Optimization control method for straw pyrolysis carbonization process

Abstract

The invention discloses an optimal control method for a straw pyrolysis carbonization process. The method comprises: performing multipoint temperature real-time acquisition and data processing on a carbonization zone of a carbonization furnace, obtaining temperature information and calculating a deviation from a preset ideal temperature curve (S01); dynamically adjusting an operating parameter of the combustion zone using a control algorithm based on the temperature deviation (S02); performing on-line monitoring on the flue gas components (S03); and regulating and controlling the return air flow and the secondary air supply amount of the combustion area in a linkage manner according to the flue gas monitoring result so as to optimize combustion and energy recovery (S04). Through accurate and intelligent process control, efficient, environment-friendly and high-valued utilization of the straw carbonization process is realized.

Description

Technical Field

[0001] The invention relates to an optimization control method for a straw pyrolysis carbonization process, belonging to the technical field of carbonization. Background Art

[0002] Mobile straw carbonization furnaces, as equipment for converting agricultural and forestry waste into charcoal on-site, have positive implications for waste resource utilization and environmental pollution reduction. Traditional mobile carbonization furnaces typically employ relatively simple control methods, such as manually observing the flame and flue gas color and setting the carbonization time based on experience to control the entire pyrolysis process. The combustion zone provides heat energy to the carbonization zone, and a portion of the flue gas is returned to the combustion zone through a "return air" structure for secondary combustion, thereby improving energy efficiency and reducing smoke emissions.

[0003] However, the mobile straw carbonization furnace in the prior art still has some shortcomings in actual operation. First, due to the lack of precise process parameter monitoring and feedback control, key conditions such as temperature and atmosphere in the carbonization process are difficult to be effectively controlled, resulting in large fluctuations in the yield and quality of charcoal, and consistency is difficult to ensure. Secondly, there is still room for improvement in energy utilization efficiency, the energy recovery in the flue gas is not sufficient, and the degree of optimization of the combustion process is not high. Thirdly, although the "return air" structure has a certain treatment effect on the flue gas, the emission reduction effect on complex flue gas components (such as CO, hydrocarbons, and other potentially harmful gases) is limited, and it may not be able to meet increasingly stringent environmental protection requirements. In addition, the operation process relies heavily on manual experience, and the level of automation and intelligence is low, making it difficult to achieve optimal operation management. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an optimization control method for the straw pyrolysis and carbonization process to overcome the shortcomings of the prior art.

[0005] The technical solution of the present invention is: a method for optimizing and controlling a straw pyrolysis and carbonization process, comprising the following steps:

[0006] S01. Performing real-time temperature acquisition and data processing on multiple points in the carbonization zone of the straw carbonization furnace to obtain the average temperature, maximum temperature, minimum temperature, and temperature field uniformity of the carbonization zone; and calculating the temperature deviation based on a preset ideal staged temperature curve for a specific raw material and the current average temperature of the carbonization zone;

[0007] S02. Based on the temperature deviation, dynamically adjust the operating parameters of the combustion zone of the carbonization furnace using a control algorithm to control the temperature of the carbonization zone to conform to the ideal staged temperature curve;

[0008] S03, online monitoring of the flue gas composition at the flue gas outlet of the carbonization furnace and / or the inlet of the return air into the combustion zone to obtain the oxygen concentration, carbon monoxide concentration and total hydrocarbon concentration in the flue gas;

[0009] S04. Based on the flue gas composition monitoring results, the return air flow rate and the secondary air supply amount of the combustion zone are jointly regulated to ensure sufficient combustion of the combustible components in the flue gas and optimize energy recovery.

[0010] As a preferred solution, in step S01, the ideal staged temperature curve is determined by: for the main type of straw to be processed, its pyrolysis characteristic parameters are obtained by experimental thermogravimetric analysis, including the temperature range corresponding to the main weight loss stage and the optimal heating rate β of each stage. i ; Set the target charcoal quality standard and determine the target temperature T for each pyrolysis stage accordingly target,i and the duration of each stage Δt i ; Comprehensively formulate the ideal stage temperature curve T by combining the pyrolysis characteristic parameters and the target charcoal quality standard ideal (t).

[0011] As a preferred solution, in step S02, the control algorithm is a proportional-integral-derivative (PID) control algorithm, and the operating parameters of the combustion zone include a fuel addition rate or a combustion air valve opening.

[0012] As a preferred solution, step S04 further includes estimating the lower calorific value of the recirculated flue gas per unit volume based on the monitored carbon monoxide concentration and total hydrocarbon concentration in the recirculated flue gas, and estimating the instantaneous thermal power brought back to the combustion zone by the recirculated flue gas based on this, for optimizing energy management.

[0013] As a preferred solution, the method also includes online sensing of the average moisture and average packing density of the straw raw material entering the furnace, and adopting a segmented feeding strategy and dynamic low-speed stirring based on the sensing results to improve carbonization uniformity.

[0014] As a preferred solution, the method also includes establishing a mathematical model between process parameters that can be monitored online during the carbonization process and the final charcoal quality indicators, and intelligently determining the carbonization endpoint by combining temperature data and model output, and adopting a graded controllable cooling method.

[0015] As a preferred solution, the method also includes using multiple fuels for co-combustion in the combustion zone, and gradient-controlling the air supply at different positions in the combustion zone to form a specific oxygen potential gradient, thereby optimizing the pyrolysis atmosphere.

[0016] As a preferred option, the method also includes online evaluation of the maturity of the materials in the carbonization zone and identification of the mature zone, discharging small batches from the mature zone and compensating by adding new raw materials to achieve quasi-continuous operation, while simultaneously subjecting the pyrolysis gas and tar to graded condensation, separation, collection and cascade energy utilization.

[0017] Compared with the prior art, the present invention has the following beneficial effects:

[0018] 1. Through real-time monitoring and closed-loop feedback control of the temperature field and flue gas composition in the carbonization zone, the accuracy and controllability of the carbonization process are significantly improved, which helps to stabilize and improve the charcoal yield and quality uniformity.

[0019] 2. By optimizing the secondary combustion and energy recovery of combustible components in the flue gas, combined with fine-tuning of the fuel and oxygen potential in the combustion zone, the overall energy efficiency of the system is improved, operating costs are reduced, and energy self-consistency is achieved.

[0020] 3. Through the effective control of flue gas emissions and the cascade utilization of various phases of pyrolysis products, environmental pollution is significantly reduced, the comprehensive utilization value of agricultural and forestry wastes is improved, and the requirements of circular economy and sustainable development are met.

[0021] 4. Production efficiency and operational safety have been further improved by monitoring the stability of the bed structure and improving to continuous / semi-continuous operation modes. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a schematic diagram of the overall flow of an embodiment of the optimization control method of the present invention;

[0023] Figure 2 A schematic diagram of the module structure of an embodiment of the optimization control system of the present invention;

[0024] Figure 3 Schematic diagram of the process of adaptive optimization control strategy based on machine learning in the method of the present invention;

[0025] Figure 4 Schematic diagram of the auxiliary control process of acoustic emission and vibration signal monitoring in the method of the present invention;

[0026] Figure 5 It is a schematic flow chart of continuous operation and cascade utilization of pyrolysis products in the method of the present invention. DETAILED DESCRIPTION

[0027] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0028] Implementation Example 1:

[0029] Reference Figure 1 The present invention provides an optimization control method for the straw pyrolysis carbonization process, which may include the following main steps: First, executing step S01: real-time monitoring and stage-by-stage control of the temperature field in the carbonization zone. N (for example, N = 6-9) armored thermocouples are symmetrically arranged along the length and height of the carbonization zone of the straw carbonization furnace to form a temperature monitoring array T. j (t)(j=1,…,N). The temperature data of each thermocouple is read in real time by the data acquisition module at a preset sampling frequency. The average temperature of the key area in the carbonization zone is calculated. And identify the maximum temperature T max (t) and the minimum temperature T min (t), to evaluate the uniformity of the temperature field ΔT field (t) = T max (t)-T min (t). According to the type of straw raw material to be processed, the ideal stage temperature curve T is determined in advance. ideal (t). The method for determining the curve may include: consulting literature or obtaining its pyrolysis characteristic parameters through experimental thermogravimetric analysis (TGA / DSC), including the temperature range corresponding to the main weight loss stages (drying stage, pre-carbonization stage, main carbonization stage, calcination stage) and the optimal heating rate β of each stage i ; Set the target charcoal quality standards (such as fixed carbon content, volatile matter content), and determine the target temperature T for each pyrolysis stage accordingly target,i and the duration of each stage Δt i The ideal stage temperature curve can be expressed as:

[0030] t ideal (t) = T start,i +β i ·(tt start,i )fort∈[t start,i ,t end,i ]

[0031] Where i represents the pyrolysis stage, T start,i is the starting temperature of stage i, t start,i is the starting time of phase i, t end,i is the end time of stage i, β i is the optimal heating rate in stage i. The real-time average temperature T avg (t) and the target temperature T of the current stage ideal (t) and calculate the temperature deviation ΔT error (t) = T ideal (t)-T avg(t). Then, step S02 is executed: combustion zone control based on temperature feedback. Proportional-integral-differential (PID) control algorithm is used, and the temperature deviation ΔT calculated in step S01 is used. error (t), dynamically adjust the operating parameters of the carbonization furnace combustion zone (such as the fuel addition rate m fuel Or combustion air valve opening α air ). The output U(t) of the PID controller can be expressed as:

[0032]

[0033] Among them, K p ,K i ,K d These are the proportional, integral, and differential gains of the PID controller, respectively. Through this regulation, the actual temperature of the carbonization zone is made to track the ideal staged temperature curve as closely as possible, ensuring the stability and efficiency of the pyrolysis process.

[0034] Then, execute step S03: online monitoring of flue gas composition. Install a flue gas analyzer at the main flue gas outlet of the carbonization furnace and before the entrance of the "return air" duct into the combustion zone to monitor the oxygen concentration in the flue gas in real time. (or ), carbon monoxide concentration C CO,out (t)(or C CO , return (t)), total hydrocarbon (THC) concentration C THC,out (t)(or C THC , return (t)) and flue gas temperature T smake,out (t). Set the emission concentration threshold C of each monitored pollutant CO,limit and C THC,limit , and the target value of excess air coefficient λ required for secondary combustion in the combustion zone target .

[0035] Then, step S04 is executed: the return air and secondary air are linked and controlled based on the smoke monitoring. According to the smoke composition data monitored in step S03, the control valve opening V on the "return air" pipe is intelligently controlled. r (t) to control the amount of backflow flue gas Q return (t), and adjust the valve opening V of the secondary air inlet of the combustion zone s,air (t) To adjust the secondary air supply Q s,air(t). The control goal is to maximize the recovery of combustible components in the flue gas while ensuring stable combustion in the combustion zone, and to ensure that they are fully burned in the combustion zone so that the final emissions meet environmental protection requirements. For example, the calorific value carried by the combustibles (such as CO, THC) in the return flue gas can be estimated based on the concentration and flow rate of the combustibles in the return flue gas. The lower heating value (LHV) of the return flue gas per unit volume is smoke (t)(MJ / Nm 3 ) can be estimated as:

[0036]

[0037] Among them, LHV CO is the lower heating value of CO, LHV THC,avg is the average lower calorific value of THC, V m is the molar volume of the gas. The instantaneous heat power P brought back to the combustion zone by the recirculating flue gas return (t) is:

[0038] P return (t) = Q return (t)·LHV smoke (t)

[0039] Secondary air supply Q s,air (t) is based on the stoichiometric air volume required for the return flue gas volume and its combustible component concentration, combined with the target excess air coefficient λ target Make adjustments to ensure complete burnout of contaminants.

[0040] The method of this embodiment may further include the following optimization steps:

[0041] 1. Online sensing and processing of raw material characteristics: Humidity sensors and volume / mass sensors are installed at the feed inlet to online sense the average humidity W of the straw entering the furnace. feed (t) and average packing density ρ feed (t) = M batch / V batch According to these parameters, a segmented intelligent feeding strategy is adopted, and a dynamic low-speed stirring device is started in the carbonization zone (especially the low-temperature preheating and main carbonization stages) (achieved by an externally driven high-temperature stirring arm, and its speed is adjusted stir (t), frequency f stir and single duration t stir ) to improve the uniformity of material distribution in the furnace and the efficiency of heat and mass transfer, and prevent coking or uneven carbonization.

[0042] 2. Intelligent judgment of carbonization endpoint and graded cooling: Establish online monitoring parameters (such as cumulative flue gas generation V gas,total (t), the change rate of the concentration of specific smoke components dC x / dt, resistivity change in the furnace Ω bed (t), etc.) and the final charcoal quality indicators (such as fixed carbon content FC char ) association model, such as FC char =f FC (T final ,Δ thold , raw material type). The temperature data and the output of this model are combined to make a multi-parameter fusion judgment of the carbonization endpoint. Once the carbonization is determined to be complete, different cooling methods are selected according to the target use of the charcoal, such as inert gas (N2) protection rapid cooling (for activated carbon raw materials), controlled rate air limited cooling (for ordinary fuel charcoal) or natural cooling. Inert gas flow rate Q N2 The estimation can be based on Q N2 =V carbonizer ·(P atm / (R·T N2,in ))·N exch ange .

[0043] 3. Multi-fuel coordination and oxygen potential gradient control in the combustion zone: Configure multiple fuel sources for the combustion zone (such as high calorific value starting fuel Fuel A and low-cost refueling B or partial pyrolysis gas), according to the thermal power demand P in the carbonization stage req,i (t) Dynamically optimize fuel ratio m A (t),m B (t), with P req,i (t) = m A (t)·LHV A ·η A +m B (t)·LHV B ·η B At the same time, the air supply to the combustion zone is controlled by different zones to form an oxygen potential gradient, optimize combustion efficiency, and actively control the composition of the atmosphere penetrating into the carbonization zone (such as increasing the (CO+H2) / (CO2+H2O) ratio) to produce charcoal with specific properties.

[0044] 4. Adaptive optimization control based on machine learning (refer to Figure 3 ): Integrate all sensor data as input feature X, and use process parameters, final product quality, energy consumption, etc. as output / target feature Y to train a machine learning model (such as LSTM). Based on this model, the model predictive control (MPC) strategy is adopted. MPC calculates the N p Internal cost function J(U k )=∑w y ||y k+i|k -y ref,k+i ||2 +∑w u ||u k+i|k || 2 +…Minimal optimal control sequence The model and control strategy are continuously learned and optimized online.

[0045] 5. Bed status monitoring based on acoustic emission and vibration signals (refer to Figure 4 ):Install acoustic emission (AE) sensors and vibration sensors on the carbonization zone shell to collect AE signals V AE,i (t) and vibration signal A vib,j (t). Extract its time domain and frequency domain features (such as AE energy Vibration RMS, etc.), the bed structure stability (such as collapse, channelization) is judged by pattern recognition algorithm, and the results are fed back to the stirring control and global optimization control system.

[0046] 6. Continuous operation and product cascade utilization (refer to Figure 5 ): Evaluate the maturity of the material at different locations in the carbonization zone (e.g., by using a resistivity probe array Ω zone,k (t)), realizing small batch and quasi-continuous discharge of charcoal in the mature zone ΔM out , and correspondingly add new material ΔM in =ΔM out / Y avg At the same time, the pyrolysis gas and tar steam produced by carbonization are condensed by graded condensation (first stage high temperature condensation T cond,1 Recovery of heavy tar, secondary medium and low temperature condensation cond,2 Recover light tar and wood vinegar) for separation and collection. The purified non-condensable pyrolysis gas (rich in CO, H2, CH4, its calorific value LHV gas,clean =Σ(Vol% i ·LHV i )) and tar are preferentially returned to the combustion zone as fuel, striving to achieve system energy self-consistency (energy self-consistency S energy (t)=(Q gas,to_comb LHV gas,clean +m tar,to_comb LHV tar ) / P char_heat (t)≥1). Excess products can be used for power generation or chemical industry.

[0047] Through the synergistic effect of the above steps, the method of the present invention can significantly improve the automation level, energy efficiency, product quality and environmental performance of the straw carbonization process.

[0048] Example 2:

[0049] Reference Figure 2The present invention also provides an optimization control system for the straw pyrolysis carbonization process, which is used to implement the above method. The system may include:

[0050] 1. Temperature monitoring and control module 201: This module includes a plurality of temperature sensors (such as a thermocouple array) 201a arranged in the carbonization zone 101 of the carbonization furnace, and a temperature controller 201b. The temperature sensor 201a is responsible for collecting temperature data of each point in the carbonization zone in real time. The temperature controller 201b receives these temperature data, calculates the average temperature, temperature field uniformity, etc., and compares it with the preset ideal staged temperature curve (stored in the storage unit or set by the host computer) to obtain the temperature deviation. Based on the deviation, the temperature controller 201b generates a control signal through a control algorithm such as PID, which is used to adjust the operation of the fuel supply unit 201c and / or the combustion air supply unit 201d of the combustion zone 102 to accurately control the temperature of the carbonization zone.

[0051] 2. Flue Gas Monitoring and Control Module 202: This module includes flue gas composition sensors (e.g., O2, CO, THC sensors) 202a, located at the flue gas exhaust manifold 103 and / or the inlet of the return air duct 104, and a flue gas controller 202b. Flue gas composition sensors 202a monitor flue gas component concentrations in real time. Flue gas controller 202b receives this concentration data and, based on preset emission limits and combustion optimization objectives, controls the opening of the return air valve 202c and the combustion zone secondary air valve 202d to optimize flue gas recirculation and secondary combustion.

[0052] 3. Raw Material Property Sensing and Processing Module 203: This module may include a humidity sensor 203a and a packing density sensor (or a unit that indirectly calculates density through mass and volume) 203b located at the feeder 105. These sensors detect the properties of the incoming raw materials. Based on this raw material property data, a feed and stirring controller 203c controls the segmented feeding actuator 203d to feed the carbonization zone 101 in a compensated or batched manner, and controls the operation (e.g., start / stop, speed, and direction) of the dynamic stirring device 106 within the carbonization zone.

[0053] 4. Machine learning and adaptive control module 204 (see Figure 3logic): This is a more advanced control module, usually composed of a high-performance processor 204a and a memory 204b, running a specialized machine learning algorithm. This module collects multi-dimensional process data from the temperature monitoring and control module 201, the flue gas monitoring and control module 202, the raw material characteristics perception and processing module 203, and other possible sensors (such as the AE / vibration sensor 205 mentioned in Example 1, the carbonization endpoint judgment-related sensor 206, the product separation and utilization part sensor 207, etc.). Based on these data, the module trains and runs a dynamic prediction model of the carbonization process, and adopts advanced control strategies such as model predictive control (MPC) or reinforcement learning (RL) to generate optimized control instructions for each execution unit (such as 201c, 201d, 202c, 202d, 203d, 106, etc.) to achieve global adaptive optimization of the entire carbonization process.

[0054] 5. Acoustic emission / vibration monitoring module 205 (see Figure 4 logic): including AE sensors and vibration sensors installed on the carbonization furnace shell, and a signal processing unit for monitoring bed stability and feeding back analysis results (such as collapse warning, uniformity assessment) to the machine learning and adaptive control module 204 or the feeding and stirring controller 203c.

[0055] 6. Carbonization endpoint judgment and cooling control module 206 (optional): contains process parameter sensors and logic judgment units for indirectly evaluating charcoal quality, for intelligently judging the carbonization endpoint and controlling the graded cooling system (such as the inert gas supply unit and the water-cooling jacket control unit).

[0056] 7. Pyrolysis product separation and utilization control module 207 (optional, refer to Figure 5 logic): including devices for graded condensation of pyrolysis gas and tar, separation devices, and corresponding flow, temperature, pressure sensors and controllers, which are used to achieve cascade utilization of pyrolysis products and closed-loop management of system energy.

[0057] These modules are interconnected and communicate through a data bus or control network, working together to achieve comprehensive, intelligent, and optimized control of the straw pyrolysis and carbonization process. The specific implementation of the system can rely on hardware platforms such as embedded microcontrollers, PLCs (programmable logic controllers), or industrial PCs.

[0058] The above is a further detailed description of the present invention in conjunction with specific preferred embodiments, and the specific implementation of the present invention should not be considered to be limited to these descriptions. For those skilled in the art of the present invention, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should be considered to fall within the scope of protection of the present invention.

Claims

1. A straw pyrolysis carbonization process optimization control method, characterized in that: The steps include: S01. Performing real-time temperature acquisition and data processing on multiple points in the carbonization zone of the straw carbonization furnace to obtain the average temperature, maximum temperature, minimum temperature, and temperature field uniformity of the carbonization zone; and calculating the temperature deviation based on a preset ideal staged temperature curve for a specific raw material and the current average temperature of the carbonization zone; S02. Based on the temperature deviation, dynamically adjust the operating parameters of the combustion zone of the carbonization furnace using a control algorithm to control the temperature of the carbonization zone to conform to the ideal staged temperature curve; S03, online monitoring of the flue gas composition at the flue gas outlet of the carbonization furnace and / or the inlet of the return air into the combustion zone to obtain the oxygen concentration, carbon monoxide concentration and total hydrocarbon concentration in the flue gas; S04. Based on the flue gas composition monitoring results, the return air flow rate and the secondary air supply amount of the combustion zone are jointly regulated to ensure sufficient combustion of the combustible components in the flue gas and optimize energy recovery.

2. The method according to claim 1, characterized in that In step S01, the ideal stage temperature curve is determined by: for the main type of straw to be processed, its pyrolysis characteristic parameters are obtained through experimental thermogravimetric analysis, including the temperature range corresponding to the main weight loss stage and the optimal heating rate of each stage. ; Set target charcoal quality standards and determine the target temperature for each pyrolysis stage accordingly and the duration of each stage ; Formulate the ideal stage temperature curve by combining the pyrolysis characteristic parameters and the target charcoal quality standard , its mathematical expression is: ,in , represents the pyrolysis stage, It's a stage The starting temperature, It's a stage The start time, It's a stage The best heating rate.

3. The method according to claim 1 or 2, characterized in that In step S02, the control algorithm is a PID control algorithm, the operating parameters of the combustion zone include the fuel addition rate or the combustion air valve opening, and the output of the PID control algorithm is The mathematical expression for adjusting the operating parameters of the combustion zone is: ,in is the temperature deviation, are the proportional, integral, and differential gains of the PID controller respectively.

4. The method according to claim 1, wherein The step S04 further includes estimating the lower calorific value of the unit volume of the return flue gas based on the monitored carbon monoxide concentration and total hydrocarbon concentration in the return flue gas. , and based on this, estimate the instantaneous heat power brought back to the combustion zone by the reflowing flue gas , The calculation formula is: ,in is the carbon monoxide concentration in the return flue gas, is the total hydrocarbon concentration in the return flue gas, is the lower calorific value of carbon monoxide, is the average lower heating value of total hydrocarbons, is the gas molar volume; The calculation formula is: ,in is the return flue gas flow rate.

5. The method according to claim 1, wherein The method further comprises the steps of: measuring the average humidity of the straw raw materials fed into the carbonization furnace at the feed port or feed conveying device of the carbonization furnace; and average packing density Perform online sensing; according to the average moisture and average packing density, adopt a segmented feeding strategy to supplement the straw raw material to the carbonization zone, and in the material drying and pre-carbonization stage and the main carbonization stage, according to the average temperature and temperature field uniformity of the carbonization zone, dynamically stir the material in the carbonization zone.

6. The method according to claim 1, characterized in that The method further includes the following steps: establishing a mathematical model between process parameters that can be monitored online during the carbonization process and the final charcoal quality indicators, and integrating the temperature data of the carbonization zone and the output of the mathematical model to intelligently determine the carbonization endpoint; and cooling the carbonized charcoal using a graded controllable cooling method according to the target use of the charcoal.

7. The method according to claim 1, characterized in that The method also includes the steps of: using multiple fuels for coordinated combustion in the combustion zone, and gradient-controlling the air supply at different positions in the combustion zone to form a specific oxygen potential gradient, thereby optimizing combustion efficiency and stability, and affecting the pyrolysis atmosphere in the carbonization zone.

8. The method according to claim 1, characterized in that The method also includes the following steps: online evaluation of the maturity of the material in the carbonization zone and identification of the mature zone that has reached the target carbonization degree; small batch discharge from the mature zone and compensatory replenishment of new raw materials from the feed end to achieve quasi-continuous operation; and graded condensation, separation and collection of the pyrolysis gas and tar generated in the carbonization process and cascade energy utilization to achieve self-consistent closed-loop operation of the system energy.

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