Study On Surrogate Of Simulation Model Of The Aquifer Contaminated By DNAPLs

Because of the petrol spilling, illegal disposal and contretemps, crude oil and various petroleum products enter into the environment, and cause environmental pollution. Petroleum products are mixtures mainly consist of alkanes, cycloalkanes and aromatic, have carcinogenic, teratogenic and mutagenic potential threats, and are toxic pollutants that are detrimental to human and the environment.The solubility of petroleum contamination is very small with water. The petroleum contamination usually exists in form of non-aqueous phase liquids (NAPLs). DNAPLs have high density, low water solubility and high interfacial tension properties. The remediation of DNAPLs is more difficult than LNAPLs, commonly used out– processing technology is difficult to control it effectively, and the cost of remediation of DNAPLs is very expensive. The cost of a single contaminated site often requires hundreds of millions of dollars. Surfactants flushing technology appearing in recent years is also called Surfactant Enhanced Aquifer Remediation (SEAR), which improves the out– processing technology. Surfactants have solubilization and mobilization for hydrophobic organic pollutants and can improve the solubility and migration of DNAPLs in water. So they allow for more freedom phase DNAPLs into the water and substantially increase the effectiveness of out– processing technology to repair DNAPLs.At present, Surfactant Enhanced Aquifer Remediation (SEAR) is still in development stage and factors effect restoration and repair costs, such as selected positions of pumping and injection wells and the concentration of surfactant are very complex.Therefore, process optimization design of aquifer remediation of contaminated site based on the field investigation, through the rational use of simulation model and optimization model is exigency and has important theoretical and practical significance, which can improve efficiency and reduce the cost of remediation.The application of simulation-optimization approaches for designing the optimal groundwater remediation systems is given more widespread attention. In the process of the use of simulation models and optimization models, the solution procedure of optimization model need repeated call for simulation model what can bring Huge computational burden for multiphase flow numerical simulation model of DNAPLs contaminated aquifer when simulation model calculation. This would seriously restrict the feasibility of the remediation application of simulation model and optimization model in DNAPLs contaminated aquifer. Therefore, the establishment of surrogate model which is reasonable and effective, so that its function can approximate numerical simulation model, and avoid repeated calls for simulation, and to shorten the computing time. It is a feasible way to solve the problem.However, the study of surrogate model is still in the exploratory and attempt stage, its accuracy is good or bad depending on the sampling method and the type of surrogate model.In this study, aiming at the problem of surfactant-enhanced DNAPLs contaminated aquifer remediation, taking the imaginary and real DNAPLs contaminated aquifers as the research object, it made a study of the theory and method of the surrogate model of Multiphase flow simulation model.First, a multiphase flow numerical simulation model of surfactant- enhanced DNAPLs contaminated aquifer was first building as the base. It was used to simulate the migrate law of water, surfactant and DNAPLs. Then study on using Monte Carlo sampling method and Latin antithetic variable composite sampling method for collecting input-output sample data of multiphase flow simulation model. And compared the results of efficiency and sampling coverage of two sampling methods. According to the input - output sample data sets got by two sampling methods, then building surrogate models of multiphase flow simulation model ---dual response surface model and radial basis function artificial neural network model. At last, choosing a new program to testing the level that surrogate model approximate the numerical simulation, and summed up the proper selection of the surrogate model of multiphase flow simulation model.Main conclusions obtained from the paper are as follows:①For the same modeling approach(Dual response surface model and Radial basis function artificial neural network model), the approximation to the simulation model based on Latin antithetic variable composite sampling method higher than Monte Carlo sampling method. This is because the Monte Carlo sampling method is a random sampling method that samples from the probability distribution using random numbers. Its samples appear completely random, which often have the problem of segregation of data points, and the overall coverage of the extracted sample is not high; However, Latin dual variable composite sample is stratified sampling, whose samples reflect the value distribution of the input probability function when ensure the efficiency of sampling. So the coverage of the sample space is guaranteed and good representative samples are taken.②For the same sampling method(Monte Carlo sampling method and Latin antithetic variable composite sampling method), the approximation to the simulation model based on Radial basis function artificial neural network higher than Dual response surface method. This is because before using the dual response surface method, it need to have a judge of the input - output function type of an issue and then to determine what form of regression equation established. However, the regression equation after judged is used as an alternative model, whose approximation of the simulation model is still limited. The radial basis function neural network makes actual output of the network gradually to approach to the desired output by continuously adjust the cluster centers of the input sample and weights between the hidden layers to output layer, and ultimately enable it to identify the characteristics of the input mode. And radial basis function artificial neural network converges fast and can find the global minimum.③Through a comprehensive comparative analysis, the calculation results and conclusions of the imaginary and real examples have been confirmed with each other. The conclusion is that the four surrogate models all can approximate the simulation model, they all possess the similar input and output relationship with simulation model, but the degree of approximation to the simulation model still exist differences. The four surrogate models sorted by the degree of approximation to the simulation model from low to high is, Dual response surface model based on Monte Carlo sampling method, Dual response surface model based on Latin antithetic variable composite sampling method, Radial basis function artificial neural network model based on Monte Carlo sampling method and Radial basis function artificial neural network model based on Latin antithetic variable composite sampling method. Therefore, the final decision of the most suitable surrogate model of remediation of DNAPLs contaminated aquifer is radial basis function artificial neural network model based on Latin antithetic variable composite sampling method.