Temperature control and monitoring system for methanol reforming hydrogen production

Abstract

The invention discloses a temperature control and monitoring system for methanol reforming hydrogen production, which comprises a reaction tube, a temperature sensor is arranged in the reaction tube, a heating device is arranged outside the reaction tube, the temperature control and monitoring system also comprises a PLC (Programmable Logic Controller), the PLC is used for receiving real-time data transmitted by the temperature sensor, a fuzzy PID (Proportion Integration Differentiation) controller is arranged in the PLC, and the fuzzy PID controller is used for controlling the temperature of the reaction tube. A fuzzy control system and a relay are arranged in the fuzzy PID controller, and the relay is used for receiving signals transmitted by the PLC and controlling start and stop of heating equipment; by utilizing the design of the PLC, the relay, the temperature sensor and the heating equipment, the temperature sensor transmits monitored data to the PLC, the data is fuzzified by the fuzzy controller in the PLC, the processed data is transmitted to the PID controller to control the heating equipment, and the temperature is adjusted through the fuzzy PID algorithm. The time for returning to a steady state is shortened, so that the temperature regulation capability of the reaction tube is further improved.

Description

Technical Field

[0001] The invention relates to the technical field of experimental equipment, and in particular to a temperature control and monitoring system for hydrogen production by methanol reforming. Background Art

[0002] During the methanol reforming reaction, cold spots are easily formed at the feed inlet, thereby reducing the reaction rate of methanol reforming to produce hydrogen. Therefore, it is necessary to install a heating device on the outer wall of the reaction tube to increase the temperature of the reaction tube to reduce the CO content in the gas generated by the methanol reforming reaction and improve the quality of the reformed gas.

[0003] When monitoring temperature, temperature sensors are usually used to collect temperature data, which is then transmitted to a programmable logic controller, or PLC for short. The temperature data is processed and the start and stop control of the heating device is performed through the PID controller inside the PLC. When the PID controller encounters interference, the ability of the entire system to return to a steady state is insufficient and the recovery time is long, which affects the control of temperature and further affects the output and quality of hydrogen production by methanol reforming. Summary of the invention

[0004] The object of the present invention is to provide a temperature control and monitoring system for hydrogen production by methanol reforming to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a temperature control and monitoring system for hydrogen production by methanol reforming, comprising a reaction tube, a temperature sensor for monitoring the temperature of the reaction tube is arranged inside the reaction tube, a heating device for heating the reaction tube is arranged outside the reaction tube, and further comprising:

[0006] PLC, the PLC is used to receive real-time data transmitted by the temperature sensor, a fuzzy PID controller is arranged inside the PLC, and a fuzzy control system is arranged inside the fuzzy PID controller;

[0007] Relay, the relay is used to receive signals transmitted by PLC and control the start and stop of the heating equipment.

[0008] Preferably, the heating device is sleeved on the outer wall of the reaction tube, one end of the heating device is electrically connected to the output end of the relay, and the input end of the relay is electrically connected to the output end of the PLC.

[0009] Preferably, the outer wall of the temperature sensor is provided with a mounting rod for fixing the temperature sensor, the outer sleeve of the mounting rod is provided with a protective member for protecting the temperature sensor, and the top and bottom ends of the reaction tube are respectively provided with threaded holes for installing the protective member.

[0010] Preferably, the protective member comprises a protective tube, the outer walls of the top and bottom of the protective tube are respectively provided with threads, the interior of the protective tube is provided with a wire groove for routing the temperature sensor wires, and the output end of the temperature sensor is electrically connected to the input end of the PLC.

[0011] Preferably, the fuzzy PID controller comprises a fuzzy controller and a PID controller, the fuzzy controller is used to generate a fuzzy output to adjust the parameters of the PID controller, and the fuzzy control system is arranged in the fuzzy controller.

[0012] Preferably, the fuzzy control system includes a fuzzy input module, a fuzzy output module and a fuzzy rule module.

[0013] Preferably, the fuzzy input module is used to perform fuzzy processing on the transmitted data;

[0014] The fuzzy rule module is used to perform regular operations on the fuzzy processed data;

[0015] The fuzzy output module is used to output defuzzified data.

[0016] Technical effects and advantages of the present invention:

[0017] The present invention utilizes the design of PLC, relay, temperature sensor and heating equipment. The temperature sensor transmits the monitored data to the PLC, and the fuzzy controller inside the PLC performs data fuzzification processing, and then transmits the processed data to the PID controller to control the heating equipment. The temperature is adjusted after the fuzzy PID algorithm is processed, and the time to return to the steady state is shortened, thereby further improving the temperature adjustment ability of the reaction tube. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the overall three-dimensional structure of the present invention.

[0019] Figure 2 It is a schematic diagram of the three-dimensional structure of the connection between the reaction tube and the protection tube of the present invention.

[0020] Figure 3 It is a schematic diagram of the three-dimensional structure of the protection tube and the mounting rod of the present invention.

[0021] Figure 4 It is a schematic diagram of the three-dimensional structure of the mounting rod and the temperature sensor of the present invention.

[0022] Figure 5 This is the control principle diagram of the fuzzy PID controller of the present invention.

[0023] Figure 6 This is a diagram of the operation steps of the fuzzy controller of the present invention.

[0024] Figure 7 It is a simulation curve diagram of the output response under the action of the PID controller of the present invention.

[0025] Figure 8 The temperature variation curve diagrams of the present invention using PID and fuzzy PID algorithms at two locations respectively.

[0026] In the figure: 1. reaction tube; 2. protection tube; 3. mounting rod; 4. temperature sensor; 5. heating equipment; 6. relay; 7. PLC. DETAILED DESCRIPTION

[0027] 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.

[0028] The present invention provides Figure 1-5 A temperature control and monitoring system for hydrogen production by methanol reforming is shown, comprising a reaction tube 1, a temperature sensor 4 for monitoring the temperature of the reaction tube 1 is arranged inside the reaction tube 1, a heating device 5 for heating the reaction tube 1 is arranged outside the reaction tube 1, and further comprising:

[0029] PLC7, PLC7 is used to receive the real-time data transmitted by the temperature sensor 4, and a fuzzy PID controller is arranged inside the PLC7, and a fuzzy control system is arranged inside the fuzzy PID controller;

[0030] Relay 6, relay 6 is used to receive the signal transmitted by PLC7 and control the start and stop of the heating device 5.

[0031] Specifically, the heating device 5 is sleeved on the outer wall of the reaction tube 1, one end of the heating device 5 is electrically connected to the output end of the relay 6, the input end of the relay 6 is electrically connected to the output end of the PLC7, the outer wall of the temperature sensor 4 is provided with a mounting rod 3 for fixing the temperature sensor 4, the outer sleeve of the mounting rod 3 is provided with a protective member for protecting the temperature sensor 4, the top and bottom ends of the reaction tube 1 are respectively provided with threaded holes for installing the protective member, the protective member includes a protective tube 2, the outer walls of the top and bottom of the protective tube 2 are respectively provided with threads, the interior of the protective tube 2 is provided with a wire groove for routing the wires of the temperature sensor 4, and the output end of the temperature sensor 4 is electrically connected to the input end of the PLC7.

[0032] Furthermore, the heating device 5 is an existing hot runner electric heating ring. Because of the hot runner design, the heating device 5 can be tightly installed on the outer wall of the reaction tube 1. A plurality of heating devices 5 are arranged at equal intervals along the vertical direction of the reaction tube 1, so that the reaction tube 1 can be heated in sections. The temperature sensor 4 is an existing K-type thermocouple. The number of temperature sensors 4 is the same as the number of heating devices 5. A slot for installing the temperature sensor 4 is opened on the outer wall of the mounting rod 3. The top of the mounting rod 3 is bonded and fixed to the top of the inner wall of the protective tube 2. The protective tube 2 can protect the temperature sensor 4 by being sleeved on the outer wall of the mounting rod 3. The wires of the temperature sensor 4 are connected to PLC7, and the relay 6 and PLC7 are connected through wires. The temperature data transmitted by the temperature sensor 4 is received by PLC7, processed, and then the signal is transmitted to the relay 6 to control the opening and closing of the heating device 5. The relay 6 is an existing intermediate relay, which is mainly used to control the current of a large load such as a heater.

[0033] Specifically, the fuzzy PID controller includes a fuzzy controller and a PID controller. The fuzzy controller is used to generate fuzzy output to adjust the parameters of the PID controller. The fuzzy control system is set in the fuzzy controller. The fuzzy control system includes a fuzzy input module, a fuzzy output module and a fuzzy rule module. The fuzzy input module is used to fuzzify the transmitted data, the fuzzy rule module is used to perform regular operations on the fuzzy-processed data, and the fuzzy output module is used to output defuzzified data.

[0034] Furthermore, the existing PID controller can calculate the control quantity according to the system error, integral and differential to adjust the dynamic response of the system, while the fuzzy PID controller combines fuzzy logic control with PID control, and adjusts the parameters of the PID controller through the fuzzy output generated by the fuzzy controller to achieve precise control of the system. The fuzzy PID controller can dynamically adjust the PID parameters according to the actual situation of the system, and has strong robustness and adaptability. The fuzzy controller is suitable for multi-input and multi-output systems. Its input includes the error between the target value and the actual value and its rate of change, and the output is the adjustment value of the proportional, integral and differential parameters of the PID controller. The operation of the fuzzy controller is mainly divided into three steps, such as Figure 6As shown in the figure, there are three steps: fuzzification, fuzzy reasoning and defuzzification. Its main function is to make decisions on fuzzy reasoning based on the designed logical rules. This rule is called language control rule, also collectively referred to as rule base. The base contains two parts: control rules and database related to this field. Define the required data and range in the database, and compare the actual domain range of input and output. The database needs to collect and define all fuzzy subsets. Through fuzzification, fuzzy reasoning and defuzzification operations, the required parameters are obtained from the database. The fuzzy PID controller parameter tuning is to self-tune the PID parameters by establishing the fuzzy relationship between the deviation e and the deviation change rate ec and the proportional, integral and differential parameters of PID. The system monitors the error e and the error change rate ec in real time. When the system has different requirements for parameters over time, the controller will adjust the controller parameters in real time according to the principle of fuzzy control, such as Figure 7, is the output response simulation curve under the action of PID controller. Based on this result, we discuss the influence of parameters Kp, Ki, Kd on the output characteristics of the system. Under different error e and error change rate ec, the real-time requirements of the controlled process for the three parameters of proportion, integration and differentiation are different: when the error |e| is large, the system is in range Ⅰ in the figure. In order to speed up the system response speed and prevent the differential from being over-saturated, resulting in the control action exceeding the specified range, it is necessary to increase the proportional gain coefficient (Kp) and reduce the differential time coefficient (Kd). At the same time, in order to avoid large overshoot due to integral saturation, the integral time should be set to zero, that is, Ki=0; when the error |e| and the error change rate |ec| are of medium size, that is, when the system is in range Ⅱ in the figure, in order to reduce the system response overshoot, it is necessary to appropriately reduce the values ​​of the proportional gain coefficient (Kp), the integral time coefficient (Ki) and the differential time coefficient (Kd) to ensure the response speed of the system. Doing so can maintain the system's response speed while minimizing overshoot; when the error |e| is small, that is, the system is in range III in the figure, in order to ensure that the system has good robustness, it is necessary to increase the values ​​of the proportional gain coefficient (Kp) and the integral time coefficient (Ki). At the same time, in order to prevent the system from oscillating near the set value, it is also necessary to consider the system's anti-interference ability and select a suitable differential time coefficient (Kd) value. If the error change rate |ec| is small, the Kd value can be increased to maintain a medium size; if |ec| is large, the Kd value should be reduced to keep the system stable; in addition, according to traditional control experience, when the error |e| and the error change rate |ec| are large, the uncertainty in the system is also large. On the contrary, when |e| and |ec| are small, the uncertainty of the system is small. Using this rule, the three parameters of PID's proportion, integration and differentiation can be adjusted and estimated. PLC7 uses the existing model S7-1200. The interior of PLC7 realizes modular programming by using FB (Function Block) and FC (Function) function blocks. The FB function block has its own data block (DB) that stores variable addresses classified by type. This classification helps to manage and organize the code more clearly.The FC function block needs to call the global data block (DB). In addition to the variable types shared with the function block, the DB data block in the FB function block also contains an independent static storage area for storing specific data related to the function block, which helps to keep the function blocks isolated and simplify maintenance. This system first uses the ladder diagram to write the calculation subroutine of the error e and the error rate ec, and then assigns the value to the fuzzy controller. The proportional integral calculated by the fuzzy controller is the difference, which is transmitted to the PID controller through the DB block to achieve the control effect of the fuzzy PID. The input of the fuzzy controller must be fuzzified before it can be used to solve the control output. In fact, it is the input interface of the fuzzy controller. The main function of the fuzzy processing is to convert the real deterministic input into a fuzzy vector, and then perform regular processing. When selecting the membership function of the input and output, it should be considered that different membership densities will cause differences in control effects. Therefore, the fuzzy PID controller designed in this system uses Gaussian membership functions at both ends of the input interval, trimf membership functions for the rest of the parts, and trimf membership functions for all outputs. Table 1 shows the subset domain conversion factor and control object of the fuzzy PID controller:.

[0035]

[0036] Table 1 and Table 2 are the fuzzy rule tables of the fuzzy PID controller:

[0037]

[0038]

[0039] Table 2

[0040] Since there are too many reaction temperature monitoring points inside the tubular reactor, in order to facilitate comparative analysis, the test results of two temperature points P1 and P2 near the feed inlet of reaction tube 1 are taken to analyze their temperature control conditions under the PID control algorithm and the fuzzy PID control algorithm. Figure 8 As shown in the figure, it can be seen that the steady-state errors of PID and fuzzy PID algorithms are small. However, the peak time and overshoot of the fuzzy PID algorithm are smaller, and the time to adjust to the steady state is shortened. Therefore, fuzzy PID control has obvious advantages. When the entire system is stable, the time consumed by the fuzzy PID control algorithm is shorter than that of the PID control algorithm, which is more conducive to temperature control.

[0041] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A temperature control and monitoring system for hydrogen production by methanol reforming, comprising a reaction tube (1), wherein a temperature sensor (4) for monitoring the temperature of the reaction tube (1) is arranged inside the reaction tube (1), and a heating device (5) for heating the reaction tube (1) is arranged outside the reaction tube (1), characterized in that: Also includes: A PLC (7), wherein the PLC (7) is used to receive real-time data transmitted by the temperature sensor (4), wherein a fuzzy PID controller is arranged inside the PLC (7), and wherein a fuzzy control system is arranged inside the fuzzy PID controller; A relay (6) is used to receive signals transmitted by a PLC (7) and control the start and stop of the heating device (5).

2. A temperature control and monitoring system for methanol reforming hydrogen production according to claim 1, characterized in that: The heating device (5) is sleeved on the outer wall of the reaction tube (1), one end of the heating device (5) is electrically connected to the output end of the relay (6), and the input end of the relay (6) is electrically connected to the output end of the PLC (7).

3. A temperature control and monitoring system for methanol reforming hydrogen production according to claim 1, characterized in that: The outer wall of the temperature sensor (4) is provided with a mounting rod (3) for fixing the temperature sensor (4), the outer sleeve of the mounting rod (3) is provided with a protective member for protecting the temperature sensor (4), and the top and bottom ends of the reaction tube (1) are respectively provided with threaded holes for installing the protective member.

4. A temperature control and monitoring system for methanol reforming hydrogen production according to claim 3, characterized in that: The protective component comprises a protective tube (2), the outer walls of the top and bottom of the protective tube (2) are respectively provided with threads, the interior of the protective tube (2) is provided with a wire groove for routing the wires of the temperature sensor (4), and the output end of the temperature sensor (4) is electrically connected to the input end of the PLC (7).

5. A temperature control and monitoring system for methanol reforming to produce hydrogen according to claim 1, characterized in that: The fuzzy PID controller comprises a fuzzy controller and a PID controller. The fuzzy controller is used to generate a fuzzy output to adjust the parameters of the PID controller. The fuzzy control system is arranged in the fuzzy controller.

6. A temperature control and monitoring system for methanol reforming hydrogen production according to claim 5, characterized in that: The fuzzy control system includes a fuzzy input module, a fuzzy output module and a fuzzy rule module.

7. A temperature control and monitoring system for methanol reforming hydrogen production according to claim 6, characterized in that: The fuzzy input module is used to perform fuzzy processing on the transmitted data; The fuzzy rule module is used to perform regular operations on the fuzzy processed data; The fuzzy output module is used to output defuzzified data.

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