A conductivity adjustment system for a dialysis mixing device

Abstract

This invention discloses a conductivity adjustment system for a dialysis mixing device, including an RO water flow sensor, a B-liquid conductivity sensor, a dialysate conductivity sensor C, an RO water pump, a B-liquid infusion pump, an A-liquid infusion pump, a B-liquid feedforward controller, a dialysate feedforward controller, a B-liquid parameter adaptive PID feedback controller, and a dialysate parameter adaptive PID feedback controller. Employing a feedforward + feedback control strategy to adaptively adjust parameter variables during the mixing process, this patent enables large-scale automated mixing of dialysate.

Description

Technical Field

[0001] This utility model relates to the field of medical device technology, and in particular to a conductivity adjustment system for a dialysis mixing device. Background Technology

[0002] Currently, hemodialysis centers commonly use centralized solution preparation systems to centrally prepare dialysate and then deliver it to each dialysis station. Traditional solution preparation systems mainly employ open-loop proportional control, which has the following technical limitations:

[0003] 1. High model dependence: Control requires a precise mixing ratio model, and frequent manual calibration is needed when the solution concentration changes;

[0004] 2. Weak anti-interference ability: Traditional PID parameters are fixed and cannot adapt to time-varying disturbances (differences between solution batches). Utility Model Content

[0005] The purpose of this invention is to address the problems and shortcomings described in the background art by providing a conductivity adjustment system and method for a dialysis mixing device.

[0006] A conductivity adjustment system for a dialysis mixing device includes an RO water flow sensor, a B-liquid conductivity sensor, a dialysate conductivity sensor C, an RO water pump, a B-liquid infusion pump, an A-liquid infusion pump, a B-liquid feedforward controller, a dialysate feedforward controller, a B-liquid parameter adaptive PID feedback controller, and a dialysate parameter adaptive PID feedback controller.

[0007] The RO water tank is connected to the B liquid mixer via the RO water pump. An RO water flow sensor is connected between the RO water pump and the B liquid mixer. A B liquid delivery pump is connected between the B liquid concentrate storage tank and the B liquid mixer. A B liquid conductivity sensor is installed inside the B liquid mixer.

[0008] The A-liquid concentrate storage tank is connected to the A-liquid mixer via the A-liquid delivery pump. The A-liquid mixer is equipped with a dialysate conductivity sensor C.

[0009] The A-liquid mixer is connected to the B-liquid mixer. After mixing, the dialysate is formed. The dialysate is finally formed in the A-liquid mixer.

[0010] The RO water flow sensor and the B-liquid infusion pump are electrically connected to the B-liquid feedforward controller.

[0011] The RO water flow sensor and A-fluid infusion pump are electrically connected to the dialysate feedforward controller.

[0012] The RO water flow sensor, the B-liquid conductivity sensor, and the B-liquid infusion pump are electrically connected to the B-liquid parameter adaptive PID feedback controller.

[0013] The RO water flow sensor, dialysate conductivity sensor, and C and A fluid infusion pump are electrically connected to the dialysate parameter adaptive PID feedback controller.

[0014] The beneficial effects of this utility model are:

[0015] This invention enables large-scale automated preparation of dialysate, adaptively adjusting parameters during the preparation process. When solution concentration changes, frequent manual calibration is unnecessary; the system automatically adjusts the infusion pump speed based on actual conditions, significantly improving its adaptability to different solution concentrations. It also responds quickly to fluctuations in RO water flow rate, suppressing interference and ensuring the dialysate conductivity remains stable within the target range. This greatly improves the accuracy and stability of dialysate preparation, providing reliable dialysate for hemodialysis treatment. The adaptive parameter adjustment mechanism automatically adapts to changes during system operation, reducing the technical threshold and workload for operators. Regarding system maintenance, the reduction of frequent calibration and adjustment work due to model dependence and fixed parameters simplifies maintenance, lowers maintenance costs, and improves equipment reliability and maintainability. Attached Figure Description

[0016] Figure 1 This is a block diagram of the structure of a dialysis mixing apparatus;

[0017] Figure 2 Here is a block diagram for conductivity adjustment control; Detailed Implementation

[0018] A conductivity adjustment system for a dialysis mixing device includes an RO water flow sensor, a B-liquid conductivity sensor, a dialysate conductivity sensor, an RO water pump, a B-liquid infusion pump, an A-liquid infusion pump, a B-liquid feedforward controller, a dialysate feedforward controller, a B-liquid parameter adaptive PID feedback controller, and a dialysate parameter adaptive PID feedback controller.

[0019] The RO water tank is connected to the B liquid mixer via the RO water pump. An RO water flow sensor is connected between the RO water pump and the B liquid mixer. A B liquid delivery pump is connected between the B liquid concentrate storage tank and the B liquid mixer. A B liquid conductivity sensor is installed inside the B liquid mixer.

[0020] The A-liquid concentrate storage tank is connected to the A-liquid mixer via the A-liquid delivery pump. The A-liquid mixer is equipped with a dialysate conductivity sensor C.

[0021] The A-liquid mixer is connected to the B-liquid mixer. After mixing, the dialysate is formed. The dialysate is finally formed in the A-liquid mixer.

[0022] The RO water flow sensor and the B-liquid infusion pump are electrically connected to the B-liquid feedforward controller.

[0023] The RO water flow sensor and A-fluid infusion pump are electrically connected to the dialysate feedforward controller.

[0024] The RO water flow sensor, the B-liquid conductivity sensor, and the B-liquid infusion pump are electrically connected to the B-liquid parameter adaptive PID feedback controller.

[0025] The RO water flow sensor, dialysate conductivity sensor, and C and A fluid infusion pump are electrically connected to the dialysate parameter adaptive PID feedback controller.

[0026] The RO water flow rate is determined based on the required amount of dialysis solution, and the RO water pump is controlled to meet the RO water flow rate requirement. The concentration of solution B and the concentration of dialysis solution are determined based on the required ion concentration in the dialysis solution, and then the set values ​​of conductivity of solution B and conductivity of dialysis solution are determined.

[0027] The entire system adopts a feedforward + feedback control strategy. The feedforward control determines the control speed of the B solution infusion pump and the A solution infusion pump based on the real-time RO water flow rate measured by the RO water flow sensor. That is, the speed of the B solution infusion pump is controlled based on the set value of the conductivity of the B solution and the real-time flow rate measurement of the RO water, and the speed of the A solution infusion pump is controlled based on the set value of the conductivity of the dialysate and the real-time flow rate measurement of the RO water.

[0028] However, during real-time control, fluctuations in RO water flow rate or changes in the concentration of saturated solution A (liquid in the A concentrate storage tank) and saturated solution B (liquid in the B concentrate storage tank) can cause deviations between the actual controlled conductivity of solution B and the conductivity of the dialysate and the set values. In this case, the system will perform feedback control on the rotation speed of the B solution infusion pump and the A solution infusion pump based on the deviation values ​​of the conductivity of solution B and the conductivity of the dialysate. The basic control logic is shown in Table 1.

[0029] Table 1. Speed ​​Control Logic for B-type and A-type Infusion Pumps

[0030] Where: C BO Set the conductivity value for liquid B, C B The conductivity value of the solution output by the B liquid mixer is the value detected in real time by the B liquid conductivity sensor. C DO C is the setpoint for the conductivity of the dialysate. D The conductivity value of the dialysate is the value detected in real time by the dialysate conductivity sensor, n. B To control the speed of the B-fluid infusion pump, n A Control the speed of the A-liquid infusion pump.

[0031]

[0032]

[0033] A method for adjusting the conductivity of a dialysis mixing device includes the following steps:

[0034] B-fluid infusion pump speed control:

[0035] Step 1: Signal Acquisition: Real-time acquisition of RO water flow rate Q water The conductivity of the solution output from the B-liquid mixer (C) B ;

[0036] Step 2: The B-liquid feedforward controller outputs the value based on the RO water flow rate n. water Calculate the control speed n of the B-fluid infusion pump B The calculation formula is as follows:

[0037] n B =k B n water

[0038] Where, k B The B solution ratio coefficient is determined based on the concentration of the B solution concentrate and the target conductivity value of the B solution.

[0039] Step 3: The B-fluid parameter adaptive PID feedback controller outputs the correction value for the control speed of the B-fluid infusion pump;

[0040] The specific correction method is as follows:

[0041] 1. System Model Construction: Assume the controlled object (B-fluid infusion pump) can be described by an autoregressive moving average (ARMA) model. The discrete-time system input is u(k), and the output is y(k). Its model expression is:

[0042]

[0043] Where, n a and n b These are the orders of autoregression and moving average, respectively, a i and b i These are the model parameters, and ξ(k) is zero-mean white noise;

[0044] Convert it to vector form and define the parameter vector. Regression vector φ(k)=[-y(k-1),-y(k-2),…,-y(k-na),u(k),u(k-1),…,u(k-nb)] T Then the system model can be expressed as y(k)=φ T (k)θ+ξ(k);

[0045] Meanwhile, the control law of the PID controller is:

[0046]

[0047] Where e(k) = r(k) - y(k) is the error, and r(k) is the set value;

[0048] 2. Initialization: Initialize parameter estimates Set it as the zero vector, that is Initialize the covariance matrix P(0) = αI, where α is a large positive number (e.g., α = 10). 6 ), where I is the identity matrix;

[0049] Choose a forgetting factor λ, with a value range of 0 < λ ≤ 1, and generally a value between 0.95 and 1;

[0050] Initialize PID parameter K p (0), K i (0) and K d (0) can be set based on experience or trial and error.

[0051] Initialize error integral The error between the previous time step and the previous time step is e(-1) = 0;

[0052] 3. Sampling Time Operations: At each sampling time k, perform the following operations:

[0053] Data acquisition and error calculation: Measure the system setpoint r(k) and actual output y(k), calculate the error e(k) = r(k) - y(k), and update the error integral. Calculate the regression vector: Based on the current time and historical input / output data, calculate the regression vector φ(k) = [-y(k-1), -y(k-2), ..., -y(k-na), u(k), u(k-1), ..., u(k-nb)] T Parameter estimation using recursive least squares:

[0054] Calculate the gain vector: Update parameter estimates: Update the covariance matrix: PID Parameter Tuning: Based on Minimizing the Sum of Squared Errors The goal is to adjust the PID parameters using gradient descent. First, the gradient of the sum of squared errors function with respect to the PID parameters is calculated: the gradient is approximated by considering the contribution of the current time-in error to the gradient.

[0055]

[0056] Where g is a constant obtained through system identification or empirical estimation;

[0057] Update PID parameters using gradient descent:

[0058] K p (k+1)=Kp (k)+η·2g·e 2 (k);

[0059]

[0060] Limit the updated PID parameters and set K. p K i and K d The upper and lower limits, i.e.

[0061] K p (k+1)=max(min(K p (k+1),K pmax ),K pmin ),

[0062] K i (k+1)=max(min(K i (k+1),K imax ),K imin ),

[0063] K d (k+1)=max(min(K d (k+1),K dmax ),K dmin ),

[0064] Where η is the learning rate, which takes a value between 0 and 1;

[0065] Calculate the control input: based on the adjusted PID parameters Kp(k), K... i (k) and K d (k), calculate the control quantity

[0066]

[0067] Output control quantity: The calculated control quantity u(k) is applied to the controlled object (B-liquid infusion pump);

[0068] Update time step: Increment time step k by 1, return to the data acquisition and error calculation step, and continue the operation at the next sampling time.

[0069] Step 4: The output of the adaptive PID feedback controller for liquid B and the output of the feedforward controller for liquid B are used to control the output speed of the liquid B infusion pump.

[0070] A. Infusion pump speed control:

[0071] Step 5: Signal Acquisition: Real-time acquisition of RO water flow rate Q water The conductivity C of the dialysate output from the A-liquid mixer D ;

[0072] Step 6: A liquid feedforward controller output, based on RO water flow rate n water Calculate the control speed n of the A-liquid infusion pump A The calculation formula is as follows:

[0073] n A =k A n water

[0074] Where, k A The ratio coefficient of solution A is determined based on the concentration of the concentrate of solution A, the concentration of the concentrate of solution B, and the conductivity value of the target dialysate.

[0075] Step 7: The A-fluid parameter adaptive PID feedback controller outputs the correction value for the control speed of the A-fluid infusion pump;

[0076] The specific correction method is as follows:

[0077] 1. System Model Construction: Assume the controlled object (A-fluid infusion pump) can be described by an autoregressive moving average (ARMA) model. The discrete-time system input is u(k), and the output is y(k). Its model expression is:

[0078]

[0079] Where, n a and n b These are the orders of autoregression and moving average, respectively, a i and b i These are the model parameters, and ξ(k) is zero-mean white noise;

[0080] Convert it to vector form and define the parameter vector. Regressor φ(k)=[-y(k-1),-y(k-2),…,-y(k-na),u(k),u(k-1),…,u(k-nb)] T Then the system model can be expressed as y(k)=φ T (k)θ+ξ(k);

[0081] Meanwhile, the control law of the PID controller is:

[0082]

[0083] Where e(k) = r(k) - y(k) is the error, and r(k) is the set value;

[0084] 2. Initialization: Initialize parameter estimates Set it as the zero vector, that is

[0085] Initialize the covariance matrix P(0) = αI, where α is a large positive number (e.g., α = 10). 6 ), where I is the identity matrix;

[0086] Choose a forgetting factor λ, with a value range of 0 < λ ≤ 1, and generally a value between 0.95 and 1;

[0087] Initialize PID parameter K p (0), K i (0) and K d (0) can be set based on experience or trial and error.

[0088] Initialize error integral The error between the previous time step and the previous time step is e(-1) = 0;

[0089] 3. Sampling Time Operations: At each sampling time k, perform the following operations:

[0090] Data acquisition and error calculation: Measure the system setpoint r(k) and actual output y(k), calculate the error e(k) = r(k) - y(k), and update the error integral.

[0091] Calculate the regression vector: Based on the current time and historical input / output data, calculate the regression vector φ.

[0092] (k)=[-y(k-1),-y(k-2),…,-y(k-na),u(k),u(k-1),…,u(k-nb)] T ;

[0093] Recursive least squares parameter estimation:

[0094] Calculate the gain vector:

[0095] Update parameter estimates:

[0096] Update the covariance matrix:

[0097] PID Parameter Tuning: Based on Minimizing the Sum of Squared Errors The goal is to adjust the PID parameters using the gradient descent method. First, the gradient of the sum of squared errors function with respect to the PID parameters is calculated:

[0098] The gradient is approximated by considering the contribution of the error at the current time step to the gradient.

[0099]

[0100] Where g is a constant obtained through system identification or empirical estimation;

[0101] Update PID parameters using gradient descent:

[0102] K p (k+1)=K p (k)+η·2g·e 2 (k);

[0103]

[0104] Limit the updated PID parameters and set K. p K i and K d The upper and lower limits, i.e.

[0105] K p (k+1)=max(min(K p (k+1),K pmax ),K pmin ),

[0106] K i (k+1)=max(min(K i (k+1),K imax ),K imin ),

[0107] K d (k+1)=max(min(K d (k+1),K dmax ),K dmin ),

[0108] Where η is the learning rate, which takes a value between 0 and 1;

[0109] Calculate the control input: based on the adjusted PID parameter K p (k), K i (k) and K d (k), calculate the control quantity

[0110]

[0111] Output control quantity: Apply the calculated control quantity u(k) to the controlled object (A-liquid infusion pump);

[0112] Update time step: Increment time step k by 1, return to the data acquisition and error calculation step, and continue the operation at the next sampling time.

[0113] Step 8: The output of the A-liquid parameter adaptive PID feedback controller + the output of the A-liquid feedforward controller control the output A-liquid infusion pump.

Claims

1. A conductivity adjustment system for a dialysis mixing apparatus, characterized in that: Including RO water flow sensor, B liquid conductivity sensor, dialysate conductivity sensor C, RO water pump, B liquid infusion pump, A liquid infusion pump, B liquid feedforward controller, dialysate feedforward controller, B liquid parameter adaptive PID feedback controller, and dialysate parameter adaptive PID feedback controller. The RO water tank is connected to the B liquid mixer via the RO water pump. An RO water flow sensor is connected between the RO water pump and the B liquid mixer. A B liquid delivery pump is connected between the B liquid concentrate storage tank and the B liquid mixer. A B liquid conductivity sensor is installed inside the B liquid mixer. The A-liquid concentrate storage tank is connected to the A-liquid mixer via the A-liquid delivery pump. The A-liquid mixer is equipped with a dialysate conductivity sensor C. The A-liquid mixer is connected to the B-liquid mixer, and the mixture is mixed to form the dialysate. The RO water flow sensor and the B-liquid infusion pump are electrically connected to the B-liquid feedforward controller. The RO water flow sensor and A-fluid infusion pump are electrically connected to the dialysate feedforward controller. The RO water flow sensor, the B-liquid conductivity sensor, and the B-liquid infusion pump are electrically connected to the B-liquid parameter adaptive PID feedback controller. The RO water flow sensor, dialysate conductivity sensor, and C and A fluid infusion pump are electrically connected to the dialysate parameter adaptive PID feedback controller.

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