Method for optimizing operation parameters and heat supply mode of small biomass boiler

Abstract

The invention provides a small biomass boiler operation parameter and heat supply mode optimization method which comprises the following steps: comparing temperature fluctuation and energy consumption data of different heat supply modes through experiments, determining intermittent heat supply as a reference mode, and setting a dynamic start-stop temperature threshold based on thermal inertia characteristics of a boiler; key operation frequency parameters influencing the combustion efficiency are selected, and a frequency parameter combination enabling the operation cost to be the lowest is screened out through multi-factor orthogonal experiment design; establishing a nonlinear relation model of operation parameters and cost indexes, and solving a global optimal parameter combination by using an intelligent optimization algorithm with a dynamic adjustment mechanism; and the optimized parameter combination is embedded into a boiler control system, and the running state of equipment is dynamically adjusted by monitoring temperature parameters in real time. According to the method, the nonlinear mathematical model of the operation parameters and the cost is established, the parameter influence is accurately quantified, the optimal parameter combination is solved through an intelligent algorithm, the operation cost is reduced, and the combustion efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a method for optimizing operating parameters and heating modes of a small-scale biomass boiler. Background Art

[0002] Biomass heating boilers are widely used in industrial production and residential heating. According to Grand View Research's "Biomass Boilers Market Size & Share | Industry Report, 2030," the global biomass boiler market is expected to reach $7.24 billion in 2024, with a projected compound annual growth rate of 8.5% from 2025 to 2030. Currently, biomass hot water boilers lack a scientific and rational method for optimizing operating parameters, resulting in suboptimal combustion efficiency. Furthermore, due to the lag in the boiler heating system, operators increase the fuel supply to reach the set temperature as quickly as possible, resulting in fuel waste. This traditional operation method relies too much on operator experience, leading to high operating costs. Therefore, there is an urgent need to improve the operating efficiency and reduce operating costs of biomass boilers. Summary of the Invention

[0003] In view of this, the present invention aims to propose a method for optimizing the operating parameters and heating mode of a small biomass boiler, so as to at least solve one problem in the background technology.

[0004] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0005] A method for optimizing operating parameters and heating mode of a small biomass boiler comprises the following steps:

[0006] Dynamic heating mode control phase: Through experimental comparison of temperature fluctuations and energy consumption data of different heating modes, intermittent heating was determined as the baseline mode. A multi-mode selection mechanism was established based on the month type and weekday / holiday attributes, and dynamic start and stop temperature thresholds were set based on the thermal inertia characteristics of the boiler.

[0007] Local optimization of operating parameters: Select key operating frequency parameters that affect combustion efficiency, screen out the frequency parameter combination that minimizes operating costs through multi-factor orthogonal experimental design, and simultaneously apply a PID controller to achieve closed-loop regulation of furnace negative pressure;

[0008] Global parameter modeling and solution phase: Establish a nonlinear relationship model between operating parameters and cost indicators, use an intelligent optimization algorithm with a dynamic adjustment mechanism to solve the global optimal parameter combination, and introduce an indoor and outdoor temperature difference feedback mechanism to dynamically correct the load adjustment threshold;

[0009] Control system integration implementation phase: The optimized parameter combination is embedded in the boiler control system, and the equipment operating status is dynamically adjusted through real-time monitoring of temperature parameters. A linkage control module including automatic feeding and supplementary combustion, coordinated air-material ratio, and safety pressure relief protection is integrated.

[0010] Furthermore, the dynamic start-stop temperature threshold setting process verifies the impact of the shutdown temperature upper limit on waste heat utilization efficiency by conducting multiple sets of comparative experiments, and tests the effect of different startup temperature lower limits on heating stability. Finally, the critical temperature value that can balance temperature fluctuations and fuel consumption is selected as the control node;

[0011] The multi-mode selection mechanism specifically includes a time-based control logic of an agricultural education mode in which the furnace is started three times a day, a daily work mode in which the furnace is started once, and a holiday mode in which the furnace is stopped and on standby.

[0012] Furthermore, the setting process of the dynamic start-stop temperature threshold: taking into account the inherent thermal inertia characteristics of a specific boiler system and the influence of fuel types, the influence of the upper limit of the shutdown temperature on the waste heat utilization efficiency is verified by conducting multiple groups of comparative experiments, and the effect of different lower limits of the start-up temperature on the heating stability is tested at the same time, and finally the critical temperature value that can balance temperature fluctuations and fuel consumption is selected as the control node; the multi-mode selection mechanism specifically includes a time-based control logic of an agricultural education mode of starting the furnace three times a day, a daily working mode of starting the furnace once, and a holiday mode of shutdown standby.

[0013] Furthermore, the multi-factor orthogonal experimental design determines the parameter ranges of the three control variables of blast, feeding, and grate, generates an experimental parameter matrix according to the orthogonal table, uses the range analysis method to identify the influence weight of each parameter on the operating cost, and selects the parameter combination that minimizes the operating cost as the local optimal solution;

[0014] The PID controller collects furnace negative pressure data in real time and dynamically adjusts the induced draft fan frequency using a proportional-integral-differential algorithm to maintain a constant negative pressure value.

[0015] Furthermore, the dynamic adjustment mechanism of the intelligent optimization algorithm automatically adjusts the crossover operation probability according to the population evolution state, and dynamically corrects the mutation operation probability based on the individual fitness, maintaining the excellent individual genetic characteristics during the iteration process, and terminates the calculation and outputs the optimal solution when the preset convergence conditions are met.

[0016] Furthermore, the control system integration is implemented by deploying a temperature monitoring network at the boiler outlet, return water outlet and heating area, building an embedded controller including an equipment control instruction generation module, and developing a human-computer interaction interface with real-time data display and alarm functions, and finally establishing a remote monitoring platform to realize cloud storage and analysis of operation data.

[0017] Further, including:

[0018] Data perception layer: composed of a temperature sensor array and a frequency detection unit, which collects heating system operation data in real time;

[0019] Algorithm processing layer: includes orthogonal test analysis module, mathematical modeling solver and intelligent optimization algorithm library;

[0020] Equipment control layer: connects to the variable frequency drive devices of the blast, unloading, and grate equipment, executes optimized control instructions, and is expanded to include an automatic ignition temperature feedback unit, a multi-bucket elevator linkage control module, and a pressure relief solenoid valve actuator;

[0021] Interactive application layer: Integrates local display terminals and remote monitoring interfaces to achieve digital management of operating status.

[0022] Furthermore, the temperature sensor array includes temperature measuring elements arranged at key nodes of the boiler water circulation pipeline and wireless temperature acquisition terminals distributed in the heating area, wherein the frequency detection unit adopts non-contact measurement technology to obtain the operating frequency of the rotating equipment in real time.

[0023] Furthermore, after receiving the sensor data, the algorithm processing layer first calls the orthogonal test analysis module to sort the parameter sensitivity, then starts the mathematical modeling solver to build a parameter-cost relationship model, and finally completes the iterative optimization of the model parameters through the intelligent optimization algorithm library.

[0024] Furthermore, the equipment control layer includes a multi-speed control circuit of the blower group, a closed-loop feeding system of the unloading mechanism, and a displacement control module of the grate drive device, which respectively achieve optimized control by precisely adjusting the air supply, equipping a weight compensation feedback mechanism, and ensuring that the fuel delivery speed is accurately adjustable.

[0025] Furthermore, the safety pressure relief protection is achieved through a dual-threshold pressure monitoring strategy. When the pipeline pressure is lower than the set lower limit, the water supply pump is activated, and when it is higher than the upper limit, the pressure relief solenoid valve is started. After the pressure exceeds the limit for a continuous timeout, the emergency shutdown protection is triggered.

[0026] Compared with the existing technology, the method for optimizing the operating parameters and heating mode of a small biomass boiler described in the present invention has the following advantages:

[0027] The method for optimizing the operating parameters and heating mode of a small biomass boiler described in the present invention can dynamically adjust the heating start and stop strategy, balance waste heat utilization and temperature stability, establish a nonlinear mathematical model of operating parameters and costs, accurately quantify the impact of parameters, and solve the optimal parameter combination through intelligent algorithms, thereby reducing operating costs and improving combustion efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings, which constitute part of the present invention, are provided to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are provided to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0029] Figure 1 This is a schematic diagram of the optimization method for the operating parameters and heating mode of a small biomass boiler;

[0030] Figure 2 Schematic diagram of the system for optimizing the operating parameters and heating mode of a small biomass boiler;

[0031] Figure 3 This is a schematic diagram of the temperature curve in the continuous heating mode;

[0032] Figure 4 This is a schematic diagram of the temperature curve of the intermittent heating mode;

[0033] Figure 5 This is a schematic diagram of the verification curve for the optimal start-stop temperature of the dynamic intermittent heating mode;

[0034] Figure 6 This is a schematic diagram of the relationship between orthogonal test parameters and operating costs;

[0035] Figure 7 This is a schematic diagram of the AGA algorithm solution flow chart;

[0036] Figure 8 Schematic diagram of the convergence curve of the adaptive genetic algorithm;

[0037] Figure 9 This is the operating logic diagram of the biomass hot water boiler. DETAILED DESCRIPTION

[0038] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0039] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, features defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "multiple" means two or more.

[0040] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0041] The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0042] To address the challenges of existing technologies, this proposal proposes a new dynamic intermittent biomass boiler heating model. First, through orthogonal experiments, we analyze the impact of operating parameters such as blast frequency, feeding frequency, and grate frequency on operating costs and establish a corresponding mathematical model. Then, we incorporate an adaptive genetic algorithm to optimize operating parameters and achieve the lowest operating cost. Practical applications have shown that compared to traditional empirical heating methods, this model reduces operating costs by an average of 24.6% while maintaining stable indoor temperatures. This achievement provides a new solution for low-cost operation of biomass boilers.

[0043] (1) Experimental platform and fuel characteristics

[0044] 1. Hardware configuration:

[0045] The present invention relies on the 2-ton biomass boiler heating pilot system of Tianjin Agricultural College. The biomass hot water boiler used is CDZL1.4-80 / 60-S with a rated power of 31.5KW. The boiler adopts a single-drum longitudinal structure mode, and the heating surface is a membrane water-cooled wall type.

[0046] 2. Fuel selection

[0047] Biomass fuel is a crucial component of biomass boiler operation systems. Its raw materials primarily come from agricultural and forestry residues. They undergo a series of processing steps, including slicing, crushing, cleaning, screening, mixing, and cooling, followed by packaging and pressing, to produce pelletized, environmentally friendly biomass fuel. Wood pellets exhibit high calorific value release and a low ash content (4.56%) during combustion. Compared to other biomass fuels (such as rice straw and corn straw), wood pellets have higher combustion efficiency and produce fewer pollutants. Furthermore, their high volatile matter content facilitates rapid ignition and stable combustion, while their moderate fixed carbon content ensures continuous and stable combustion. These characteristics make wood pellets an efficient and environmentally friendly biomass fuel. Therefore, wood pellets were selected as the biomass fuel in this study.

[0048] (2) Implementation steps of heating mode

[0049] Currently, there are two main heating modes for boilers:

[0050] Continuous Heating Mode: The boiler runs continuously, maintaining a low combustion intensity, continuously supplying heat to the heating system, stabilizing the indoor temperature and reducing temperature fluctuations, creating a comfortable and stable indoor environment for users. Intermittent Heating Mode: The boiler starts and stops according to preset daily schedules to provide heating. This mode provides energy according to actual user needs at different times of the day. The boiler is shut down during non-heating hours, avoiding energy waste and reducing energy costs to a certain extent.

[0051] To determine the most suitable heating mode for small biomass boilers, this study conducted comparative experiments comparing continuous and intermittent heating modes. Through a rigorously designed experimental plan, precise measurement and analysis of indoor temperature fluctuations and key heating cost indicators under different modes were conducted. A comprehensive performance evaluation of the two modes was conducted to identify the most cost-effective heating mode.

[0052] (1) Continuous heating

[0053] The continuous heating experiment aims to explore the heating stability and energy consumption of small biomass boilers under continuous and uninterrupted combustion conditions. At the beginning of the experiment, the outlet water temperature rose slowly and reached 41.74°C in the first 60 minutes. As the combustion continued, the return water temperature also began to rise gradually, from the initial 7.85°C to 26.67°C after 1 hour, and continued to rise in the subsequent time. At the end of the 4-hour experiment, the outlet water temperature reached 81.9°C. In terms of fuel consumption, a total of 633kg of fuel was consumed in 4 hours. Figure 3It is not difficult to find that even if the feeding frequency is adjusted to the lowest, the boiler water temperature continues to rise and is difficult to maintain within a stable and controllable range. This shows that it is currently difficult for small biomass boilers to achieve continuous and controllable operation and heating.

[0054] (2) Dynamic intermittent heating

[0055] The intermittent heating experiment aims to explore the indoor temperature fluctuation control and energy efficiency under the condition of alternating start and stop of a small biomass boiler in a specific time cycle. Since the boiler outlet water temperature directly affects the indoor temperature, the outlet water temperature is selected as the condition for the boiler to start and stop periodically. The test results are as follows Figure 4 As shown in the figure, at the beginning of the experiment, the outlet water temperature rose rapidly, reaching 55°C in the first 27 minutes. As the combustion continued, the return water temperature also began to rise rapidly, from the initial 32°C to 42°C after 30 minutes. In terms of fuel consumption, a total of 327 kg of fuel was consumed in 4 hours. Figure 4 It can be seen that by adopting this heating mode, the outlet water temperature, return water temperature and indoor temperature can be effectively controlled within the expected reasonable range.

[0056] According to the test of the heating mode of the biomass hot water boiler, the intermittent heating mode can meet the heating demand. However, the different setting values ​​of the outlet water temperature of different biomass boilers will lead to different indoor temperatures. Therefore, in order to find the dynamic heating start-stop setting value suitable for the indoor temperature, the present invention will carry out a dynamic intermittent heating test. In the initial experiment, the biomass boiler outlet water temperature was set to stop combustion at 70 degrees Celsius and start combustion at 45 degrees Celsius. Because the residual heat of the system continues to release heat after stopping at 70 degrees Celsius, 45 degrees Celsius is too early to start the combustion to supplement the heat, and the indoor temperature gradually rises and exceeds the human comfort range. In view of this, the stop temperature is adjusted to 55 degrees Celsius, with the intention of reducing the heat output of a single combustion cycle. Since the heat dissipation of the system is reduced when the combustion is stopped at 55 degrees Celsius, it is impossible to maintain a suitable indoor temperature, and frequent starting and stopping will bring additional energy consumption costs. Therefore, in order to balance the indoor heat supply and demand, the stop temperature is adjusted to 65 degrees Celsius again. The experimental results are as follows Figure 5 As shown. Figure 5 As can be seen, the indoor temperature stabilizes at a moderate level. At this point, the system's stored heat meets basic indoor heating needs without overheating the room. Restarting at 40°C replenishes heat, effectively preventing large temperature fluctuations and reducing the number of boiler starts and stops, ensuring a stable indoor temperature while reducing energy consumption.

[0057] (3) Specific process of parameter optimization

[0058] 1. Implementation of orthogonal experiments

[0059] According to the equipment characteristics of biomass hot water boilers and preliminary tests, the blast frequency directly affects the oxygen supply and airflow distribution in the furnace, which in turn plays an important role in combustion efficiency; the feeding frequency determines the supply rate of biomass fuel, which is closely related to the combustion intensity and heat generation; the grate frequency controls the residence time and movement speed of the fuel in the furnace, affecting the completeness of fuel combustion. Therefore, the blast frequency, feeding frequency, and grate frequency were selected as the three factors of this orthogonal experiment. Based on the previous heating experience, four levels were selected for each factor, and the orthogonal experiment optimization combination was carried out according to the L16 (43) table.

[0060] The orthogonal experimental design and results are shown in Table 1.

[0061] Table 1 Orthogonal experimental design and results

[0062]

[0063] Table 1 shows that the optimal operating parameter combination is a blast frequency of 20.5 Hz, a fuel feed frequency of 6.5 Hz, and a grate operating frequency of 13.5 Hz. This combination minimizes operating costs while also meeting indoor temperature requirements. After determining this optimal parameter combination, a two-day validation experiment was conducted to further verify its stability and superiority over long-term operation. With this parameter combination, the cost per furnace startup was approximately 159.4 yuan.

[0064] 2. Model Fitting

[0065] After obtaining a set of parameter combinations with good experimental performance through orthogonal experimental design and intuitive analysis, the present invention introduces a curve fitting method to establish a mathematical model to further explore the potential of the system and achieve more refined parameter control, so as to explore better parameter configurations and the intrinsic quantitative relationship between variables.

[0066] Combining data analysis and actual operational experience, we found that the relationships between variables exhibit significant nonlinear characteristics, making it difficult for simple linear models to accurately describe their complex behavior. Therefore, this patent uses a polynomial nonlinear curve fitting method to construct a mathematical model to better reflect the nonlinear relationships between variables.

[0067] 3. Optimizing operating parameters

[0068] In order to find the minimum value of the above fitting function, that is, to find the operating parameter combination with the lowest operating cost, this patent selects the adaptive genetic algorithm (AGA). The algorithm implementation process is as follows: Figure 6 In order to more intuitively reflect the optimization effect of the algorithm, Figure 7 The convergence curve of the AGA algorithm optimization process is given.

[0069] (IV) Engineering Verification

[0070] This patent successfully obtained the values ​​of blast frequency, feeding frequency and grate frequency when the operation cost of biomass hot water boiler is minimized by using adaptive genetic algorithm (AGA). To verify the validity of the result, this patent conducted a one-day furnace burning test experiment. Figure 8 During the experiment, the operation data was recorded every 5 minutes, including key information such as the outflow and return water temperature, indoor temperature, and equipment operation status. Figure 8 The results clearly show that when operating according to the parameters derived from the AGA algorithm, the biomass hot water boiler was able to meet the indoor temperature requirements throughout the day. Regarding fuel consumption, a total of 1,120 kg of biomass fuel was consumed throughout the day, a 26% decrease compared to the average daily consumption of 1,520 kg using parameters set based on previous experience. This represents an 18% decrease compared to the average daily consumption of 1,360 kg using parameters derived from the orthogonal experiment.

[0071] The optimization method of this solution during implementation is as follows:

[0072] 1. Dynamic intermittent heating mode setting:

[0073] (1) By comparing the experimental data of the continuous heating mode and the intermittent heating mode, it is determined that the intermittent heating mode is a better heating method.

[0074] (2) Design a dynamic intermittent heating test to determine the optimal start and stop temperature setting values ​​(outlet water temperature_start = 40℃, outlet water temperature_stop = 65℃) to balance indoor temperature stability and energy consumption.

[0075] 2. Orthogonal experimental parameter screening:

[0076] Taking blast frequency (20.5-23.5Hz), feeding frequency (5.5-8.0Hz), and grate frequency (10.5-13.5Hz) as variables, an L16 (4³) orthogonal experiment was designed, and the local optimal parameter combination was determined to be (20.5Hz, 6.5Hz, 13.5Hz) through range analysis.

[0077] 3. Adaptive genetic algorithm optimization:

[0078] Establish an operating cost polynomial model and use the AGA algorithm to solve the global optimal parameters

[0079] 、 、 They are respectively the blast, unloading and grate frequencies, in Hz.

[0080] Crossover probability in genetic algorithm and mutation probability Adaptive adjustments are made to speed up the convergence of the algorithm and improve the solution quality. The adjustment formula is as follows:

[0081]

[0082] in, It is the maximum value of the individual objective function of the algorithm in the current iteration process, which reflects the objective function value of the individual with the best performance in the current group; It is the average value of individuals in the current group and is used to measure the average performance level of the entire group; is the objective function value corresponding to the individual participating in the crossover operation and with a larger target value; F is the objective function value of the individual acted upon by the mutation operator; 、 、 、 These constants may vary in different optimization problems and need to be reasonably selected through a large number of preliminary experiments and analyses to ensure that the algorithm can strike a good balance between exploring new solutions and retaining excellent solutions, thereby efficiently searching for the optimal combination of operating parameters.

[0083] In the dynamic intermittent heating mode, the temperature data collection frequency is not less than 5 minutes / time, and the sensors include: outlet water temperature sensor (accuracy ±0.5℃), installed at the boiler outlet; return water temperature sensor (accuracy ±0.5℃), installed at the boiler return water outlet; indoor temperature sensor (accuracy ±1℃), installed at the center of the heating area.

[0084] A small biomass boiler operation optimization system includes a sensor module for collecting temperature and operating parameters; a controller module for outputting control signals; actuators for the induced draft fan, blower, unloading motor, grate motor, circulating pump motor, igniter, ignition fan, slag discharger, multi-bucket elevator, and feeding cage motor, which receive controller signals; a human-machine interface for real-time display of operating status, cost data, and alarm information; a remote monitoring platform for real-time transmission and control of boiler operating status through Internet of Things technology; and a fuzzy PID controller for adjusting the initiator frequency, maintaining negative pressure in the furnace, and improving combustion stability.

[0085] In addition, the control system involved in the actual use of this solution is described in detail as follows:

[0086] Regarding the biomass boiler monitoring part: the biomass boiler monitoring system is mainly designed using touch screen and cloud configuration to achieve local and remote control as well as data query and analysis.

[0087] The touch screen is a key component of the boiler control system. Based on the local control requirements and operating environment of the biomass boiler, the host computer uses a Kunlun Tongtai touch screen. Screen editing processes the actual operating conditions and workflow of the boiler, enabling dynamic interface design. By linking the touch screen with relevant PLC variables, various control commands such as start, stop, and parameter adjustment can be issued. A user rights management interface is designed to protect the system from unauthorized access, ensuring data security for device communications and preventing data leakage and loss. A report interface is designed to record and save data generated during the biomass boiler system's operation, facilitating subsequent data query and analysis.

[0088] To fully ensure the security and stability of system operation, the USR-PLCNET510 UCloud gateway was selected. This integrates multiple monitoring data types, including configuration screens, real-time data, historical data, and camera data, creating a unified monitoring window and resolving data silos. Remote viewing via a PC or mobile phone allows for intuitive and visual monitoring of on-site operating conditions anytime, anywhere.

[0089] For the biomass boiler control system, a Delta AS200 series PLC was selected for the control system of a 2-ton biomass hot water boiler. The AS200 series PLC offers stability, reliability, high performance, and a modular design, meeting the control requirements of the boiler system. Nine expansion modules were selected based on the number of control points. The control system's primary task is to utilize the PLC to start and stop the biomass boiler's motor, valves, water pump, and other equipment, collecting and processing relevant operational data. It also manages related equipment such as the water system, fans, and multi-bucket elevators. This control process effectively improves control accuracy and rationally allocates and executes various control tasks. During the processing, operations follow logical relationships and control sequences.

[0090] Regarding the software for biomass boiler control systems: To address the energy-saving challenges of biomass boilers that integrate power, grid, and load, we are working on key technologies to match boiler heating efficiency with load at different times and locations. We are developing multiple operating modes based on different conditions, such as the month, holidays, weekdays, daytime, and region, to overcome the technical bottleneck of low boiler control system flexibility. To address the challenge of intelligent biomass boiler control systems, we are developing programs for automatic loading and slag discharge, ignition, load adjustment, and air-to-fuel ratio adjustment, and developing an intelligent biomass boiler control system.

[0091] The control strategy for the aforementioned structure is designed as follows: The biomass hot water boiler heats water by burning biomass pellets, which is then supplied to users, achieving heating. The control strategy aims to automatically adjust the boiler's operating state to meet energy demand in different scenarios, thereby achieving energy savings. Combustion experiments were conducted in different months to identify suitable combustion parameters for each month, constructing a combustion empirical model. Subsequently, a decision-making program was developed in Ispsoft software based on these monthly combustion parameters for easy later use. Before the boiler is operational, the user selects a different operating mode via the on-site touch screen or mobile phone cloud. Each mode determines the number of times the boiler is restarted daily. Mode 1, for agricultural education, involves three daily restarts; Mode 2, for daily operations, involves one daily restart; and Mode 3, for holidays, requires no restarts. Even when the boiler is not running, the circulation pump is activated based on the outdoor temperature to prevent freezing. Once the operating mode is selected, the boiler will start at the designated time interval. During operation, the loading system ensures a constant supply of fuel in the boiler hopper, while the water system module handles water treatment and heat transfer. The air-feed system ensures complete fuel combustion. The ignition system ignites the biomass pellets. Once ignition is successful, combustion parameters appropriate for the current month are selected until the desired temperature is reached. If a boiler malfunctions during operation, the control system enters fault handling mode, ensuring the boiler stops operating and preventing further malfunctions.

[0092] By applying this control strategy, the intelligence level and operating efficiency of biomass boilers can be effectively improved, ensuring the effective utilization of biomass pellet fuel and reducing operating costs. At the same time, through continuous monitoring and analysis of operating data, it provides support for subsequent control optimization and improvement, allowing biomass hot water boilers to play a greater role in the heating industry.

[0093] Regarding program design: The control system program primarily includes data acquisition, motor control, alarm, and automatic control programs. During normal boiler operation, data collection and analysis provide a comprehensive understanding of the system's status. This data analysis primarily relies on the visual monitoring interface of the touch screen and cloud platform. By collecting and processing various operating parameters in real time, this information provides crucial insights for efficient system operation.

[0094] The data acquisition program is the core component of the automation control system, which is used to receive a continuous range of physical signals.

[0095] Such as temperature, pressure, flow, etc., convert the input current signal into a digital signal, convert it with the corresponding sensor range, and finally output the corresponding sensor collection data, that is, convert the collected 4~20mA current signal into a 6553~32767 digital signal, which includes the conversion of data type to ensure the real-time nature of the data.

[0096] The automatic loading system uses upper and lower level sensors to monitor the amount of biomass fuel in the boiler hopper in real time. When the biomass fuel level falls below the set lower level, the feed valve rotates to the boiler hopper side, subsequently activating the feed cage motor and the multi-bucket elevator until the biomass fuel in the boiler hopper reaches the upper level switch. When the biomass fuel level exceeds the set upper level, the system stops the multi-bucket elevator and the feed cage motor, ensuring that the boiler is always fueled. The automatic loading system reduces reliance on manual labor and saves labor costs.

[0097] The automatic air-material ratio system mainly relies on pressure sensors, feeders and induced draft fans. The pressure sensor monitors the internal pressure of the boiler in real time, and uses the furnace negative pressure as input value to adjust the frequency of the induced draft fan through the PID controller.

[0098] The rate of combustion is controlled to maintain a constant furnace negative pressure. The feeder motor's feed speed is adjusted by a frequency converter based on the boiler's load requirements. A blower ensures the required oxygen for combustion, thereby ensuring sufficient fuel combustion. The automatic air-to-fuel ratio system improves combustion efficiency. PID control technology achieves an optimal fuel-air balance, enhancing the boiler's economic and environmental performance.

[0099] The automatic ignition system primarily utilizes a temperature sensor, igniter, and ignition blower to achieve automatic ignition. First, when the biomass pellets reach the ignition position, the ignition blower is turned on to provide sufficient oxygen and prevent the igniter from burning out. The igniter is then activated to ignite the biomass fuel. Finally, the temperature sensor above the igniter measures the success of the ignition. If successful, the system proceeds to the next step. If unsuccessful, an alarm is triggered, requesting manual ignition.

[0100] The automatic load adjustment system adjusts the boiler's output and fuel supply based on the difference between the indoor temperature sensor and the user's desired temperature. When the difference is large, high-fire mode is used; when the difference is small, combustion parameters are gradually reduced until the boiler is shut down. This automatic load adjustment system improves energy utilization and ensures efficient and safe boiler operation.

[0101] The automatic water replenishment system uses pressure sensors to monitor the pressure in the water system pipelines in real time. When the pressure falls below the set lower limit, the system activates the water replenishment pump. When the pressure rises above the set upper limit, the system stops the pump. If the pressure in the pipeline reaches the upper limit warning, the system activates the pressure relief solenoid valve to relieve the pressure, ensuring that the boiler pipeline pressure remains within a safe range. The application of automatic water replenishment effectively reduces boiler accidents caused by water level problems and improves the safety and reliability of overall operation.

[0102] The automatic dust removal and slag discharge system primarily consists of a slag discharger and a cyclone dust collector. After the boiler successfully ignites, the dust removal and slag discharge process automatically begins. The solid combustion residue is transported to a designated location by the slag discharger, while the combustion dust is transported to the ash hopper by the rotation of the cyclone dust collector. This automatic dust removal and slag discharge system is key to improving boiler operating efficiency, protecting the environment, and extending equipment life.

[0103] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing the operating parameters and heating mode of a small biomass boiler, characterized in that: The following steps are involved: Dynamic heating mode control phase: Through experimental comparison of temperature fluctuations and energy consumption data of different heating modes, intermittent heating was determined as the baseline mode. A multi-mode selection mechanism was established based on the month type and weekday / holiday attributes, and dynamic start and stop temperature thresholds were set based on the thermal inertia characteristics of the boiler. Local optimization of operating parameters: Select key operating frequency parameters that affect combustion efficiency, screen out the frequency parameter combination that minimizes operating costs through multi-factor orthogonal experimental design, and simultaneously apply a PID controller to achieve closed-loop regulation of furnace negative pressure; Global parameter modeling and solution phase: Establish a nonlinear relationship model between operating parameters and cost indicators, use an intelligent optimization algorithm with a dynamic adjustment mechanism to solve the global optimal parameter combination, and introduce an indoor and outdoor temperature difference feedback mechanism to dynamically correct the load adjustment threshold; Control system integration implementation phase: The optimized parameter combination is embedded in the boiler control system, and the equipment operating status is dynamically adjusted through real-time monitoring of temperature parameters. A linkage control module including automatic feeding and supplementary combustion, coordinated air-material ratio, and safety pressure relief protection is integrated.

2. A method for optimizing operating parameters and heating mode of a small biomass boiler according to claim 1, characterized in that: The dynamic start-stop temperature threshold setting process verifies the impact of the shutdown temperature upper limit on waste heat utilization efficiency by conducting multiple sets of comparative experiments, and at the same time tests the effect of different start-up temperature lower limits on heating stability. Finally, the critical temperature value that can balance temperature fluctuations and fuel consumption is selected as the control node; The multi-mode selection mechanism specifically includes a time-based control logic of an agricultural education mode in which the furnace is started three times a day, a daily work mode in which the furnace is started once, and a holiday mode in which the furnace is stopped and on standby.

3. The method for optimizing operating parameters and heating mode of a small-scale biomass boiler according to claim 1, characterized in that: The multi-factor orthogonal experimental design determines the parameter ranges of the three control variables of blast, feeding, and grate, generates an experimental parameter matrix according to the orthogonal table, uses the range analysis method to identify the influence weight of each parameter on the operating cost, and selects the parameter combination that minimizes the operating cost as the local optimal solution; The PID controller collects furnace negative pressure data in real time and dynamically adjusts the induced draft fan frequency using a proportional-integral-differential algorithm to maintain a constant negative pressure value.

4. The method for optimizing operating parameters and heating mode of a small-scale biomass boiler according to claim 1, characterized in that: The dynamic adjustment mechanism of the intelligent optimization algorithm automatically adjusts the crossover operation probability according to the population evolution state, and dynamically corrects the mutation operation probability based on the individual fitness, maintaining the excellent individual genetic characteristics during the iteration process, and terminates the calculation and outputs the optimal solution when the preset convergence conditions are met.

5. The method for optimizing operating parameters and heating mode of a small biomass boiler according to claim 1, characterized in that: The control system integration is implemented by deploying a temperature monitoring network at the boiler outlet, return water outlet and heating area, building an embedded controller including an equipment control instruction generation module, and developing a human-computer interaction interface with real-time data display and alarm functions, and finally establishing a remote monitoring platform to realize cloud-based storage and analysis of operation data.

6. The method for optimizing operating parameters and heating mode of a small-scale biomass boiler according to claim 1, characterized in that: Data perception layer: composed of a temperature sensor array and a frequency detection unit, which collects heating system operation data in real time; Algorithm processing layer: includes orthogonal test analysis module, mathematical modeling solver and intelligent optimization algorithm library; Equipment control layer: connects to the variable frequency drive devices of the blast, unloading, and grate equipment, executes optimized control instructions, and is expanded to include an automatic ignition temperature feedback unit, a multi-bucket elevator linkage control module, and a pressure relief solenoid valve actuator; Interactive application layer: Integrates local display terminals and remote monitoring interfaces to achieve digital management of operating status.

7. The method for optimizing operating parameters and heating mode of a small-scale biomass boiler according to claim 6, characterized in that: The temperature sensor array includes temperature measuring elements arranged at key nodes of the boiler water circulation pipeline and wireless temperature acquisition terminals distributed in the heating area. The frequency detection unit uses non-contact measurement technology to obtain the operating frequency of the rotating equipment in real time.

8. The method for optimizing operating parameters and heating mode of a small-scale biomass boiler according to claim 6, characterized in that: After receiving sensor data, the algorithm processing layer first calls the orthogonal test analysis module to sort parameter sensitivity, then starts the mathematical modeling solver to build a parameter-cost relationship model, and finally completes the iterative optimization of model parameters through the intelligent optimization algorithm library.

9. The method for optimizing operating parameters and heating mode of a small-scale biomass boiler according to claim 6, characterized in that: The equipment control layer includes a multi-speed control circuit for the blower group, a closed-loop feeding system for the unloading mechanism, and a displacement control module for the grate drive device. These layers achieve optimized control by precisely adjusting the air supply, equipping it with a weight compensation feedback mechanism, and ensuring that the fuel delivery speed is precisely adjustable.

10. The method for optimizing operating parameters and heating mode of a small-scale biomass boiler according to claim 6, characterized in that: The safety pressure relief protection is achieved through a dual-threshold pressure monitoring strategy. When the pipeline pressure is lower than the set lower limit, the water supply pump is activated, and when it is higher than the upper limit, the pressure relief solenoid valve is started. After the pressure exceeds the limit for a continuous timeout, the emergency shutdown protection is triggered.

Images