System and method for predicting reaction conditions of menthamide cooling agent

Abstract

The invention discloses a system and a method for predicting reaction conditions of a menthane carboxamide cooling agent, and aims to optimize the reaction conditions, improve the experiment efficiency, reduce the production cost and ensure the stable product quality through an intelligent method. According to the system, the design experiment DoE and the response surface method RSM technology are combined, the relation between reaction conditions and products is systematically analyzed, multi-scale modeling is utilized, a molecular reaction mechanism and a macroscopic reactor model are combined, seamless connection from a laboratory to an industrial scale is achieved, and challenges in the amplification process are solved. By means of real-time data monitoring and feedback adjustment, the system can dynamically optimize the reaction process, controllability and stability of the production process are improved, the scientificity and intelligent level of reaction optimization are improved, and an efficient, controllable and environment-friendly solution is provided for large-scale production of the menthane carboxamide cooling agent.

Description

Technical Field

[0001] The present invention relates to the technical field of prediction of reaction conditions of menthyl amide cooling agents, and in particular to a system and method for predicting reaction conditions of menthyl amide cooling agents. Background Art

[0002] In the synthesis of menthyl amide cooling agents, reaction conditions (such as temperature, reaction time, concentration, and catalyst type) significantly influence product yield and purity. Traditional experimental methods typically require extensive trial and error testing to find optimal reaction conditions. This is not only time-consuming and labor-intensive, but the optimization process is often blind, making it difficult to ensure efficient experimental design and optimal reaction results. Experimental design is inefficient: Traditional manual experimental design and adjustment of reaction conditions typically require significant time and experimental resources. Inability to fully assess complex interactions between multiple factors: Different reaction factors may interact in complex ways, and traditional methods cannot quickly identify the impact of these interactions on reaction outcomes. In chemical reaction research and production, especially before large-scale production, extensive experimental verification can lead to high experimental costs. Traditional methods often rely on numerous experiments and cannot quickly determine optimal reaction conditions, resulting in unnecessary waste of resources such as experimental equipment, reagents, and catalysts. Summary of the Invention

[0003] A method for predicting reaction conditions of a menthamide cooling agent comprises the following steps: S1. Data Collection and Preparation: Identify the main raw materials of the menthamide cooling agent, including menthol, amidation reagent, solvent, and catalyst. Design different reaction conditions, including temperature, reaction time, solvent type, catalyst type, and reactant concentration. Record the product characteristics under different reaction conditions, including yield, purity, reaction rate, and cooling effect. Use molecular dynamics simulation to simulate the reaction mechanism, determine the reaction path and energy barrier, and identify the interactions between reactants, solvent, and catalyst, and analyze their effects on product selectivity. S2. Data Preprocessing and Feature Extraction: Process experimental data, remove outliers and missing values, ensure data accuracy and completeness, extract key variables from the experimental data: temperature, time, concentration, solvent, and catalyst as input features, and use molecular simulation results to extract reaction kinetic characteristics, including reaction rate and activation energy. S3. Build a model linking reaction conditions and product properties: Use supervised learning methods to model data, analyze the relationship between reaction conditions and product properties, optimize the selection of reaction conditions, and gradually optimize product yield or purity through interaction with the environment (experimental results). Combine molecular-level reaction mechanism models with macro-reactor models to build a multi-scale prediction model. S4. Model Training and Validation: Divide the data into a training set and a validation set. Use the training set to train the model and use cross-validation to evaluate the model's accuracy. Adjust model parameters and use metrics such as mean squared error (MSE) and R² to evaluate the model's predictive accuracy. Based on the validated model, input new reaction conditions for prediction to obtain the expected product characteristics (e.g., yield, purity, etc.). S5. Reaction Condition Optimization and Practical Verification: Use Design of Experiments (DoE) to systematically optimize reaction conditions, generate different experimental schemes, and evaluate product performance. Introduce online monitoring technologies (such as spectral analysis and infrared analysis) to track reaction progress in real time, obtain real-time data to optimize reaction conditions, and adjust the model based on experimental results. Optimize reaction conditions through real-time monitoring data and experimental feedback. S6. Multi-factor synergistic optimization: Consider multiple factors (such as solvent polarity, pH value, reactor type, etc.) for synergistic optimization to ensure the optimal combination of reaction conditions. Simultaneously, the thermodynamic and kinetic conditions during the reaction process are optimized to ensure the energy efficiency and reaction rate of the reaction. S7. Real-time data-driven closed-loop control: Utilize an automated experimental platform to collect real-time data and dynamically adjust the reaction condition model. Based on real-time reaction data and real-time feedback from the predictive model, the reaction conditions are automatically adjusted to ensure that the reaction is always in the optimal state.

[0004] Furthermore, a method for predicting the reaction conditions of menthyl amide cooling agent is provided. In step S3, the product yield and purity are optimized step by step through interaction with the environment, and a multi-scale prediction model is established by combining the molecular-level reaction mechanism model and the macro-reactor model. The specific steps are as follows; S31. Environmental interaction data collection and definition: Reaction conditions: including temperature, time, reactant concentration, catalyst type and concentration, solvent type; Reactor characteristics: including reactor volume, stirring rate, and heat exchange efficiency; Real-time monitoring data: Collect real-time temperature, concentration, pH value, and product concentration data during the reaction process through sensors or online analytical tools (such as infrared spectroscopy, mass spectrometry, and gas chromatography); Based on the above data, a molecular-level reaction mechanism model is established: through quantum chemical calculation methods, an intermolecular interaction model is established to describe the interaction and transformation of reactants, catalysts, and solvents at the molecular level; Combined with the fluid dynamics, thermodynamic behavior, and mass transfer effects in the reactor, a macroscopic model of the reactor is established to simulate the overall reaction process; S32. Model Building and Environmental Interaction Design: Use quantum chemical methods to simulate the interaction between reactants and catalysts, infer key information such as reaction pathways and activation energies, apply molecular dynamics (MD) simulations to analyze molecular motion and reaction progress, and predict reaction rates and pathways. Use heat and mass transfer equations to simulate heat exchange and material transfer within the reactor, optimizing reaction rates and material conversion efficiency. Combine molecular mechanism models with macroscopic reactor models to form a multiscale model from micro to macro, describing the microscopic mechanisms of the reaction process at the molecular level while predicting the overall reaction status within the reactor at the macro level. S33. Environmental Interaction Optimization and Prediction: This system uses real-time monitoring data (temperature, concentration, and pH) as input parameters for dynamic model adjustments. This system monitors the progress of reactions using real-time data and automatically adjusts reaction conditions. Feedback from real-time environmental data helps quickly identify changes during the reaction, thereby optimizing product yield and purity. After the experiment and reaction, a feedback system (integrated between the control system and the model prediction system) adjusts the next reaction conditions to more closely align predictions with actual results. Based on environmental feedback, model parameters are automatically adjusted and regularly updated to continuously improve prediction accuracy. S34. Experimental Verification and Model Calibration: Based on the predictions of the multi-scale model, conduct small-scale and pilot-scale experimental verification to ensure the reliability of the model predictions. Compare the reaction process monitoring data (such as yield and purity) with the model predictions to verify the model's accuracy. Use experimental data to calibrate the model, especially if new reaction mechanisms or behavioral characteristics emerge during the reaction process, which require feedback adjustment of the model. S35. Continuous Optimization and Intelligent Adjustment: This system integrates multiscale models with the reactor control system to form a closed-loop control system. This system adjusts reaction conditions based on real-time data, gradually optimizing the reaction process. Model predictions are continuously fed back into actual data, automatically adjusting control system parameters such as temperature, pressure, and reaction time. During the experiment, a reinforcement learning algorithm is introduced to continuously optimize the reaction process through continuous interaction with the environment, thereby improving product yield and purity. S36. Achieve industrialization and large-scale production: After successful verification in the laboratory and pilot scale, apply the multi-scale prediction model to the large-scale production process. Through feedback and optimization of the multi-scale model, ensure that the scaled-up production process can achieve yields and purities similar to those under laboratory conditions. As data accumulates during the production process, continuously optimize the multi-scale model and upgrade it to meet new process requirements or new reaction conditions.

[0005] Furthermore, a method for predicting the reaction conditions of menthyl amide cooling agent is provided. In step S5, the design of experiments (DoE) method is used to systematically optimize the reaction conditions, generate different experimental schemes and evaluate the performance of the products. The specific steps are as follows: S51. Define the experimental objectives and response variables: Clarify the optimization objectives of the experiment, including improving product yield, purity, and other key properties such as reaction rate and cooling effect. Response variables are indicators used to measure experimental results, including reaction yield, product purity, reaction time, and temperature. These variables need to be quantified to analyze their relationship with reaction conditions. S52. Determine factors and levels: Factors are factors that affect the reaction results. In the optimization of menthyl amide cooling agent, factors include: reaction temperature, reaction time, reactant concentration, catalyst type and concentration, solvent type, stirring rate, and each factor should be set to several different levels: the range of values ​​includes: reaction temperature: low temperature (20°C), medium temperature (40°C), high temperature (60°C); reaction time: short time (1 hour), medium time (3 hours), long time (5 hours); reactant concentration: low concentration (0.1 mol / L), medium concentration (0.5 mol / L), high concentration (1 mol / L); the number of factor levels depends on the experimental requirements and computing power; S53. Perform experiments and collect data: Conduct experiments according to the designed experimental plan (full factorial design). In each experiment, ensure that the reaction conditions (temperature, time, and concentration) meet the predetermined factors and levels, and accurately record the reaction results. After each experiment, measure and record the response variable (e.g., yield, purity, etc.). During the experiment, ensure that the detailed conditions and response data of each experiment are recorded, and indicate the experiment number and other relevant information. S54. Data Analysis and Modeling: Use analysis of variance to test whether each factor and its interaction significantly affects the response variable. Use regression models to analyze the relationship between the response variable and each factor. Use regression analysis to derive the mathematical relationship between the reaction condition and the response variable. S55. Optimization and Prediction: Use experimental data to verify the accuracy and reliability of the regression model. Evaluate the model's predictive ability using goodness of fit metrics such as R² and mean square error. Utilize response surface methodology to predict product yield and purity under different reaction conditions. Use gradient descent to find the optimal combination of reaction conditions to achieve the highest product quality. Adjust reaction conditions based on the optimization results, and conduct experiments to verify the reliability of the optimization results. Ensure that the predicted reaction conditions can achieve the optimal results in actual operation. S56. Continuous Improvement and Iteration: Based on the deviation between experimental results and model predictions, continuously adjust the experimental design and model parameters. During the optimization process, increase or decrease the levels of certain factors to further improve the accuracy of the model and the optimization effect. Based on the laboratory scale, gradually expand the experimental scale to ensure the feasibility of the optimized reaction conditions in large-scale production.

[0006] A menthamide cooling agent reaction condition prediction system, the menthamide cooling agent reaction condition prediction system is used to implement any menthamide cooling agent reaction condition prediction method; the menthamide cooling agent reaction condition prediction system comprises: a data acquisition module, an experimental design module, a mathematical modeling and analysis module, an optimization and prediction module, an experimental feedback module, and a multi-scale integration module; Data acquisition module: real-time collection of data on various reaction conditions and response variables during the experiment, real-time monitoring of product yield, purity, and reaction rate response variables; Experimental Design Module: Design different experimental schemes, including factor selection, level setting and experimental combination, and generate experimental plans based on different experimental design methods: full factorial design, fractional factorial design, and response surface method; Mathematical Modeling and Analysis Module: Based on experimental data, mathematical models are established using regression analysis to analyze the effects of reaction conditions (factors) on product properties (response variables), and to calculate the interactions between factors and their contributions to the response; Optimization and prediction module: Utilizes regression models to optimize, predict the best combination of reaction conditions, and adjusts reaction conditions based on optimization algorithms to achieve optimal product yield and purity; Experimental feedback module: Based on the optimized prediction results, experimental verification is carried out to check the difference between the prediction and the actual reaction results. Through experimental feedback, the model is adjusted and optimized to gradually improve the prediction accuracy; Multi-scale integration module: Integrates the molecular-level reaction mechanism model with the macro-reactor model to form a multi-scale model, combining the microscopic reaction process (such as molecular dynamics simulation) and the macro-reactor process (such as fluid mechanics model) for prediction.

[0007] The beneficial effects of the present invention are as follows: by using techniques such as design of experiments (DoE) and response surface methodology (RSM), the number of blind experiments can be effectively reduced. By optimizing the experimental design, key reaction factors and optimal reaction conditions can be quickly identified, avoiding a large number of trial and error processes. Optimizing the experimental design can obtain accurate reaction conditions in a shorter time, avoid lengthy debugging processes, and improve the response speed of the experiment. By intelligently optimizing the experimental design, the waste of raw materials, reagents, and catalysts can be effectively reduced. Reliable results can be obtained with a smaller number of experiments, saving the cost of expensive experimental materials and equipment. Manual adjustments and ineffective inputs in the experimental process are reduced, making the experimental process more automated and efficient, and reducing the workload of personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flowchart of a method for predicting reaction conditions of a menthamide cooling agent; DETAILED DESCRIPTION

[0009] A method for predicting reaction conditions of a menthamide cooling agent comprises the following steps: S1. Data Collection and Preparation: Identify the main raw materials of the menthamide cooling agent, including menthol, amidation reagent, solvent, and catalyst. Design different reaction conditions, including temperature, reaction time, solvent type, catalyst type, and reactant concentration. Record the product characteristics under different reaction conditions, including yield, purity, reaction rate, and cooling effect. Use molecular dynamics simulation to simulate the reaction mechanism, determine the reaction path and energy barrier, and identify the interactions between reactants, solvent, and catalyst, and analyze their effects on product selectivity. S2. Data Preprocessing and Feature Extraction: Process experimental data, remove outliers and missing values, ensure data accuracy and completeness, extract key variables from the experimental data: temperature, time, concentration, solvent, and catalyst as input features, and use molecular simulation results to extract reaction kinetic characteristics, including reaction rate and activation energy. S3. Build a model linking reaction conditions and product properties: Use supervised learning methods to model data and analyze the relationship between reaction conditions and product properties. Introduce reinforcement learning algorithms to optimize the selection of reaction conditions. Gradually optimize product yield or purity through interaction with the environment (experimental results). Combine molecular-level reaction mechanism models with macroscopic reactor models to build a multiscale prediction model. S4. Model Training and Validation: Divide the data into a training set and a validation set. Use the training set to train the model and use cross-validation to evaluate the model's accuracy. Adjust model parameters and use metrics such as mean squared error (MSE) and R² to evaluate the model's predictive accuracy. Based on the validated model, input new reaction conditions for prediction to obtain the expected product characteristics (e.g., yield, purity, etc.). S5. Reaction Condition Optimization and Practical Verification: Use Design of Experiments (DoE) to systematically optimize reaction conditions, generate different experimental schemes, and evaluate product performance. Introduce online monitoring technologies (such as spectral analysis and infrared analysis) to track reaction progress in real time, obtain real-time data to optimize reaction conditions, and adjust the model based on experimental results. Optimize reaction conditions through real-time monitoring data and experimental feedback. S6. Multi-factor synergistic optimization: Consider multiple factors (such as solvent polarity, pH value, reactor type, etc.) for synergistic optimization to ensure the optimal combination of reaction conditions. Simultaneously, the thermodynamic and kinetic conditions during the reaction process are optimized to ensure the energy efficiency and reaction rate of the reaction. S7. Real-time data-driven closed-loop control: Utilize an automated experimental platform to collect real-time data and dynamically adjust the reaction condition model. Based on real-time reaction data and real-time feedback from the predictive model, the reaction conditions are automatically adjusted to ensure that the reaction is always in the optimal state.

[0010] Furthermore, a method for predicting the reaction conditions of menthyl amide cooling agent is provided. In step S3, the product yield and purity are optimized step by step through interaction with the environment, and a multi-scale prediction model is established by combining the molecular-level reaction mechanism model and the macro-reactor model. The specific steps are as follows; S31. Environmental interaction data collection and definition: Reaction conditions: including temperature, time, reactant concentration, catalyst type and concentration, solvent type; Reactor characteristics: including reactor volume, stirring rate, and heat exchange efficiency; Real-time monitoring data: Collect real-time temperature, concentration, pH value, and product concentration data during the reaction process through sensors or online analytical tools (such as infrared spectroscopy, mass spectrometry, and gas chromatography); Based on the above data, a molecular-level reaction mechanism model is established: through quantum chemical calculation methods, an intermolecular interaction model is established to describe the interaction and transformation of reactants, catalysts, and solvents at the molecular level; Combined with the fluid dynamics, thermodynamic behavior, and mass transfer effects in the reactor, a macroscopic model of the reactor is established to simulate the overall reaction process; S32. Model Building and Environmental Interaction Design: Use quantum chemical methods to simulate the interaction between reactants and catalysts, infer key information such as reaction pathways and activation energies, apply molecular dynamics (MD) simulations to analyze molecular motion and reaction progress, and predict reaction rates and pathways. Use heat and mass transfer equations to simulate heat exchange and material transfer within the reactor, optimizing reaction rates and material conversion efficiency. Combine molecular mechanism models with macroscopic reactor models to form a multiscale model from micro to macro, describing the microscopic mechanisms of the reaction process at the molecular level while predicting the overall reaction status within the reactor at the macro level. S33. Environmental Interaction Optimization and Prediction: This system uses real-time monitoring data (temperature, concentration, and pH) as input parameters for dynamic model adjustments. This system monitors the progress of reactions using real-time data and automatically adjusts reaction conditions. Feedback from real-time environmental data helps quickly identify changes during the reaction, thereby optimizing product yield and purity. After the experiment and reaction, a feedback system (integrated between the control system and the model prediction system) adjusts the next reaction conditions to more closely align predictions with actual results. Based on environmental feedback, model parameters are automatically adjusted and regularly updated to continuously improve prediction accuracy. S34. Experimental Verification and Model Calibration: Based on the predictions of the multi-scale model, conduct small-scale and pilot-scale experimental verification to ensure the reliability of the model predictions. Compare the reaction process monitoring data (such as yield and purity) with the model predictions to verify the model's accuracy. Use experimental data to calibrate the model, especially if new reaction mechanisms or behavioral characteristics emerge during the reaction process, which require feedback adjustment of the model. S35. Continuous Optimization and Intelligent Adjustment: This system integrates multiscale models with the reactor control system to form a closed-loop control system. This system adjusts reaction conditions based on real-time data, gradually optimizing the reaction process. Model predictions are continuously fed back into actual data, automatically adjusting control system parameters such as temperature, pressure, and reaction time. During the experiment, a reinforcement learning algorithm is introduced to continuously optimize the reaction process through continuous interaction with the environment, thereby improving product yield and purity. S36. Achieve industrialization and large-scale production: After successful verification in the laboratory and pilot scale, apply the multi-scale prediction model to the large-scale production process. Through feedback and optimization of the multi-scale model, ensure that the scaled-up production process can achieve yields and purities similar to those under laboratory conditions. As data accumulates during the production process, continuously optimize the multi-scale model and upgrade it to meet new process requirements or new reaction conditions.

[0011] Furthermore, a method for predicting the reaction conditions of menthyl amide cooling agent is provided. In step S5, the design of experiments (DoE) method is used to systematically optimize the reaction conditions, generate different experimental schemes and evaluate the performance of the products. S51. Define the experimental objectives and response variables: Clarify the optimization objectives of the experiment, including improving product yield, purity, and other key properties such as reaction rate and cooling effect. Response variables are indicators used to measure experimental results, including reaction yield, product purity, reaction time, and temperature. These variables need to be quantified to analyze their relationship with reaction conditions. S52. Determine factors and levels: Factors are factors that affect the reaction results. In the optimization of menthyl amide cooling agent, factors include: reaction temperature, reaction time, reactant concentration, catalyst type and concentration, solvent type, stirring rate, and each factor should be set to several different levels: the range of values ​​includes: reaction temperature: low temperature (20°C), medium temperature (40°C), high temperature (60°C); reaction time: short time (1 hour), medium time (3 hours), long time (5 hours); reactant concentration: low concentration (0.1 mol / L), medium concentration (0.5 mol / L), high concentration (1 mol / L); the number of factor levels depends on the experimental requirements and computing power; S53. Perform experiments and collect data: Conduct experiments according to the designed experimental plan (full factorial design). In each experiment, ensure that the reaction conditions (temperature, time, and concentration) meet the predetermined factors and levels, and accurately record the reaction results. After each experiment, measure and record the response variable (e.g., yield, purity, etc.). During the experiment, ensure that the detailed conditions and response data of each experiment are recorded, and indicate the experiment number and other relevant information. S54. Data Analysis and Modeling: Use analysis of variance to test whether each factor and its interaction significantly affects the response variable. Use regression models to analyze the relationship between the response variable and each factor. Use regression analysis to derive the mathematical relationship between the reaction condition and the response variable. S55. Optimization and Prediction: Use experimental data to verify the accuracy and reliability of the regression model. Evaluate the model's predictive ability using goodness of fit metrics such as R² and mean square error. Utilize response surface methodology to predict product yield and purity under different reaction conditions. Use gradient descent to find the optimal combination of reaction conditions to achieve the highest product quality. Adjust reaction conditions based on the optimization results, and conduct experiments to verify the reliability of the optimization results. Ensure that the predicted reaction conditions can achieve the optimal results in actual operation. S56. Continuous Improvement and Iteration: Based on the deviation between experimental results and model predictions, continuously adjust the experimental design and model parameters. During the optimization process, increase or decrease the levels of certain factors to further improve the accuracy of the model and the optimization effect. Based on the laboratory scale, gradually expand the experimental scale to ensure the feasibility of the optimized reaction conditions in large-scale production.

[0012] A menthamide cooling agent reaction condition prediction system, the menthamide cooling agent reaction condition prediction system is used to implement any menthamide cooling agent reaction condition prediction method; the menthamide cooling agent reaction condition prediction system comprises: a data acquisition module, an experimental design module, a mathematical modeling and analysis module, an optimization and prediction module, an experimental feedback module, and a multi-scale integration module; Data acquisition module: real-time collection of data on various reaction conditions and response variables during the experiment, real-time monitoring of product yield, purity, and reaction rate response variables; Experimental Design Module: Design different experimental schemes, including factor selection, level setting and experimental combination, and generate experimental plans based on different experimental design methods: full factorial design, fractional factorial design, and response surface method; Mathematical Modeling and Analysis Module: Based on experimental data, mathematical models are established using regression analysis to analyze the effects of reaction conditions (factors) on product properties (response variables), and to calculate the interactions between factors and their contributions to the response; Optimization and prediction module: Utilizes regression models to optimize, predict the best combination of reaction conditions, and adjusts reaction conditions based on optimization algorithms to achieve optimal product yield and purity; Experimental feedback module: Based on the optimized prediction results, experimental verification is carried out to check the difference between the prediction and the actual reaction results. Through experimental feedback, the model is adjusted and optimized to gradually improve the prediction accuracy; Multi-scale integration module: Integrates the molecular-level reaction mechanism model with the macro-reactor model to form a multi-scale model, combining the microscopic reaction process (such as molecular dynamics simulation) and the macro-reactor process (such as fluid mechanics model) for prediction.

Claims

1. A method for predicting the reaction conditions of menthamide cooling agents, characterized in that: The following steps are included: S1. Data Collection and Preparation: Identify the main raw materials of the menthamide cooling agent, including menthol, amidation reagent, solvent, and catalyst. Design different reaction conditions, including temperature, reaction time, solvent type, catalyst type, and reactant concentration. Record the product characteristics under different reaction conditions, including yield, purity, reaction rate, and cooling effect. Use molecular dynamics simulation to simulate the reaction mechanism, determine the reaction path and energy barrier, and identify the interactions between reactants, solvent, and catalyst, and analyze their effects on product selectivity. S2. Data Preprocessing and Feature Extraction: Process experimental data, remove outliers and missing values, ensure data accuracy and completeness, extract key variables from the experimental data: temperature, time, concentration, solvent, and catalyst as input features, and use molecular simulation results to extract reaction kinetic characteristics, including reaction rate and activation energy. S3. Build a model linking reaction conditions and product properties: Use supervised learning methods to model data, analyze the relationship between reaction conditions and product properties, optimize the selection of reaction conditions, and gradually optimize product yield and purity through interaction with the environment. Combine molecular-level reaction mechanism models with macro-reactor models to build a multi-scale prediction model. S4. Model Training and Validation: Divide the data into a training set and a validation set. Use the training set to train the model and use cross-validation to evaluate the model's accuracy. Adjust model parameters and use metrics such as mean squared error and R² to evaluate the model's predictive accuracy. Based on the validated model, input new reaction conditions for prediction to obtain the expected product characteristics, such as yield and purity. S5. Reaction Condition Optimization and Practical Verification: We use Design of Experiments (DoE) to systematically optimize reaction conditions, generate different experimental schemes, and evaluate product performance. We also introduce online monitoring technology, spectral analysis, to track reaction progress in real time, acquire real-time data to optimize reaction conditions, and adjust the model based on experimental results. We optimize reaction conditions through real-time monitoring data and experimental feedback. S6. Multi-factor synergistic optimization: Consider multiple factors such as solvent polarity, pH value, and reactor type for synergistic optimization to ensure the optimal combination of reaction conditions. Simultaneously, the thermodynamic and kinetic conditions during the reaction are optimized to ensure the energy efficiency and reaction rate of the reaction. S7. Real-time data-driven closed-loop control: Utilize an automated experimental platform to collect real-time data and dynamically adjust the reaction condition model. Based on real-time reaction data and real-time feedback from the predictive model, the reaction conditions are automatically adjusted to ensure that the reaction is always in the optimal state.

2. The method for predicting the reaction conditions of a menthamide cooling agent according to claim 1, wherein: In step S3, the product yield and purity are optimized step by step through interaction with the environment, and a multi-scale prediction model is established by combining the molecular-level reaction mechanism model and the macro-reactor model. The specific steps are as follows; S31. Environmental interaction data collection and definition: Reaction conditions: including temperature, time, reactant concentration, catalyst type and concentration, solvent type; Reactor characteristics: including reactor volume, stirring rate, and heat exchange efficiency; Real-time monitoring data: sensors are used to collect real-time temperature, concentration, pH value, and product concentration data during the reaction process; Based on the above data, a molecular-level reaction mechanism model is established: through quantum chemical calculation methods, an intermolecular interaction model is established to describe the interaction and transformation of reactants, catalysts, and solvents at the molecular level; Combined with the fluid dynamics, thermodynamic behavior, and mass transfer effects in the reactor, a macroscopic model of the reactor is established to simulate the overall reaction process; S32. Model Building and Environmental Interaction Design: Use quantum chemical methods to simulate the interaction between reactants and catalysts, infer key information such as reaction pathways and activation energies, apply molecular dynamics (MD) simulations to analyze molecular motion and reaction progress, and predict reaction rates and pathways. Use heat and mass transfer equations to simulate heat exchange and material transfer within the reactor, optimizing reaction rates and material conversion efficiency. Combine molecular mechanism models with macroscopic reactor models to form a multiscale model from micro to macro, describing the microscopic mechanisms of the reaction process at the molecular level while predicting the overall reaction status within the reactor at the macro level. S33. Environmental Interaction Optimization and Prediction: This system uses real-time monitoring data (temperature, concentration, and pH) as input parameters for dynamic model adjustments. This system monitors the progress of reactions using real-time data and automatically adjusts reaction conditions. Feedback from real-time environmental data helps quickly identify changes during the reaction, thereby optimizing product yield and purity. After the experiment and reaction, a feedback system (integrated between the control system and the model prediction system) adjusts the next reaction conditions to more closely align predictions with actual results. Based on environmental feedback, model parameters are automatically adjusted and regularly updated to continuously improve prediction accuracy. S34. Experimental Verification and Model Calibration: Based on the predictions of the multi-scale model, conduct small-scale and pilot-scale experimental verification to ensure the reliability of the model predictions. Compare the reaction process monitoring data, such as yield and purity, with the model predictions to verify the model's accuracy. Use experimental data to calibrate the model, especially if new reaction mechanisms or behavioral characteristics emerge during the reaction process, which require feedback adjustment of the model. S35. Continuous Optimization and Intelligent Adjustment: This system integrates multiscale models with the reactor control system to form a closed-loop control system. This system adjusts reaction conditions based on real-time data, gradually optimizing the reaction process. Model predictions are continuously fed back into actual data, automatically adjusting control system parameters such as temperature, pressure, and reaction time. During the experiment, a reinforcement learning algorithm is introduced to continuously optimize the reaction process through continuous interaction with the environment, thereby improving product yield and purity. S36. Achieve industrialization and large-scale production: After successful verification in the laboratory and pilot scale, apply the multi-scale prediction model to the large-scale production process. Through feedback and optimization of the multi-scale model, ensure that the scaled-up production process can achieve yields and purities similar to those under laboratory conditions. As data accumulates during the production process, continuously optimize the multi-scale model and upgrade it to meet new process requirements or new reaction conditions.

3. The method for predicting reaction conditions of a menthamide cooling agent according to claim 1, wherein: In step S5, a design of experiments (DoE) method is used to systematically optimize the reaction conditions, generate different experimental schemes, and evaluate the performance of the products. The specific steps are as follows: S51. Define the experimental objectives and response variables: Clarify the optimization objectives of the experiment, including improving product yield, purity, and other key properties such as reaction rate and cooling effect. Response variables are indicators used to measure experimental results, including reaction yield, product purity, reaction time, and temperature. These variables need to be quantified to analyze their relationship with reaction conditions. S52. Determine factors and levels: Factors are factors that influence reaction outcomes. In the optimization of menthyl amide cooling agent, factors include: reaction temperature, reaction time, reactant concentration, catalyst type and concentration, solvent type, and stirring rate. Each factor should be assigned several different levels. The number of levels depends on experimental requirements and computing power. S53. Perform experiments and collect data: Conduct experiments according to the designed experimental plan (full factorial design). In each experiment, ensure that the reaction conditions (temperature, time, and concentration) meet the predetermined factors and levels, and accurately record the reaction results. After each experiment, measure and record the response variables (yield, purity). During the experiment, ensure that the detailed conditions and response data of each experiment are recorded, and indicate the experiment number and other relevant information. S54. Data Analysis and Modeling: Use analysis of variance to test whether each factor and its interaction significantly affects the response variable. Use regression models to analyze the relationship between the response variable and each factor. Use regression analysis to derive the mathematical relationship between the reaction condition and the response variable. S55. Optimization and Prediction: Use experimental data to verify the accuracy and reliability of the regression model. Evaluate the model's predictive ability using goodness-of-fit metrics such as R² and mean square error. Utilize response surface methodology to predict product yield and purity under different reaction conditions. Use gradient descent to identify the optimal combination of reaction conditions to achieve the highest product quality. Adjust reaction conditions based on the optimization results. Conduct experiments to verify the reliability of the optimization results. S56. Continuous Improvement and Iteration: Based on the deviation between experimental results and model predictions, continuously adjust the experimental design and model parameters. During the optimization process, increase or decrease the levels of certain factors to further improve the accuracy and optimization effect of the model. Based on the laboratory scale, gradually expand the scale of the experiment.

4. A menthamide cooling agent reaction condition prediction system, characterized in that: The menthamide cooling agent reaction condition prediction system is used to implement the menthamide cooling agent reaction condition prediction method according to any one of claims 1 to 3; the menthamide cooling agent reaction condition prediction system comprises: a data acquisition module, an experimental design module, a mathematical modeling and analysis module, an optimization and prediction module, an experimental feedback module, and a multi-scale integration module; Data acquisition module: real-time collection of data on various reaction conditions and response variables during the experiment, real-time monitoring of product yield, purity, and reaction rate response variables; Experimental Design Module: Design different experimental schemes, including factor selection, level setting and experimental combination, and generate experimental plans based on different experimental design methods: full factorial design, fractional factorial design, and response surface method; Mathematical Modeling and Analysis Module: Based on experimental data, mathematical models are established using regression analysis to analyze the effects of reaction conditions on product properties and calculate the interactions between factors and their contributions to the response; Optimization and prediction module: Utilizes regression models to optimize, predict the best combination of reaction conditions, and adjusts reaction conditions based on optimization algorithms to achieve optimal product yield and purity; Experimental feedback module: Based on the optimized prediction results, experimental verification is carried out to check the difference between the prediction and the actual reaction results. Through experimental feedback, the model is adjusted and optimized to gradually improve the prediction accuracy; Multi-scale integration module: Integrates the molecular-level reaction mechanism model with the macro-reactor model to form a multi-scale model, combining the microscopic reaction process and the macro-reactor process for prediction.

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