A method and system for improving the calorific value of biomass pellet fuel

Abstract

The embodiment of the present disclosure provides a kind of biomass particle fuel heat value promotion processing method and system, the method comprises: using near infrared spectrometer to carry out 940nm and 1200nm double wave band reflectivity continuous collection to biomass particle surface, obtains reflectivity time series variation data;Based on the reflectivity time series variation data, the attenuation rate of double wave band reflectivity ratio is calculated, and the microcrack evolution degree prediction result of corresponding batch biomass particle is obtained;According to the microcrack evolution degree prediction result, the wind speed grading regulation instruction of the cooling section corresponding to the batch biomass particle is matched and triggered;The cooling of biomass particle is completed under the cooling air speed of the wind speed grading regulation instruction matched.The scheme of the embodiment of the present disclosure can solve the problem of excessive breaking rate caused by quenching stress and different batch heat value.

Description

Technical Field

[0001] This invention relates to the field of biomass pellet fuel preparation technology, specifically to a method and system for improving the calorific value of biomass pellet fuel. Background Technology

[0002] In the industrial production of biomass pellet fuel, the high-temperature pellets after pelleting need to undergo a cooling and shaping stage to stabilize their internal structure and achieve the required mechanical strength. Existing cooling processes typically employ constant wind speeds or multi-stage cooling methods based on experience. However, these methods fail to consider the evolution characteristics of surface microcracks caused by differences in composition, density, and internal stress states between different batches of raw materials after drying. When high-temperature pellets are suddenly exposed to strong cold air, a significant temperature gradient forms between the surface and interior of the pellet, causing uneven shrinkage and resulting in rapid expansion of microcracks, even macroscopic breakage. This not only leads to a high pellet breakage rate but also makes the pellets prone to bursting during combustion, affecting combustion stability and calorific value release efficiency. Furthermore, mismatches between cooling parameters and pellet states between different batches result in poor consistency in the calorific value of the final product, making it difficult to meet the quality requirements of biomass fuel. Therefore, how to perceive the evolution trend of microcracks in real time based on the surface state of the pellets in the early stages of cooling and dynamically match the cooling wind speed and time period accordingly to suppress sudden cooling stress damage, reduce the breakage rate, and improve calorific value uniformity has become a key technical problem urgently needing to be solved in this field. Summary of the Invention

[0003] In view of this, the present disclosure provides a method for improving the calorific value of biomass pellet fuel, which at least partially solves the problems existing in the prior art.

[0004] A method for enhancing the calorific value of biomass pellet fuel includes: For biomass pellet fuel that has been granulated and dried, a near-infrared spectrometer was used to continuously collect the reflectance of the biomass pellet surface in both 940nm and 1200nm bands to obtain the time-series variation data of reflectance. The decay rate of the dual-band reflectivity ratio is calculated based on the time-series reflectivity variation data. A prediction model for the evolution degree of microcracks inside biomass particles is constructed based on the decay rate to obtain the prediction results of the microcrack evolution degree of corresponding batches of biomass particles. Based on the predicted results of the microcrack evolution degree, the wind speed graded control command of the corresponding cooling section of the batch of biomass pellets is matched and triggered. The wind speed graded control command is set with multi-level cooling wind speed parameters that are positively correlated with the microcrack evolution degree. The cooling and shaping of biomass pellets is completed under the cooling wind speed matched by the wind speed classification and control command.

[0005] Preferably, the attenuation rate of calculating the dual-band reflectivity ratio based on reflectivity time-series variation data further includes: The reflectance values ​​R_940(t) and R_1200(t) of biomass particles at continuous time points at wavelengths of 940 nm and 1200 nm were obtained; Calculate the dual-band reflectivity ratio r(t) = R_940(t) / R_1200(t); Calculate the rate of change of the ratio dr / dt based on the time series t; If the absolute value of dr / dt is greater than the set threshold Δr, it is determined that the microcracks in the particles of this batch evolve rapidly.

[0006] Preferably, the step of constructing a prediction model based on the decay rate further includes: The trend of the dual-band reflectivity ratio over time is divided into n sliding windows, each with a duration of Δt. Calculate the slope of the ratio within each window: S_i = (r(t+Δt) - r(t)) / Δt; Construct a regression model using the S_i value within the sliding window; If the variance Var(S_i) of S_i is less than the threshold σ², the microcrack evolution process is considered to be relatively stable.

[0007] Preferably, the matching and triggering of the cooling fan speed graded control command further includes: A model is established to establish the relationship between the degree of microcrack evolution and the cooling wind speed: F(m) = k × m + b, where m is the degree of microcrack evolution, k is the proportionality coefficient, and b is the base value. The normalization algorithm maps m to the wind speed parameter range [V_min, V_max]. The actual wind speed V is calculated using linear interpolation: V = V_min + (V_max - V_min) × (m / m_max); If V is greater than the set maximum wind speed V_max, then V = V_max.

[0008] Preferably, the step of triggering the cooling section wind speed graded control command based on the microcrack evolution degree prediction result further includes: Multiple wind speed levels are set: V1, V2, V3, and V4, which correspond to low, medium, high, and ultra-high wind speeds, respectively. The degree of microcracks is divided into four levels: M1 (0≤m) <m1),M2(m1≤m<m2),M3(m2≤m<m3),M4(m≥m3); Match wind speed levels using a preset mapping table: M1→V1, M2→V2, M3→V3, M4→V4; If m exceeds the M4 threshold range, an emergency cooling process will be initiated, increasing the wind speed level by two levels.

[0009] Preferably, the cooling and shaping stage further includes: Multiple sub-segments are set in the cooling zone, each corresponding to a different wind speed level; The particle surface temperature T_surface is collected using a real-time temperature detection module. If T_surface drops to the critical point T_threshold, then switch to the next wind speed level; Select the appropriate control logic based on the wind speed level: V1 → constant temperature cooling, V2 → gradient cooling, V3 → rapid cooling adjustment, V4 → rapid shaping.

[0010] Preferably, the reduction in breakage rate caused by the rapid cooling stress further includes: Calculate the breakage rate P_b = (n_b / n_total) × 100%, where n_b is the number of broken particles and n_total is the total number of particles; Define the breakage rate limit target value as P_target = 5%; The cooling fan speed is dynamically adjusted through a feedback mechanism. If P_b > P_target, then increase the wind speed to the next level by a fixed step size ΔV until P_b is below the limit.

[0011] Preferably, the microcrack evolution prediction model further includes: Extract the principal component variables PC1, PC2, and PC3 from the reflectance data; The mathematical relationship between PC and microcrack evolution is established using a multiple linear regression model: m = a1×PC1 + a2×PC2 + a3×PC3 + ​​c, where a1, a2, and a3 are regression coefficients, and c is the intercept term; A correction factor λ is introduced to eliminate errors caused by equipment deviation; If the corrected model residual exceeds the threshold ε, the abnormal data verification process will be initiated.

[0012] Preferably, the airflow control of the cooling section further includes: Set a time window ΔT for each cooling section and switch the fan speed level according to the preset time point; Calculate the current particle's residence time t_remaining to control the timing of the response; If t_remaining < 5 × ΔT, then the acceleration / deceleration mechanism is activated; If t_remaining≥5×ΔT, then a smooth deceleration method is adopted to reduce temperature shock.

[0013] Preferably, the wind speed grading control command for triggering the cooling section further includes: Set different initial velocity values ​​in the wind speed stratification mechanism: V0_1, V0_2, V0_3, V0_4; The initial wind speed is dynamically adjusted based on the degree of microcrack evolution (m). The initial wind speed is set using the following formula: V0 = V0_min + (V0_max - V0_min) × (m / m_max); If m exceeds m_max, then V0_max will be forced to be used as the initial wind speed.

[0014] This disclosure provides a method for enhancing the calorific value of biomass pellet fuel, comprising: continuously acquiring reflectance data of the biomass pellet surface in both 940nm and 1200nm bands using a near-infrared spectrometer, and obtaining time-series variation data of reflectance; calculating the decay rate of the ratio of the two-band reflectance based on the time-series variation data of reflectance, constructing a prediction model for the degree of microcrack evolution inside the biomass pellet based on the decay rate, and obtaining the prediction result of the degree of microcrack evolution of the corresponding batch of biomass pellets; matching and triggering a wind speed graded control command for the corresponding cooling section of the batch of biomass pellets according to the prediction result of the degree of microcrack evolution, wherein the wind speed graded control command is set with multi-level cooling wind speed parameters positively correlated with the degree of microcrack evolution; and completing the cooling and shaping of the biomass pellets under the cooling wind speed matched by the wind speed graded control command. The solution of this disclosure can predict the degree of surface microcrack evolution based on the temporal change of near-infrared reflectivity of particle surface, and trigger the corresponding batch of cooling section wind speed and time segment graded control command to solve the problems of excessive breakage rate caused by rapid cooling stress and uneven heat value of different batches. Attached Figure Description

[0015] In the accompanying drawings, unless otherwise specified, the same reference numerals throughout the various drawings denote the same or similar parts or elements. These drawings are not necessarily drawn to scale. It should be understood that these drawings depict only some embodiments disclosed in this application and should not be construed as limiting the scope of this application.

[0016] Figure 1 This is a flowchart of a method for improving the calorific value of biomass pellet fuel; Figure 2 This is a block diagram of a biomass pellet fuel calorific value enhancement system. Detailed Implementation

[0017] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0018] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0019] First, refer to Figure 1 The present invention describes the specific steps of the biomass pellet fuel calorific value enhancement treatment method, which specifically includes: S101: For biomass pellet fuel that has been granulated and dried, a near-infrared spectrometer is used to continuously collect the reflectance of the biomass pellet surface in both 940nm and 1200nm bands to obtain the time-series variation data of reflectance. Specifically, after pelleting and drying, the biomass pellet fuel is evenly spread on the surface of the conveyor belt by a vibrating feeder, forming a single-layer pellet flow. A portable near-infrared spectrometer based on an InGaAs sensor is installed approximately 50 mm above the pellet surface on the conveyor belt. This spectrometer is equipped with two narrow-band filters at 940 nm and 1200 nm, and the sampling frequency is set to 10 Hz to ensure continuous capture of the pellet surface condition. In this application, the 940nm band exhibits extremely high specificity and sensitivity to the free water, bound water, and hydroxyl structure on the surface of biomass particles, enabling precise capture of the core factors driving the initiation and propagation of microcracks during the cooling process. Meanwhile, the 1200nm band is highly sensitive to the core chemical bond state of cellulose and lignin, which constitute the mechanical strength framework of the particles, and is minimally affected by fluctuations in surface moisture. It can stably reflect the intrinsic state of the particle structure and stress damage changes, and can also serve as a stable reference benchmark. By calculating the reflectance ratio of the two bands, it effectively offsets various common-mode interferences in industrial production scenarios, such as light source attenuation, ambient stray light, fluctuations in particle surface roughness and spreading state, and equipment vibration, extracting high signal-to-noise ratio quantification signals that are strongly correlated only with microcrack evolution. At the same time, both bands belong to the near-infrared short-wave region, and the detection depth is focused on the particle surface, enabling precise capture of early signals of microcrack initiation from the surface.

[0020] As particles pass through the detection area at a belt speed of 0.2 m / s, the spectrometer continuously collects the reflectance values ​​of each particle surface at wavelengths of 940 nm and 1200 nm at a rate of once every 0.1 seconds. Since the particles are continuously distributed on the conveyor belt, the acquisition system performs mean filtering on the reflectance values ​​of multiple particles acquired within each time window (e.g., 0.5 seconds) to form representative reflectance data R_940(t) and R_1200(t) for that moment. To ensure data stability, a 2-meter-long pre-detection section is set up before the particles enter the cooling zone, with continuous acquisition time of no less than 10 seconds, thereby obtaining time-series reflectance variation data covering the critical period of initial temperature field change in the particles. All data is transmitted to the industrial control computer in real time and undergoes normalization preprocessing to eliminate light source attenuation and environmental stray light interference.

[0021] S102: Calculate the attenuation rate of the dual-band reflectivity ratio based on the time-series reflectivity variation data, construct a prediction model for the evolution degree of microcracks inside biomass particles based on the attenuation rate, and obtain the prediction results of the microcrack evolution degree of corresponding batches of biomass particles. After acquiring the time-series reflectance variation data, the system first calculates the dual-band reflectance ratio r(t) = R_940(t) / R_1200(t). The 940nm wavelength is sensitive to the surface moisture and hydroxyl structure of the particles, while the 1200nm wavelength mainly reflects the chemical bond state of cellulose and lignin. The ratio of the two decreases regularly with the migration of internal moisture and the initiation of microcracks. Specifically, the control computer plots the change curve of the ratio r(t) with the time series t as the abscissa and uses the five-point moving average method to eliminate high-frequency noise. Then, the decay rate dr / dt is calculated, that is, the slope of the ratio change is calculated in every 0.5-second time window. If the absolute value of the slope exceeds the preset threshold Δr (Δr is 0.015 / s in this embodiment), it is determined that the microcrack evolution rate inside the particles of this batch is relatively fast, and a more moderate cooling strategy needs to be adopted in the subsequent cooling.

[0022] Furthermore, to construct a predictive model for the degree of microcrack evolution, the system divides the entire time series window into n sliding windows, each with a duration of Δt = 2 seconds and an overlap rate of 50%. The slope S_i of the ratio within each window is calculated, and a linear regression model is constructed using all S_i values ​​to reflect the overall trend of ratio decay. Simultaneously, the variance Var(S_i) of the slope of each window is calculated. When Var(S_i) is less than a set threshold σ² (σ² is 0.002 in this embodiment), the microcrack evolution process is considered relatively stable; otherwise, it indicates drastic evolution. Finally, a prediction model is established by correlating the window slope sequence with the pre-calibrated microcrack area proportions using multiple linear regression: m = 0.32·|dr / dt|_avg + 0.18·Var(S_i) + ε, where m is the quantified value of the degree of microcrack evolution (dimensionless, range 0~1), and ε is a correction term. The model is dynamically updated for each batch of particles, enabling real-time prediction of the evolution of microcracks.In the microcrack evolution prediction model of this application, the core function of the correction term ε is to compensate for the systematic deviation between the fitted value of the dual-band reflectivity characteristics and the actual microcrack evolution degree of the particles. Its determination needs to be completed in conjunction with laboratory benchmark calibration, working condition adaptation correction and dynamic iteration at the production end. Specifically, firstly, standard calibration samples of biomass particles covering the entire working condition range of industrial production are prepared, including particle samples with different raw material types, molding pressure, drying moisture content and initial internal stress state. Micro-CT scanning detection is used as the benchmark true value method to accurately measure the microcrack evolution degree of each calibration sample. The crack area ratio was quantified to obtain the dimensionless measured value m_actual of the actual microcrack evolution degree. Subsequently, under the same testing environment, equipment parameters, and operating conditions as the production line, the time-series data of the 940nm and 1200nm dual-band reflectivity of each calibration sample were simultaneously collected. The two core model input variables, the absolute value of the average attenuation rate |dr / dt|_avg and the variance of the sliding window slope Var(S_i), were calculated for the corresponding samples. Then, the input variables and measured values ​​of all calibration samples were linearly fitted using the least squares method to determine the model core. After setting the coefficients to 0.32 and 0.18, the deviation Δm = m_actual - m_pred between the predicted value m_pred and the measured value m_actual for each calibration sample is calculated. Outlier data exceeding the 95% confidence interval are removed using the Grubbs test. The arithmetic mean of the remaining valid deviation data is used as the basic calibration value for the correction term ε. Based on this, considering the fluctuations in production line environmental temperature and humidity, and the changes in operating conditions caused by batch variations of raw materials, a dynamic adaptation range for ε is set. Simultaneously, the calibration results of the equipment system deviation correction factor λ are used to adjust ε. A second correction is performed to eliminate additional errors caused by inherent deviations in equipment and environment, such as spectrometer light source attenuation, optical path offset, and stray light. Finally, in the continuous industrial production process, ε is periodically updated by using online breakage rate detection data of each batch of particles and measured values ​​of microcracks obtained from regular micro-CT inspections. When the prediction residual after model correction exceeds the set threshold, calibration samples of newly added working conditions are immediately added to refit and correct the value of ε, ensuring that the correction term ε can stably compensate for the model system deviation and continuously guarantee the accuracy and robustness of the microcrack evolution prediction model under all working conditions.

[0023] In another embodiment, to address the issue that reflectance time-series data in the continuous industrial production of biomass pellets is susceptible to interference from production line vibration and stray light from the environment, the 940nm and 1200nm dual-band reflectance time-series data continuously acquired by a near-infrared spectrometer are first decomposed and reconstructed using db4 wavelet transform in three layers to remove high-frequency random noise and baseline drift interference, resulting in smoothed and denoised time-series reflectance sequences R_940(t) and R_1200(t). Then, a non-overlapping sliding window is constructed with a step size of 1s and a window width of 3s. Within each sliding window, the dual-band reflectance ratio r(t) = R_940(t) / R_1200(t) is calculated, and the average decay of the window is calculated by the ratio of the cumulative change in r(t) within the window to the window duration. The deceleration rate v_i is used as an auxiliary feature parameter. Then, the range and kurtosis of r(t) within the window are extracted simultaneously. The decay rate feature set of biomass pellet standard samples with different microcrack evolution degrees, which are pre-calibrated by scanning electron microscopy and micro-CT and cover different raw materials, moisture content, molding pressure conditions and different microcrack evolution degrees, is used as the training set to construct a microcrack evolution degree prediction model based on gradient boosting decision tree. The real-time decay rate sequence features and auxiliary features of the current batch are input into the model. At the same time, the real-time surface temperature and initial drying moisture content of the pellets in this batch are introduced as covariates to correct the model output. Finally, the dimensionless microcrack evolution degree quantitative prediction result of the current batch of pellets is output, and the model prediction confidence is output simultaneously. When the confidence is lower than 90%, the data re-sampling and secondary prediction process is triggered.

[0024] In another embodiment, considering the phased evolution characteristics of moisture migration and thermal stress release during the cooling process of biomass pellets, the time-series data of reflectance in the 940nm and 1200nm dual-band wavelengths collected by the near-infrared spectrometer are first segmented. The first 5 seconds before the pellets enter the detection zone are taken as the initial baseline segment, and each subsequent 2 seconds constitutes a dynamic monitoring segment. The baseline segment data is first normalized to eliminate the initial reflectance deviation caused by differences in raw materials from different batches of pellets and the initial surface state. Then, the dual-band reflectance ratio r(t) = R_940(t) / R_1200(t) is calculated segment by segment. Based on the Arrhenius dynamic equation, the decay dynamic curve of r(t) within each monitoring segment is fitted, and the rate constant k of the curve is extracted as the characteristic decay rate of that monitoring segment. Simultaneously... The rate of change Δk between adjacent monitoring segments was calculated as the core characteristic index of accelerated microcrack evolution. Subsequently, 200 sets of standard calibration samples covering four microcrack evolution levels (low, medium, high, and extremely high) were selected. The full-cycle decay rate constant sequence and the true value data of the corresponding microcrack volume ratio measured by micro-CT were collected. After dimensionality reduction of the high-dimensional rate feature set by principal component analysis, a partial least squares regression prediction model was constructed. At the same time, an online self-calibration link was embedded in the model. After every 10 consecutive batches, the offline microcrack sampling data of the 10 batches were used to incrementally learn and update the model. Finally, the real-time characteristic decay rate sequence of the current batch was input into the calibrated model, and the microcrack evolution degree level classification results and continuous quantitative prediction values ​​of the corresponding batch of biomass particles were output.

[0025] S103: Based on the predicted result of the microcrack evolution degree, match and trigger the wind speed graded control command of the cooling section corresponding to the batch of biomass particles. The wind speed graded control command is set with multi-level cooling wind speed parameters that are positively correlated with the microcrack evolution degree. Based on the predicted microcrack evolution degree *m* obtained in step S102, the control system calls the pre-stored wind speed graded control command library. In this embodiment, four cascaded cooling sub-segments are set in the cooling zone, each 1.5 meters long, corresponding to four wind speed levels: V1, V2, V3, and V4. The wind speed control command adopts a mapping mechanism positively correlated with the microcrack evolution degree: when *m* < 0.2, it is determined to be a low evolution degree, triggering the V1 level (wind speed 1.5 m / s); when *0.2* ≤ *m* < 0.4, it triggers the V2 level (wind speed 2.5 m / s); when *0.4* ≤ *m* < 0.6, it triggers the V3 level (wind speed 3.8 m / s); and when *m* ≥ 0.6, it triggers the V4 level (wind speed 5.0 m / s). If the m value exceeds 0.8, the system classifies it as a high-risk batch. In addition to executing the V4 setting, an emergency cooling process is initiated, which involves adding a pre-cooling air curtain at the front of the cooling zone to increase the initial wind speed to 6.0 m / s and extending the gradient cooling time. All wind speed commands are executed through a variable frequency fan with a response time of less than 0.5 seconds, ensuring real-time control. Furthermore, the system establishes a relational model F(m) = 2.5·m + 1.2 for continuous domain wind speed calculation and uses linear interpolation to smoothly transition between settings, avoiding secondary thermal shock to particles caused by sudden wind speed changes.

[0026] S104: The cooling and shaping of biomass pellets is completed under the cooling wind speed matched by the wind speed graded control command.

[0027] Under the execution of wind speed graded control commands, biomass pellets sequentially pass through four cooling sub-sections. Each sub-section inlet is equipped with an infrared temperature sensor to monitor the pellet surface temperature in real time. When the pellet surface temperature drops from an initial approximately 90°C to below 45°C, the control system switches to the next cooling level based on the current wind speed: V1 uses constant temperature cooling, maintaining a temperature gradient of less than 5°C / min; V2 uses gradient cooling, with a cooling rate controlled at 8-12°C / min; V3 implements rapid cooling adjustment, ensuring the surface and center temperature difference does not exceed 25°C; V4 is used for rapid shaping, combined with extended residence time to homogenize the internal temperature of the pellets. After cooling, the pellets enter the collection chamber, where an online breakage rate detection device counts the number of broken pellets. In this embodiment, statistics were compiled for 10 batches of continuous production, and the average breakage rate decreased from 12.6% in the traditional process to 7.8%, with the lowest batch reaching 6.9%, all below the target limit of 8.5%. Meanwhile, calorific value testing was conducted according to GB / T30727-2014 standard. The average lower heating value of the treated particles increased from 16.8 MJ / kg in the original process to 17.5 MJ / kg, an increase of approximately 4.2%. Furthermore, the batch-to-batch standard deviation of calorific value decreased from 0.35 MJ / kg to 0.12 MJ / kg, significantly improving the uniformity of calorific value. Experimental data show that by predicting the evolution of microcracks in real time and implementing graded wind speed control, crack propagation and breakage caused by rapid cooling stress were effectively suppressed, ensuring the integrity of the particle structure and thus achieving a stable increase in combustion calorific value.

[0028] In this invention, after acquiring the reflectance time-series data collected by the near-infrared spectrometer, the decay rate of the dual-band reflectance ratio is further calculated. Specifically, the industrial control computer first extracts the 940nm and 1200nm reflectance values ​​corresponding to each sampling time t, denoted as R_940(t) and R_1200(t) respectively, with a sampling frequency of 10Hz and a sampling duration of 15 seconds, totaling 150 sets of data. Subsequently, the system calculates the dual-band reflectance ratio r(t) = R_940(t) / R_1200(t) point by point, forming a ratio time-series sequence. To calculate the decay rate, this embodiment uses the central difference method to approximate the derivative: for the internal time point t_i, the rate of change dr / dt = [r(t_{i+1}) - r(t_{i-1})] / (2Δt), where Δt = 0.1 seconds is the sampling interval; for the first and last endpoints, forward difference and backward difference are used for calculation respectively. After obtaining a continuous decay rate sequence, the system sets a threshold Δr = 0.012 / s. This threshold was determined through prior orthogonal experiments. When the absolute value of the decay rate exceeds 0.012 / s, it indicates that the surface moisture evaporation rate of the particles is accelerating, internal stress release is severe, and the microcrack propagation rate enters a high-risk range. The system monitors the absolute value of dr / dt at each sampling point in real time. If the absolute values ​​at three consecutive sampling points are all greater than Δr, it is determined that the microcrack evolution rate inside the particles in this batch is relatively fast, and an early warning indicator is generated on the control interface, providing a quantitative basis for subsequent wind speed regulation. This calculation process is executed independently for each batch to ensure the real-time nature and accuracy of the judgment.

[0029] Furthermore, in constructing the prediction model based on the attenuation rate, the system divides the time-series data of the dual-band reflectivity ratio r(t) changing over time into n sliding windows. The window duration Δt is set to 2 seconds, and the overlap rate between windows is 50%, meaning it slides once every 1 second. For each window, the ratio sequence within the window is extracted, and the least squares method is used for linear fitting to calculate the slope S_i=[r(t+Δt)-r(t)] / Δt, where t is the start time of the window. Through the above operations, the system obtains a series of slope values ​​S_1, S_2, ..., S_n, forming a slope sequence. Subsequently, the system uses these slope values ​​to construct a regression model, specifically using linear regression analysis to determine the trend of slope change over time, in order to determine whether there are acceleration or deceleration characteristics in the evolution of microcracks. Simultaneously, the system calculates the variance Var(S_i) of all S_i. If the variance is less than the preset threshold σ²=0.0015, it is considered that the slope fluctuations of each window are small, the microcrack evolution process is relatively stable, and the stress release inside the particles is relatively uniform. Conversely, if the variance exceeds the threshold, it indicates that there is significant instability in the evolution process, and a more conservative wind speed strategy needs to be adopted in subsequent cooling control. In this embodiment, this analysis method and the attenuation rate threshold judgment complement each other, together forming a comprehensive evaluation system for the degree of microcrack evolution.

[0030] In addition, in the process of matching and triggering the cooling wind speed graded control command, the system establishes a linear relationship model F(m) = k·m + b between the microcrack evolution degree m and the cooling wind speed V, where the proportional coefficient k is determined according to the wind speed adjustment range of the cooling equipment, and the base value b corresponds to the minimum cooling wind speed. In this embodiment, k is taken as 4.5 and b is taken as 1.2, that is, the relationship model expression is V = 4.5m + 1.2, in m / s. This model linearly maps the microcrack evolution degree m (value range 0~1) to the theoretical wind speed range [1.2, 5.7] m / s. To achieve fine control, the system uses a normalization algorithm to map m to the wind speed parameter range [V_min, V_max], where V_min = 1.5 m / s is the minimum effective wind speed allowed by the equipment, and V_max = 5.5 m / s is the maximum safe wind speed. Specifically, the system calculates the actual operating wind speed using a linear interpolation formula: V = V_min + (V_max - V_min) × (m / m_max), where m_max is set to 1.0. Taking a batch with a predicted m=0.45 as an example, the calculated V = 1.5 + (5.5 - 1.5) × (0.45 / 1.0) = 3.3 m / s. If the m value exceeds the calibration range due to abnormal conditions, causing the calculated wind speed V to be greater than V_max, the system automatically sets V = V_max and alerts the operator to check the raw materials or process status via an alarm. This linear mapping and upper limit cutoff mechanism ensures that wind speed regulation is closely related to the degree of microcrack evolution while being strictly controlled within the safe operating boundaries of the equipment.

[0031] To further simplify the control logic and improve system response efficiency, this embodiment additionally sets up a discrete wind speed level mapping mechanism. Specifically, the system pre-classifies the cooling fans into four wind speed levels: V1=1.8m / s corresponds to low wind speed, V2=2.8m / s corresponds to medium wind speed, V3=4.2m / s corresponds to high wind speed, and V4=5.5m / s corresponds to ultra-high wind speed. Simultaneously, the microcrack evolution degree m is divided into four levels: M1 corresponds to 0≤m<0.2, M2 corresponds to 0.2≤m<0.4, M3 corresponds to 0.4≤m<0.6, and M4 corresponds to m≥0.6. The system has a built-in mapping table. When the predicted m value is obtained, the corresponding wind speed level is directly matched by looking up the table: if m belongs to M1, V1 is triggered; if it belongs to M2, V2 is triggered; if it belongs to M3, V3 is triggered; and if it belongs to M4, V4 is triggered. In this embodiment, an emergency cooling process is also set up to cope with extreme operating conditions: when the m value exceeds the M4 threshold range, i.e., m≥0.8, the system determines that the evolution of microcracks inside the particles is close to the critical state, and the conventional M4 setting is insufficient to effectively suppress crack propagation. At this time, the wind speed level is forcibly increased by two levels, i.e., directly jumping from V4 to V6 (in this embodiment, V5=6.2m / s and V6=7.0m / s are predefined as emergency wind speeds), and the residence time in the front section of the cooling zone is shortened, so that a hardened layer can be quickly formed on the particle surface to resist internal expansion stress. During the execution of this emergency process, the system synchronously records abnormal batch information for subsequent process optimization reference. By combining discrete level mapping and emergency mechanism, a balance is achieved between the simplicity of control logic and robustness in dealing with extreme operating conditions.

[0032] Furthermore, during the cooling and shaping stage, this embodiment further refines the segmented control of the cooling process. The cooling zone is divided into four sequentially connected sub-segments along the conveyor belt's running direction: a pre-cooling segment, a slow cooling segment, a rapid cooling segment, and a final cooling segment. Each sub-segment is 1.8 meters long and is independently equipped with a variable frequency centrifugal fan and an airflow equalization device, enabling independent adjustment of different wind speed levels. At the end of each sub-segment, a non-contact infrared temperature sensor is installed, with the sensor probe approximately 100 mm from the particle surface. The measurement accuracy is ±0.5℃, used to collect the particle surface temperature T_surface in real time after passing through that sub-segment. The system presets a critical point temperature T_threshold of 55℃. This temperature value is based on previous experiments, indicating that when the particle surface temperature drops below 55℃, the internal moisture migration rate significantly decreases, thermal stress sensitivity weakens, and it is suitable to switch to the next cooling wind speed. The specific control logic is as follows: When the particles enter the first sub-section (pre-cooling section) of the cooling zone, the initial matched wind speed level is executed; the T_surface at the outlet of the first sub-section is monitored in real time. If the temperature drops below T_threshold, the control system automatically switches to the next wind speed level when the particles enter the second sub-section (slow cooling section); subsequent sub-sections follow the same logic. At the same time, the system selects the corresponding control logic according to the current wind speed level: when the wind speed level is V1, a constant temperature cooling mode is adopted, and the temperature difference between the ambient temperature and the particle surface is maintained within 5℃ through PID regulation to avoid sudden temperature changes; when the wind speed level is V2, a gradient cooling mode is adopted, and the cooling rate per meter is controlled to not exceed 8℃ / m to ensure that the internal temperature field of the particles decreases uniformly; when the wind speed level is V3, a rapid cooling regulation mode is adopted, allowing the cooling rate to be increased to 15℃ / m, but reducing thermal shock through pulsed air supply; when the wind speed level is V4, a rapid shaping mode is adopted, and a continuous strong wind is used to quickly reduce the surface temperature of the particles to room temperature, so that the surface layer hardens rapidly. Through the above-mentioned segmented control and logic matching, a high degree of coordination between the cooling process and the degree of microcrack evolution was achieved.

[0033] In addition, after cooling and shaping, the biomass pellets are conveyed to an online inspection device via a conveyor belt. This device includes a vibrating sieving unit and a machine vision unit. The vibrating sieving unit uses a three-layer standard test sieve with sieve aperture sizes of 4.0mm, 3.0mm, and 2.0mm, separating intact pellets from broken pellets through vibration at an amplitude of 0.5mm and a frequency of 25Hz. Under white LED backlighting, the machine vision unit acquires images of the oversize and undersize particles, identifies and counts the number of broken particles (n_b) and the total number of particles (n_total) using an edge detection algorithm, and then calculates the breakage rate P_b = (n_b / n_total) × 100%. In this embodiment, a breakage rate limit target value P_target = 5% is defined, which is set according to the product quality standard. To achieve feedback control, the system sets up the following dynamic adjustment mechanism: after each batch inspection is completed, the actual breakage rate P_b is compared with the target value P_target. If P_b > P_target, it indicates that the current wind speed setting is insufficiently matched to the microcrack evolution. The system automatically increases the wind speed level corresponding to the same evolution stage in the next batch by a fixed step size ΔV = 0.5 m / s. For example, if the original matching V2 = 2.8 m / s, and the breakage rate exceeds the limit, the wind speed for the next batch will be adjusted to 3.3 m / s. After each adjustment, the system continues to monitor P_b of subsequent batches. If three consecutive batches are all below P_target, the wind speed will be gradually reduced back to the original setting to avoid excessive cooling and energy waste. If P_b of a batch exceeds 8.5%, the system will not only increase the wind speed but also trigger an alarm to prompt operators to check the raw material moisture content or molding pressure and other pre-process parameters. This feedback mechanism uses actual breakage rate data to reverse-correct the wind speed matching model, enabling the control parameters to adaptively optimize with changes in production conditions.

[0034] To further improve the accuracy and robustness of the microcrack evolution prediction model, this embodiment introduces principal component analysis and multiple linear regression methods. In the reflectance time-series data preprocessing stage, the system extracts all time-series data of 940nm and 1200nm dual-band reflectance within a 15-second acquisition period to form the original feature matrix. Subsequently, principal component analysis is used for dimensionality reduction and feature extraction: first, the original data is standardized to eliminate the influence of dimensions; then, the correlation coefficient matrix is ​​calculated, and eigenvalues ​​and eigenvectors are solved; the top three principal components with a cumulative variance contribution rate of over 95% are extracted, denoted as PC1, PC2, and PC3, respectively. In this embodiment, PC1 mainly reflects the overall reflectance drift caused by moisture migration, PC2 mainly reflects the dynamic characteristics of the dual-band ratio change, and PC3 captures high-frequency fluctuation information. When establishing the prediction model, the system uses multiple linear regression to establish the mathematical relationship between the principal components and the degree of microcrack evolution: m = a1·PC1 + a2·PC2 + a3·PC3 + ​​c, where the regression coefficients a1, a2, a3 and the intercept term c are determined through previous calibration experiments. In this embodiment, a1 = 0.23, a2 = 0.41, a3 = 0.16, and c = 0.05 are obtained by fitting 50 sets of calibration samples. To eliminate prediction errors caused by equipment deviations such as spectrometer light source attenuation and ambient temperature drift, the system introduces a correction factor λ. This factor is obtained by testing the standard reference board daily after powering on and calculating the deviation between the model prediction value and the calibration value. The regression model output is corrected during actual prediction: m_corrected = λ·m. If the prediction residual of the corrected model (i.e., the difference between the corrected predicted value and the measured value from micro-CT) exceeds the threshold ε=0.08, the system determines that the model is abnormal and immediately initiates the abnormal data verification process: automatic wind speed control for this batch is suspended and switched to the preset safe wind speed level. Simultaneously, the reflectance data, predicted values, and measured values ​​for this batch are uploaded to the cloud analysis platform for review and model parameter updates by engineers. Through principal component analysis dimensionality reduction, multivariate regression modeling, and correction factor adjustments, the prediction model significantly improves accuracy and environmental adaptability while maintaining computational efficiency.

[0035] In the process of wind speed control in the cooling section, this embodiment further introduces a time window mechanism and dynamic control of residence time to improve the timeliness of wind speed switching and the effect of suppressing temperature shocks. The cooling zone is divided into three sub-segments along the conveyor belt running direction. Each sub-segment is equipped with an independent fan unit and airflow equalization device. The system sets a time window ΔT for each sub-segment. In this embodiment, ΔT is set to 5 seconds. This time window matches the average residence time of particles in each sub-segment. Within each sub-segment, the system switches the wind speed level sequentially according to preset time points. Specifically, taking the moment when the particles enter the sub-segment as the starting point, the wind speed level is switched step by step at three time points: 0 seconds, 5 seconds, and 10 seconds, thereby achieving gradient cooling and avoiding sudden temperature changes caused by a one-time large adjustment of the wind speed.

[0036] To achieve precise response timing control, the system calculates the remaining residence time t_remaining of the particles in the cooling zone in real time. This calculation is based on the belt speed signal v (set to 0.2 m / s in this embodiment) fed back by the conveyor encoder and the distance L_remain from the particle's current position to the cooling zone outlet, obtained through the formula t_remaining = L_remain / v, with an update frequency of once per second. The system formulates different control strategies based on the multiple relationship between t_remaining and the time window ΔT: when t_remaining < 5 × ΔT (i.e., less than 25 seconds), it indicates that the particles are about to leave the cooling zone, and the system activates an acceleration and deceleration mechanism. The specific operation of this mechanism is as follows: if the current wind speed level has not yet reached the target matching value, the intermediate level is skipped, and the wind speed is rapidly increased to the target wind speed at a rate of one level every 2 seconds, ensuring that the main cooling process is completed before the particles leave the cooling zone; at the same time, the fan unit adopts a feedforward compensation method, and immediately increases the fan speed response slope after detecting that t_remaining has entered the threshold, so that the airflow output reaches 90% of the set value within 3 seconds. When t_remaining ≥ 0.5 × ΔT (i.e., greater than or equal to 2.5 seconds), the system adopts a smooth deceleration method. This involves introducing a first-order inertial filter during wind speed switching, setting the filter time constant to 1.2 seconds. This allows the fan frequency to smoothly transition from the current value to the target value according to an exponential curve, avoiding sudden wind speed changes that could cause instantaneous impact on the particle surface. Taking a batch of measured data as an example, when t_remaining = 4 seconds, the system determines that the smooth deceleration condition is met and extends the switching time from V3 to V4 from instantaneous to 2.5 seconds. The maximum wind speed change rate during the switching process is 1.2 m / s². Infrared thermal imager monitoring shows that the particle surface temperature fluctuation range is reduced from ±3.2℃ in the original process to ±1.1℃, effectively reducing the peak thermal stress. Through the coordinated control of the time window division and residence time response, refined wind speed regulation and minimized temperature shock are achieved.

[0037] In the initial stage of triggering the cooling section wind speed graded control command, this embodiment introduces a dynamic initial velocity value setting method from the wind speed stratification mechanism to further enhance the matching accuracy between the control command and the microcrack evolution level. The system pre-sets four different initial velocity values, corresponding to different microcrack evolution levels: V0_1=1.2m / s corresponds to low evolution level, V0_2=2.0m / s corresponds to low-to-medium evolution level, V0_3=3.0m / s corresponds to medium-high evolution level, and V0_4=4.2m / s corresponds to high evolution level. Unlike fixed-mapped discrete wind speed levels, the initial velocity value in this embodiment is not directly used as the execution wind speed, but rather as the base speed for fan startup at each level, and is fine-tuned based on other feedback parameters during subsequent cooling processes.

[0038] Specifically, the system dynamically adjusts the initial wind speed of each cooling segment based on the microcrack evolution degree *m* predicted in step two. The initial wind speed is set using a continuous domain mapping formula: V0 = V0_min + (V0_max - V0_min) × (m / m_max), where V0_min is the minimum initial speed (1.0 m / s in this embodiment), V0_max is the maximum initial speed (5.0 m / s in this embodiment), and m_max is 1.0. Taking a batch with a predicted m=0.35 as an example, V0 = 1.0 + (5.0 - 1.0) × (0.35 / 1.0) = 2.4 m / s is calculated, and the system uses this value as the initial wind speed of the first cooling segment. Subsequently, the system matches the corresponding initial speed group according to the level to which the m value belongs. For example, if m=0.35 falls into level M2, the system sets the initial speed benchmark for this batch to 2.4 m / s, and then makes fine adjustments within a range of ±0.3 m / s based on subsequent temperature feedback.

[0039] When the degree of microcrack evolution m exceeds the preset maximum threshold m_max (m_max=1.0 in this embodiment), for example, if the predicted value of m reaches 1.2 due to abnormal raw materials or detection equipment failure, the system determines that the batch of particles is in an extremely high-risk state. At this time, V0_max is forcibly used as the initial wind speed, that is, the initial wind speed is directly set to 5.0 m / s, and linear interpolation calculation is no longer performed. At the same time, the system locks the initial wind speed of the batch to V0_max and maintains this wind speed throughout the cooling process until the particle surface temperature drops below 55°C, so as to suppress the further propagation of microcracks to the greatest extent. In addition, the system generates alarm information and highlights the abnormal batch number on the operation interface to remind the operator to intervene and check. Through the dynamic setting of the initial speed and the forced handling mechanism of abnormal state, the adaptive optimization of the wind speed graded control command under different operating conditions is realized, which not only ensures the efficient cooling of the regular batches, but also provides reliable safety protection for the extreme batches.

[0040] In addition, such as Figure 2 As shown, the present invention also provides a biomass pellet fuel calorific value enhancement treatment system 200, comprising: The data acquisition system 201 uses a near-infrared spectrometer to continuously acquire the reflectance of the biomass pellets in the 940nm and 1200nm dual-bands to obtain the time-series variation data of reflectance. Prediction module 202 calculates the attenuation rate of the dual-band reflectivity ratio based on the time-series reflectivity change data, constructs a prediction model for the degree of microcrack evolution inside biomass particles based on the attenuation rate, and obtains the prediction result of the degree of microcrack evolution of corresponding batches of biomass particles. The control module 203 matches and triggers the wind speed graded control command of the cooling section corresponding to the batch of biomass pellets based on the predicted result of the microcrack evolution degree. The wind speed graded control command is set with multi-level cooling wind speed parameters that are positively correlated with the microcrack evolution degree. The cooling module 204 completes the cooling and shaping of biomass pellets under the cooling wind speed matched by the wind speed graded control command.

[0041] The functions of each module of the biomass pellet fuel calorific value enhancement system 200 of the present invention have been described above with reference to the method embodiments, and will not be repeated here.

[0042] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for enhancing the calorific value of biomass pellet fuel, characterized in that, include: For biomass pellet fuel that has been granulated and dried, a near-infrared spectrometer was used to continuously collect the reflectance of the biomass pellet surface in both 940nm and 1200nm bands to obtain the time-series variation data of reflectance. The decay rate of the dual-band reflectivity ratio is calculated based on the time-series reflectivity variation data. A prediction model for the evolution degree of microcracks inside biomass particles is constructed based on the decay rate to obtain the prediction results of the microcrack evolution degree of corresponding batches of biomass particles. Based on the predicted results of the microcrack evolution degree, the wind speed graded control command of the corresponding cooling section of the batch of biomass pellets is matched and triggered. The wind speed graded control command is set with multi-level cooling wind speed parameters that are positively correlated with the microcrack evolution degree. The cooling and shaping of biomass pellets is completed under the cooling wind speed matched by the wind speed classification and control command.

2. The method for enhancing the calorific value of biomass pellet fuel according to claim 1, characterized in that, The attenuation rate of calculating the dual-band reflectivity ratio based on reflectivity temporal variation data further includes: The reflectance values ​​R_940(t) and R_1200(t) of biomass particles at continuous time points at wavelengths of 940 nm and 1200 nm were obtained; Calculate the dual-band reflectivity ratio r(t) = R_940(t) / R_1200(t); Calculate the rate of change of the ratio dr / dt based on the time series t; If the absolute value of dr / dt is greater than the set threshold Δr, it is determined that the microcracks in the particles of this batch evolve rapidly.

3. The method for enhancing the calorific value of biomass pellet fuel according to claim 2, characterized in that, The step of constructing a prediction model based on the attenuation rate further includes: The trend of the dual-band reflectivity ratio over time is divided into n sliding windows, each with a duration of Δt. Calculate the slope of the ratio within each window: S_i = (r(t+Δt) - r(t)) / Δt; Construct a regression model using the S_i value within the sliding window; If the variance Var(S_i) of S_i is less than the threshold σ², the microcrack evolution process is considered to be relatively stable.

4. The method for enhancing the calorific value of biomass pellet fuel according to claim 3, characterized in that, The matching and triggering of the cooling fan speed graded control command further includes: A model is established to establish the relationship between the degree of microcrack evolution and the cooling wind speed: F(m) = k × m + b, where m is the degree of microcrack evolution, k is the proportionality coefficient, and b is the base value. The normalization algorithm maps m to the wind speed parameter range [V_min, V_max]. The actual wind speed V is calculated using linear interpolation: V = V_min + (V_max - V_min) × (m / m_max); If V is greater than the set maximum wind speed V_max, then V = V_max.

5. The method for enhancing the calorific value of biomass pellet fuel according to claim 4, characterized in that, The step of triggering a graded control command for the cooling section wind speed based on the prediction results of the microcrack evolution degree further includes: Multiple wind speed levels are set: V1, V2, V3, and V4, which correspond to low, medium, high, and ultra-high wind speeds, respectively. The degree of microcracks is divided into four levels: M1 (0≤m) <m1),M2(m1≤m<m2),M3(m2≤m<m3),M4(m≥m3); Match wind speed levels using a preset mapping table: M1→V1, M2→V2, M3→V3, M4→V4; If m exceeds the M4 threshold range, an emergency cooling process will be initiated, increasing the wind speed level by two levels.

6. The method for enhancing the calorific value of biomass pellet fuel according to claim 5, characterized in that, The cooling and shaping stage further includes: Multiple sub-segments are set in the cooling zone, each corresponding to a different wind speed level; The particle surface temperature T_surface is collected using a real-time temperature detection module. If T_surface drops to the critical point T_threshold, then switch to the next wind speed level; Select the appropriate control logic based on the wind speed level: V1 → constant temperature cooling, V2 → gradient cooling, V3 → rapid cooling adjustment, V4 → rapid shaping.

7. The method for enhancing the calorific value of biomass pellet fuel according to claim 6, characterized in that, The reduction in breakage rate caused by the quenching stress further includes: Calculate the breakage rate P_b = (n_b / n_total) × 100%, where n_b is the number of broken particles and n_total is the total number of particles; Define the breakage rate limit target value as P_target = 5%; The cooling fan speed is dynamically adjusted through a feedback mechanism; If P_b > P_target, then increase the wind speed to the next level by a fixed step size ΔV until P_b is below the limit.

8. The method for enhancing the calorific value of biomass pellet fuel according to claim 7, characterized in that, The microcrack evolution prediction model further includes: Extract the principal component variables PC1, PC2, and PC3 from the reflectance data; The mathematical relationship between PC and microcrack evolution is established using a multiple linear regression model: m = a1×PC1 + a2×PC2 + a3×PC3 + ​​c, where a1, a2, and a3 are regression coefficients, and c is the intercept term. A correction factor λ is introduced to eliminate errors caused by equipment deviations; If the corrected model residual exceeds the threshold ε, the abnormal data verification process will be initiated.

9. The method for enhancing the calorific value of biomass pellet fuel according to claim 8, characterized in that, The airflow control of the cooling section further includes: Set a time window ΔT for each cooling section and switch the fan speed level according to the preset time point; Calculate the current particle's residence time t_remaining to control the timing of the response; If t_remaining < 5 × ΔT, then the acceleration / deceleration mechanism is activated; If t_remaining≥0.5×ΔT, then a smooth deceleration method is adopted to reduce temperature shock.

10. The method for enhancing the calorific value of biomass pellet fuel according to claim 9, characterized in that, The wind speed grading control command for triggering the cooling section further includes: Set different initial velocity values ​​in the wind speed stratification mechanism: V0_1, V0_2, V0_3, V0_4; The initial wind speed is dynamically adjusted based on the degree of microcrack evolution (m). The initial wind speed is set using the following formula: V0 = V0_min + (V0_max - V0_min) × (m / m_max); If m exceeds m_max, then V0_max will be forced to be used as the initial wind speed.

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