Biomass gasifier coupled trough-type photothermal heating system

Abstract

The present invention relates to the field of new energy application technology, and discloses a biomass gasifier coupled trough type photothermal heating system, comprising: an intelligent control module, used for real-time monitoring and adjustment of the operating status of the biomass gasifier and the photothermal system; an intelligent prediction and optimization control module, used for judging the operating efficiency and fault points of the gasifier; an energy storage module, used for storing the energy generated by the system, including: a biomass gasifier, a biomass gas combustion furnace and a trough type photothermal system equipment; an energy complementary integration module, used for optimizing the configuration and scheduling of multiple energy sources; an energy management optimization module, used for intelligently adjusting the utilization ratio of biomass gas and solar energy for optimization; an efficient heat exchange module, used for realizing multi-layer cascade utilization of energy; an adaptive temperature control module, and performing preventive maintenance in advance to reduce unexpected downtime and provide strong decision support for management.

Description

Technical Field

[0001] The present invention relates to the field of new energy application technology, and in particular to a biomass gasifier coupled trough-type photothermal heating system. Background Art

[0002] The history of biomass gasification technology can be traced back to the mid-19th century, when a gas production industry using coal and biomass as raw materials was established in London, England. Luz first used its self-developed trough collector in the SEGS series of solar thermal power stations in California, USA. This was an early application of trough solar thermal technology. With the improvement of environmental awareness and the promotion of renewable energy, researchers began to explore the possibility of combining biomass gasification technology with trough solar thermal technology.

[0003] The traditional system has high requirements for biomass gasification furnaces. When using coarse materials, the gas production is very small or no gas is produced, and the gas production is stable. In addition, the trough type solar thermal heating system mainly relies on solar energy and is affected by factors such as geographical latitude, season, climate and meteorology. When the ambient temperature is low, the heat dissipation of the system will increase accordingly, reducing the heating efficiency.

[0004] After searching, the Chinese patent number CN110285474B discloses a photovoltaic thermal power generation and heating system and control method, which heats the water in the first water tank by the heat of the power generating solar panel body itself, and then temporarily stores the heated hot water in the second water tank, and finally stores the hot water in the third water tank for users to use. The second water tank wrapped outside the first water tank has a good insulation effect on the first water tank;

[0005] However, the traditional system in the above patent is difficult to predict future energy demand and output based on historical data and real-time data, which makes it difficult to adjust the operation strategy in time. It often relies on manual operation and experience judgment, lacks automated and intelligent control algorithms, and is difficult to dynamically adjust system parameters based on real-time data. Therefore, a biomass gasifier coupled trough type solar thermal heating system is proposed. Summary of the invention

[0006] The present invention proposes a biomass gasifier coupled trough type solar thermal heating system to solve the problem in existing patents that it is difficult to predict future energy demand and output based on historical data and real-time data, resulting in difficulty in timely adjusting operation strategies and lack of automated and intelligent control algorithms.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] Intelligent control module, used to monitor and adjust the operating status of biomass gasifier and solar thermal system in real time;

[0009] Intelligent prediction and optimization control module, used to determine the operating efficiency and fault points of the gasifier;

[0010] Energy storage module, used to store the energy generated by the system, including: biomass gasification furnace, biomass gas combustion furnace and trough type solar thermal system equipment;

[0011] Energy complementary integration module, used for optimizing configuration and scheduling of multiple energy sources;

[0012] Energy management optimization module, used to intelligently adjust the utilization ratio of biomass gas and solar energy for optimization;

[0013] High-efficiency heat exchange module for realizing multi-layer cascade utilization of energy;

[0014] Adaptive temperature control module, used to automatically adjust the temperature setting of the gasifier;

[0015] Intelligent maintenance plan module, used to formulate intelligent maintenance plans for gasifiers and heating systems;

[0016] Remote monitoring and maintenance module, used for remote monitoring and maintenance.

[0017] The above technical solution further includes:

[0018] The intelligent prediction and optimization control module collects historical data, including the operating conditions of the gasifier and the corresponding gas production or heat output, and trains a linear regression model to predict the performance of the gasifier under given operating conditions. The linear regression model is used when predicting the gas production of the biomass gasifier or the heat output of the solar thermal heating system.

[0019] y=ax+b

[0020] Where y is the predicted value, x is the input feature, a and b are regression coefficients obtained through training data, and fuzzy logic is used to define fuzzy sets and fuzzy rules for reasoning and decision making, using the two processes of forward propagation and back propagation.

[0021] a l+1 =σ(z l+1 )

[0022] z l+1 =W l+1 a l +b l+1

[0023] Back propagation is used to update weights and biases and build an intelligent control system to automatically adjust the operating parameters of the biomass gasifier and the control strategy of the solar thermal heating system, and automatically adjust the operating conditions according to real-time data.

[0024] The intelligent control module monitors various parameters of the biomass gasifier and the trough-type solar thermal heating system in real time, continuously collects data through high-precision sensors, and predicts the gas production efficiency of the biomass gasifier or the heat output of the trough-type solar thermal heating system based on historical data and current operating conditions, and then adjusts the feed speed and temperature setting of the biomass gasifier, or adjusts the reflector angle of the trough-type solar thermal heating system, and continuously corrects and optimizes the control strategy according to the actual operating data of the system;

[0025] The energy storage module stores excess energy when the system has excess energy output, and quickly releases the previously stored energy when the system energy demand is greater than the supply, thereby smoothing out energy fluctuations in the system and matching the system's energy demand and supply. It selects appropriate energy storage media based on system requirements and conditions, and monitors the system's energy demand and supply in real time.

[0026] The energy complementary integration module integrates the biomass gas generated by the biomass gasifier and the heat energy generated by the trough type solar thermal heating system, adjusts the operation of the biomass gasifier and the trough type solar thermal heating system according to energy demand and supply conditions, monitors the output and consumption of various energy sources in real time, and performs automatic regulation accordingly to optimize the energy use structure.

[0027] The energy management optimization module calculates the gradient of the objective function with respect to each control parameter and adjusts the parameter value in the direction of gradient descent.

[0028]

[0029] Among them, x_k is the coordinate of the current iteration point, α k is the learning rate or step size, It is the gradient of the objective function at point x_k. In the optimization of the biomass gasifier coupled trough-type solar thermal heating system, the gradient descent method adjusts the control parameters of the system.

[0030] The efficient heat exchange module, through optimized design, transfers heat to the working medium quickly and evenly, while reducing energy consumption and increasing gas production;

[0031] The adaptive temperature control module performs self-learning and optimization according to historical data and real-time operation conditions, monitors the temperature in the furnace in real time, and makes intelligent adjustments according to the set temperature range. It detects the temperature in the gasification furnace in real time through a temperature sensor and transmits the data to the control system. It automatically adjusts the feed speed and air volume parameters according to the set temperature range and real-time temperature data.

[0032] The intelligent maintenance plan module uses sensors and data analysis technology to monitor the operating status of the equipment in real time, and establishes an equipment failure prediction model based on historical data and machine learning algorithms to predict potential failures by analyzing equipment status data.

[0033] The remote monitoring and maintenance module uses sensors and Internet of Things technology to collect the operating data of the equipment in real time, and remotely displays the real-time operating status of the equipment through a data visualization platform. Once the equipment data is abnormal, the alarm mechanism will be triggered immediately. Based on data analysis technology, the collected equipment data will be processed and analyzed, and the equipment will be remotely debugged and repaired through a remote desktop or dedicated software, and data will be transmitted through a dedicated network channel.

[0034] The present invention has the following beneficial effects:

[0035] 1. In the present invention, advanced prediction algorithms and big data analysis are used to accurately predict future energy demand and output. Through real-time data monitoring and feedback, system parameters are automatically adjusted to ensure that the gasifier and solar thermal heating system operate in the optimal state, thereby improving energy output efficiency and system stability.

[0036] 2. In the present invention, the energy output of biomass gasification and solar thermal heating is intelligently allocated according to real-time demand and energy supply, the operation mode is automatically adjusted to reduce energy consumption and unnecessary emissions, possible problems of equipment are predicted, and preventive maintenance is performed in advance to reduce unexpected downtime, providing strong decision-making support for management. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is the overall system block diagram of the biomass gasification furnace coupled trough-type photothermal heating system proposed in the present invention. DETAILED DESCRIPTION

[0038] Embodiment 1

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] like Figure 1As shown, the biomass gasifier coupled trough type photothermal heating system proposed in the present invention includes: an intelligent control module for real-time monitoring and adjustment of the operating status of the biomass gasifier and the photothermal system; an intelligent prediction and optimization control module for judging the operating efficiency and fault points of the gasifier; an energy storage module for storing the energy generated by the system, including: a biomass gasifier, a biomass gas combustion furnace and a trough type photothermal system equipment;

[0041] Energy complementary integration module, used for optimizing the configuration and scheduling of multiple energy sources; energy management optimization module, used for intelligently adjusting the utilization ratio of biomass gas and solar energy; high-efficiency heat exchange module, used for realizing multi-layer cascade utilization of energy; adaptive temperature control module, used for automatically adjusting the temperature setting of the gasifier; intelligent maintenance plan module, used for formulating intelligent maintenance plans for gasifiers and heating systems; remote monitoring and maintenance module, used for remote monitoring and maintenance;

[0042] The intelligent control module collects historical data, including the operating conditions of the gasifier and the corresponding gas production or heat output, and trains a linear regression model to predict the performance of the gasifier under given operating conditions. The linear regression model is used to predict the gas production of the biomass gasifier or the heat output of the solar thermal heating system.

[0043] y=ax+b

[0044] Where y is the predicted value, x is the input feature, a and b are regression coefficients obtained through training data, and fuzzy logic is used to define fuzzy sets and fuzzy rules for reasoning and decision making, using the two processes of forward propagation and back propagation.

[0045] a l+1 =σ(z l+1 )

[0046] z l+1 =W l+1 a l +b l+1

[0047] Back propagation is used to update weights and biases, build an intelligent control system, and automatically adjust the operating parameters of the biomass gasifier and the control strategy of the solar thermal heating system, and automatically adjust the operating conditions according to real-time data.

[0048] The intelligent control module monitors the various parameters of the biomass gasifier and the trough-type solar thermal heating system in real time, collects data continuously through high-precision sensors, and predicts the gas production efficiency of the biomass gasifier or the heat output of the trough-type solar thermal heating system based on historical data and current operating conditions, and then adjusts the feed speed and temperature setting of the biomass gasifier, or adjusts the reflector angle of the trough-type solar thermal heating system, and continuously corrects and optimizes the control strategy according to the actual operating data of the system;

[0049] The energy storage module stores excess energy when the system produces excess energy. When the system energy demand exceeds the supply, it quickly releases the previously stored energy to smooth out the energy fluctuations in the system and match the system's energy demand and supply. It selects appropriate energy storage media based on system requirements and conditions, and monitors the system's energy demand and supply in real time.

[0050] The energy complementary integration module integrates the biomass gas generated by the biomass gasifier and the heat energy generated by the trough type solar thermal heating system. According to the energy demand and supply situation, it adjusts the operation of the biomass gasifier and the trough type solar thermal heating system, monitors the output and consumption of various energy sources in real time, and performs automatic regulation accordingly to optimize the energy use structure.

[0051] In this embodiment, historical data is collected through the intelligent control module, and a linear regression model is trained to predict the performance of the gasifier under given operating conditions. At the same time, data from the intelligent prediction and optimization control module is obtained. Various parameters of the biomass gasifier and the trough type solar thermal heating system are monitored in real time, and the data is transmitted to the energy storage module. When the system energy output is in excess, the excess energy is stored, and the data is transmitted to the energy complementary integration module. By integrating the biomass gas generated by the biomass gasifier and the heat generated by the trough type solar thermal heating system, the operation of the biomass gasifier and the trough type solar thermal heating system is adjusted according to energy demand and supply.

[0052] Embodiment 2

[0053] like Figure 1 As shown, in the embodiment of the present invention, the energy management optimization module calculates the gradient of the objective function with respect to each control parameter and adjusts the parameter value in the direction of gradient descent.

[0054]

[0055] Among them, x_k is the coordinate of the current iteration point, α k is the learning rate or step size, is the gradient of the objective function at point x_k. In the optimization of the biomass gasifier coupled trough-type solar thermal heating system, the gradient descent method adjusts the control parameters of the system;

[0056] High-efficiency heat exchange module, through optimized design, transfers heat to the working medium quickly and evenly, while reducing energy consumption and increasing gas production;

[0057] The adaptive temperature control module conducts self-learning and optimization based on historical data and real-time operation conditions, monitors the temperature in the furnace in real time, and makes intelligent adjustments according to the set temperature range. It also detects the temperature in the gasifier in real time through a temperature sensor and transmits the data to the control system. It automatically adjusts the feed speed and air volume parameters according to the set temperature range and real-time temperature data.

[0058] The intelligent maintenance planning module uses sensors and data analysis technology to monitor the operating status of equipment in real time, and builds an equipment failure prediction model based on historical data and machine learning algorithms to predict potential failures by analyzing equipment status data;

[0059] The remote monitoring and maintenance module uses sensors and Internet of Things technology to collect equipment operation data in real time, and remotely displays the real-time operation status of the equipment through a data visualization platform. Once the equipment data is abnormal, the alarm mechanism will be triggered immediately. Based on data analysis technology, the collected equipment data will be processed and analyzed, and the equipment will be remotely debugged and repaired through a remote desktop or dedicated software, and data will be transmitted through a dedicated network channel.

[0060] In this embodiment, the energy management optimization module calculates the gradient of the objective function with respect to each control parameter, adjusts the parameter value in the direction of gradient descent, and transmits the data to the high-efficiency heat exchange module through the energy complementary integration module, so that the heat is quickly and evenly transferred to the working medium, and at the same time, the adaptive temperature control module is used;

[0061] It conducts self-learning and optimization based on historical data and real-time operation conditions, monitors the temperature inside the furnace in real time, and uses the intelligent maintenance plan module, sensors and data analysis technology to monitor the operating status of the equipment in real time. It also establishes an equipment failure prediction model based on historical data and machine learning algorithms, and uses sensors and Internet of Things technology through the remote monitoring and maintenance module to collect equipment operation data in real time.

[0062] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Biomass gasifier coupled trough type photothermal heating system, characterized in that: include: Intelligent control module, used to monitor and adjust the operating status of biomass gasifier and solar thermal system in real time; Intelligent prediction and optimization control module, used to determine the operating efficiency and fault points of the gasifier; Energy storage module, used to store the energy generated by the system, including: biomass gasification furnace, biomass gas combustion furnace and trough type solar thermal system equipment; Energy complementary integration module, used for optimizing configuration and scheduling of multiple energy sources; Energy management optimization module, used to intelligently adjust the utilization ratio of biomass gas and solar energy for optimization; High-efficiency heat exchange module for realizing multi-layer cascade utilization of energy; Adaptive temperature control module, used to automatically adjust the temperature setting of the gasifier; Intelligent maintenance plan module, used to formulate intelligent maintenance plans for gasifiers and heating systems; Remote monitoring and maintenance module, used for remote monitoring and maintenance; The intelligent prediction and optimization control module collects historical data, including the operating conditions of the gasifier and the corresponding gas production or heat output, and trains a linear regression model to predict the performance of the gasifier under given operating conditions. The linear regression model is used when predicting the gas production of the biomass gasifier or the heat output of the solar thermal heating system. y=ax+b Where y is the predicted value, x is the input feature, a and b are regression coefficients obtained through training data, and fuzzy logic is used to define fuzzy sets and fuzzy rules for reasoning and decision making, using the two processes of forward propagation and back propagation. a l+1 =σ(z l+1 ) z l+1 =W l+1 a l +b l+1 Back propagation is used to update weights and biases, build an intelligent control system, and automatically adjust the operating parameters of the biomass gasifier and the control strategy of the solar thermal heating system, and automatically adjust the operating conditions according to real-time data. The intelligent control module monitors various parameters of the biomass gasifier and the trough-type solar thermal heating system in real time, continuously collects data through high-precision sensors, and predicts the gas production efficiency of the biomass gasifier or the heat output of the trough-type solar thermal heating system based on historical data and current operating conditions, and then adjusts the feed speed and temperature setting of the biomass gasifier, or adjusts the reflector angle of the trough-type solar thermal heating system, and continuously corrects and optimizes the control strategy according to the actual operating data of the system; The energy storage module stores excess energy when the system has excess energy output, and quickly releases the previously stored energy when the system energy demand is greater than the supply, thereby smoothing out energy fluctuations in the system and matching the system's energy demand and supply. It selects appropriate energy storage media according to system requirements and conditions, and monitors the system's energy demand and supply in real time. The energy complementary integration module integrates the biomass gas generated by the biomass gasifier and the heat energy generated by the trough type solar thermal heating system, adjusts the operation of the biomass gasifier and the trough type solar thermal heating system according to energy demand and supply conditions, monitors the output and consumption of various energy sources in real time, and performs automatic regulation accordingly to optimize the energy use structure.

2. The biomass gasifier coupled trough type photothermal heating system according to claim 1 is characterized in that: The energy management optimization module calculates the gradient of the objective function with respect to each control parameter and adjusts the parameter value in the direction of gradient descent. x k+1 =x k -α k ▽f(x k ) Among them, x_k is the coordinate of the current iteration point, α k is the learning rate or step size, ▽f(x k ) is the gradient of the objective function at point x_k. In the optimization of the biomass gasifier coupled trough-type solar thermal heating system, the gradient descent method is used to adjust the control parameters of the system.

3. The biomass gasifier coupled trough type photothermal heating system according to claim 1 is characterized in that: The efficient heat exchange module, through optimized design, transfers heat to the working medium quickly and evenly, while reducing energy consumption and increasing gas production; The adaptive temperature control module performs self-learning and optimization according to historical data and real-time operation conditions, monitors the temperature in the furnace in real time, and makes intelligent adjustments according to the set temperature range. It detects the temperature in the gasification furnace in real time through a temperature sensor and transmits the data to the control system. It automatically adjusts the feed speed and air volume parameters according to the set temperature range and real-time temperature data.

4. The biomass gasifier coupled trough type photothermal heating system according to claim 1 is characterized in that: The intelligent maintenance plan module uses sensors and data analysis technology to monitor the operating status of the equipment in real time, and establishes an equipment failure prediction model based on historical data and machine learning algorithms to predict potential failures by analyzing equipment status data.

5. The biomass gasifier coupled trough type photothermal heating system according to claim 1 is characterized in that: The remote monitoring and maintenance module uses sensors and Internet of Things technology to collect the operating data of the equipment in real time, and remotely displays the real-time operating status of the equipment through a data visualization platform. Once the equipment data is abnormal, the alarm mechanism will be triggered immediately. Based on data analysis technology, the collected equipment data will be processed and analyzed, and the equipment will be remotely debugged and repaired through a remote desktop or dedicated software, and data will be transmitted through a dedicated network channel.

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