11 results about "Response surface methodology" patented technology

The method, system, and equipment for screening and optimizing reservoir gas injection parameters based on response surface methodology include: determining the gas injection parameters affecting reservoir gas drive recovery and assigning initial weights; selecting basic spatial sample points using the central composite method; establishing a response surface function; constructing a response surface function model; validating the response surface model and verifying its accuracy; obtaining the gas injection parameter variable set; obtaining the distribution results of the gas injection parameter variable set converted into weight coefficients; and obtaining the quadratic response distribution of the weight coefficients through quadratic response surface analysis to obtain the discrimination criteria for gas drive in fractured-vuggy carbonate reservoirs. The reservoir gas injection parameter screening method provided by this invention uses response surface analysis combined with multiple linear regression to screen reservoir gas injection parameters. It uses graphical technology to display the functional relationships, providing intuitive graphs to clearly identify the optimization region, allowing researchers to observe and select the optimal conditions in experimental design.

The invention belongs to the technical field of offshore converter station construction design, and provides an offshore structural pipe node lightweight design method and system based on a response surface method.The method comprises the steps that a target function is established with the minimum total weight of structural pipe nodes as the target and the condition that the ultimate strength stress ratio is not larger than a preset allowable value as the constraint condition; adopting Latin hypercube sampling to generate a series of test sample points; extracting a structure response value corresponding to each sample point; according to the structure response value, a response surface method is adopted for fitting to obtain a polynomial function; obtaining a lightweight design scheme according to the polynomial function; according to the method, a stress ratio response surface in a high-dimensional design space can be effectively fitted based on a sampled high-precision response surface model, collaborative optimization is carried out on a plurality of key design parameters of a complex pipe node on the premise of meeting safety specifications, constraint conditions of different diagonal bracings are met, structural redundancy can be accurately released, and the design efficiency is improved. And obvious weight reduction is realized on the basis of not sacrificing safety.

The invention relates to the technical field of computational modeling and numerical optimization, and particularly discloses a multi-parameter objective function comprehensive optimization method based on a response surface and multiple rounds of orthogonal tests, which comprises the following steps of: firstly, establishing a response surface relationship between parameters and an objective function by using a response surface method, solving constants in the response surface function through a sample library obtained by the orthogonal tests, and calculating a response surface relationship between the parameters and the objective function; solving a response surface function to obtain a preliminary optimal parameter combination; and then, performing round-by-round reduction and iterative optimization on a parameter value range by combining a multi-round orthogonal test method, and finally outputting an optimal parameter combination of an objective function by calculating an objective function value of each orthogonal scheme and performing comparison and screening. The method has the advantages of being high in optimization precision, high in calculation efficiency, wide in application range and high in expandability, the system or model performance can be remarkably improved, the test frequency and calculation cost are reduced, the optimization period is shortened, and the method is suitable for multi-parameter and multi-target optimization of physical models, engineering optimization and other scenes.

This invention discloses a method and system for optimizing the proportion of tailings cementitious materials based on response surface methodology, relating to the field of tailings recycling technology. The method includes: using lead-zinc tailings content, fly ash content, sand-cement ratio, and water-cement ratio as process parameters, determining multiple cementitious material formulations based on response surface methodology; preparing physical cementitious material test blocks according to these formulations; conducting compressive strength tests to obtain the compressive strength results; establishing a compressive strength prediction model based on the results using regression analysis; and determining a target combination of process parameters based on the compressive strength prediction model, with a target compressive strength or target cost-effectiveness ratio as the objective. The target combination of process parameters is the proportion of lead-zinc tailings content, fly ash content, sand-cement ratio, and water-cement ratio. The cementitious material is then produced using this target combination of process parameters. This invention systematically optimizes the proportion of tailings cementitious materials through response surface methodology, establishing a compressive strength prediction model to achieve controllable performance prediction, providing a scientific solution for tailings resource utilization.

This invention belongs to the field of oil and gas reservoir development technology, and relates to a numerical simulation and parameter optimization method for composite gas-driven reservoirs based on response surface methodology, electronic equipment, and storage medium. The numerical simulation and parameter optimization method for composite gas-driven reservoirs includes the following steps: Step S1: Selecting gas injection parameters; Step S2: Determining the range and experimental point values ​​of the gas injection parameters; Step S3: Establishing a predictive model of the relationship between the gas injection parameters and the cumulative oil increase as the dependent variable; Step S4: Fitting and analyzing variance of the data obtained from the predictive model to determine the response surface model and the optimal parameter combination; Step S5: Response surface analysis. This invention establishes an accurate response surface model within a limited number of experiments, enabling the analysis and optimization of the impact of various operating parameters on reservoir recovery.

The application discloses a lead styphnate compound process optimization method based on a response surface method and an artificial neural network, belongs to the field of primers and pyrotechnics, and solves the problem that in the existing lead styphnate production line, compound process parameters are adjusted depending on artificial experience, leading to great product purity fluctuation, wide particle size distribution, poor flowability and insufficient batch consistency. The method comprises the following steps: determining key compound process parameters influencing the performance of lead styphnate based on collected lead styphnate compound process related data; and establishing a mathematical model for describing the relationship between the key compound process parameters, the interaction thereof and the performance index of lead styphnate. The method forms a stable and reliable performance influence rule by optimizing the lead styphnate compound process parameters, improves production efficiency, and solves the problem that the performance control mainly relies on artificial experience setting in the past.

This application discloses a method for calculating and optimizing the magnetic field of high-current lead wires based on the response surface methodology, comprising: constructing a parameterized three-dimensional nonlinear electromagnetic-thermal bidirectional coupled finite element model; selecting representative sample points in the design space using the optimal Latin hypercube sampling method, training a surrogate model based on the sample points and dynamically adding points, and outputting a set of representative sample points; performing finite element calculations on each output representative sample point, extracting the performance indicators corresponding to each sample point, and then constructing a Kriging gradient-enhanced hybrid response surface model; establishing an optimization objective function to guide the optimization direction, and then using a multi-objective optimization algorithm to quickly optimize the high-precision hybrid response surface model, generating a Pareto optimal solution set; selecting the optimal design scheme from the Pareto optimal solution set based on the entropy weight-TOPSIS dynamic decision method and performing double verification, and outputting optimization parameters and a performance report after successful verification. This application achieves synergistic optimization of performance, cost, and reliability.

The application provides a process parameter optimization method, device and program product. The method comprises the following steps: constructing a yield prediction model according to a pre-acquired experimental data set; performing an explainability analysis on the yield prediction model to obtain key process parameters; establishing a kinetic model and a computational fluid dynamics model according to kinetic parameters of microbial growth, and coupling the kinetic model to the computational fluid dynamics model; generating a plurality of process parameter combinations according to the key process parameters by using a response surface method; performing numerical simulation on the plurality of process parameter combinations by using the computational fluid dynamics model to obtain a plurality of corresponding methane yields; and determining an optimal process parameter combination according to the plurality of process parameter combinations and the plurality of corresponding methane yields. The scientificity and efficiency of process parameter optimization can be improved, a reliable basis can be provided for industrial scaling of a biological methanation trickle bed, and thus the conversion efficiency of the trickle bed can be improved and the methane yield can be improved.

The present application relates to a kind of vertical axis tidal current energy turbine multi-parameter coupling experiment and intelligent model collaborative optimization method, the method includes: based on orthogonal experimental design and response surface method to construct the multi-level interaction working condition of blade key geometric parameters, obtain power and thrust coefficient performance curve, introduce global optimization of genetic algorithm and obtain optimal geometric configuration;Using variable pitch mechanism, determine the optimal variable pitch strategy considering power improvement and load fluctuation suppression;Introduce turbulence intensity correction coefficient, represent the performance degradation law of turbine under complex inflow, construct robust prediction model;Phase-locked PIV flow field measurement is executed under different working conditions, establish the quantitative correlation model of flow field characteristics and instantaneous load fluctuation, and feedback optimal parameter combination back to experimental design.Relative to the prior art, the present application has disclosed the multi-parameter nonlinear coupling mechanism of turbine energy trapping efficiency and operating stability, provides physical basis for its optimization design and the like advantages.

This invention discloses a process for optimizing the extraction of total flavonoids from Houttuynia cordata stems and leaves based on response surface methodology, relating to the field of natural product extraction technology. The method includes the following steps: Step 1, obtaining dried and pulverized Houttuynia cordata stem and leaf powder; Step 2, extracting total flavonoids from the powder using ultrasound, selecting ethanol concentration, extraction temperature, extraction time, and liquid-to-solid ratio as single-factor experiments, and employing response surface methodology with total flavonoid content as the response value to establish a second-order polynomial regression model to predict the optimal extraction conditions; Step 3, determining the optimized extraction parameter combination based on the model; Step 4, performing the extraction operation according to the optimized extraction parameter combination. This invention utilizes response surface methodology to optimize the extraction process of total flavonoids from Houttuynia cordata stems and leaves, determining a process that can effectively extract flavonoids from Houttuynia cordata stems and leaves, providing a theoretical basis for the research and utilization of flavonoids from Houttuynia cordata stems and leaves.

To address the limitations of traditional orthogonal experimental methods, which rely on experience and have limited parameter search capabilities, and response surface methodology, which is heavily reliant on the number of finite element simulation samples and has high computational costs, in optimizing the structural parameters of a six-dimensional force sensor elastomer, this invention proposes a method for optimizing the structural parameters of a six-dimensional force sensor elastomer based on a physical information neural network. The technical approach involves: taking the six-dimensional force sensor elastomer structure as the research object, selecting the length of the floating beam, the length of the strain beam, the width of the floating beam, the width of the strain beam, and the beam thickness as design variables; constructing multiple combinations of structural dimensions through orthogonal experiments, and obtaining the strain response data corresponding to each dimension combination using finite element analysis; establishing an elastomer mechanical model, and introducing the constitutive relations, governing equations, and boundary conditions of the mechanical model as physical constraints into the neural network training process; simultaneously using finite element strain data to supervise and correct the network, thereby constructing a high-precision mapping model between structural parameters and strain response; and further combining this mapping model with a multi-objective genetic algorithm (NSGA-III) to optimize the search for the elastomer structural parameters. This invention reduces the requirement for finite element simulation samples while achieving high-precision prediction of the mechanical response of the elastomer structure, significantly improving the efficiency of structural parameter optimization.