Method for operating adjustment of a sludge dryer based on online measurement of moisture content and calorific value

Abstract

This invention discloses a method for adjusting the operation of a sludge dryer based on online measurement of moisture content and calorific value, relating to the field of sludge dryer operation adjustment technology. The method includes the following steps: Step 1: Measuring the moisture content C1 of the wet sludge at the dryer inlet using a microwave moisture content meter installed on the pipeline between the sludge conveying pump and the dryer sludge inlet. This invention, by using a microwave moisture content meter installed on the pipeline between the sludge conveying pump and the dryer sludge inlet, makes measuring the moisture content of the sludge before it enters the dryer more convenient and faster. It allows for real-time detection of the moisture content before drying, and the measurement of the sludge moisture content C2 at the dryer outlet. Adjustment is based on the C2 measurement feedback. Furthermore, after drying, the sludge exiting the dryer has a relatively low moisture content, generally in granular form. The moisture content of the dried sludge is retrieved using hyperspectral imaging, making sludge moisture content detection more flexible.

Description

Technical Field

[0001] This invention relates to the field of sludge dryer operation adjustment technology, specifically a sludge dryer operation adjustment method based on online measurement of moisture content and calorific value. Background Technology

[0002] With the development of drying technology, modeling the drying process has become increasingly challenging. Pure mathematical models have gradually shown their limitations. For example, in most cases, before establishing a mathematical model, it is necessary to assume that discrete parameters are lumped parameters, nonlinear cases are linear cases, and unsteady systems are steady systems.

[0003] Traditional sludge moisture content measurement methods based on drying and weighing are complex, time-consuming, and lack real-time monitoring capabilities, making them unsuitable for guiding adjustments to sludge dryer operation. To facilitate setting reasonable sludge moisture content at the dryer outlet, sampling and using the bomb calorimeter method to measure the higher heating value of the dried sludge are typically employed. However, this method also suffers from complexity, time consumption, and lack of real-time monitoring. Currently, adjustments to dryer operation rely primarily on manual adjustments based on operator experience. However, manual adjustments suffer from significant human error and slow response times. The sludge moisture content control system for the dryer outlet is a system with high inertia, high delay, nonlinearity, and variable characteristics. By controlling the sludge flow rate into the dryer, the outlet sludge moisture content can be adjusted. Due to the simple structure of the PID control algorithm, which allows for adjustment of the sludge flow rate and achieves satisfactory control performance, it has been used in the sludge drying control process. However, because the sludge drying process is influenced by various factors, conventional PID control algorithms are insufficient to meet practical application requirements. Summary of the Invention

[0004] Based on this, the purpose of this invention is to provide a method for adjusting the operation of a sludge dryer based on online measurement of moisture content and calorific value, in order to solve the technical problem mentioned above that the traditional sludge moisture content measurement method based on drying and weighing is complex, time-consuming and cannot provide real-time monitoring, and therefore cannot be used to guide the adjustment of the operating conditions of the sludge dryer.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for adjusting the operation of a sludge dryer based on online measurement of moisture content and calorific value, comprising the following steps:

[0006] Step 1: The moisture content C1 of the wet sludge at the dryer inlet is measured by a microwave moisture content meter installed on the pipeline between the sludge conveying pump and the sludge inlet of the dryer. The pipeline sludge moisture content microwave transmitter is installed between the wet sludge conveying pump and the sludge dryer.

[0007] Step 2: The moisture content of the dried sludge is determined by hyperspectral method. The sludge moisture content detection device based on hyperspectral method is set on the sludge conveyor belt connected to the sludge outlet of the dryer.

[0008] Step 3: By calibrating the functional relationship between the reflectance spectra of sludge with different components and the higher heating value of sludge, the higher heating value of sludge at the sludge outlet of the dryer is estimated by the Dulong formula, that is, higher heating value HHV=32810C +142246(HO / 8) +9273S, in kJ / kg. Based on the mass fraction of carbon (C), hydrogen (H), oxygen (O) and sulfur (S) in the sludge, combined with the element content estimation model, the higher heating value of the dryer sludge is estimated.

[0009] Step 4: By monitoring the sludge moisture content (C1) and sludge moisture content (C2) in real time, and combining them with the set sludge moisture content, the residence time of the sludge in the dryer is adjusted by changing the sludge inlet flow rate (Q), thereby changing the sludge moisture content at the outlet.

[0010] As a preferred technical solution of the sludge dryer operation adjustment method based on online measurement of moisture content and calorific value of the present invention, since the moisture content of the sludge entering the furnace is relatively high, generally greater than 80%, a pipeline-type microwave sludge concentration and moisture content meter can be used to measure the moisture content C1 of the sludge entering the furnace.

[0011] As a preferred technical solution of the sludge dryer operation adjustment method based on online measurement of moisture content and calorific value of the present invention, the measurement band range of the hyperspectral camera is 400~1700 nm, and the band range λ used for inverting sludge moisture content is 1300~1450 nm.

[0012] As a preferred technical solution of the sludge dryer operation adjustment method based on online measurement of moisture content and calorific value of the present invention, the acquired spectrum is subjected to noise reduction, baseline fitting, baseline subtraction, and second derivative calculation to obtain the second-order spectrum of the corresponding band. Based on the least squares fitting algorithm, by calibrating the functional relationship between different moisture contents and sludge reflectance spectra, the functional relationship between the second-order spectrum and sludge moisture content is finally obtained. For the dry sludge moisture content at the dryer outlet: C2 = f (λ).

[0013] As a preferred technical solution of the sludge dryer operation adjustment method based on online measurement of moisture content and calorific value of the present invention, the characteristic spectral bands in the hyperspectral image are selected as 572, 643, 685, 766, 819, and 964 nm. A quantitative analysis model is established based on the relationship between hyperspectral image data and carbon (C), hydrogen (H), oxygen (O), and sulfur (S) elements in sludge composition using six characteristic spectral variables. By calibrating the functional relationship between the reflectance spectrum of sludge with different components and the higher heating value of sludge, the partial least squares algorithm is applied, combined with multiple spectral preprocessing algorithms, to establish a quantitative analysis model for the content of carbon (C), hydrogen (H), oxygen (O), and sulfur (S) elements in sludge. Finally, the functional relationship between the higher heating value of sludge and the characteristic bands in the hyperspectral image can be obtained: HHV = f (λ1, λ2, λ3, λ4, λ5, λ6).

[0014] As a preferred technical solution of the sludge dryer operation adjustment method based on online measurement of moisture content and calorific value of the present invention, the estimation of the higher heating value of sludge at the sludge outlet of the dryer is approximated by the Dulong formula, that is, higher heating value HHV=32810C + 142246(HO / 8) +9273S, in kJ / kg. Based on the mass fraction of elements such as carbon (C), hydrogen (H), oxygen (O) and sulfur (S) in the sludge, combined with the element content estimation model, the higher heating value of the sludge in the dryer is estimated.

[0015] As a preferred technical solution of the sludge dryer operation adjustment method based on online measurement of moisture content and calorific value of the present invention, the sludge conveying pump of the dryer adopts a screw pump, and its sludge inlet flow rate can be calculated using the following formula: Q = V n, where Q represents the flow rate of the screw pump, V represents the volume of the screw pump chamber, and n represents the rotational speed of the screw pump. By changing the rotational speed of the screw pump, the flow rate of the sludge entering the furnace can be adjusted, thereby adjusting the moisture content of the sludge exiting the furnace.

[0016] As a preferred technical solution of the sludge dryer operation adjustment method based on online measurement of moisture content and calorific value of the present invention, the PID control algorithm based on BP neural network realizes online self-tuning of PID parameters, so that the three important parameters kp, ki, and kd of the PID control system can be dynamically adjusted according to the sludge drying situation. By using BP neural network, a self-learning PID control system of parameters kp, ki, and kd can be established to control the speed of sludge screw pump.

[0017] As a preferred technical solution of the sludge dryer operation regulation method based on online measurement of moisture content and calorific value of the present invention, the PID controller based on BP neural network consists of two parts: a classic PID controller and a neural network.

[0018] As a preferred technical solution of the sludge dryer operation adjustment method based on online measurement of moisture content and calorific value of the present invention, the classic PID controller directly performs closed-loop control on the controlled object and adjusts the three parameters kp, ki, and kd online. The neural network adjusts the parameters of the PID controller according to the operating state of the system to achieve the optimization of a certain performance index. Even if the output state of the output layer neuron corresponds to the three adjustable parameters kp, ki, and kd of the PID controller, through the self-learning and weighting coefficient adjustment of the neural network, its stable state corresponds to the controller parameters of the PID under a certain optimal control law.

[0019] In summary, the present invention has the following main beneficial effects:

[0020] 1. This invention utilizes a microwave moisture content meter installed on the pipeline between the sludge conveying pump and the sludge inlet of the dryer. This makes it more convenient and faster to measure the moisture content of sludge before it enters the dryer. The moisture content of the sludge before drying can be detected in real time. The moisture content C2 of the sludge at the dryer outlet can be measured and adjusted based on the C2 measurement feedback. After drying in the sludge dryer, the moisture content of the sludge exiting the dryer is relatively low and is generally granular. The moisture content of the dried sludge is retrieved using a hyperspectral method, making the detection of sludge moisture content more flexible.

[0021] 2. This invention, by comparing the moisture content of sludge before and after drying during the detection of sludge moisture content, uses an algorithm formula to calculate the required drying time after the sludge enters the dryer. This provides data for the operation of the sludge dryer, allowing the dryer to be adjusted in real time and ensuring that the moisture content of the sludge after it is discharged from the dryer meets the requirements.

[0022] 3. This invention, through testing the discharged sludge and obtaining its structure, infers the calorific value of the sludge using a formula, determines whether the sludge meets the standards, and facilitates subsequent reuse of the sludge. Based on a BP neural network-based PID control algorithm, it achieves online self-tuning of PID parameters, enabling detailed adjustments to the entire drying process based on the detected data. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0025] The embodiments of the present invention will now be described.

[0026] Operational adjustment methods for sludge dryers based on online measurement of moisture content and calorific value, such as... Figure 1 As shown, it includes the following steps:

[0027] Step 1: The moisture content C1 of the wet sludge at the dryer inlet is measured by a microwave moisture content meter installed on the pipeline between the sludge conveying pump and the sludge inlet of the dryer. The pipeline-type microwave sludge moisture content transmitter is set between the wet sludge conveying pump and the sludge dryer. Since the moisture content of the sludge entering the furnace is relatively high, generally greater than 80%, the pipeline-type microwave sludge moisture content meter can be used to measure the moisture content C1 of the sludge entering the furnace.

[0028] Step 2: The moisture content of the dried sludge is retrieved using hyperspectral imaging. A hyperspectral sludge moisture content detection device is installed on the sludge conveyor belt connected to the sludge outlet of the dryer. The hyperspectral camera's measurement band range is 400~1700 nm. The band range λ used for retrieving the sludge moisture content is 1300~1450 nm. The acquired spectra undergo noise reduction, baseline fitting, baseline subtraction, and second derivative calculation to obtain the second-order spectrum for the corresponding band. Based on the least squares fitting algorithm, the functional relationship between different moisture contents and the sludge reflectance spectrum is calibrated to finally obtain the functional relationship between the second-order spectrum and the sludge moisture content. For the moisture content of the dried sludge at the dryer outlet: C2 = f(λ);

[0029] Step 3: By calibrating the functional relationship between the reflectance spectra of sludge with different components and the higher heating value of the sludge, the Duron formula is used to approximate the estimation of the higher heating value of the sludge at the dryer sludge outlet, i.e., higher heating value HHV = 32810C + 142246(H₂O / ₈) + 9273S, in kJ / kg. Based on the mass fractions of carbon (C), hydrogen (H), oxygen (O), and sulfur (S) in the sludge, combined with the element content estimation model, the higher heating value of the dryer sludge is estimated. The characteristic spectral bands in the hyperspectral image are selected as 572, 643, 685, 766, 819, and 964. A quantitative analysis model was established using six characteristic spectral variables based on hyperspectral image data and the carbon (C), hydrogen (H), oxygen (O), and sulfur (S) elements in sludge composition. By calibrating the functional relationship between the reflectance spectra of sludge with different compositions and the higher heating value of the sludge, a quantitative analysis model of the carbon (C), hydrogen (H), oxygen (O), and sulfur (S) content in the sludge was established using partial least squares algorithm combined with multiple spectral preprocessing algorithms. Finally, the functional relationship between the higher heating value of the sludge and the characteristic bands in the hyperspectral spectrum was obtained: HHV = f (λ1, λ2, λ3, λ4, λ5, λ6). The Duron formula was used to approximate the estimation of the higher heating value of the sludge at the dryer sludge outlet, i.e., higher heating value HHV = 32810C + 142246(HO / 8)+9273S, unit kJ / kg, based on the mass fraction of elements such as carbon (C), hydrogen (H), oxygen (O), and sulfur (S) in the sludge, combined with the element content estimation model, to estimate the higher heating value of the sludge in the dryer;

[0030] Step 4: By monitoring the real-time moisture content of the sludge entering the dryer (C1) and exiting the dryer (C2), and combining this with the set sludge moisture content, the residence time of the sludge in the dryer is adjusted by changing the sludge inlet flow rate (Q), thereby changing the moisture content of the sludge exiting the dryer. The sludge conveying pump in the dryer is a screw pump, and its inlet flow rate can be calculated using the following formula: Q = V n, where: Q represents the flow rate of the screw pump, V represents the volume of the screw pump chamber, and n represents the rotational speed of the screw pump. By changing the speed of the screw pump, the flow rate of sludge entering the furnace can be adjusted, thereby adjusting the moisture content of the sludge exiting the furnace. A PID control algorithm based on a BP neural network enables online self-tuning of the PID parameters, allowing the three important parameters kp, ki, and kd of the PID control system to be dynamically adjusted according to the sludge drying status. Using a BP neural network, a self-learning PID control system with parameters kp, ki, and kd can be established to control the speed of the sludge screw pump. The BP neural network-based PID controller consists of two parts: a classic PID controller and a neural network. The classic PID controller directly performs closed-loop control on the controlled object and adjusts the three parameters kp, ki, and kd online. The neural network adjusts the parameters of the PID controller according to the system's operating state to achieve optimization of a certain performance index. Even if the output state of the output layer neurons corresponds to the three adjustable parameters kp, ki, and kd of the PID controller, through the neural network's self-learning and weighting coefficient adjustment, its stable state corresponds to the PID controller parameters under a certain optimal control law.

[0031] In use, a microwave moisture content meter installed on the pipeline between the sludge conveying pump and the sludge inlet of the dryer makes it more convenient and faster to measure the moisture content of the sludge before it enters the dryer. It allows for real-time monitoring of the moisture content before drying. After drying, the sludge exiting the dryer has a relatively low moisture content and is generally granular. At this stage, microwave measurement is not suitable. Hyperspectral imaging is used to retrieve the moisture content of the dried sludge, making moisture content detection more flexible. During the sludge moisture content detection process, the comparison of the moisture content before and after drying, using algorithms... The formula calculates the drying time required after the sludge enters the dryer, providing data for the dryer's operation. This allows for real-time adjustments, ensuring that the sludge's moisture content meets requirements after discharge. Furthermore, by analyzing the discharged sludge's structure, the formula infers the sludge's calorific value, determining whether it meets standards. This facilitates subsequent reuse of the sludge. A PID control algorithm based on a BP neural network enables online self-tuning of PID parameters, allowing for detailed adjustments to the drying process based on detected data.

[0032] Although embodiments of the present invention have been shown and described, these specific embodiments are merely explanations of the invention and are not intended to limit it. The specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. After reading this specification, those skilled in the art may make modifications, substitutions, and variations to the embodiments as needed without departing from the principles and spirit of the invention, but such modifications, substitutions, and variations are protected by patent law as long as they are within the scope of the claims of the present invention.

Claims

1. A method for adjusting the operation of a sludge dryer based on online measurement of moisture content and calorific value, characterized in that, Includes the following steps: Step 1: The moisture content (C1) of the sludge entering the dryer at the inlet is measured by a microwave moisture content meter installed on the pipeline between the sludge conveying pump and the sludge inlet of the dryer. Step 2: The moisture content of the dried sludge is determined by hyperspectral method. The sludge moisture content detection device based on hyperspectral method is set on the sludge conveyor belt connected to the sludge outlet of the dryer. The wavelength range λ used for inverting the moisture content of dry sludge is 1300~1450 nm. The acquired spectra are subjected to noise reduction, baseline fitting, baseline subtraction, and second derivative calculation to obtain the second-order spectra of the corresponding bands. Based on the least squares fitting algorithm, the functional relationship between different moisture contents and sludge reflectance spectra is calibrated, and finally the functional relationship between the second-order spectra and sludge moisture content is obtained. For the moisture content of the sludge exiting the dryer: C2 = f (λ). Step 3: By calibrating the functional relationship between the reflectance spectra of sludge with different components and the higher heating value of sludge, the higher heating value of sludge at the sludge outlet of the dryer is estimated by the Dulong formula, that is, higher heating value HHV=32810C +142246(HO / 8) +9273S, in kJ / kg. Based on the mass fraction of carbon (C), hydrogen (H), oxygen (O) and sulfur (S) elements in the sludge, combined with the element content estimation model, the higher heating value of the dryer sludge is estimated. The characteristic spectral bands of 572, 643, 685, 766, 819, and 964 nm in the hyperspectral image were selected. A quantitative analysis model was established based on the relationship between hyperspectral image data and carbon (C), hydrogen (H), oxygen (O), and sulfur (S) elements in sludge composition using six characteristic spectral variables. By calibrating the functional relationship between the reflectance spectrum of sludge with different compositions and the higher heating value of sludge, a quantitative analysis model of the content of carbon (C), hydrogen (H), oxygen (O), and sulfur (S) elements in sludge was established by applying the partial least squares algorithm and combining multiple spectral preprocessing algorithms. Finally, the functional relationship between the higher heating value of sludge and the characteristic bands in the hyperspectral image was obtained: HHV = f (λ1, λ2, λ3, λ4, λ5, λ6). Step 4: By monitoring the sludge moisture content (C1) and sludge moisture content (C2) in real time, and combining them with the set sludge moisture content, the residence time of the sludge in the dryer is adjusted by changing the sludge inlet flow rate (Q), thereby changing the sludge moisture content (C2) at the outlet.

2. The sludge dryer operation adjustment method based on online measurement of moisture content and calorific value according to claim 1, characterized in that: The sludge transfer pump uses a screw pump, and its sludge inlet flow rate is calculated using the following formula: Q = V n, where: Q represents the flow rate of the screw pump, V represents the volume of the screw pump chamber, and n represents the rotational speed of the screw pump. By changing the rotational speed of the screw pump, the flow rate of the sludge entering the furnace can be adjusted, thereby adjusting the moisture content (C2) of the sludge exiting the furnace.

3. The sludge dryer operation adjustment method based on online measurement of moisture content and calorific value according to claim 2, characterized in that: The PID control algorithm based on BP neural network realizes online self-tuning of PID parameters. The three important parameters of the PID control system, kp, ki, and kd, are dynamically adjusted according to the sludge drying status. By using BP neural network, a self-learning PID control system of parameters kp, ki, and kd is established to control the speed of the screw pump.

4. The sludge dryer operation adjustment method based on online measurement of moisture content and calorific value according to claim 3, characterized in that: The PID controller based on BP neural network consists of two parts: a classic PID controller and a neural network.

5. The sludge dryer operation adjustment method based on online measurement of moisture content and calorific value according to claim 4, characterized in that: The classic PID controller directly performs closed-loop control on the controlled object and adjusts three parameters kp, ki, and kd online. The neural network adjusts the parameters of the PID controller according to the system's operating state to achieve optimization of a certain performance index. Even if the output state of the output layer neurons corresponds to the three adjustable parameters kp, ki, and kd of the PID controller, through the neural network's self-learning and weighting coefficient adjustment, its stable state corresponds to the PID controller parameters under a certain optimal control law.

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