Fourth generation catalytic fiber optic chemical and artificial intelligence chemical system

Abstract

The present application relates to a fourth-generation catalytic optical fiber chemical and artificial intelligence chemical system, which combines optical fiber chemistry and artificial intelligence technology to achieve efficient, accurate and intelligent control of chemical reactions. The system includes an optical fiber chemical membrane electrode, a light source spectrometer module, a proton / ion exchange layer, an electric field control module, a reaction path optimization module, a photocatalytic reaction layer, an artificial intelligence laboratory and a factory. The optical fiber chemical membrane electrode optimizes light distribution through the optical fiber catalytic layer to improve catalytic effect; the light source spectrometer module supports multiple wavelength inputs to adapt to different reaction requirements; the proton / ion exchange layer ensures ion migration and charge balance. The electric field control module accurately adjusts the reaction path to optimize product generation. The reaction path optimization module adjusts the reaction conditions in combination with artificial intelligence to improve selectivity and efficiency. The system also continuously optimizes experimental configuration through autonomous learning and data feedback, and integrates a large model module to provide decision support for reaction results. This system is suitable for energy conversion, catalytic synthesis and environmental protection, etc. It promotes the intelligent transformation of the chemical industry and has wide application prospects and economic value.

Description

I. Technical Field

[0001] This invention relates to chemical reaction control and optimization technology, and more particularly to an artificial intelligence-based fiber optic chemistry system. Specifically, this invention integrates fiber optic chemical membrane electrodes, multi-field synergistic catalysis technology, artificial intelligence optimization modules, large-scale model modules, and autonomous learning modules, enabling efficient control and optimization of chemical reactions. This system improves reaction efficiency and product quality by precisely adjusting reaction pathways and optimizing production conditions, and has wide applications in chemical industries, energy conversion, materials synthesis, and intelligent manufacturing.

[0002] This invention also relates to an intelligent chemical reaction plant, which, through an autonomous learning and optimization control system, dynamically adjusts the production process and reaction conditions according to market demand, raw material costs, and product quality requirements, to achieve efficient and economically viable production.

[0003] Keywords:

[0004] Fiber optic chemistry; artificial intelligence; chemical reaction control; fiber optic chemical membrane electrode; reaction pathway optimization; autonomous learning; large model modules; multi-field synergistic catalysis; intelligent production; chemical reaction optimization; data feedback; industrial automation; intelligent manufacturing; reaction condition regulation; green chemistry; sustainable development II. Background Technology

[0005] Since its inception in the 19th century, catalytic chemistry has undergone a profound evolution from empirical observation to multi-field synergistic systems. Its development can be divided into four main stages, with each generation of technological paradigms representing a leap forward in the concepts and methods of chemical reaction control.

[0006] First generation: Thermocatalysis

[0007] In the 19th century, Berzelius first proposed the concept of "catalysis," pointing out that certain substances can change the rate of a reaction without being consumed. Subsequently, Ostwald systematically elucidated the energetic nature of catalysis and established its scientific definition. Representative achievements of this period include the high-temperature, high-pressure ammonia synthesis reaction system established by Haber and Bosch (the Haber–Bosch process), which achieves direct nitrogen-hydrogen synthesis using iron-based catalysts at 200–300 atm and 400–500 °C. First-generation catalysis relied on thermodynamic drive and a high-energy reaction environment, limiting its energy efficiency and selectivity.

[0008] Second generation: Photocatalysis and Electrocatalysis

[0009] In the latter half of the 20th century, with the development of semiconductor physics and electrochemistry, catalysis entered an era dependent on electron migration and photogenerated carriers. Fujishima and Honda achieved the photoelectrochemical splitting of water using TiO2 electrodes, laying the foundation for semiconductor photocatalysis; Gerischer et al.'s mechanistic studies of photoelectrode interface processes, and... The exploration of visible light-driven artificial photosynthesis systems has driven the rapid development of photocatalytic systems. Meanwhile, electrocatalysis has found wide application in fuel cells, hydrogen oxidation reactions, and other fields, with representative studies covering the oxygen reduction reaction (ORR) and hydrogen evolution reaction (HER).

[0010] Third generation: Photoelectrochemical and Multifield Coupling Catalysis

[0011] In the 21st century, the synergistic effect of multiple energy fields has become an important means to improve reaction rates and selectivity. Maeda et al. developed a photocatalytic system that can produce hydrogen under natural sunlight, and Mills and Le Hunte systematically summarized the mechanism and application of semiconductor photocatalysis. Building on this, reaction systems such as CO2 reduction and water electrolysis have introduced synergistic driving forces from multiple fields, including light, electricity, and heat. Through carrier concentration regulation, local temperature adjustment, and electric field enhancement, energy utilization and product selectivity have been significantly improved.

[0012] The fourth generation: Optical Fiber Chemistry (OFC), which is the subject of this invention.

[0013] Building upon multi-field coupled catalysis, Gu Shiping et al. proposed fiber optic chemistry, whose core component is a sandwich-type fiber optic chemical membrane electrode. It utilizes photonic crystal fibers or refractive index-tunable fibers to achieve uniform distribution and localized focusing of excitation light, integrating photocatalysis, electrocatalysis, thermocatalysis, and proton conduction on a nano- to micro-scale. OFC can construct two-dimensional or three-dimensional controllable light fields through a beam splitter and multiple parallel fibers, combined with tunable electric fields and ion migration paths, to achieve programmable control of reaction conditions. This system has been extended to AI-driven programmable chemistry and has shown significant potential in areas such as ambient temperature and pressure ammonia synthesis and noble metal-free fuel cells.

[0014] In summary, the development of catalytic chemistry has progressed from high-energy-consuming, single-field-driven thermocatalysis to the era of fiber optic chemistry, characterized by multi-field synergy and programmable chemistry. This evolution has not only improved the energy efficiency and controllability of reactions but also provided new technological pathways for green energy and sustainable chemical industries.

[0015] With the continuous development of catalysis technology, photocatalysis and electrocatalysis, as key technologies for green chemical reactions, have been widely applied in energy conversion, pollution control, and chemical synthesis. Traditional catalytic systems mostly rely on a single mechanism of thermal catalysis, photocatalysis, or electrocatalysis. These systems suffer from low reaction efficiency and poor selectivity under certain conditions. In recent years, fiber optic chemistry, as an emerging catalysis technology, has provided new solutions for the high efficiency and controllability of catalytic reactions due to its synergistic effects of light, electricity, protons, and ions.

[0016] Fiber optic chemistry systems utilize fiber arrays to guide and uniformly scatter excited light fields. By precisely controlling the effects of four fields—light, electricity, protons, and ions—at the reaction interface, the efficiency and selectivity of catalytic reactions can be significantly improved. Compared to traditional photocatalysis and electrocatalysis methods, fiber optic chemistry combines fiber optic catalytic membrane electrodes with advanced electrocatalysis, photocatalysis, and thermocatalysis technologies to form a composite catalytic structure, enabling more efficient reactions with lower energy consumption.

[0017] With the development of artificial intelligence technology, AI agent modules combining large models and machine learning have provided new pathways for optimizing fiber optic chemistry systems. These AI modules can automatically generate reaction pathways based on reaction conditions, reactant types, and target products, and adjust key parameters (such as voltage and light intensity) to achieve optimal reaction results. This intelligent control enables real-time optimization of the reaction process, significantly improving reaction selectivity and product yield.

[0018] Currently, although fiber optic chemical catalysis systems have shown great potential, how to achieve a high degree of automation and intelligence in practical applications, especially how to further improve the system's adaptability and self-optimization capabilities through artificial intelligence, remains a research hotspot in this field. III. Summary of the Invention

[0019] 3.1 Problems to be solved

[0020] This invention aims to solve the following technical problems:

[0021] • Highly efficient chemical reaction control and optimization: In existing chemical reaction processes, reaction conditions and pathways often rely on experience and manual adjustments, making it difficult to precisely control reaction rates, selectivity, and product quality. This invention integrates fiber optic chemical membrane electrodes, tunable optical fields, controllable currents, reaction pathway optimization modules, and artificial intelligence technology to achieve precise control and real-time optimization of chemical reaction processes, significantly improving reaction efficiency and selectivity.

[0022] • Intelligent adjustment of reaction pathways and production conditions: Traditional reaction pathway optimization relies on extensive experimentation and experience, and it is difficult to adjust production conditions based on real-time feedback. By integrating a large model and an autonomous learning module, this invention can dynamically adjust reaction pathways and production conditions during chemical reactions, thereby improving product conversion rates and purity, and reducing byproduct generation.

[0023] • Data-driven decision-making and response strategy updates: Existing systems struggle to effectively integrate multi-dimensional data from the reaction process for real-time decision-making and reaction path optimization. The system of this invention collects reaction data in real-time through a data feedback and monitoring module, inputting the data into a large model module. Machine learning and data mining techniques are then used to automatically optimize the reaction path and production parameters, ensuring the reaction proceeds under optimal conditions.

[0024] • Resource optimization and cost control: Existing chemical production systems often waste resources and energy and struggle to cope with production scheduling issues arising from changes in market demand. Through the integration of an AI-powered chemical plant, this invention can dynamically adjust production processes and reaction conditions based on market demand, raw material costs, and product quality requirements, achieving efficient and economically viable autonomous production while reducing raw material consumption and production waste.

[0025] The challenge of multi-field synergistic catalysis and optimization: Achieving precise control of multiple physical fields, such as light, electricity, protons, and ions, remains a technical challenge in multi-field synergistic catalysis reactions. This invention solves the problem of multi-field synergistic catalysis control by using a fiber optic chemical membrane electrode system combined with tunable light and controllable current to precisely regulate the distribution of each physical field, thereby improving the efficiency and selectivity of the catalytic reaction.

[0026] • Experimental Efficiency and Reproducibility: Existing chemical reaction systems often suffer from imprecise control of reaction conditions and difficulty in replicating experimental results. This invention integrates a large-scale model and autonomous learning module into an AI-powered chemistry laboratory, enabling real-time analysis of experimental data and adaptive optimization of reaction pathways. This improves experimental efficiency and reproducibility, ensuring consistency of experimental results.

[0027] By addressing the aforementioned problems, this invention provides an efficient, intelligent, and sustainable chemical reaction optimization system that can improve reaction efficiency and selectivity while reducing production costs and ensuring product quality stability. 3.2 Summary of the Invention

[0029] This invention relates to an artificial intelligence-based fiber optic chemistry system, and more particularly to a multi-field synergistic catalytic reaction system integrating artificial intelligence and fiber optic chemistry technologies. The core feature of this system is the combination of fiber optic chemical membrane electrodes, an artificial intelligence optimization module, a large-scale model module, and an artificial intelligence chemistry laboratory and factory, enabling precise control and autonomous optimization of chemical reactions. By comprehensively utilizing multiple physical fields such as light, electrons, and ions, the system can adjust chemical reaction pathways, optimize reaction selectivity, and achieve the efficient synthesis of different target products.

[0030] Fiber optic chemical membrane electrode

[0031] The fiber optic chemical membrane electrode of this invention is a multifunctional catalytic interface, comprising an anode fiber catalytic layer, a central proton or ion exchange membrane, and a symmetrical cathode fiber catalytic layer. The anode and cathode are connected by multiple parallel optical fibers forming a light scattering surface, using either unclad photonic crystal fibers or ordinary optical fibers with a higher refractive index on the outside and lower on the inside (with the protective layer removed). This fiber structure allows excitation light to be uniformly distributed and focused on the fiber surface, ensuring efficient utilization of light energy and promoting the catalytic reaction. This design, through the interaction of physical and chemical fields, enables charge migration, reactant desorption, and the catalytic reaction to occur simultaneously on a unified catalytic interface, thereby significantly improving reaction efficiency.

[0032] Fiber optic chemical membrane electrodes are not only used for photocatalysis, but also enable precise control of reaction pathways by adjusting the electric field, light field, and ion current. Through this integrated catalytic effect, the membrane electrode can efficiently catalyze various chemical reactions under the synergistic effect of multiple fields.

[0033] Light source beam splitting module

[0034] The light source splitting module couples external light from the source onto multiple parallel optical fibers via a splitter, and then scatters the light onto the anode and cathode planes of the fiber membrane electrode via an optical fiber array. This module supports multi-wavelength light source input and can optimize the photocatalytic reaction effect by adjusting the proportion of different wavelengths according to the light energy and wavelength required for different reactions. In this way, the system can cope with different reaction conditions, adapt to complex reaction systems, and improve the selectivity and efficiency of the catalytic reaction.

[0035] Proton / ion exchange layer

[0036] The selection of proton exchange membranes (PEMs) or anion exchange membranes (AEMs) and cation exchange membranes (CEMs) provides the necessary ionic conductivity for fiber optic chemical membrane electrodes. This membrane layer ensures the directional migration of ions within the membrane, maintaining interfacial charge balance, thereby providing a stable electrochemical environment for multi-field synergistic catalytic reactions. The design of the proton / ion exchange layer provides a closed-loop electrochemical environment for the reaction system and supports mass exchange during the catalytic process, ensuring the continuous and efficient execution of the reaction.

[0037] Electric field control module

[0038] The electric field control module can precisely adjust the voltage and current between the anode and cathode to achieve programmable control of the chemical reaction pathway. By adjusting the electric field strength and distribution, the module can effectively guide the directional migration of reactants on the membrane electrode surface, optimize charge transfer, prevent electron-ion recombination, and significantly improve reaction rate and selectivity. The flexibility of the electric field control module allows for dynamic adjustment of reaction conditions according to different reaction requirements, thereby achieving precise control of the reaction process.

[0039] Reaction Path Optimization Module

[0040] The reaction path optimization module, incorporating artificial intelligence technology, can automatically adjust the light field and current intensity based on externally input reaction conditions. This precisely controls the formation of different products from the chemical reaction and adjusts the reaction direction and rate. The module acquires data during the reaction through a real-time feedback mechanism and uses machine learning algorithms to analyze the reaction process, automatically optimizing reaction conditions. Based on real-time feedback data, the module can dynamically adjust parameters such as light field intensity, electric field configuration, and reaction temperature to ensure optimal reaction performance.

[0041] Photocatalytic reaction layer

[0042] The photocatalytic reaction layer utilizes a photonic crystal fiber structure. By introducing liquids with different refractive indices, the photonic crystal fiber can modulate the intensity and frequency of incident light, thereby generating scattered light with shorter wavelengths. This scattered light effectively enhances the photocatalytic effect, further optimizing reaction selectivity and conversion efficiency. By adjusting the light intensity distribution and frequency, the photocatalytic reaction layer can provide the most suitable photocatalytic conditions, accelerating the conversion of reactants under illumination.

[0043] Self-learning module and artificial intelligence chemistry laboratory

[0044] The autonomous learning module continuously optimizes reaction pathways and experimental configurations through real-time feedback and adjustment of reaction parameters to improve reaction efficiency and selectivity. Based on an artificial intelligence agent and incorporating extensive experimental data, this module uses deep learning algorithms to continuously improve the control precision of catalytic reactions. The AI-powered chemistry laboratory integrates large-scale models and AI agents, providing powerful decision support capabilities for the experimental platform. This platform can automatically adjust reaction conditions according to experimental needs and objectives, and optimize reaction pathways in real time to ensure optimal results in multiple synergistic catalytic reactions.

[0045] Artificial intelligence chemical factory

[0046] An AI-powered chemical plant is an automated production system built by an AI-powered chemical laboratory. Through autonomous learning and decision optimization, the AI-powered chemical plant can regulate the entire experimental and production process according to market demand and production goals, ensuring efficient autonomous production and product quality control. All production processes within the plant are monitored and optimized in real time by AI modules. The system can automatically adjust reaction conditions, raw material inputs, and reaction times to maximize production efficiency and economic benefits.

[0047] Large model modules and data feedback

[0048] This invention employs a Large Model Module (LLM) combined with deep learning and data mining techniques to analyze chemical reaction data, predict reaction outcomes, optimize reaction pathways, and provide decision support for AI-powered chemistry laboratories and factories. The LLM can process and analyze large amounts of experimental data, providing model-based reaction predictions and pathway optimization suggestions. The data feedback and monitoring module monitors key parameters such as current, voltage, temperature, pressure, and product types during the reaction process through real-time sensors and a data collection system. All data is transmitted to the LLM in real-time to further optimize reaction conditions and pathways, achieving closed-loop control of the reaction process.

[0049] Advantages and effects of the invention

[0050] This invention provides a highly efficient and tunable artificial intelligence-based fiber optic chemistry system. Through the combination of multi-field synergistic catalysis, artificial intelligence, and large-scale models, the system achieves precise control of chemical reactions and autonomous optimization of reaction pathways. Under the combined influence of three physical fields—light, electricity, and protons—the system can optimize reaction selectivity, increase reaction rates, and generate different target products. The integration of the artificial intelligence chemistry laboratory with the factory enables the system to learn and optimize autonomously, adapting to ever-changing market demands and achieving efficient production. The system's intelligence and autonomy significantly improve experimental and production efficiency, reduce resource waste, and achieve a sustainable production model. This system demonstrates excellent economic efficiency and application prospects in chemical reaction control and industrial production.

[0051] Composite fiber structure of optical fiber chemical film electrode

[0052] This invention provides a novel fiber optic chemical membrane electrode, wherein both the anode and cathode fiber optic catalytic layers employ a composite fiber structure. Specifically, the anode and cathode consist of multiple parallel-arranged optical fibers forming a light scattering surface, with electrocatalytic, photocatalytic, and thermal catalytic particles embedded on the fiber surfaces to achieve multi-field synergistic catalytic effects. By integrating different types of catalytic particles on the fiber surfaces, the composite fiber structure can simultaneously exert thermal, electrocatalytic, and photocatalytic effects within the same catalytic layer.

[0053] Distribution of electrocatalytic and photocatalytic particles

[0054] In the optical fiber catalytic layer of this invention, electrocatalytic particles, photocatalytic particles, and thermal catalytic particles are precisely selected and arranged to ensure that different catalytic mechanisms can exert their maximum effect under different reaction conditions. The catalytic particles covering the optical fiber surface can work synergistically in multiple catalytic fields:

[0055] Electrocatalytic particles: Electrocatalytic particles can promote electron transfer reactions under the action of an electric field, thereby increasing the current density and catalytic activity of the reaction;

[0056] • Photocatalytic particles: Photocatalytic particles use light-induced electrons and holes to excite chemical reactions, thereby accelerating the reaction process;

[0057] • Thermal catalytic particles: Thermal catalytic particles promote chemical reactions by providing thermal energy, further improving catalytic efficiency.

[0058] The combined effect of these particles enables fiber optic chemical membrane electrodes to carry out catalytic reactions efficiently under different physical fields.

[0059] The function of the electric field control module

[0060] The electric field control module of this invention can precisely adjust the voltage and current between the anode and cathode, thereby controlling charge migration and reactant conversion within the fiber optic chemical membrane electrode. Specifically, the electric field control module adjusts the electric field strength and distribution, enabling charge carriers such as electrons and ions to migrate directionally within the membrane electrode, thus enhancing the interaction between reactants and the catalytic surface.

[0061] Adjustment of electric field strength and distribution

[0062] The electric field control module creates an ideal electrochemical reaction environment within the fiber optic membrane electrode by precisely adjusting the strength and distribution of the electric field. Changes in the electric field can affect the electronic structure of the reactants, thereby altering their chemical reaction rates. For example, the reduction reaction of reactants may be slow under a low electric field, while the electrocatalytic reaction will be significantly accelerated under a high electric field. Furthermore, the electric field control module can also optimize the distribution of reactants within the membrane electrode by adjusting the current distribution, thereby improving reaction selectivity and efficiency.

[0063] Precise regulation of charge transfer and reactant transformation

[0064] The fiber optic chemical membrane electrode of this invention not only promotes charge migration under the influence of an electric field, but also precisely controls the directional migration path of reactants in the electric field. This precise charge control ensures that reactants can fully participate in the catalytic reaction on the electrode surface, avoiding recombination of electrons and ions, thereby significantly improving reaction efficiency.

[0065] Advantages of multi-field synergistic catalysis

[0066] This invention achieves a multi-field synergistic catalytic effect by combining electrocatalysis, photocatalysis, and thermocatalysis in the same composite optical fiber catalytic layer. The conversion rate of reactants is effectively improved under different catalytic modes:

[0067] • Electrocatalysis: Driven by an external electric field, it can accelerate electron transfer and improve the efficiency of electrochemical reactions;

[0068] • Photocatalysis: With the help of an external light source, photocatalytic particles can excite electron-hole pairs, promoting the redox reaction of reactants;

[0069] • Thermocatalysis: The reaction activity of a catalyst or reactant is enhanced by heating it.

[0070] Through the synergistic effect of these three catalytic mechanisms, the selectivity and conversion efficiency of the reaction are significantly improved, enabling it to adapt to different reaction conditions and reactant requirements.

[0071] Advantages of the present invention

[0072] 1. Highly Efficient Catalytic Reactions: The composite fiber structure combined with multi-field catalysis effectively improves the efficiency of catalytic reactions, especially in high current density, high light intensity, and high temperature conditions. 2. Precise Electric Field Control: The electric field control module can precisely regulate charge migration and reactant conversion during the reaction, thereby optimizing the reaction path and product selectivity. 3. Synergistic Multi-Field Catalysis: The fiber optic chemical membrane electrode utilizes the synergistic effects of electrocatalysis, photocatalysis, and thermocatalysis to achieve more efficient chemical conversions and adapt to various chemical reaction conditions. 4. Programmable Chemical Reactions: By precisely adjusting the electric field intensity, light field intensity, and catalyst surface state, programmable control of the reaction direction and products can be achieved to meet different reaction requirements.

[0073] The reaction path optimization module is combined with the large model.

[0074] The reaction path optimization module of this invention combines a large model with an artificial intelligence (AI) agent, enabling the automatic generation of experimental configurations and reaction parameters based on input of chemical reaction requirements via natural language. This module not only automatically generates experimental configurations and reaction parameters based on input reaction objectives (such as target products and reaction conditions), but also receives experimental feedback data in real time and automatically adjusts reaction conditions according to changes in various parameters during the reaction process.

[0075] Combination of large models and AI agents

[0076] The large-scale model in the reaction pathway optimization module is based on deep learning and big data analytics. Trained on a large amount of chemical reaction data, it can analyze reaction pathways, predict reaction results, and optimize reaction parameters. Utilizing advanced Natural Language Processing (NLP) technology, the large-scale model module can generate corresponding reaction pathways and experimental configurations based on user-provided natural language input (such as reaction requirements, target products, and reaction conditions).

[0077] The AI ​​agent acts as the core decision-making unit of the system. After receiving the experimental configuration output by the large model, it can adjust the reaction parameters to optimize the reaction path based on real-time experimental feedback. Through continuous learning and optimization, the AI ​​agent improves the system's adaptability and optimization capabilities to different reaction conditions, ensuring the efficiency and accuracy of the experimental process.

[0078] Input chemical reaction requirements via natural language

[0079] The reaction path optimization module of this invention supports natural language input, enabling users to express reaction objectives and conditions in concise and clear language. For example, a user can input "synthesize methanol at room temperature and pressure, with the target product being high-purity methanol" in natural language, and the system can automatically understand and generate a reaction path, select appropriate catalysts and reaction conditions according to the reaction requirements, and then automatically adjust the experimental configuration and operating steps.

[0080] Automatic generation of experimental configurations and reaction parameters

[0081] Based on the user's input of reaction requirements, the reaction path optimization module automatically generates the corresponding experimental configuration and reaction parameters. Specifically, the module can determine the following aspects:

[0082] • Reactant selection and concentration: Automatically determine the types and concentrations of reactants based on the target product and its synthetic route;

[0083] • Catalyst selection and dosage: Based on the catalytic mechanism of the reaction, the most suitable catalyst is automatically selected and its dosage is optimized;

[0084] • Reaction temperature and pressure: Automatically adjusts environmental conditions such as reaction temperature and pressure to ensure that the reaction proceeds under optimal conditions;

[0085] • Reaction time and rate: Based on the reaction pathway and reaction kinetics, the reaction time and rate are automatically adjusted to achieve the optimal product conversion rate.

[0086] Adjust reaction conditions based on real-time feedback.

[0087] During the experiment, the reaction pathway optimization module receives real-time feedback data, including key parameters such as temperature, current, type and concentration of reaction products, and reaction conversion rate. Based on this real-time feedback, the module can adjust the reaction conditions and further optimize the reaction pathway.

[0088] Specifically, the module automatically iterates and optimizes experiments through the synergy of AI agents and large models. For example, when unexpected products or low reaction rates occur during experiments, the reaction pathway optimization module can promptly adjust reaction conditions, re-optimize the selection and dosage of catalysts, adjust temperature or pressure, and even change the reaction pathway to ensure reaction efficiency and high product selectivity.

[0089] Advantages and effects of the system

[0090] The integration of the reaction path optimization module enables the system to automatically optimize reaction paths, significantly reducing the need for manual intervention and repeated trials. Specific advantages include:

[0091] • High efficiency and automation: Through natural language input, users do not need to know the specific experimental details. The system automatically generates the optimal experimental configuration and reaction conditions, which significantly improves experimental efficiency.

[0092] • Flexibility and adaptability: The system can dynamically adjust reaction conditions based on reaction feedback, achieving adaptive adjustment for different reactions;

[0093] • Precise control and optimization: Through real-time feedback and reaction parameter adjustment, the system can find the optimal reaction path and conditions in the shortest time, thereby optimizing reaction selectivity and product conversion rate;

[0094] • Intelligence and Autonomy: By combining large models with AI agents, the system can not only learn and optimize itself, but also react quickly to constantly changing experimental data, thereby improving the system's level of autonomy.

[0095] The introduction of this module enables the system to be more intelligent and autonomous, maximizing reaction efficiency, reducing resource waste during the experiment, and achieving precise control over complex reaction systems.

[0096] Design and function of light source beam splitting module

[0097] The light source splitting module of this invention supports multi-wavelength light source input and adjusts the proportion of different wavelengths through a splitter to adapt to the light energy required by different types of reactions. By precisely controlling the wavelength and energy distribution of the light source, this module can select the optimal light source configuration according to the needs of different reactions, thereby optimizing the photocatalytic reaction effect.

[0098] Specifically, the light source splitting module consists of a beam splitter, a wavelength adjustment device, and a wavelength selector. The beam splitter transmits the input composite light source through an optical fiber array and distributes different wavelengths of the light source onto different optical fibers according to the reaction requirements. The wavelength selector automatically adjusts the proportion of different wavelengths of light source based on the preset reaction target and the absorption characteristics of the reactants, ensuring that the reaction system obtains the optimal light intensity and frequency.

[0099] Multi-wavelength light source input

[0100] Multi-wavelength light source input can adapt to the needs of different types of reactions. Especially in catalytic reactions that require multiple wavelengths of light, the system can excite reactants according to different wavelengths of light and precisely control the photocatalytic effect by adjusting the light intensity and frequency. For example, for some photocatalytic reactions, some reactants may have higher absorption efficiency under specific wavelengths of light, while other wavelengths may provide additional excitation energy, thereby further improving the catalytic reaction rate.

[0101] Applications of photonic crystal fibers and control of photocatalytic effects

[0102] In this invention, the photonic crystal fiber, by combining with liquids of different refractive indices, can adjust the light intensity distribution and frequency, precisely controlling the photocatalytic effect of the reaction. Through its microstructure design, the photonic crystal fiber can achieve precise control over light transmission and scattering, thereby improving the selectivity and efficiency of the reaction.

[0103] Design and Function of Photonic Crystal Fiber

[0104] Photonic crystal fiber (PCF) is a type of optical fiber with a periodic structure, consisting of multiple micropores or microstructures that allow for alteration of light propagation characteristics according to design requirements. By changing the size, arrangement, and refractive index of the micropores within the fiber, precise control over light intensity distribution and frequency can be achieved.

[0105] When a liquid flows through a photonic crystal fiber, the light field within the fiber undergoes varying degrees of scattering and focusing due to the difference in refractive index between the liquid and the fiber. By adjusting the refractive index of the liquid within the fiber, the system can precisely control the distribution and frequency of light intensity. The selection of liquids with different refractive indices can adjust the speed of light and its propagation mode within the photonic crystal fiber, thereby altering the distribution of excitation light at the reaction interface.

[0106] Light intensity distribution and light frequency modulation

[0107] The refractive index modulation capability of photonic crystal fibers can influence the light transmission characteristics, thereby affecting the distribution and frequency of light in the reaction. Specifically, matching the refractive index of the liquid in the photonic crystal fiber with the refractive index of the fiber material enables the light to be uniformly distributed within the reaction region, avoiding excessive concentration or dispersion of light energy, thus ensuring that the photocatalytic reaction proceeds under ideal conditions.

[0108] In this way, photonic crystal fibers can effectively adjust the intensity and frequency of the light field, maximizing the efficiency of photocatalytic reactions. Especially in high-precision photocatalytic reactions, fine-tuning of light intensity and frequency can significantly improve reactant conversion rates and reduce unnecessary side reactions.

[0109] Advantages and effects of the present invention

[0110] The combination of the light source beam-splitting module and photonic crystal fiber in this invention provides great flexibility and precision control, enabling the photocatalytic reaction to be precisely adjusted according to specific needs. Specific advantages include:

[0111] • Flexible wavelength adjustment: Multi-wavelength light source input can adapt to the needs of different reactions. By precisely adjusting the proportion of different wavelength light sources, reaction conditions can be optimized.

[0112] • Precise control of light intensity distribution: Photonic crystal fibers, by combining with liquids of different refractive indices, can precisely adjust the distribution and frequency of light intensity, ensuring the high efficiency of photocatalytic reactions;

[0113] • Highly efficient photocatalytic reaction: By adjusting the wavelength and frequency distribution of the light source, precise control of the reaction path is achieved, improving reaction efficiency and product selectivity;

[0114] • High adaptability: The system can automatically adjust the light source configuration according to the absorption characteristics of the reactants to adapt to different types of chemical reactions.

[0115] By combining this multi-wavelength light source with photonic crystal fiber, this invention provides a highly tunable and efficient photocatalytic reaction platform that can optimize the photocatalytic effect under various reaction conditions, significantly improving the conversion rate and selectivity of the reaction. The design and function of the chemical reaction control module are also discussed.

[0116] The chemical reaction control module in this invention is a highly integrated system capable of optimizing the selectivity of reaction products and achieving adaptive reaction path adjustment through interface adjustment. This module, through a real-time feedback and adjustment mechanism, can dynamically optimize the chemical reaction path based on changes in reaction conditions, thereby improving reaction efficiency and selectivity. By integrating artificial intelligence and data analysis technologies, the chemical reaction control module can adjust reaction parameters based on real-time monitoring data to adapt to different reaction requirements and maximize product formation efficiency.

[0117] Interface conditioning and reaction product selectivity optimization

[0118] The chemical reaction control module is adjusted through a user-friendly interface. The interface design allows users to easily input reaction requirements and adjust reaction conditions such as temperature, pressure, and reactant concentrations in real time. Simultaneously, the interface provides real-time reaction data feedback, including reaction rate, product type, and conversion rate, helping users understand the reaction progress immediately.

[0119] By adjusting reaction conditions, the control module can precisely regulate the contact conditions between reactants and catalysts, as well as the distribution of physical fields such as electric and light fields, thereby optimizing the selectivity of reaction products. Particularly in multi-product reactions, the module can automatically adjust the reaction path based on feedback data, prioritizing the generation of the target product and suppressing the formation of byproducts.

[0120] Adaptive adjustment of reaction pathway

[0121] During the reaction, the chemical reaction control module automatically adjusts the reaction path based on real-time feedback data. By integrating with a large model and AI agents, the control module can determine in real-time whether the reaction deviates from the predetermined path based on data such as changes in reactants and reaction rates, and automatically make corresponding adjustments. This adaptive capability enables the system to maintain efficient reaction control and optimize product generation even in complex reaction environments.

[0122] For example, when byproducts are generated during the reaction, the module can suppress their formation by adjusting the input amounts of reactants, the concentration of the catalyst, or the reaction temperature, and readjust the reaction path to guide the reaction toward the formation of the intended target product. This adaptive reaction path adjustment not only improves reaction efficiency but also saves raw materials and energy, reducing waste during the reaction process.

[0123] Dynamic adjustment and feedback mechanism

[0124] One of the core functions of the control module is dynamic adjustment based on a real-time feedback mechanism. This module monitors multiple key parameters during the reaction process in real time through sensors and a data acquisition system, such as temperature, pressure, current, voltage, reactant concentration, and product concentration. Within each experimental cycle, the data feedback mechanism provides accurate reaction information, and the control module automatically adjusts the reaction conditions based on this data to ensure the reaction process remains in an optimal state.

[0125] For example, if the conversion rate of the reaction products is lower than expected during the reaction process, the module can automatically adjust the amount of catalyst used or the reaction temperature to accelerate the reaction rate. Through this continuous adaptive adjustment, the system can effectively improve the yield and selectivity of the reaction products.

[0126] Advantages and effects of the system

[0127] The chemical reaction control module in this invention offers the following advantages through interface adjustment and adaptive reaction path adjustment:

[0128] • Optimize reaction selectivity: By precisely adjusting reaction conditions, the contact conditions between reactants and catalysts, the distribution of physical fields such as electric field and light field are optimized to improve the selectivity of target products;

[0129] • Adaptive reaction path adjustment: Based on real-time feedback, the reaction path is automatically adjusted to ensure that the reaction always proceeds in the direction of the target product and to reduce the generation of by-products;

[0130] • Improve reaction efficiency: Through precise adjustment of the reaction path and real-time feedback regulation, the system can significantly improve reaction efficiency and product conversion rate, and reduce resource waste;

[0131] • Save energy and raw materials: Through intelligent reaction control, optimize reaction conditions, reduce raw material consumption and energy waste, and lower production costs;

[0132] • Flexibility and efficiency: The interface adjustment function allows users to easily set and adjust reaction conditions, and the system can be optimized and adjusted in real time according to experimental needs.

[0133] The chemical reaction control module of this invention enables the system to automatically adjust under changing reaction conditions, ensuring efficient reaction and maximizing the production of the target product. The introduction of this module significantly enhances the system's intelligence, adaptability, and flexibility, offering substantial technical advantages in chemical reaction optimization.

[0134] Design and Function of Fiber Optic Chemical Membrane Electrode Reaction Platform

[0135] The reaction platform constructed using the fiber optic chemical membrane electrode of this invention integrates multiple functional modules, including tunable optical field, controllable current, and programmable reaction path, enabling efficient control and optimization of chemical reactions. With the fiber optic chemical membrane electrode as its core, this platform ensures that the reaction proceeds under optimal conditions by precisely adjusting the intensity and distribution of the optical field, the magnitude and direction of the current, and combining this with programmable reaction path control. This significantly improves the efficiency, selectivity, and product conversion rate of the reaction.

[0136] Adjustable light field

[0137] The fiber optic chemical membrane electrode reaction platform achieves precise control of the light field during the reaction by adjusting the wavelength, intensity, and distribution of the light source. Specifically, the platform's light source can provide multi-wavelength illumination through a beam splitter module, and the light energy is uniformly scattered onto the anode and cathode planes of the fiber optic membrane electrode through a fiber array. The platform's light field can be adjusted according to different reaction requirements, ensuring that the light energy distribution during the reaction process meets the optimal requirements for the catalytic reaction.

[0138] By adjusting the intensity and uniformity of the light field, the system can ensure the full excitation and transformation of reactants in photocatalytic reactions. For example, in photocatalysis, a stronger light field can accelerate the generation of electrons and holes, increasing the reduction or oxidation rate of reactants. This tunable light field capability gives the reaction platform extremely high flexibility, enabling it to adapt to the requirements of different chemical reactions and improve reaction efficiency and selectivity.

[0139] Controllable current

[0140] The current control module in the reaction platform can precisely adjust the current between the anode and cathode, thereby regulating the electric field distribution and charge migration process inside the membrane electrode. By adjusting the current intensity, the system can control the rate of reduction or oxidation reactions of the reactants, precisely achieving the conversion of reactants and the selective generation of products.

[0141] The current control module can not only regulate the reaction rate, but also control the reaction path by changing the current direction or electric field strength. In some reaction systems, changing the current direction can optimize the reaction selectivity, suppress the formation of by-products, and increase the yield of the target product.

[0142] Programmable reaction pathway

[0143] The reaction platform, through an integrated programmable control system, can automatically adjust the reaction path based on real-time data and feedback during the chemical reaction process. The programmable reaction path function allows the system to dynamically adjust reaction conditions, such as temperature, pressure, light intensity, and current, at different reaction stages to achieve efficient reaction control.

[0144] This system utilizes artificial intelligence technology, combining large-scale models with real-time feedback data, to automatically adjust the reaction path according to the experimental progress. For example, during the reaction, the system can automatically adjust the light field, current intensity, and other reaction conditions based on real-time data of reactant conversion rates, product types, and other key reaction parameters to ensure the reaction is always maintained in an optimal state.

[0145] Advantages and effects of the system

[0146] The reaction platform constructed using fiber optic chemical membrane electrodes in this invention offers the following significant advantages through the integration of tunable optical fields, controllable currents, and programmable reaction paths:

[0147] • High-efficiency reaction control: By precisely adjusting the light field, current and reaction path, the system can optimize reaction conditions and significantly improve the reaction rate and reactant conversion rate;

[0148] • Flexible reaction path adjustment: The programmable reaction path can dynamically adjust the reaction conditions based on real-time feedback to ensure that the reaction always proceeds under optimal conditions;

[0149] • Enhanced reaction selectivity: Through the combined effect of tunable light field and controllable current, the system can improve the selectivity of target products and suppress the formation of by-products;

[0150] • High adaptability: The reaction platform can automatically adjust the light field intensity, current and other reaction conditions according to different chemical reaction requirements, adapting to a variety of chemical reactions;

[0151] • Intelligent and automated: By combining artificial intelligence technology, the system can automatically adjust the reaction path and optimize the reaction conditions based on experimental feedback, reducing human intervention and improving experimental efficiency.

[0152] These advantages enable the fiber optic chemical membrane electrode reaction platform to provide efficient, flexible, and intelligent reaction control under various reaction conditions, providing important technical support for the optimization of chemical reactions.

[0153] Integration and Functions of Artificial Intelligence Chemistry Laboratory

[0154] The AI-powered chemistry laboratory in this invention integrates a large-scale model module and a self-learning module. By combining real-time feedback and historical data analysis, it can automatically optimize reaction paths and production parameters, and autonomously predict and control reaction results. As the core control unit of the system, the AI-powered chemistry laboratory can continuously improve the efficiency and accuracy of experiments through self-learning and optimization, ensuring that the reaction process is always kept in an optimal state.

[0155] Functions and roles of the large model module

[0156] The Large Model Module (LMM) is a crucial component of the AI-powered chemistry lab. Based on deep learning and big data technologies, and incorporating extensive chemical reaction data, this module learns from historical experimental data to generate efficient reaction pathways and experimental configurations. The LMM not only predicts reaction outcomes but also analyzes the advantages and disadvantages of reaction pathways and provides optimization suggestions.

[0157] Specifically, the large model module can:

[0158] • Reaction pathway prediction: By learning from a large amount of chemical reaction data, large models can predict the conversion rate and selectivity of different reaction pathways, providing accurate reaction pathway predictions for experiments;

[0159] • Reaction parameter optimization: Based on inputs such as reactant type, concentration, temperature, and pressure, the large model can calculate the optimal reaction parameter configuration to improve reaction efficiency and product quality;

[0160] • Real-time feedback analysis: By combining real-time data feedback during the experiment, the large model module can continuously update and optimize the reaction path and production parameters to ensure that the reaction process is always maintained under optimal conditions.

[0161] • Multi-objective optimization: For complex reaction systems, large models can balance multiple objectives, such as increasing yield, improving selectivity, and reducing byproduct formation, providing a comprehensive reaction optimization solution.

[0162] The Role and Optimization of the Self-Learning Module

[0163] The Self-Learning Module (SLM) is another key component of the AI-powered chemistry laboratory. This module can learn from real-time feedback data and historical experimental data, progressively optimizing reaction pathways and production parameters. Working closely with the large-scale model module, the SLM continuously improves the system's adaptability to different reaction conditions through ongoing learning.

[0164] The functions of the self-learning module include:

[0165] • Real-time data feedback: The autonomous learning module can dynamically adjust the reaction conditions based on the data collected in real time by sensors (such as reaction temperature, pressure, product concentration, etc.) to ensure that the reaction process is carried out under optimal conditions.

[0166] • Adaptive optimization: The self-learning module can adaptively optimize based on feedback data, continuously adjusting the reaction path and reaction conditions to improve reaction efficiency and product selectivity;

[0167] • Learning from historical data: By analyzing historical experimental data, the self-learning module can identify patterns and trends in the reaction process, automatically predict potential problems, and make adjustments accordingly;

[0168] • Iterative feedback: The module can gradually optimize the reaction configuration through multiple experimental iterations, which not only improves reaction efficiency, but also reduces waste and energy consumption in the reaction process.

[0169] Reaction Result Prediction and Regulation

[0170] One of the core advantages of an AI-powered chemistry lab is its ability to autonomously predict and control reaction outcomes. Through the collaborative work of a large model module and an autonomous learning module, the lab can not only predict the final result of a reaction but also control the reaction process in real time during the experiment.

[0171] Reaction Result Prediction

[0172] The large model module, through analysis of reaction pathways and parameters, can predict the final products, conversion rates, and byproduct formation of reactions. The system can provide accurate predictions based on historical data in the early stages of experiments, effectively reducing trial-and-error processes and saving time and resources.

[0173] Reaction Regulation

[0174] During the experiment, the AI-powered chemistry lab can monitor reaction conditions in real time and automatically adjust them based on feedback data. For example, if the system detects excessive byproduct formation, the module will automatically adjust reaction conditions (such as temperature, current, and catalyst concentration) to guide the reaction toward the target product. This real-time control ensures the stability and efficiency of the reaction process, maximizing product quality and yield.

[0175] Advantages and effects of the system

[0176] The artificial intelligence chemistry laboratory of this invention, combining a large model module and a self-learning module, achieves the following advantages:

[0177] • High-efficiency reaction optimization: Through the combination of a large model and an autonomous learning module, the system can optimize reaction paths in complex reaction systems in real time, improving reaction efficiency and selectivity;

[0178] • Autonomous learning and adjustment: The system has the ability to learn on its own and can continuously optimize reaction conditions based on real-time feedback and historical data to achieve more efficient reaction control;

[0179] • Prediction and control: The laboratory can accurately predict reaction results and control the reaction in real time according to the experimental process to ensure efficient and high-quality production;

[0180] • Intelligence and Automation: The AI-powered chemistry laboratory is characterized by a high degree of intelligence and automation, which can reduce human intervention, lower experimental risks, and save time and resources;

[0181] • Sustainable development: Through intelligent optimization, the system can reduce the waste of raw materials and energy, improve resource utilization efficiency, and promote the goals of green chemistry and sustainable development.

[0182] Overall, the AI-powered chemistry laboratory, by integrating large models and autonomous learning modules, has significantly improved the efficiency and accuracy of reaction optimization, demonstrating strong adaptability and intelligence, and showing great application potential in the control and optimization of complex chemical reactions.

[0183] Design and Function of Artificial Intelligence Chemical Plant

[0184] The AI-powered chemical plant of this invention, through its autonomous learning and optimization control system, can dynamically adjust production processes and reaction conditions based on market demand, raw material costs, and product quality requirements, ensuring both high efficiency and economic benefits. This plant integrates advanced artificial intelligence technology; through autonomous learning and real-time data feedback, the system can autonomously adjust production parameters, optimize reaction paths and production processes, achieving a highly efficient production mode, reducing resource waste, lowering production costs, and guaranteeing product quality.

[0185] Autonomous Learning and Optimization Control System

[0186] Autonomous learning and optimization control systems are core components of AI-powered chemical plants. Through continuous learning and optimization, this system can adjust various variables (such as reaction temperature, pressure, raw material concentration, and catalyst dosage) in real time during production, ensuring efficient operation at every stage and maximizing production efficiency.

[0187] Market demand and production scheduling

[0188] AI-powered chemical plants can automatically adjust production plans and schedules based on market demand. The system analyzes current market trends, product demand, and customer requirements, optimizing production processes and reaction conditions to adjust production pace in a timely manner to adapt to market changes. For example, when market demand for a particular product increases, the system automatically increases production and adjusts reaction conditions to maintain product quality; conversely, when market demand decreases, the system reduces production to avoid overproduction and resource waste.

[0189] Raw material cost and production cost optimization

[0190] The AI-powered chemical plant of this invention monitors raw material costs in real time and adjusts raw material procurement and usage based on production needs. The system can adjust the amount of raw materials input according to the actual progress of the reaction, maximizing raw material utilization and reducing waste. Simultaneously, by optimizing reaction conditions and production processes, the system can effectively reduce production costs and ensure the economic efficiency of the production process. For example, the system optimizes parameters such as catalyst usage, reaction temperature, and pressure based on raw material cost fluctuations, thereby reducing raw material and energy consumption.

[0191] Product quality control and optimization

[0192] A key function of AI-powered chemical plants is to automatically adjust production parameters based on product quality requirements, ensuring consistent quality for each batch. During production, the system monitors multiple critical parameters in real time, such as product concentration, conversion rate, reaction temperature, and pressure. If product quality deviates from predetermined standards, the system automatically adjusts reaction conditions to restore product quality. For example, the system can automatically lower the temperature when it is too high or adjust the catalyst concentration when the reaction rate is too fast, thereby optimizing product quality and purity.

[0193] Data feedback and production control

[0194] The system continuously monitors various parameters during the production process through real-time sensors and data acquisition modules, transmitting the data to the central control system. The data feedback module provides real-time production status reports, helping the system analyze and adjust for any deviations in the production process. When the system detects anomalies in production, it can automatically make adjustments, such as changing raw materials or adjusting reaction conditions, thereby ensuring smooth production and reducing production risks.

[0195] Advantages and effects of the system

[0196] The artificial intelligence chemical plant of the present invention has the following significant advantages:

[0197] • High-efficiency production: Through autonomous learning and optimization control systems, the factory can adjust production plans and response conditions in a short period of time to maximize production efficiency and ensure product quality;

[0198] • Adapt to market demand: The system can automatically adjust production volume and pace according to fluctuations in market demand, avoiding resource waste and adapting to market changes;

[0199] • Reduce production costs: By optimizing raw material usage and various parameters in the production process, the system can effectively reduce raw material consumption and energy waste, thereby lowering production costs;

[0200] • Quality control and stability: The AI-powered chemical plant can monitor product quality in real time and automatically adjust reaction conditions to ensure product consistency and quality stability;

[0201] • Intelligence and automation: The intelligent and automated control of the system minimizes human intervention, improves production efficiency, and reduces the risk of human error.

[0202] These advantages enable AI-powered chemical plants to achieve efficient, flexible, and economical production models, reducing production costs and increasing efficiency while ensuring product quality, thus promoting the goals of green chemistry and sustainable production.

[0203] Design and Functions of Large Model Module

[0204] The large-scale model module in this invention combines multi-dimensional experimental data with chemical reaction models, utilizing advanced machine learning and data mining techniques to provide the system with optimized reaction paths and product characteristic predictions, and supports factory production scheduling and reaction strategy updates. As a crucial component of the system, this module provides highly accurate reaction predictions and strategy recommendations based on extensive historical experimental data and reaction models, offering robust support for chemical reaction control and production process optimization.

[0205] Using multidimensional experimental data and chemical reaction models

[0206] The large-scale model module utilizes multi-dimensional experimental data, including parameters such as temperature, pressure, reactant concentration, catalyst concentration, current, and voltage, to create accurate chemical reaction models through data analysis and modeling. These models can describe in detail the reaction pathways, reaction rates, and product distribution under different reaction conditions.

[0207] Integration and analysis of experimental data

[0208] The large-scale model module collects multi-dimensional data from the reaction process and, combined with a chemical reaction model, can identify the relationships between different parameters. For example, when reactant concentrations change, the system can predict changes in the reaction rate and adjust relevant reaction conditions based on the reaction pathway optimization model. Furthermore, the module can use data mining techniques to discover potential patterns and regularities in the reaction process, further optimizing the reaction pathway and production conditions.

[0209] Application of chemical reaction models

[0210] The large model module applies chemical reaction models to reaction pathway optimization, predicting possible products and key parameters such as reaction time and conversion rate. The reaction model not only predicts reaction rates but also analyzes the effectiveness of different reactant and catalyst combinations, optimizing reaction pathways and selecting optimal reaction conditions. This process enables more precise control of chemical reactions, improving selectivity and efficiency.

[0211] Applications of machine learning and data mining techniques

[0212] The large model module combines machine learning and data mining techniques, enabling it to continuously learn from new experimental data, optimize itself, and improve predictive accuracy. Machine learning algorithms can extract patterns and trends from large amounts of historical data, thereby providing a basis for new response paths and optimization strategies.

[0213] Machine learning optimizes reaction pathways

[0214] The machine learning algorithms in the large model module can automatically identify and recommend optimal reaction paths. When reaction conditions change, the machine learning algorithms automatically adjust the reaction path and predict the conversion efficiency of reactants through data analysis. This automatic optimization capability allows the reaction path to be flexibly adjusted according to different experimental needs, ensuring the high efficiency of the reaction.

[0215] Data mining technology supports production decisions

[0216] Data mining technology, through in-depth analysis of historical data, can uncover potential influencing factors and bottlenecks in the production process, providing real-time feedback. This technology helps the system discover optimal production scheduling solutions and adjust production plans in a timely manner to improve production efficiency and reduce energy waste. For example, when the system detects excessive byproduct generation in a certain reaction pathway, data mining technology can identify potential problems and suggest adjustments to reaction conditions to avoid overproduction of byproducts.

[0217] Reaction pathway and product characteristic prediction

[0218] The large model module can not only optimize reaction pathways but also predict product characteristics such as purity, yield, and selectivity. By analyzing experimental data under different reaction conditions, the system can predict the quality of the product under specific conditions and provide adjustment plans in advance to optimize product characteristics.

[0219] Reaction Result Prediction

[0220] The large model module can predict the final reaction products and their characteristics based on the current reaction conditions. For example, it can predict the purity and conversion rate of the products at a given reaction temperature and catalyst concentration. Based on these predictions, the system can adjust the reaction conditions in advance to achieve higher product quality and yield.

[0221] Support for production scheduling and response strategy updates

[0222] The large-scale model module provides decision support for factory production scheduling and response strategy updates by offering optimized reaction paths and product characteristic predictions. Through real-time data feedback and analysis results from the large-scale model module, the system automatically adjusts production plans and response conditions to meet constantly changing market demands and production requirements.

[0223] Production scheduling optimization

[0224] Based on the reaction path and product characteristic prediction of the large model module, the system can automatically adjust production scheduling according to market demand, raw material supply, and production targets. For example, the system can adjust the batch production sequence of the reaction based on reaction path prediction and dynamically allocate production resources according to product demand.

[0225] Automatic updating of reaction strategy

[0226] When new data emerges or reaction conditions change during production, the large model module can automatically analyze and update the reaction strategy. This self-optimization capability ensures the continuous and efficient operation of the production process. For example, if excessive byproduct generation is detected during the reaction, the module can adjust the reaction path and optimize reaction conditions to ensure product selectivity and conversion rate.

[0227] Advantages and effects of the system

[0228] The large model module in this invention has the following advantages:

[0229] • Precise reaction path optimization: By combining multi-dimensional experimental data and chemical reaction models, the system can provide accurate reaction path predictions and optimize reaction conditions to improve reaction efficiency;

[0230] • Product characteristic prediction: It can predict the quality and characteristics of products during the reaction process, providing a basis for product optimization in the production process;

[0231] • Adaptive optimization and learning: Through machine learning and data mining techniques, the system can continuously learn and optimize reaction paths and production strategies to improve reaction efficiency and quality;

[0232] • Efficient production scheduling: The system automatically adjusts production scheduling and response strategies based on market demand, raw material costs, and production targets to ensure an efficient and economical production process;

[0233] • Real-time feedback and adjustment: Through real-time data feedback, the system can quickly identify reaction deviations and automatically adjust reaction conditions and production plans to ensure stable production.

[0234] The technical solution of this invention significantly improves the level of automation and intelligence in the production process, effectively reduces production costs, improves product quality, and adjusts production strategies according to real-time demand, thereby achieving a more efficient, economical and sustainable production mode.

[0235] Design and Functions of Data Feedback and Monitoring Module

[0236] The data feedback and monitoring module in this invention monitors and collects multiple key parameters during the reaction process in real time, such as voltage, current, temperature, and reaction product concentration, and inputs this data into the large model module. The main function of this module is to track the progress of the chemical reaction in real time, provide accurate reaction state data, and dynamically optimize the reaction conditions and pathways based on this data, thereby ensuring that the reaction process always proceeds under optimal conditions, improving reaction efficiency and product quality.

[0237] Real-time monitoring and data acquisition

[0238] The data feedback and monitoring module is equipped with a series of high-precision sensors for real-time monitoring of voltage, current, temperature, reactant concentration, product concentration, and other relevant parameters during the reaction process. These sensors enable the system to comprehensively understand each stage of the reaction process and collect reaction data in real time, providing accurate data for subsequent analysis and optimization.

[0239] Voltage and current monitoring

[0240] During the reaction, voltage and current are key parameters determining the reaction rate and pathway. The data feedback and monitoring module can measure the voltage difference and reaction current between the electrodes in real time, determine the progress of the electrochemical reaction, and monitor for current fluctuations or voltage instability. When a deviation of voltage or current from the preset range is detected, the module can immediately transmit this data to the large model module, which can then optimize the electric field distribution and reaction pathway based on this feedback.

[0241] Temperature and reactant concentration monitoring

[0242] Temperature and reactant concentration are key factors affecting the rate and selectivity of catalytic reactions. Using temperature and concentration sensors, the data feedback module can acquire reaction temperature and reactant concentration data in real time. Temperature changes can significantly impact reaction rate and selectivity, while reactant concentration directly affects the extent of reaction progression. The module monitors temperature and concentration changes and inputs the data into a large-scale model module for reaction pathway optimization and temperature control adjustment.

[0243] Reaction product concentration monitoring

[0244] In multi-product reactions, monitoring the concentration of reaction products is particularly important. The data feedback and monitoring module can measure the concentration of reaction products in real time, and evaluate the yield and selectivity of the reaction. If the concentration of reaction products deviates from the expected value, the module can adjust the reaction conditions based on real-time data to ensure high selectivity and high yield of the target product.

[0245] Data input to large model module and optimization

[0246] The collected real-time data is directly input into the large model module, which uses deep learning and big data analytics, combined with historical experimental data and reaction models, to evaluate the current reaction conditions and propose optimization suggestions. The large model module can dynamically adjust the reaction path and production parameters based on data feedback to ensure that the reaction process is always in an optimal state.

[0247] Reaction pathway optimization

[0248] The large model module can optimize reaction pathways based on real-time data feedback during the reaction process. For example, when data feedback indicates that a certain reaction pathway has low product selectivity, the module will suggest adjusting the reaction temperature, current, or catalyst concentration to improve selectivity and reduce byproduct formation. Through this process, the reaction pathway can be continuously optimized to ensure that the reaction products meet the expected requirements.

[0249] Adjustment of reaction conditions

[0250] Upon receiving data feedback, the large model module can adjust reaction conditions in real time. For example, the system may adjust catalyst concentration, reaction time, reaction temperature, or light intensity based on parameters such as voltage, current, and temperature, thereby improving reaction efficiency and selectivity. This feedback mechanism ensures that the reaction process is always under optimal control.

[0251] Multidimensional data fusion and optimization decision making

[0252] By fusing and analyzing multidimensional data (such as voltage, current, temperature, and concentration), the large model module can make comprehensive decisions and optimize reaction conditions. For example, if the reactant concentration is too high, leading to an excessively fast reaction rate, the module may adjust the reaction temperature to slow down the reaction rate or increase the catalyst concentration to improve reaction selectivity. This optimization decision-making based on multidimensional data analysis makes the reaction process more intelligent and efficient.

[0253] Advantages and effects of the system

[0254] The data feedback and monitoring module in this invention has the following significant advantages by collecting and processing various data during the reaction process in real time:

[0255] • High-efficiency reaction monitoring: Real-time monitoring of key parameters such as voltage, current, temperature, reactant concentration, and product concentration during the reaction to ensure the stability of the reaction process;

[0256] • Precise data feedback: Real-time data is accurately input into the large model module, providing a basis for optimizing the reaction path and adjusting the reaction conditions;

[0257] • Dynamically optimize reaction conditions: Through real-time feedback data, the system can dynamically adjust reaction conditions, optimize reaction paths, and improve reaction efficiency and selectivity;

[0258] • Intelligence and automation: The integration of data feedback and monitoring modules makes the reaction process more intelligent, reduces manual intervention, and improves production efficiency;

[0259] • Improve reaction selectivity and product quality: Through precise monitoring and optimized decision-making, the system can significantly improve the selectivity and quality of reaction products.

[0260] These advantages enable the data feedback and monitoring module to adjust and continuously optimize the entire reaction process in real time, providing strong technical support for the efficient conduct of chemical reactions and the stability of product quality.

[0261] 3.3 Beneficial Effects

[0262] The artificial intelligence-based fiber optic chemistry system provided by this invention has the following significant advantages:

[0263] • Highly efficient reaction control and optimization: By integrating fiber optic chemical membrane electrodes, tunable optical fields, controllable currents, and reaction path optimization modules, this invention enables precise control and real-time optimization of chemical reaction processes. The system can automatically adjust reaction paths and production conditions based on real-time feedback, significantly improving reaction efficiency and selectivity, reducing byproduct formation, and increasing product yield.

[0264] • Intelligent reaction path optimization: Combining a large-scale model with a self-learning module, this invention can optimize the reaction path through multi-dimensional experimental data analysis and automatically adjust reaction conditions based on experimental feedback. The system can intelligently predict reaction results and regulate the reaction path to ensure efficient reaction and maximize product quality.

[0265] Resource optimization and cost control: The AI-powered chemical plant of this invention, through its autonomous learning and optimization control system, can dynamically adjust production processes and reaction conditions based on market demand, raw material costs, and product quality requirements. By optimizing reaction conditions and production plans, the system achieves optimal use of raw materials and energy, significantly reducing production costs and minimizing resource waste.

[0266] • Real-time data feedback and decision support: Through the data feedback and monitoring module, the system can monitor multiple key parameters in the reaction process in real time and input the data into the large model module. The large model module can optimize the reaction path and production parameters based on the feedback data, ensuring that the reaction is always in an optimal state and improving the stability and consistency of the production process.

[0267] • Multi-field synergistic catalytic effect: Through the tunable light field and controllable current in the fiber optic chemical membrane electrode system, this invention can precisely adjust the distribution of multiple physical fields such as light, electricity, and protons, promoting the efficient execution of multi-field synergistic catalytic reactions. The system can optimize reaction efficiency under the synergistic effect of photocatalysis, electrocatalysis, and thermocatalysis, adapting to the needs of different reactions.

[0268] • Improved experimental efficiency and reproducibility: The reaction path optimization and experimental control functions integrated with artificial intelligence technology make the experimental process more efficient, accurate, and reproducible. The system can reduce human intervention, decrease human error, and improve the predictability and consistency of experimental results.

[0269] • Green Chemistry and Sustainable Development: By reducing waste in reactions, optimizing reaction pathways, and lowering energy consumption, this invention promotes the realization of green chemistry, aligning with sustainable development goals. The system achieves the highest product conversion rate with minimal raw material and energy consumption, supporting environmentally friendly production methods.

[0270] In summary, this invention, by integrating technologies such as fiber optic chemistry, artificial intelligence, and multi-physics synergistic catalysis, solves the problems of precision, efficiency, and cost in traditional chemical reaction control, providing a highly efficient, intelligent, and economical chemical reaction optimization system. This system not only improves reaction selectivity and yield but also reduces production costs, achieving precise control and optimization of the chemical reaction process, and has broad application prospects and market value.

[0271] 3.4 Novelty, inventiveness and industrial applicability analysis

[0272] 3.4.1 Novelty Analysis

[0273] This invention relates to an artificial intelligence-based fiber optic chemistry system, combining fiber optic chemical membrane electrodes, an artificial intelligence optimization module, a large model module, and a self-learning module, among other technologies. A similar combination has not been seen in the prior art. The novelty of this invention is embodied in the following:

[0274] • Combination of Fiber Optic Chemical Membrane Electrode with Artificial Intelligence Technology: This invention employs a fiber optic chemical membrane electrode system combined with artificial intelligence technology. By precisely controlling reaction conditions through tunable optical fields and controllable currents, the reaction pathway is optimized. While existing technologies utilize fiber optic catalysis and artificial intelligence, the integration of these two technologies in reaction pathway optimization has not yet been disclosed.

[0275] • Application of the large model module: This invention automates the prediction of reaction paths and product characteristics by combining a large model module with deep learning and data mining techniques. Existing large model modules are generally applied to a single domain or process, lacking the multi-domain, multi-dimensional comprehensive optimization application described in this invention.

[0276] • Adaptive Response Path Adjustment: The autonomous learning module in this invention can adaptively adjust the response path based on real-time feedback data and historical data, ensuring optimal response efficiency and selectivity. Most existing technologies rely on manual adjustment or fixed models, lacking the ability of this invention to adaptively adjust the response path based on artificial intelligence.

[0277] Therefore, the technical combination of the present invention provides a completely new solution based on the prior art, and has significant novelty.

[0278] 3.4.2 Creativity Analysis

[0279] This invention combines multiple advanced technological fields such as fiber optic chemistry, artificial intelligence, and autonomous learning, and creatively proposes the following innovative points:

[0280] • Multi-field synergistic catalysis and intelligent optimization: This invention combines fiber optic chemical membrane electrodes with artificial intelligence technology to optimize reaction pathways and production conditions in multi-field synergistic catalysis reactions through real-time feedback and adaptive adjustment. This technology, which controls reactions under the synergistic effect of multiple physical fields such as light, electricity, and protons, is something that traditional catalytic reactions cannot achieve.

[0281] • Combining a large model with autonomous learning: This invention integrates a large model with an autonomous learning module into reaction path optimization, enabling the system to automatically adjust reaction conditions based on real-time feedback. Such a system for real-time optimization and reaction path adjustment based on artificial intelligence is unprecedented in existing technologies.

[0282] • Intelligent Chemical Reaction Plant: Through the design of an artificial intelligence-based chemical plant, this invention enables the automatic adjustment of production processes and reaction conditions based on market demand, raw material costs, and product quality requirements. This intelligent production process is highly innovative in industrial applications, significantly improving production efficiency and reducing resource consumption.

[0283] These innovative technological integrations enable this invention to surpass existing technologies in multiple fields, demonstrating significant technological progress and innovation.

[0284] 3.4.3 Industrial Applicability Analysis

[0285] This invention has broad industrial applicability and can be applied in multiple industries, particularly in the fields of chemical reactions, catalysis, energy, environmental protection, and materials manufacturing. Specific industrial applicability includes:

[0286] • Chemical Industry: The fiber optic chemical membrane electrode system of this invention can efficiently catalyze chemical reactions and is widely used in various fields such as chemical synthesis, petroleum refining, and fine chemical production. The system can automatically optimize the reaction path and production parameters according to the type of reactants, reaction conditions, and product requirements, ensuring a high-efficiency and high-quality production process.

[0287] • Energy and Environmental Protection: This invention optimizes energy conversion processes, such as fuel cells, hydrogen production, and carbon dioxide capture. By precisely controlling reaction pathways and catalytic reactions, this invention can significantly improve energy efficiency and reduce environmental pollution.

[0288] • Materials Manufacturing: This invention can be applied to materials synthesis processes, such as the synthesis of novel catalysts and functional materials. Through optimization of the large model module and the autonomous learning module, the system can automatically adjust reaction conditions according to the material properties, improving product quality and production efficiency.

[0289] • Intelligent Manufacturing and Automated Production: The AI-powered chemical plant in this invention can automatically adjust its production process based on market demand, raw material costs, and product quality, representing a significant application in the field of intelligent manufacturing. Through this system, the production process can achieve high efficiency, automation, low energy consumption, and low pollution, adapting to production needs of different scales and types.

[0290] In summary, the technical solution of this invention has broad industrial applicability and can realize automated and intelligent chemical reaction control and production process optimization in multiple industries, demonstrating significant market prospects and application value. IV. Description of the attached drawings

[0291] Figure 1 A schematic diagram of an AI-based fiber optic chemistry system. This system integrates fiber optic chemical membrane electrodes, a light source dispersion module, a proton / ion exchange layer, an electric field control module, a reaction path optimization module, a photocatalytic reaction layer, and an AI-powered chemistry laboratory and factory, achieving efficient and precise reaction control.

[0292] Figure 2 Schematic diagram of fiber optic chemical membrane electrode structure. From top to bottom, the membrane electrode comprises: an anode fiber array, an anode catalyst layer, a proton / ion exchange membrane, a cathode catalyst layer, and a cathode fiber array. Incident light is coupled to the anode and cathode fiber arrays respectively, forming a composite light field of "uniform scattering + local focusing" on the surfaces of the catalyst layers on both sides. An electric field is constructed at the anode and cathode ends through external extraction, driving H... + / OH - It migrates directionally within the exchange membrane, maintaining interfacial charge balance and promoting the reaction.

[0293] Figure 3 A schematic diagram of the light source beam splitting module. The beam splitter couples external light from the light source to a parallel fiber array, scattering it onto the anode and cathode planes of the fiber membrane electrode. This supports multi-wavelength light source input to meet different reaction requirements.

[0294] Figure 4 Schematic diagram of the reaction path optimization module. Combined with an artificial intelligence system, it automatically adjusts the light field and current intensity based on real-time feedback and externally input reaction conditions to precisely control the reaction and generate the target product.

[0295] Figure 5 A schematic diagram of an AI-powered chemical plant. The system, through autonomous learning and optimized control, adjusts production processes and reaction conditions based on market demand and production targets, achieving efficient autonomous production and product quality control.

[0296] Figure 6Diagram of the large model module. By analyzing experimental data using a large language model and deep learning, it predicts reaction results and provides decision support for optimizing chemical reaction pathways and scheduling production in factories.

[0297] Figure 7 This is a flowchart of the timing control of the artificial intelligence-based optical fiber chemical system described in this invention. V. Detailed Implementation Methods

[0298] To better understand the technical solution of this invention, the implementation process and system construction method of this invention will be described in detail below with reference to specific embodiments. The embodiments described are merely examples, and the specific implementation details can be adjusted according to actual application needs.

[0299] Example 1: Fiber Optic Chemical Film Electrode and Artificial Intelligence Optimization System

[0300] In this embodiment, the fiber optic chemical membrane electrode system is used for the control and optimization of catalytic reactions. The core of this system includes an anode fiber catalytic layer, a central proton or ion exchange membrane, and a symmetrical cathode fiber catalytic layer. By integrating the fiber optic chemical membrane electrode and an artificial intelligence optimization module, the system can adjust the reaction path in real time, optimizing reaction efficiency and selectivity.

[0301] Construction of fiber optic chemical membrane electrodes

[0302] The fiber optic chemical membrane electrode consists of multiple parallel optical fibers forming a light scattering surface. These fibers are either unclad photonic crystal fibers (PCF) or ordinary optical fibers with a higher refractive index on the outside and lower on the inside (with the protective layer removed). This special fiber design enhances the scattering and focusing of the light field, ensuring a uniform distribution of light energy during the reaction process.

[0303] Fiber Optic Selection and Arrangement

[0304] To maximize photocatalytic efficiency, the fiber membrane electrode employs either unclad photonic crystal fiber (PCF) or ordinary fiber with the protective layer removed. Due to its unique structure, PCF can provide a more uniform light field distribution under different reaction conditions, improving the efficiency of the catalytic reaction. Furthermore, ordinary fiber, with its higher refractive index on the outside and lower on the inside, can achieve a stronger light focusing effect, further enhancing the conversion rate of the photocatalytic reaction.

[0305] These optical fibers are arranged in parallel to form a uniformly distributed light-scattering surface. The symmetrical arrangement of the fibers at the anode and cathode ensures a uniform light field distribution on the reaction surface, promoting homogeneous catalysis of the reactants. Through precise fiber arrangement and light energy distribution, fiber-optic chemical membrane electrodes can maximize the rate and selectivity of photocatalytic reactions.

[0306] Proton / ion exchange membrane

[0307] The proton or ion exchange membrane in the middle plays a crucial role in fiber optic chemical membrane electrodes. This membrane layer ensures the directional migration of ions during the reaction and maintains charge balance. Proton exchange membranes (PEMs) or anion exchange membranes (AEMs) can be used for different types of reactions to ensure efficient conductivity of the electrolyte in an electric field, providing a stable electrochemical environment for the reaction. The directional migration of ions within the membrane guarantees the stability and efficiency of the reaction.

[0308] Application of AI optimization module

[0309] This invention utilizes an artificial intelligence optimization module, combining a large model with autonomous learning technology, to adjust the reaction path and production parameters in real time. The main function of this module is to automatically optimize reaction conditions based on real-time feedback data, ensuring the reaction is always in an optimal state.

[0310] Structure and function of the artificial intelligence optimization module

[0311] The AI ​​optimization module comprises a large-scale model module and a self-learning module. The large-scale model module uses machine learning and data mining techniques to predict the optimal reaction path based on reactant types, reaction conditions, and historical data, and generates corresponding reaction parameters. The self-learning module continuously optimizes the reaction path using real-time feedback data. Working together, these two modules can monitor parameters such as temperature, current, light intensity, and reactant concentration in real time during the reaction process, and adjust reaction conditions based on real-time data.

[0312] Real-time adjustment of reaction pathway

[0313] The AI ​​optimization module dynamically adjusts the reaction path through an integrated feedback mechanism. At each reaction stage, the system uses real-time feedback data (such as temperature, current, and reactant concentration) to determine if the current reaction path meets expectations. If a deviation is detected, the system automatically adjusts the reaction conditions according to a preset model. For example, the system can automatically increase the light source intensity when the light intensity is insufficient, or reduce the input amount when the reactant concentration is too high, avoiding the formation of byproducts. Through this adaptive reaction path adjustment, the system ensures that the reaction process remains highly efficient and selective at all times.

[0314] Adjusting reaction conditions and production parameters

[0315] The AI ​​optimization module can not only adjust the reaction path but also modify production parameters in real time. Based on feedback data, the system can adjust key reaction conditions such as reaction temperature, pressure, current, and catalyst concentration. For example, in a certain reaction, if the system detects that the reaction temperature is too high, which may lead to an increase in by-product formation, the module will automatically lower the reaction temperature to reduce by-product formation and improve the purity of the target product.

[0316] Furthermore, the AI ​​optimization module can automatically adjust production parameters based on market demand, production targets, and raw material costs to ensure that production costs are minimized while meeting reaction efficiency and selectivity requirements. For example, the module can automatically adjust the production cycle of reaction batches and optimize the amount of catalyst used based on production needs.

[0317] Feedback mechanism and optimization loop

[0318] This system features a robust feedback mechanism that continuously optimizes the reaction process. Whenever reaction conditions change, the data acquisition system inputs reaction data into the optimization module in real time. The large model module then reassesses the reaction path and production parameters based on this data, proposing new optimization strategies. Through this continuous feedback and optimization cycle, the system can continuously improve reaction efficiency, reduce unnecessary resource consumption, and ensure high selectivity and stability of the reaction process.

[0319] Summary of Implementation Examples

[0320] Through the implementation of this embodiment, the combination of the fiber optic chemical membrane electrode system and the artificial intelligence optimization module achieves precise control and efficient optimization of the chemical reaction process. The system can adaptively adjust the reaction path and production parameters based on real-time feedback data, significantly improving reaction efficiency and product quality. Simultaneously, through large-scale modeling and autonomous learning technology, the system possesses powerful predictive and self-optimization capabilities, enabling highly automated and intelligent reaction optimization. This provides a new technical solution for catalytic reaction optimization and intelligent manufacturing in the chemical industry.

[0321] Example 2: Integration of the large model module and the autonomous learning module

[0322] In this embodiment, the fiber optic chemical membrane electrode system is combined with the large model module and the autonomous learning module to realize intelligent reaction path optimization and production scheduling, thereby improving reaction efficiency, product quality and the self-adaptive capability of the production process.

[0323] Functions and Implementation of Large Model Module

[0324] The large model module is one of the core components of the system. It is responsible for analyzing, predicting, and optimizing reaction pathways by combining experimental data with chemical reaction models, utilizing machine learning and data mining techniques. The workflow of this module is as follows:

[0325] • Reaction Path Prediction: When reaction parameters such as reactants, catalysts, temperature, and pressure are input, the large model module analyzes the reaction mechanism based on historical data and chemical reaction theory, using deep learning algorithms to predict the reaction path and final product characteristics. The system generates preliminary experimental plans based on these predictions and provides guidance for subsequent optimization of reaction conditions.

[0326] • Reaction Condition Optimization: The large model module not only predicts reaction pathways but also optimizes reaction conditions. By monitoring the reaction process in real time and combining the analysis results of the large model, the system automatically adjusts key parameters such as reaction temperature, pressure, and catalyst dosage to ensure optimal reaction rate and maximize product selectivity.

[0327] • Multi-objective optimization: By predicting reaction pathways, the large model module can weigh multiple objectives, such as product yield, reaction time, energy consumption, and raw material utilization efficiency, thereby providing multi-dimensional optimization solutions for the production process. The system will automatically select the optimal reaction conditions based on different production needs.

[0328] The core advantage of this module lies in its ability to continuously improve the accuracy of predictions by learning from a large amount of experimental data. For example, in continuous reaction experiments, the large model can gradually accumulate experience and continuously adjust the reaction path and optimization strategy by analyzing the feedback results of different reaction conditions, thereby improving the reliability of the experiment and the stability of the product.

[0329] Implementation and Function of Self-directed Learning Module

[0330] The self-learning module is the second core part of the system, responsible for continuously learning and automatically optimizing reaction paths and production conditions through a combination of real-time feedback and historical data. Its implementation and function specifically include the following aspects:

[0331] • Real-time feedback analysis: The self-learning module monitors key data points during the experiment in real time, such as reactant consumption, temperature, pressure, product type, and conversion rate. This data is used as the module's learning basis, helping the system determine whether the reaction is proceeding as expected. If the system detects a deviation, the module can respond immediately, adjusting reaction conditions to prevent reaction failure.

[0332] • Self-learning and path optimization: After each experiment, the module automatically compares the experimental results with the predicted results and analyzes the reasons for the differences. The self-learning module gradually optimizes the reaction path and its conditions by updating the internal model. After each experiment, the module adjusts the model parameters based on the latest data, enabling the system to more accurately predict and control future experiments.

[0333] • Deviation Correction and Process Control: When deviations occur during the reaction process (such as abnormal temperature increases or products not meeting expectations), the autonomous learning module will promptly detect and correct these deviations through a feedback mechanism. For example, if the reaction temperature exceeds the set range, the module will automatically adjust the temperature control system to bring the reaction back to the ideal state. The autonomous learning module can proactively adjust based on real-time changes in the experiment, maintaining the stability of the reaction process and production efficiency.

[0334] • Cyclic Optimization and Intelligent Scheduling: The self-learning module not only optimizes in a single experiment but also improves the efficiency of the overall reaction process through continuous cyclic learning. In production scheduling, the module can automatically adjust reaction conditions and production plans based on historical data and real-time feedback, thereby achieving parallel processing of multiple tasks and intelligent scheduling of the production process.

[0335] A key feature of the self-learning module is its "adaptive" capability. It can automatically adjust its strategy based on feedback from each experiment, continuously optimizing the reaction process and thus improving the overall performance of the system. Through the self-learning module, the system can not only cope with preset reaction conditions but also flexibly respond to sudden changes and anomalies, thereby maintaining efficient operation in uncertain environments.

[0336] Synergistic effect of large model and autonomous learning module

[0337] The large model module and the autonomous learning module complement and collaborate with each other, jointly driving the system towards intelligence and automation. The large model module provides preliminary response optimization suggestions through comprehensive analysis and prediction of response paths and conditions, while the autonomous learning module continuously adjusts and optimizes the response strategy through real-time feedback data, enabling the system to perform efficient and precise response control in complex and ever-changing environments.

[0338] The combination of these two modules not only improves the accuracy of the reaction pathways but also enhances the flexibility and adaptability of the production process. The large model module is responsible for the overall design and optimization of the reaction pathways, while the autonomous learning module performs fine-tuning under local conditions, ensuring optimal performance in every experiment and production process.

[0339] Example 3: Implementation of an AI-powered chemical plant

[0340] This example demonstrates how to utilize artificial intelligence (AI) in a chemical plant for efficient production and cost control. Through autonomous learning and optimization of its control system, the AI-powered chemical plant can dynamically adjust production processes and reaction conditions based on market demand, raw material costs, and product quality requirements, thereby achieving intelligent, flexible, and efficient production management.

[0341] Intelligent control of production processes and scheduling

[0342] The core advantage of AI-powered chemical plants lies in their intelligent production process control and scheduling capabilities. By integrating large-scale models and autonomous learning modules, the plant can acquire market demand and production data in real time and dynamically adjust production plans and reaction conditions based on this information.

[0343] • Market Demand Response: The AI-powered chemical plant can flexibly adjust its production plan based on changes in market demand. When demand for a product increases, the system automatically increases the production volume of that product; conversely, when market demand decreases, the plant automatically reduces production to avoid overproduction and resource waste. The system allocates resources in an optimal manner based on production targets and market demand forecasts.

[0344] • Reaction Condition Optimization: AI-powered chemical plants can dynamically adjust reaction conditions based on real-time feedback data and reaction monitoring information. For example, during production, the system can automatically adjust parameters such as reaction temperature, pressure, and catalyst concentration to improve reaction conversion and product quality. By optimizing the reaction pathway, the plant can ensure optimal results in every production run, maximizing product yield and minimizing energy consumption.

[0345] • Automated Scheduling and Production Planning Optimization: By integrating a self-learning module, the factory can automatically adjust itself based on real-time data and feedback during production. When unexpected situations arise during production (such as equipment failure or raw material supply interruptions), the system can promptly adjust the production plan and optimize the production process through intelligent scheduling. This allows the factory to avoid production bottlenecks and ensure the continuous and efficient operation of the production line.

[0346] Resource and cost optimization

[0347] In AI-powered chemical plants, resource and cost optimization is key to achieving efficient production and maximum economic benefits. Through autonomous learning and optimization control systems, the plant can perform refined control of the production process, optimizing resource utilization and production costs.

[0348] • Maximizing raw material utilization: The system can automatically adjust the amount of raw materials used based on real-time data feedback. Through precise control of the reaction process, the factory can maximize the conversion rate of raw materials and reduce waste. For example, the system monitors the progress of the reaction in real time and adjusts the supply of reactants based on feedback information, thereby reducing excess raw material input.

[0349] • Catalyst concentration and reaction condition optimization: The system adjusts the catalyst concentration and other reaction conditions according to the requirements of different reaction pathways. During catalyst use, the system can optimize its dosage and extend its lifespan by monitoring catalyst consumption in real time. This not only improves reaction efficiency but also reduces catalyst costs.

[0350] • Energy Saving in Production Processes: By optimizing parameters such as reaction temperature, pressure, and fluid flow rate, AI-powered chemical plants can minimize energy consumption. During production, the system can monitor energy consumption in real time and adjust operating conditions to avoid unnecessary energy waste. Through a self-learning module, the plant can continuously optimize energy efficiency and reduce energy costs in the production process.

[0351] • Reduced production costs: By optimizing reaction pathways and production processes, the system can reduce unnecessary operations and intermediate steps, thereby lowering overall production costs. Simultaneously, through efficient resource allocation and refined production scheduling, the AI-powered chemical plant can further reduce overall production costs while ensuring product quality.

[0352] Economic benefits and sustainable development

[0353] The intelligent production of AI-powered chemical plants not only improves production efficiency but also has a positive impact on economic benefits and sustainable development. By maximizing resource utilization, reducing energy consumption, and minimizing raw material waste, the plants can achieve a highly efficient and environmentally friendly production model.

[0354] • Improved economic efficiency: By autonomously optimizing production processes and scheduling, the factory can maintain low-cost operations during stable production, thereby improving overall economic efficiency. Intelligent manufacturing processes reduce manual intervention and management costs while enhancing the market competitiveness of products.

[0355] Sustainable Development: AI-powered chemical plants prioritize environmentally friendly production methods, contributing to sustainable development by reducing resource consumption, waste emissions, and energy conservation. Intelligent systems automatically adjust production parameters to achieve more environmentally friendly production processes, reducing the burden on the environment.

[0356] Through autonomous learning and optimization control systems, AI-powered chemical plants can achieve comprehensive management and optimization of the entire production process, not only improving production efficiency but also reducing costs, bringing significant economic benefits to enterprises. In practical applications, this system demonstrates strong flexibility and adaptability, enabling rapid response to market demands and production changes, and driving enterprises towards intelligent, automated, and green development.

[0357] Example 4: Application of the Data Feedback and Monitoring Module

[0358] In this embodiment, the data feedback and monitoring module provides real-time data on the reaction process by monitoring parameters such as voltage, current, temperature, and reactant concentration in real time, and inputs this data into the large model module to further optimize the reaction conditions and path.

[0359] Real-time monitoring and data acquisition

[0360] The data feedback and monitoring module is equipped with high-precision sensors, enabling real-time monitoring of key parameters such as voltage, current, temperature, reactant concentration, and product concentration during the reaction. Through high-frequency sampling technology, the module can acquire data in milliseconds. When any abnormal fluctuations occur during the reaction (such as abnormal current or excessively high temperature), the module can promptly provide feedback to the system. At this point, the system automatically initiates corresponding optimization strategies, including adjusting reaction conditions, regulating catalyst concentration, or adjusting the reaction flow rate, to ensure the reaction continues under predetermined operating conditions. This process ensures the stability, reliability, and controllability of the reaction.

[0361] Data Input and Path Optimization

[0362] The collected real-time data is input into the large-scale model module via a feedback mechanism. Leveraging its powerful data analysis capabilities, the large-scale model module analyzes the current state of the reaction process based on the real-time data and automatically adjusts the reaction path and production parameters. Specifically, the module analyzes the reaction rate, product distribution, and selectivity in real time based on input data such as voltage, current, and reactant concentration. Then, the large-scale model module automatically optimizes the reaction path and adjusts the reaction conditions according to preset optimization objectives to ensure the reaction process proceeds under optimal conditions. For example, if deviations occur in the reaction temperature or concentration, the module automatically adjusts the parameters of the heating or cooling system, or adjusts the reactant injection rate, to maintain the high efficiency and selectivity of the reaction.

[0363] Summary of Implementation Examples

[0364] This embodiment demonstrates how the different technical modules of the present invention work collaboratively in chemical reaction control, production scheduling, cost optimization, and quality control. By integrating fiber optic chemical membrane electrodes, an artificial intelligence optimization module, a large model module, and a self-learning module, the present invention achieves intelligent, automated, and efficient control of chemical reaction processes, improving production efficiency, reducing production costs, and effectively enhancing product quality. This system is not only suitable for optimizing chemical reactions but can also be widely applied to other intelligent manufacturing and automated production fields, providing an innovative solution for the chemical industry and intelligent manufacturing. This solution closely integrates traditional chemical reaction processes with artificial intelligence technology, driving the intelligent transformation of the chemical industry.

Claims

1. A fourth-generation catalytic fiber optic chemistry and artificial intelligence chemistry system, characterized in that, The system includes: • Fiber optic chemical membrane electrode, including an anode fiber catalytic layer, a middle proton or ion exchange membrane, and a symmetrical cathode fiber catalytic layer. Both the anode and cathode are composed of multiple parallel optical fibers forming a light scattering surface. The use of unclad photonic crystal fiber or ordinary optical fiber with a high refractive index on the outside and a low refractive index on the inside after removing the protective layer can achieve uniform scattering and local focusing of the excitation light. • A light source beam splitting module is used to couple external light source light onto multiple parallel optical fibers through a beam splitter. The optical fiber array scatters the light onto the anode and cathode planes of the optical fiber film electrode. • Proton / ion exchange layer: using highly selectively conductive proton exchange membrane (PEM) or anion exchange membrane (AEM) or cation exchange membrane (CEM) to ensure ion directional migration within the membrane and maintain interfacial charge balance, thus constructing an intrinsic closed-loop reaction; • Electric field control module, used to adjust the voltage and current between the anode and cathode to achieve programmable control of the chemical reaction path; • The reaction path optimization module, combined with an artificial intelligence system, can automatically adjust the light field and current intensity based on externally input reaction conditions, precisely control the chemical reaction to generate different products, and adjust the reaction direction and rate; • The photocatalytic reaction layer, including a photonic crystal fiber structure, allows liquids with different refractive indices to flow into it. Through photocatalysis, the intensity and frequency of the incident light are adjusted to generate scattered light with shorter wavelengths, thereby optimizing the catalytic reaction effect. • The autonomous learning module, based on an artificial intelligence agent, continuously optimizes the reaction path and experimental configuration through real-time feedback and adjustment of reaction parameters, thereby improving reaction efficiency and selectivity; • The chemical reaction control module is used to adjust the directionality of chemical reactions. By combining tunable light fields and currents, it can achieve precise control of chemical reactions and generate different target products. The AI-powered chemistry laboratory integrates the above modules and provides an autonomously optimized experimental platform through a large model and AI agent. This platform is used to efficiently carry out multi-field synergistic catalytic reactions and automatically adjusts reaction conditions according to experimental needs and objectives. • An AI-powered chemical plant is a production system built by an AI-powered chemical laboratory. It can optimize reaction conditions and regulate the entire process of experimentation and production based on market demand and production goals through autonomous learning and decision-making, thereby achieving efficient autonomous production and product quality control. • The large model module, which uses large language model (LLM) and combines deep learning and data mining methods, can analyze chemical reaction data, predict reaction results, optimize reaction paths, and provide decision support for artificial intelligence chemical laboratories and factories; The data feedback and monitoring module monitors various parameters during the reaction process, such as current, voltage, temperature, pressure, product type, and reaction conversion rate, through real-time sensors and a data collection system, and feeds the data back to the large model module to optimize the reaction strategy.

2. The system according to claim 1, wherein the anode fiber catalytic layer and the cathode fiber catalytic layer of the fiber optic chemical membrane electrode are both composite fiber structures, and adopt a multi-field synergistic catalytic mode with electrocatalysis, photocatalysis and thermal catalysis particle distribution; the electric field control module can achieve precise regulation of charge migration and reactant conversion within the fiber optic chemical membrane electrode by adjusting the electric field strength and distribution.

3. The system according to claim 1, wherein the reaction path optimization module combines a large model with an AI agent, automatically generates experimental configurations and reaction parameters by inputting chemical reaction requirements through natural language, and adjusts reaction conditions based on real-time feedback.

4. The system according to claim 1, wherein the light source beam splitting module supports multi-wavelength light source input, and the proportion of different wavelength light sources is adjusted by the beam splitter to adapt to the light energy required by different types of reactions; the photonic crystal fiber, by combining with liquids of different refractive indices, adjusts the light intensity distribution and frequency, and precisely controls the photocatalytic effect of the reaction.

5. The system according to claim 1, wherein the chemical reaction control module supports optimizing the selectivity of reaction products through interface adjustment and can achieve adaptive reaction path adjustment.

6. The system according to claim 1, wherein the reaction platform constructed by the optical fiber chemical film electrode has the advantages of tunable optical field, controllable current and programmable reaction path, realizing efficient control and optimization of chemical reaction.

7. The system according to claim 1, wherein the artificial intelligence chemistry laboratory integrates a large model module and an autonomous learning module, which can automatically optimize reaction paths and production parameters based on real-time feedback and historical data analysis, and autonomously predict and control reaction results.

8. The system according to claim 1, wherein the artificial intelligence chemical plant, through autonomous learning and optimization control system, adjusts production process and reaction conditions according to market demand, raw material cost and product quality requirements, to achieve efficient and economically viable autonomous production.

9. The system according to claim 1, wherein the large model module can utilize multi-dimensional experimental data and chemical reaction models, combined with machine learning and data mining techniques, to provide optimized reaction paths and product characteristic predictions, and support factory production scheduling and reaction strategy updates.

10. The system according to claim 1, wherein the data feedback and monitoring module monitors and collects changes in voltage, current, temperature and reaction products in real time during the reaction process, and inputs these data into the large model module to further optimize the reaction conditions and path.

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