solutions in the embodiments of the present application or the prior art clearer, the accompanying drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, those skilled in the art can obtain other drawings according to these drawings without any creative effort.

[0075] Embodiment 1

[0076] An alumina ore pulp grinding advanced process control method, comprising the following steps:

[0077] S1, a data acquisition module acquires data in an alumina ore grinding process;

[0078] S2, modeling the ore grinding process to obtain the output characteristics and response curve of the mill;

[0079] S3, using soft instrument technology, calculating the ore pulp specific gravity according to the auxiliary variable through soft instrument algorithm;

[0080] S4, soft measurement model online estimation of dominant variable: the value of pulp density combined with quality controller, dynamic control of liquid-solid ratio data to realize stable control of production index;

[0081] S5, using model predictive control algorithm, according to the liquid-solid ratio parameter prediction result set by the quality controller and the optimization target, real-time adjustment of the input parameters of the mill.

[0082] Data processing is needed during data collection, such as adjusting the liquid flow signal, adding a total liquid flow filter calculation block FY101 in DCS;

[0083] FY101 input is the total liquid flow FF101;

[0084] The filter algorithm is:

[0085]

[0086] Where T is the filter time, the initial filter time is 20 seconds;

[0087] In PLC, use the existing liquid flow control loop FC101, change the input to FY101 after filtering.

[0088] In step S3, soft instrument measurement is used to calculate the ratio of total liquid to ore quantity of main mill. The purpose of calculation is that when the belt scale frequency of ore quantity is used as the controller manipulated variable to control the liquid-solid ratio, the liquid-solid ratio is the controlled variable of the controller, which is adjusted by the ore quantity. For the ore quality controller, this variable is also the manipulated variable.

[0089] The calculation formula is:

[0090] FFY1101=FF101 / XKL

[0091] Calculation input:

[0092] FF101, total liquid quantity of main mill, m 3 / h;

[0093] XKL, main mill ore flow, t / h.

[0094] Calculation output:

[0095] FFY1101, liquid-solid ratio, m 3 / t.

[0096] The calculation passes through the pulp tank outlet pulp density And the soft measurement calculation value of leaching solution .

[0097]

[0098]

[0099] Wherein:

[0100] : Ore density, unit t / m 3 , reference value: 2.35;

[0101] : Al2O3 content, reference value: 49%;

[0102] : Al2O3 dissolution rate, reference value: 84%;

[0103] : Dispensing liquid density, unit t / m 3 , reference value: 1.32;

[0104] : Al2O3 content, unit g / L, reference value: 111;

[0105] : Caustic soda concentration, unit g / L, reference value 198;

[0106] : Main mill ore quantity, unit t / h;

[0107] : Main mill dispensing liquid quantity, unit t / h;

[0108] : Auxiliary mill ore quantity, unit t / h;

[0109] : Auxiliary mill dispensing liquid quantity, unit t / h.

[0110] The pulp specific gravity and The calculated value is obtained by the above calculation. And record the pulp specific gravity Manual measurement value And Laboratory test value As a periodic manual correction.

[0111] The soft instrument correction algorithm is:

[0112]

[0113] Wherein, y out is the soft instrument output value, Q is the above soft instrument calculation formula, K i is the auxiliary variable correction coefficient, i represents the auxiliary variable calculation number, q fi is the auxiliary variable deviation value, B测 The artificial measurement data is corrected by a bias value, and f1 is an artificial correction coefficient.

[0114] After the soft instrument measurement data is obtained by the above algorithm, it is further transmitted to the grinding circuit controller to realize the predictive control of the grinding control circuit. In step S4, the grinding circuit controller adopts model-based predictive control technology to realize the set value or regional control of a series of related variables (controlled variables) by adjusting a series of independent variables (manipulated variables). The controller can also perform feedforward adjustment on the disturbance variables.

[0115] The manipulated variables (MV), controlled variables (CV) and disturbance variables (DV) of the grinding circuit controller are shown in the following table.

[0116]

[0117] Grinding circuit controller gain matrix:

[0118]

[0119] The grinding quality controller controls the pulp specific gravity at the outlet of the pulp tank by adjusting the controlled variable of the grinding circuit controller, i.e. the liquid-solid ratio. and leaching solution .

[0120] In step S5, the grinding quality controller adopts model-based predictive control technology, which can effectively suppress the fluctuations caused by measurable feedforward disturbances to the process, provide more stable and strict control for the production process, and realize "edge clipping" operation.

[0121] The specific optimization and control target definitions are as follows:

[0122]

[0123] Grinding quality controller gain matrix:

[0124]

[0125] According to the collected data, obvious error data is removed, and data records under normal working conditions are intercepted for analysis and modeling to obtain an alumina slurry grinding system data model.

[0126] The data model of the adjusting liquid and the adjusting valve opening degree is:

[0127]

[0128] The data model of the ore quantity and the liquid-solid ratio is:

[0129]

[0130] Buffer tank level and buffer pump frequency data model:

[0131]

[0132] Mine mill quality control model ore pulp specific gravity change data model:

[0133]

[0134] Mine mill quality control model alpha K change change data model:

[0135]

[0136] After the control model is established, the model data is input into the model predictive controller for control. The controller used in this project has the following basic principles:

[0137] The future output state and output can be predicted by the model:

[0138]

[0139]

[0140] The estimated input is corrected by feedback:

[0141]

[0142] Among them The estimated value of the current output from the historical input and state

[0143]

[0144] The estimated value of the jth output in the future P j step is

[0145]

[0146] For single-value estimation algorithm, the control time domain L = 1, i.e. = u(k), i > 0. Therefore

[0147]

[0148] Among them

[0149]

[0150] The value of the jth output response in p j . The optimal control can be obtained by the feedback correction algorithm for estimation:

[0151]

[0152] wherein

[0153]

[0154]

[0155]

[0156] According to the controller structure and the data model, the control platform is built, and the whole control system structure is shown in Figure 3 After the DCS obtains the field measurement values, the liquid-solid ratio, the ore pulp specific gravity, and the conductivity value are calculated through the soft instrument calculation model, and are further compared and corrected with the pipeline temperature, the electric conductivity calculation, and the manual detection value, and the soft instrument measurement value is output. The liquid-solid ratio, the ore pulp specific gravity, and the conductivity value measured by the soft instrument are input as input variables to the quality controller, and the quality controller dynamically outputs the liquid-solid ratio given value according to the model prediction algorithm. The quality controller is input to the grinding system controller, and the system controller controls multiple inputs according to the control model to finally form the control output instruction output to the DCS, so as to complete the adjustment of the system state and the production index. Example 2

[0157] An advanced process control system for an alumina ore pulp grinding process, applying the advanced process control method for the alumina ore pulp grinding process, characterized in that, as shown in the system comprises a DCS control subsystem, an OPC server, and an APC server;

[0158] Figure 1 The control subsystem comprises a data acquisition module, a central processing unit CPU, and a network communicator, which is used to collect the mill process control data to the operation station and the server, and to execute the command output and the alarm signal;

[0159] The server is a server for collecting DCS signals, which is used to establish a link with the APC server, and to transmit all key signals to the APC server;

[0160] The DCS performs final limiting processing on the output instruction accepted from the APC server, so as to ensure that the equipment works in a reasonable range. The DCS and the APC server establish a heartbeat signal, and once the heartbeat signal is lost for more than a set time, the APC output instruction is automatically switched to the safety output instruction set manually.

[0161] The DCS performs final limiting processing on the output instruction accepted from the APC server, so as to ensure that the equipment works in a reasonable range. The DCS and the APC server establish a heartbeat signal, and once the heartbeat signal is lost for more than a set time, the APC output instruction is automatically switched to the safety output instruction set manually.

[0162] ​After installing the APC server on-site, the communication between the APC control system and the DCS system was tested. The data sent and received by the APC system was verified, and after confirming its accuracy, the APC system sent analog control commands for testing. Once the DCS system received data normally, the MV manipulator switching test was performed. The on / off buttons could switch normally between the APC controller and DCS manual values. The software instrument calibration window was tested; input values ​​were observed to ensure normal data changes. Finally, the alarm function was tested: a color-changing indicator appeared when the controlled variable exceeded its limit; a pop-up alarm appeared after communication was interrupted, and the system could switch from APC system to DCS manual control.

[0163] The server includes a soft instrument, a mill system predictive controller, and a quality controller. The soft instrument is used to generate soft instrument data based on the soft measurement model. The mill system predictive controller generates control signals based on the alumina slurry mill system data model to stabilize and adjust production status such as liquid flow rate and slurry tank level. The quality controller uses the soft instrument data to guide the dynamic modification of the liquid-solid ratio to achieve the stabilization and optimization of production indicators.

[0164] Preferably, the DCS control subsystem is connected to the field instrument electrical signals via a 4-20mA signal. The signals to be collected include ore feed rate, mixing liquid flow rate, transfer pump frequency, buffer tank level, slurry tank level, conductivity, and slurry temperature.

[0165] After the liquid level control is put into operation, such as Figure 4 The liquid level fluctuations shown are significantly improved. The buffer tank liquid level can be automatically controlled by adjusting the buffer tank slurry pump through the intelligent grinding system. The three buffer tank slurry pumps can automatically maintain a balanced frequency of operation.

[0166] After the advanced control system of the mill is started, the mixing fluid can be automatically adjusted, such as... Figure 5 The fluctuation in the flow rate of the blending liquid decreased significantly after the addition of the shown product, which is beneficial to the liquid-to-solid ratio and the slurry tank outlet. Stability.

[0167] like Figure 6 As shown, the liquid-to-solid ratio is controlled by the frequency of the conveyor belt scale for ore loading, and the influence of weighing changes on the liquid-to-solid ratio is promptly overcome. During liquid load adjustment, the frequency of the conveyor belt scale for ore loading is automatically adjusted to ensure a stable liquid-to-solid ratio throughout the load adjustment process.

[0168] To effectively compare the effects of intelligent grinding systems (i.e., on...) The data of 2 / 15 7:27 to 4 / 6 11:57 (the intelligent grinding system was started, the intelligent grinding system was basically continuously running, and the conductivity 3 meter output was normal) and 5 / 12 0:00 to 7 / 11 23:59 (the intelligent grinding system was stopped, and the conductivity 3 meter output was normal) were selected, and the abnormal conductivity 3 data were corrected (the correction limit was +-30% of the average value) for data analysis.

[0169]

[0170] From the above table, we can see that the stage of running the intelligent grinding system greatly slows down the fluctuation of the index on the basis of meeting the range, and reduces 42.5% by standard deviation. From the above table, we can see that the system running state is obviously more stable after the advanced control is started. Figure 7

[0171] Although the present application has been described in detail with reference to the preferred embodiments, the present application is not limited to the preferred embodiments. The skilled in the art can make various equivalent modifications or replacements to the embodiments of the present application without departing from the spirit and essence of the present application, and these modifications or replacements should be within the scope of the present application. Any skilled in the art within the technical scope disclosed by the present application can easily think of changes or replacements, and these changes or replacements should be within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.​

Claims

1. An alumina slurry mill advanced process control method, characterized by, It comprises the following steps: S1, a data acquisition module acquires data in an alumina ore grinding process; S2, the ore grinding process is modeled to obtain an output characteristic and a response curve of the mill; S3, using soft instrument technology, according to the auxiliary variable through soft instrument algorithm to calculate With the pulp specific gravity ; S4, online estimation of dominant variables by soft sensor model , value of pulp specific gravity combined with the quality controller, dynamically control the liquid-solid ratio data to realize the stable control of production index S5, a model predictive control algorithm is used to adjust the input parameters of the mill in real time according to the prediction result of the liquid-solid ratio parameter set by the quality controller and the optimization target; The soft gauge algorithm calculates The calculation method of the pulp specific gravity The calculation method is: Wherein: : ore density, in t / m 3 , reference value: 2.35; Al203content, reference value: 49%; : Al203 dissolution rate, reference value: 84%; : density of the formulation, in t / m 3 , reference value: 1.32; : Al203 content in g / L, reference value: 111; : caustic concentration in g / L, reference value 198; : Main mill underflow, unit t / h; : Main mill adjusted liquid amount, unit t / h; : auxiliary grinding of the ore, in t / h; : auxiliary grinding adjustment liquid amount, unit t / h.

2. The alumina slurry mill advanced process control method of claim 1, wherein, The data of S1 needs to be screened to remove obvious error data in the modeling process of S2, and the data records under normal working conditions are intercepted for analysis and modeling to obtain an alumina ore slurry grinding system data model.

3. The alumina slurry mill advanced process control method of claim 1, wherein, The mass controller can provide the The mass controller can provide the The mass controller can provide the 4. The alumina slurry mill advanced process control method of claim 2, wherein, The alumina ore slurry grinding system data model generates a liquid flow, a buffer pump frequency output, and a belt scale speed output, and predicts and optimizes the control of the liquid flow, the ore slurry tank liquid level, and the liquid-solid ratio parameter in the production process.

5. The alumina slurry mill advanced process control method of claim 1 wherein, The basic principle of the quality controller is: For an input-output system: x(k+1)=Ax(k)+Bu(k) y(k+1)=Cx(k+1) x(k) and x(k+1) represent the state of the ore grinding process at the kth time and the k+1th time, respectively; y(k+1) represents the state of the system at the k+1th time; u(k) represents the control input at the kth time; A(k) and B(k) represent two corresponding parameter matrices at the kth time; C is a system matrix with appropriate dimensions; The future output state and output can be predicted from the model: The estimated input is corrected by feedback: wherein a predicted value of a current output from historical inputs and states The estimated value of the jth output at future P j steps is For the single-value prediction algorithm, the control time domain L = 1, that is = u(k), i > 0; therefore Wherein For the jth output response at p j The optimal control from the feedback correction algorithm for the jth output response at p Wherein 。 6. An alumina slurry mill advanced process control system applying the alumina slurry mill advanced process control method of any one of claims 1 to 5, characterized by, The system comprises a DCS control subsystem, an OPC server, and an APC server. The DCS control subsystem comprises a data acquisition module, a central processing unit (CPU), and a network communicator, which is used to collect mill process control data to the operation station and the OPC server, and execute command output and alarm signals. The OPC server is a server for collecting DCS signals, which is used to establish a link with the APC server and transmit all key signals to the APC server. The APC server comprises a soft instrument, a mill system predictive controller, and a quality controller. The soft instrument is used to generate soft instrument data according to a soft measurement model. The mill system predictive controller generates control signals to stabilize the liquid flow, the ore slurry tank liquid level, and the production state. The quality controller guides dynamic modification of the liquid-solid ratio to stabilize and optimize the production index.

7. The advanced process control system for an alumina slurry mill as claimed in claim 6, wherein, The DCS control subsystem is connected with field instrument electrical signals through 4-20MA signals. The signals to be collected include ore quantity, liquid flow, transfer pump frequency, buffer tank liquid level, ore slurry tank liquid level, conductivity, and ore slurry temperature.

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