Multi-stage circulating fluidized bed hydrogen peroxide integrated production method

Abstract

The invention discloses a multistage circulating fluidized bed hydrogen peroxide integrated production method, and relates to the field of hydrogen peroxide preparation.The method comprises the steps that continuous efficient synthesis of hydrogen peroxide is achieved by constructing a multistage serial circulating fluidized bed reaction system; meanwhile, a genetic algorithm based on historical data is introduced to perform off-line optimization on process parameters such as reaction temperature, flow velocity, catalyst particle size, gas-liquid ratio and retention time, so that the hydrogen peroxide yield is improved, the energy consumption per unit yield is reduced, and the generation of byproducts is inhibited; the constructed optimization objective function adopts a weighted mean square error structure, multiple objectives are balanced, and it is ensured that the overall working condition is optimal; an optimization result is used for guiding initial setting and steady-state operation of a reaction system, and efficient reaction can be realized without real-time sensor control. The system has the advantages of high process structure integration level, high control strategy intelligence degree, good operation stability and the like, and is suitable for continuous optimization operation of medium and large hydrogen peroxide production devices.

Description

Technical Field

[0001] The present invention relates to the field of hydrogen peroxide preparation, and in particular to a multi-stage circulating fluidized bed integrated production method of hydrogen peroxide. Background Art

[0002] As a green chemical, hydrogen peroxide is now widely used in the chemical, pharmaceutical, and environmental protection sectors. Industrial production primarily utilizes the anthraquinone process, with traditional hydrogenation processes often utilizing fixed-bed reactors. In recent years, fluidized-bed processes have demonstrated significant advantages in catalyst utilization, reaction uniformity, and heat transfer efficiency, gradually replacing traditional fixed-bed processes. Fluidized-bed hydrogenation technology has become the mainstream choice, particularly among large international chemical companies, and is poised to gradually replace fixed-bed processes.

[0003] However, existing fluidized bed reaction systems rely primarily on empirical parameter settings and lack a systematic optimization strategy. The control of key variables such as reaction temperature, flow rate, and gas-liquid ratio during the production process largely relies on manual experience and lacks a unified quantitative evaluation mechanism. This can easily lead to problems such as fluctuating hydrogen peroxide yields, increased byproducts, and high energy consumption per unit of output, limiting the efficient operation of fluidized bed units. Furthermore, multi-stage reaction conditions lack precise matching and overall planning, resulting in insufficient overall collaborative optimization capabilities.

[0004] To address the above issues, the present invention proposes an integrated hydrogen peroxide production method in a multi-stage circulating fluidized bed. By introducing a genetic algorithm based on historical data, an optimization function with a multi-objective weighted mean square error structure is constructed to optimize key process parameters that affect reaction performance. The optimization results are then applied to the initial settings of system operation, thereby achieving the optimal combination and efficient control of operating conditions, significantly improving hydrogen peroxide yield and overall energy efficiency. Summary of the Invention

[0005] In response to the above problems, the present invention provides a multi-stage circulating fluidized bed integrated production method for hydrogen peroxide to solve the problems of the prior art lacking systematic parameter optimization and coordinated control, resulting in unstable yield, high energy consumption and a large number of by-products.

[0006] To solve the above technical problems, the present invention provides the following technical solution: a multi-stage circulating fluidized bed integrated hydrogen peroxide production method, comprising the following steps: Step S1, historical data analysis and optimization preparation, by comprehensively collecting and organizing various historical data from the previous multi-stage circulating fluidized bed hydrogen peroxide production process, identifying key process variables that affect hydrogen peroxide yield, unit energy consumption, and by-product concentration; Step S2, process parameter optimization based on genetic algorithm, using genetic algorithm to optimize the process parameters of hydrogen peroxide production, converting process variables such as reaction temperature, flow rate, catalyst particle size, gas-liquid ratio and residence time into genotype codes of genetic algorithm individuals, performing optimization calculations as chromosomes, and setting appropriate evolution parameters; Step S3, system initialization under optimized conditions, by completing the setting of key parameters such as temperature and flow rate and equipment calibration before the start of the reaction, to ensure that the system starts operating under optimal conditions; Step S4, a multi-stage fluidized bed reaction process, wherein the raw gas-liquid mixture is introduced into a multi-stage circulating fluidized bed reaction system under controlled conditions, and is sequentially passed through each stage of the fluidized bed reactor to fully contact with the catalyst and react; Step S5, product separation and medium reuse, the reaction product is separated and purified, the hydrogen peroxide product is separated from the reaction mixture and purified and collected, and the remaining reaction medium is recycled and reintroduced into the production process.

[0007] In step S1, the following sub-steps are also included: S1-1, collect historical production data, collect historical data from previous operations, including reaction temperature, flow rate, catalyst particle size, gas-liquid ratio, residence time process parameters, and corresponding hydrogen peroxide yield, unit output energy consumption and by-product concentration performance indicators; S1-2, clean and preprocess the collected historical data, including removing outliers, filling in missing data, unifying measurement units and dimensions, and smoothing and normalizing the data as needed; S1-3, using statistical analysis and correlation analysis methods to conduct in-depth analysis of historical data after pretreatment, to identify the main process factors and their action patterns that affect hydrogen peroxide yield, energy consumption and by-product formation; S1-4, based on the results of the key factor analysis, determine the reasonable value range and initial optimization settings of each process variable, clarify the upper and lower limits of the reaction temperature and residence time, set the selection range of the catalyst particle size, and set the constraints of the gas-liquid ratio and flow rate; At the same time, the sorted historical data and the determined parameter boundaries are input into the genetic algorithm optimization model as the basis for the algorithm's initial population generation and fitness evaluation; During the optimization process, the above-mentioned multiple process objectives are constructed into a comprehensive objective function in the form of weighted mean square error with variable weights, so that the algorithm can comprehensively balance the weight relationship between yield, energy consumption and by-products, thereby achieving multi-objective optimization.

[0008] In step S2, the following sub-steps are also included: S2-1, real number coding parameter representation, each process variable to be optimized is represented by a real number coding method as a chromosome gene of the genetic algorithm. Each individual consists of five genes, corresponding to the five process parameters of reaction temperature, flow rate, catalyst particle size, gas-liquid ratio and residence time; S2-2, initialize the population settings. Set the initial population size of the genetic algorithm to 60 individuals, and randomly generate the initial population based on the parameter value range determined in step S1. Each individual represents a set of feasible process parameter combinations. Set the evolution termination condition to one of the following two situations: the evolution generation reaches the preset maximum generation number of 150 generations; the change rate of the population fitness is lower than the set threshold, which is judged as fitness convergence; S2-3, objective function construction, constructs a fitness evaluation function based on the optimization goal, and transforms the three goals of increasing hydrogen peroxide yield, reducing unit output energy consumption, and reducing by-product concentration into a comprehensive evaluation objective function, as shown in the formula:

[0009] in, is the target yield of hydrogen peroxide, is the actual yield of hydrogen peroxide, is the target energy consumption per unit output, is the actual energy consumption per unit output, is the target by-product concentration, is the actual by-product concentration, is the weight coefficient of each target item; A weighted mean square error structure with variable weights is used, that is, the square of the deviation of each indicator is multiplied by the corresponding weight and the sum is used as the fitness value. By adjusting the weight of each target, the focus can be placed on improving yield, saving energy consumption, and reducing by-products according to production needs; S2-4, the genetic algorithm evolution process, uses the genetic algorithm to iteratively evolve the population. In each generation, the roulette wheel selection method is used to select excellent individuals as parents based on their fitness values. Individuals with higher fitness have a greater probability of being selected. Then, the selected parent individuals are subjected to genetic recombination using the two-point crossover operator, that is, gene segments are exchanged at two locations on the chromosome to produce new offspring individuals with mixed parameter characteristics of the parent generation. Subsequently, the Gaussian mutation operator is applied to the newly generated offspring individuals, adding small perturbations that follow a Gaussian distribution to certain gene values ​​in the chromosomes with a small probability, thereby enhancing the diversity of the population and preventing it from falling into a local optimum. Through the repeated effects of selection, crossover, and mutation, the overall fitness of the population improves generation by generation. S2-5, obtaining the optimal parameter combination. After 150 generations of iterative evolution or after the fitness stabilizes, the genetic algorithm obtains the globally optimal or near-optimal process parameter combination, that is, a set of optimized reaction temperatures, flow rates, catalyst particle sizes, gas-liquid ratios, and residence times. This parameter combination performs best in the fitness evaluation, indicating that it can achieve the combined goals of maximizing hydrogen peroxide yield, minimizing specific energy consumption, and minimizing by-product concentrations. This optimal parameter combination is used for subsequent system initialization and reaction processes.

[0010] In step S3, the following sub-steps are also included: S3-1, catalyst preparation and loading: Select and prepare a suitable catalyst based on the optimization results, adjust its particle size to the specified range, measure the catalyst dosage according to the process requirements, and evenly add the catalyst to each reaction zone of the multi-stage fluidized bed reactor to ensure that the catalyst is evenly distributed in each reaction bed layer to prepare for the fluidized reaction; S3-2, parameter setting and equipment calibration: According to the optimized process parameters, set and calibrate the various control parameters of the reaction system, adjust the heating system to preheat the temperature of each reactor to the optimized reaction temperature, adjust the feed pump and gas flow meter to provide the flow rate and gas-liquid ratio required for optimization, adjust the feed rate or reactor liquid level according to the target residence time, and calibrate and verify the relevant sensors and controllers; S3-3, Pre-operation Check and Stability: Before formally introducing the reactants, the system is tested with no load or inert medium to verify the stability of the system under the conditions of various optimized parameters. Specifically: Introduce inert gas to fluidize the catalyst bed, observe the fluidization conditions and circulation characteristics of each level of fluidized bed, and ensure good gas-solid contact and no local blockage. At the same time, maintain each level of reactor at the optimized temperature for a period of time to observe the stability of temperature control. Through the above pre-run, the entire system reaches a stable and controlled state under the optimized settings, and potential operational hazards are eliminated. S3-4, the reaction start-up preparation is completed. When it is confirmed that all system indicators are stable and meet the optimized set values, the input of inert medium is stopped. The system is in the optimal initial state and can enter the formal multi-stage fluidized bed reaction process.

[0011] In step S4, the following sub-steps are also included: S4-1, after the system is ready, the reaction raw materials are introduced into the first-stage fluidized bed reactor according to the optimized gas-liquid ratio and flow rate to start the reaction. The raw materials, including hydrogen-containing gas and oxygen-containing gas, are fed into the bottom of the fluidized bed through a uniform distributor to fully contact the catalyst. The feed rate of the gas-liquid mixture is strictly controlled at the optimized value to ensure a stable fluidized state and good mass and heat transfer conditions, thereby smoothly starting the hydrogen peroxide synthesis reaction; S4-2, multi-stage reaction advancement, where the reaction mixture passes through multiple stages of fluidized bed reactors in series, with partial conversion occurring in each stage. Each stage of the reactor is maintained under optimized reaction conditions, utilizing the parameters set in the previous step to ensure full reaction progress. After the mixture flows out of the first stage reactor, the incompletely converted portion enters the next stage for further reaction, and this progression continues step by step, allowing the reactants to have multiple opportunities to react. S4-3, Catalyst circulation and fluidization maintenance: During the reaction process, the fluidized bed system realizes continuous circulation and reuse of the catalyst by setting up a circulation pipeline; the catalyst particles flow through each stage of the reactor with the gas-solid mixture, are carried to the separation device, and then return to the lower reaction zone; S4-4, process control and reaction completion. During the multi-stage reaction process, the process control system monitors key parameters to ensure that they remain within the optimized range. If deviations are found, the control unit is adjusted appropriately for fine-tuning. However, since the optimized parameters have been pre-set, the reaction process can run stably without major adjustments. When all the raw materials have passed through each stage of the reactor and the reaction has reached the predetermined conversion degree, the feed is stopped and the hydrogen peroxide synthesis reaction is completed. At this time, the hydrogen peroxide product mixture produced by each reactor flows into the discharge pipeline and enters the next step of the product separation process.

[0012] In step S5, the following sub-steps are also included: S5-1, catalyst separation and recovery: The product mixture from the last stage fluidized bed reactor is passed through a separation unit to separate the catalyst solids; a separation device is used to separate the catalyst particles from the product mixture. The separated catalyst is properly cleaned and collected and returned to the catalyst feed port or circulation pipeline of the reaction system for subsequent reactions, thereby achieving the recycling of the catalyst in the field and reducing the need for catalyst replenishment; S5-2, after separation of the catalyst, extracting and purifying the product mixture containing hydrogen peroxide; if the product mixture is an organic phase system, contacting it with an aqueous phase to perform liquid-liquid extraction to transfer the hydrogen peroxide to the aqueous phase, and then concentrating the hydrogen peroxide in the aqueous phase to the desired purity by distillation or evaporation; if the product is mainly an aqueous solution system, directly using evaporation concentration and rectification to remove impurities and excess water to obtain a high-purity hydrogen peroxide product, and the purified hydrogen peroxide is cooled, collected, and stored; S5-3, after separating the hydrogen peroxide product, the remaining medium and unreacted materials are recycled, including separating and collecting the unconsumed hydrogen and oxygen in the reaction tail gas, and returning them to the raw material supply system through compression technology to participate in the next round of reaction; recycling the working liquid after separation and extraction back to the raw material distribution unit for replenishing feed; and purifying the recycled inert medium and returning it to the system for further use.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention adopts a multi-stage circulating fluidized bed structure in series and performs a segmented design for the hydrogen peroxide production process, so that the reaction processes such as hydrogenation and oxidation are independently optimized in different beds, avoiding conflicts in working conditions at different reaction stages, and improving the overall reaction efficiency and yield of hydrogen peroxide. At the same time, the gas, solid and liquid three-phases in the fluidized bed are fully contacted, which is conducive to the enhancement of the mass and heat transfer process, making the reaction more uniform and stable, suppressing local hot spots, reducing the probability of side reactions, and significantly improving the selectivity of the reaction.

[0014] 2. This invention, for the first time, introduces a genetic algorithm into the continuous hydrogen peroxide production process. This method constructs an objective function based on historical production data and uses mechanisms such as real number encoding, roulette wheel selection, two-point crossover, and Gaussian mutation to perform multi-objective optimization of key process parameters that influence hydrogen peroxide synthesis efficiency. The resulting weighted mean square error evaluation model simultaneously balances the three objectives of maximizing hydrogen peroxide yield, minimizing energy consumption per unit of output, and minimizing byproduct concentration, avoiding the process instability often associated with single-objective optimization.

[0015] 3. The optimization method used in the present invention is completely based on offline execution of historical data. The optimal parameter combination obtained can be directly used for system startup and steady-state operation, reducing the dependence on high-precision sensing systems and complex feedback control mechanisms, simplifying engineering implementation and maintenance costs. At the same time, the algorithm can be continuously updated and iterated through historical data, and has good scalability and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. It is understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in combination with the drawings 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.

[0019] Please refer to Figure 1 , Figure 1 A flow chart of a multi-stage circulating fluidized bed integrated hydrogen peroxide production method provided in an embodiment of the present invention includes the following steps: Step S1, historical data analysis and optimization preparation, comprehensively collects and organizes various historical data from the previous multi-stage circulating fluidized bed hydrogen peroxide production process, identifies key process variables that affect hydrogen peroxide yield, unit output energy consumption, and by-product concentration, and sets reasonable parameter ranges and evaluation criteria for subsequent optimization.

[0020] S1-1, collect historical production data, collect historical data from previous operations, including process parameters such as reaction temperature, flow rate, catalyst particle size, gas-liquid ratio, residence time, and corresponding performance indicators such as hydrogen peroxide yield, energy consumption per unit output, and by-product concentration, to ensure that the collected data is comprehensive and representative.

[0021] S1-2, clean and preprocess the collected historical data, including removing outliers, filling missing data, unifying measurement units and dimensions, and smoothing and normalizing the data as needed. Preprocessing improves data quality and lays the foundation for accurate analysis and optimization.

[0022] S1-3, use statistical analysis and correlation analysis methods to conduct in-depth analysis of historical data after pretreatment to identify the main process factors and their action rules that affect hydrogen peroxide yield, energy consumption and by-product generation.

[0023] Analyze trends in the relationship between reaction temperature, gas-liquid ratio, and yield and byproduct concentrations to identify favorable conditions and sensitive parameters for improving yield and reducing energy consumption. This analysis determines the relative importance of each process variable, providing targeted reference information for optimization.

[0024] S1-4, based on the results of key factor analysis, determine the reasonable value range and initial optimization settings of each process variable, clarify the upper and lower limits of reaction temperature and residence time, set the selection range of catalyst particle size, and the constraints of gas-liquid ratio and flow rate.

[0025] At the same time, the sorted historical data and the determined parameter boundaries are input into the genetic algorithm optimization model as the basis for the algorithm's initial population generation and fitness evaluation. Through the above preparations, it is ensured that the genetic algorithm can efficiently find the optimal process parameter combination within a reasonable search space.

[0026] Step S2, process parameter optimization based on genetic algorithm, uses genetic algorithm (GA) to optimize the process parameters of hydrogen peroxide production. By encoding key variables such as reaction temperature, flow rate, catalyst particle size, gas-liquid ratio, residence time, etc. into chromosomes of genetic algorithm individuals and setting appropriate evolution parameters, GA can find the optimal parameter combination that increases hydrogen peroxide yield, reduces energy consumption per unit output, and reduces by-product concentration through multiple generations of evolution.

[0027] During the optimization process, the above-mentioned multiple process objectives are constructed into a comprehensive objective function in the form of a weighted mean square error with variable weights, so that the algorithm can comprehensively balance the weight relationship between yield, energy consumption and by-products, thereby achieving multi-objective optimization. This optimization is performed offline based on historical data, and stable and reliable optimization results can be obtained without relying on real-time sensor feedback.

[0028] S2-1, real number coding parameter representation, each process variable that needs to be optimized is represented by real number coding as the chromosome gene of the genetic algorithm. Each individual consists of five genes, which correspond to the five process parameters of reaction temperature, flow rate, catalyst particle size, gas-liquid ratio and residence time.

[0029] S2-2, initialize the population settings, set the initial population size of the genetic algorithm to 60 individuals, and randomly generate the initial population according to the parameter value range determined in step S1. Each individual represents a set of feasible process parameter combinations, and set the evolution termination condition to one of the following two situations: the evolutionary generation reaches the preset maximum generation of 150 generations; the rate of change of the population fitness is lower than the set threshold, and it is judged that the fitness converges; thereby ensuring that the algorithm has enough iterations to explore the entire parameter space.

[0030] S2-3, objective function construction, constructs a fitness evaluation function based on the optimization goal, and transforms the three goals of increasing hydrogen peroxide yield, reducing unit output energy consumption, and reducing by-product concentration into a comprehensive evaluation objective function, as shown in the formula:

[0031] in, is the objective function, where is the target yield of hydrogen peroxide, is the actual production rate of hydrogen peroxide, the goal is to maximize it; is the target energy consumption per unit output, is the actual energy consumption per unit of output, with the goal of minimizing it; is the target by-product concentration, is the actual by-product concentration, the goal is to minimize; is the weight coefficient of each target item; A weighted mean square error structure with variable weights is adopted, that is, the square of the deviation of each indicator is multiplied by the corresponding weight and then summed to obtain the fitness value. By adjusting the weight of each target, the focus can be placed on improving yield, saving energy or reducing by-products according to production needs, so that the genetic algorithm can flexibly balance multi-objective optimization requirements.

[0032] S2-4, the genetic algorithm evolution process, uses a genetic algorithm to iteratively evolve the population. In each generation, roulette wheel selection is used to select excellent individuals as parents based on their fitness values. Individuals with higher fitness are more likely to be selected. The selected parents are then genetically recombined using a two-point crossover operator. This operator swaps gene segments at two locations on the chromosome to produce new offspring individuals with mixed parental parameter characteristics.

[0033] Subsequently, the Gaussian mutation operator is applied to the newly generated offspring individuals, and small perturbations that obey the Gaussian distribution are added to certain gene values ​​in the chromosomes with a small probability, thereby enhancing the diversity of the population and preventing it from falling into local optimality. Through the repeated effects of selection, crossover and mutation, the overall fitness of the population improves from generation to generation.

[0034] S2-5, obtaining the optimal parameter combination. After 150 generations of iterative evolution or after the fitness has stabilized, the genetic algorithm obtains the globally optimal or near-optimal process parameter combination, that is, a set of optimized reaction temperatures, flow rates, catalyst particle sizes, gas-liquid ratios, and residence times. This parameter combination performs best in the fitness evaluation, indicating that it can achieve the combined goals of maximizing hydrogen peroxide yield, minimizing specific energy consumption, and minimizing byproduct concentration. This optimal parameter combination is then used for subsequent system initialization and reaction processes.

[0035] Step S3, system initialization under optimized conditions, ensures that the system starts operating under optimal conditions by completing the setting of key parameters such as temperature and flow rate and equipment calibration before the reaction starts, thereby avoiding repeated debugging during the startup process and providing a controlled and consistent environment for the hydrogen peroxide reaction.

[0036] S3-1, catalyst preparation and loading: select and prepare the appropriate catalyst according to the optimization results, adjust its particle size to the specified range of optimization, measure the catalyst dosage according to the process requirements, and evenly add the catalyst to the reaction zones of each stage of the multi-stage fluidized bed reactor to ensure that the catalyst is evenly distributed in each reaction bed layer to prepare for the fluidized reaction.

[0037] S3-2, parameter setting and equipment calibration. According to the optimized process parameters, set and calibrate the various control parameters of the reaction system, adjust the heating system to preheat the temperature of each reactor to the optimized reaction temperature, adjust the feed pump and gas flow meter to provide the flow rate and gas-liquid ratio required for optimization, and adjust the feed rate or reactor liquid level according to the target residence time. Calibrate and verify related sensors and controllers to ensure accurate measurements of temperature, flow, etc., and that all equipment is in good working condition.

[0038] S3-3, Pre-operation Check and Stability: Before formally introducing the reactants, the system is tested with no load or inert medium to verify the stability of the system under the conditions of each optimized parameter. Specifically: Introduce inert gas (such as N2) to fluidize the catalyst bed, observe the fluidization conditions and circulation characteristics of each stage of the fluidized bed, and ensure good gas-solid contact and no local blockage; at the same time, maintain each stage of the reactor at the optimized temperature for a period of time, and observe the stability of temperature control; through the above pre-operation, the entire system can reach a stable and controlled state under the optimized settings, and eliminate potential operational hazards.

[0039] S3-4, the reaction start-up preparation is completed. When it is confirmed that all system indicators are stable and meet the optimized set values, the input of inert medium is stopped. The system is in the optimal initial state and can enter the formal multi-stage fluidized bed reaction process.

[0040] Step S4, a multi-stage fluidized bed reaction process, introduces the raw gas-liquid mixture into a multi-stage circulating fluidized bed reaction system under controlled conditions, and passes through each stage of the fluidized bed reactor in sequence, fully contacts with the catalyst and reacts.

[0041] S4-1, after the system is ready, the reaction raw materials are introduced into the first-stage fluidized bed reactor according to the optimized gas-liquid ratio and flow rate to start the reaction. The raw materials include hydrogen-containing gas and oxygen-containing gas, which are sent to the bottom of the fluidized bed through a uniform distributor to fully contact with the catalyst. The feed rate of the gas-liquid mixture is strictly controlled at the optimized value to ensure a stable fluidized state and good mass and heat transfer conditions, thereby smoothly starting the hydrogen peroxide synthesis reaction.

[0042] S4-2, multi-stage reaction advancement, the reaction mixture passes through multiple stages of fluidized bed reactors in series, and partial conversion occurs in each stage; each stage reactor is maintained at the optimized reaction temperature and other conditions, and the parameters set in the previous step are used to ensure that the reaction proceeds fully; when the mixture flows out of the first stage reactor, the incompletely converted portion enters the next stage to continue the reaction, and the reaction is advanced step by step, so that the reactants have multiple reaction opportunities, significantly improving the overall conversion rate and the cumulative yield of hydrogen peroxide.

[0043] The multi-stage reaction mode enables the single-stage reaction to be operated at a more suitable conversion rate, reducing the generation of by-products caused by violent reactions, and gradually increasing the concentration of the target product through step-by-step reactions.

[0044] S4-3, catalyst circulation and fluidization maintenance. During the reaction process, the fluidized bed system realizes the continuous circulation and reuse of the catalyst by setting up a circulation pipeline; the catalyst particles flow through the reactors at each stage along with the gas-solid mixture, are carried to the separation device and then returned to the lower reaction zone, thereby ensuring that the amount of catalyst in the reactors at each stage is sufficient and evenly distributed. The circulating fluidized operation mode extends the effective residence time of the catalyst in the system, improves the catalyst utilization rate, helps to remove the reaction heat and achieve uniform temperature distribution, avoids local overheating and catalyst sintering, and ensures that the reaction process proceeds smoothly and continuously.

[0045] S4-4, process control and reaction completion. During the multi-stage reaction, the process control system monitors key parameters to ensure they remain within the optimized range. If deviations are detected, fine-tuning can be achieved by adjusting control units such as heating or flow. However, since the optimized parameters are pre-set, the reaction process typically requires no major adjustments for stable operation. Once all the raw materials have passed through each reactor and the reaction has reached the predetermined conversion level, feeding is stopped, completing the hydrogen peroxide synthesis reaction. At this point, the hydrogen peroxide product mixture produced by each reactor flows into the discharge pipeline and enters the next step, the product separation process.

[0046] It should be noted that the multi-stage reaction structure and circulating fluidization design provide the reactants with sufficient residence time and contact opportunities, thereby significantly increasing the production rate of hydrogen peroxide and suppressing the generation of by-products. During the reaction process, the temperature and feed rate of each reactor are strictly maintained near the optimized set value to ensure that the reaction is always carried out under the best process conditions. The multi-stage series reaction mode also allows each stage of the reaction to gradually complete the conversion under milder conditions, improving the overall conversion rate while reducing the possibility of local side reactions. After the reaction is completed, the product slurry or mixture flows out of the last reactor and enters the subsequent separation and recovery step.

[0047] Step S5, product separation and medium reuse, this step is to separate and purify the reaction product, the hydrogen peroxide product is separated from the reaction mixture and then purified and collected, and the remaining reaction medium is recycled and reintroduced into the production process.

[0048] S5-1, catalyst separation and recovery, the product mixture flow from the last stage fluidized bed reactor is sent to the separation unit (solid-gas or solid-liquid separation unit) to separate the catalyst solids; the catalyst particles are separated from the product mixture by a separation device cyclone separator or a filter device. The separated catalyst can be collected after proper cleaning and returned to the catalyst feeding port or circulation pipeline of the reaction system for subsequent reactions, so as to realize the recycling of the catalyst in the field and reduce the demand for catalyst replenishment.

[0049] S5-2, after separation of the catalyst, extracting and purifying the product mixture containing hydrogen peroxide; if the product mixture is an organic phase system, contacting it with an aqueous phase to perform liquid-liquid extraction to transfer the hydrogen peroxide to the aqueous phase, and then concentrating the hydrogen peroxide in the aqueous phase to the desired purity by distillation or evaporation; if the product is mainly an aqueous solution system, directly remove impurities and excess water by evaporation concentration, rectification, etc. to obtain a high-purity hydrogen peroxide product. The purified hydrogen peroxide is cooled, collected, and stored for future use or for downstream applications.

[0050] S5-3, after separating the hydrogen peroxide product, the remaining medium and unreacted materials are recycled, including separating and collecting the unconsumed hydrogen and oxygen in the reaction tail gas, and returning them to the raw material supply system through compression technology to participate in the next round of reaction; recycling the working liquid after separation and extraction to the raw material distribution unit for replenishing the feed; and purifying the recycled inert medium and returning it to the system for continued use; through the reuse of the above-mentioned medium, the recycling of raw materials and media is achieved, which reduces production costs, reduces waste emissions, and ensures the continuous and stable operation of the process.

[0051] In summary, the present invention constructs a dual-circulation reaction system with a three-stage series fluidized bed reactor as the core; wherein, the three sequentially connected reactors constitute the main reaction path, and the raw gas-liquid mixed flow is reacted step by step in each stage of the reactor. Each stage sets an independent temperature, catalyst dosage and residence time according to the reaction progress, forming a staged conversion mechanism, thereby achieving a step-by-step increase in the concentration of hydrogen peroxide, significantly improving the overall conversion rate and suppressing the formation of by-products, taking into account reaction efficiency, process stability and control accuracy.

[0052] At the same time, the present invention introduces a double circulation path into the reaction system, including: The solid phase circulation path of the catalyst, through the separation unit installed at the top of each reactor and the return pipeline at the bottom, realizes the continuous reflux and reuse of catalyst particles between reactors, maintains the stable fluidized state of the bed, and effectively extends the life of the catalyst; The closed-loop circulation path of gas-liquid raw materials separates and purifies the unreacted hydrogen and oxygen in the reaction tail gas and then returns them, and purifies and refluxes the liquid working medium, thereby building a recycling mechanism for gas-liquid two-phase materials, reducing raw material consumption and waste emissions.

[0053] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A multi-stage circulating fluidized bed integrated production method of hydrogen peroxide, characterized in that: The following steps are involved: Step S1, historical data analysis and optimization preparation, by comprehensively collecting and organizing various historical data from the previous multi-stage circulating fluidized bed hydrogen peroxide production process, identifying key process variables that affect hydrogen peroxide yield, unit energy consumption, and by-product concentration; Step S2, process parameter optimization based on genetic algorithm, using genetic algorithm to optimize the process parameters of hydrogen peroxide production, converting process variables such as reaction temperature, flow rate, catalyst particle size, gas-liquid ratio and residence time into genotype codes of genetic algorithm individuals, performing optimization calculations as chromosomes, and setting appropriate evolution parameters; Step S3, system initialization under optimized conditions, by completing the setting of key parameters such as temperature and flow rate and equipment calibration before the start of the reaction, to ensure that the system starts operating under optimal conditions; Step S4, a multi-stage fluidized bed reaction process, wherein the raw gas-liquid mixture is introduced into a multi-stage circulating fluidized bed reaction system under controlled conditions, and is sequentially passed through each stage of the fluidized bed reactor to fully contact with the catalyst and react; Step S5, product separation and medium reuse, the reaction product is separated and purified, the hydrogen peroxide product is separated from the reaction mixture and purified and collected, and the remaining reaction medium is recycled and reintroduced into the production process.

2. The multi-stage circulating fluidized bed integrated production method of hydrogen peroxide according to claim 1, characterized in that: In step S1, the following sub-steps are also included: S1-1, collect historical production data, collect historical data from previous operations, including reaction temperature, flow rate, catalyst particle size, gas-liquid ratio, residence time process parameters, and corresponding hydrogen peroxide yield, unit output energy consumption and by-product concentration performance indicators; S1-2, clean and preprocess the collected historical data, including removing outliers, filling in missing data, unifying measurement units and dimensions, and smoothing and normalizing the data as needed; S1-3, using statistical analysis and correlation analysis methods to conduct in-depth analysis of historical data after pretreatment, to identify the main process factors and their action patterns that affect hydrogen peroxide yield, energy consumption and by-product formation; S1-4, based on the results of the key factor analysis, determine the reasonable value range and initial optimization settings of each process variable, clarify the upper and lower limits of the reaction temperature and residence time, set the selection range of the catalyst particle size, and set the constraints of the gas-liquid ratio and flow rate; At the same time, the sorted historical data and the determined parameter boundaries are input into the genetic algorithm optimization model as the basis for the algorithm's initial population generation and fitness evaluation; During the optimization process, the above-mentioned multiple process objectives are constructed into a comprehensive objective function in the form of weighted mean square error with variable weights, so that the algorithm can comprehensively balance the weight relationship between yield, energy consumption and by-products, thereby achieving multi-objective optimization.

3. The multi-stage circulating fluidized bed integrated production method of hydrogen peroxide according to claim 1, characterized in that: In step S2, the following sub-steps are also included: S2-1, real number coding parameter representation, each process variable to be optimized is represented by a real number coding method as a chromosome gene of the genetic algorithm. Each individual consists of five genes, corresponding to the five process parameters of reaction temperature, flow rate, catalyst particle size, gas-liquid ratio and residence time; S2-2, initialize the population settings. Set the initial population size of the genetic algorithm to 60 individuals, and randomly generate the initial population based on the parameter value range determined in step S1. Each individual represents a set of feasible process parameter combinations. Set the evolution termination condition to one of the following two situations: the evolution generation reaches the preset maximum generation number of 150 generations; the change rate of the population fitness is lower than the set threshold, which is judged as fitness convergence; S2-3, objective function construction, constructs a fitness evaluation function based on the optimization goal, and transforms the three goals of increasing hydrogen peroxide yield, reducing unit output energy consumption, and reducing by-product concentration into a comprehensive evaluation objective function, as shown in the formula: in, is the target yield of hydrogen peroxide, is the actual yield of hydrogen peroxide, is the target energy consumption per unit output, is the actual energy consumption per unit output, is the target by-product concentration, is the actual by-product concentration, is the weight coefficient of each target item; A weighted mean square error structure with variable weights is used, that is, the square of the deviation of each indicator is multiplied by the corresponding weight and the sum is used as the fitness value. By adjusting the weight of each target, the focus can be placed on improving yield, saving energy consumption, and reducing by-products according to production needs; S2-4, the genetic algorithm evolution process, uses the genetic algorithm to iteratively evolve the population. In each generation, the roulette wheel selection method is used to select excellent individuals as parents based on their fitness values. Individuals with higher fitness have a greater probability of being selected. Then, the selected parent individuals are subjected to genetic recombination using the two-point crossover operator, that is, gene segments are exchanged at two locations on the chromosome to produce new offspring individuals with mixed parameter characteristics of the parent generation. Subsequently, the Gaussian mutation operator is applied to the newly generated offspring individuals, adding small perturbations that follow a Gaussian distribution to certain gene values ​​in the chromosomes with a small probability, thereby enhancing the diversity of the population and preventing it from falling into a local optimum. Through the repeated effects of selection, crossover, and mutation, the overall fitness of the population improves generation by generation. S2-5, obtaining the optimal parameter combination. After 150 generations of iterative evolution or after the fitness stabilizes, the genetic algorithm obtains the globally optimal or near-optimal process parameter combination, that is, a set of optimized reaction temperatures, flow rates, catalyst particle sizes, gas-liquid ratios, and residence times. This parameter combination performs best in the fitness evaluation, indicating that it can achieve the combined goals of maximizing hydrogen peroxide yield, minimizing specific energy consumption, and minimizing by-product concentrations. This optimal parameter combination is used for subsequent system initialization and reaction processes.

4. The multi-stage circulating fluidized bed integrated production method of hydrogen peroxide according to claim 1, characterized in that: In step S3, the following sub-steps are also included: S3-1, catalyst preparation and loading: Select and prepare a suitable catalyst based on the optimization results, adjust its particle size to the specified range, measure the catalyst dosage according to the process requirements, and evenly add the catalyst to each reaction zone of the multi-stage fluidized bed reactor to ensure that the catalyst is evenly distributed in each reaction bed layer to prepare for the fluidized reaction; S3-2, parameter setting and equipment calibration: According to the optimized process parameters, set and calibrate the various control parameters of the reaction system, adjust the heating system to preheat the temperature of each reactor to the optimized reaction temperature, adjust the feed pump and gas flow meter to provide the flow rate and gas-liquid ratio required for optimization, adjust the feed rate or reactor liquid level according to the target residence time, and calibrate and verify the relevant sensors and controllers; S3-3, Pre-operation Check and Stability: Before formally introducing the reactants, the system is tested with no load or inert medium to verify the stability of the system under the conditions of various optimized parameters. Specifically: Introduce inert gas to fluidize the catalyst bed, observe the fluidization conditions and circulation characteristics of each level of fluidized bed, and ensure good gas-solid contact and no local blockage. At the same time, maintain each level of reactor at the optimized temperature for a period of time to observe the stability of temperature control. Through the above pre-run, the entire system reaches a stable and controlled state under the optimized settings, and potential operational hazards are eliminated. S3-4, the reaction start-up preparation is completed. When it is confirmed that all system indicators are stable and meet the optimized set values, the input of inert medium is stopped. The system is in the optimal initial state and can enter the formal multi-stage fluidized bed reaction process.

5. The multi-stage circulating fluidized bed integrated production method of hydrogen peroxide according to claim 1, characterized in that: In step S4, the following sub-steps are also included: S4-1, after the system is ready, the reaction raw materials are introduced into the first-stage fluidized bed reactor according to the optimized gas-liquid ratio and flow rate to start the reaction. The raw materials, including hydrogen-containing gas and oxygen-containing gas, are fed into the bottom of the fluidized bed through a uniform distributor to fully contact the catalyst. The feed rate of the gas-liquid mixture is strictly controlled at the optimized value to ensure a stable fluidized state and good mass and heat transfer conditions, thereby smoothly starting the hydrogen peroxide synthesis reaction; S4-2, multi-stage reaction advancement, where the reaction mixture passes through multiple stages of fluidized bed reactors in series, with partial conversion occurring in each stage. Each stage of the reactor is maintained under optimized reaction conditions, utilizing the parameters set in the previous step to ensure full reaction progress. After the mixture flows out of the first stage reactor, the incompletely converted portion enters the next stage for further reaction, and this progression continues step by step, allowing the reactants to have multiple opportunities to react. S4-3, Catalyst circulation and fluidization maintenance: During the reaction process, the fluidized bed system realizes continuous circulation and reuse of the catalyst by setting up a circulation pipeline; the catalyst particles flow through each stage of the reactor with the gas-solid mixture, are carried to the separation device, and then return to the lower reaction zone; S4-4, process control and reaction completion. During the multi-stage reaction process, the process control system monitors key parameters to ensure that they remain within the optimized range. If deviations are found, the control unit is adjusted appropriately for fine-tuning. However, since the optimized parameters have been pre-set, the reaction process can run stably without major adjustments. When all the raw materials have passed through each stage of the reactor and the reaction has reached the predetermined conversion degree, the feed is stopped and the hydrogen peroxide synthesis reaction is completed. At this time, the hydrogen peroxide product mixture produced by each reactor flows into the discharge pipeline and enters the next step of the product separation process.

6. The multi-stage circulating fluidized bed integrated hydrogen peroxide production method according to claim 1, characterized in that: In step S5, the following sub-steps are also included: S5-1, catalyst separation and recovery, the product mixture from the last stage fluidized bed reactor is passed through a separation unit to separate the catalyst solids; A separation device is used to separate the catalyst particles from the product mixture. The separated catalyst is properly cleaned and collected, and then returned to the catalyst feed port or circulation pipeline of the reaction system for subsequent reactions, thereby realizing the recycling of the catalyst in the field and reducing the need for catalyst replenishment. S5-2, after separation of the catalyst, extracting and purifying the product mixture containing hydrogen peroxide; if the product mixture is an organic phase system, contacting it with an aqueous phase to perform liquid-liquid extraction to transfer the hydrogen peroxide to the aqueous phase, and then concentrating the hydrogen peroxide in the aqueous phase to the desired purity by distillation or evaporation; if the product is mainly an aqueous solution system, directly using evaporation concentration and rectification to remove impurities and excess water to obtain a high-purity hydrogen peroxide product, and the purified hydrogen peroxide is cooled, collected, and stored; S5-3, after separating the hydrogen peroxide product, the remaining medium and unreacted materials are recycled, including separating and collecting the unconsumed hydrogen and oxygen in the reaction tail gas, and returning them to the raw material supply system through compression technology to participate in the next round of reaction; recycling the working liquid after separation and extraction back to the raw material distribution unit for replenishing feed; and purifying the recycled inert medium and returning it to the system for further use.

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