Sobol Global Sensitivity Analysis Research Articles

Impact of aleatory and epistemic uncertainties on thermal risk and production assessment: Application to the hydrogenation of levulinic acid and butyl levulinate

The hydrogenation of levulinates, derivatives from cellulose hydrolysis, leads to the platform molecule γ-valerolactone (GVL). While prior investigations indicate the propensity for thermal runaways in this reaction, a comprehensive sensitivity analysis has been conspicuously absent in the existing literature. This study endeavors to scrutinize the influence of input parameters on both thermal risks and the production rate during GVL synthesis. Distinguished by its dual focus on sensitivity analyses, this manuscript also contrasts the implications of aleatory and epistemic uncertainties on thermal risk and production metrics. Aleatory uncertainties arise from the inherent variability of initial conditions, while epistemic uncertainties emanate from incomplete knowledge of the kinetic model. Employing Sobol global sensitivity analysis on the kinetic model, we delineate the hierarchical importance of various parameters. This analysis incorporates a quantification of uncertainty, and algorithmic approaches are proffered. The findings reveal that the initial temperature, the initial mass of the Ru/C catalyst, and hydrogen pressure are pivotal factors that are directly proportional to thermal risk and production efficiency. The implications of uncertainty on these variables are also discussed.

A comprehensive parametric study and multi-objective optimization of turbulent jet array impingement for uniform cooling of gas turbine blades with minimized compression power

In the present paper, a comprehensive parametric study and multi-objective optimizations on jet array impingement cooling are conducted for mid-chord sections of gas turbine blades to maximize the heat transfer uniformity on the target plate and minimize the air compression power consumption at different desired Nusselt numbers. The validated numerical method based on RANS equations is utilized to determine the effects of Reynolds number (2500≤Re≤35000), jet spacings (3≤Px,Py≤8), and the jet-to-target distance (0.75≤Pz≤3) on air compression power (Wc), average Nusselt number (Nu‾), and heat transfer uniformity index (UI). According to the parametric study, the increase of Re improves Nu‾ and UI, while intensively increasing Wc. The effect of jet-to-target distance is a function of jet spacings; with the increase of Pz at low jet spacings, Wc,Nu‾, and UI are reduced. Although, at large spacings, Nu‾ and UI increase with Pz, and Wc is independent of Pz. Additionally, the increase of Px decreases Wc, Nu‾, and UI. Increasing Py reduces Nu‾ and Wc. But at small Px and Pz, the UI is descending; while at large Px and Pz, the UI tends to ascend. Three high-accuracy surrogate models are developed using backpropagation artificial neural networks (ANN) for estimating Wc, Nu‾, and UI for input design variables. Sobol global sensitivity analysis is also performed based on the developed models for quantifying the influence of design variables and their interactions on objective functions. As expected, the results indicate that Wc and Nu‾ are the most sensitive to Re, and UI is mainly affected by Px, whereas Py is more affecting the uniformity index rather than compression power. Finally, to find out the best design and flow conditions, optimizations are conducted by the NSGA-II algorithm. The optimal Pareto frontier and final decided solutions by TOPSIS and LINMAP methods are then demonstrated for the desired Nusselt number (NuD) of 70. The TOPSIS method indicates Px=4.70, Py=3.25, Pz=1.40, and Re = 13800 as the best compromise for optimization. The analysis of Pareto solutions in the range of NuD from 35 to 130 suggests a variety of optimal flow and geometrical arrangements for a trade-off between objective functions; therefore, at most, 50% less compression power or 5% more uniformity is approachable corresponding to the design requirements.

Global Sensitivity Analysis of Factors Influencing the Surface Temperature of Mold during Autoclave Processing.

During the process of forming carbon fiber reinforced plastics (CFRP) in an autoclave, deeply understanding the global sensitivity of factors influencing mold surface temperature is of paramount importance for optimizing large frame-type mold thermally and enhancing curing quality. In this study, the convective heat transfer coefficient (CHTC), the thickness of composite laminates (TCL), the thickness of mold facesheet (TMF), the mold material type (MMT), and the thickness of the auxiliary materials layer (TAL) have been quantitatively assessed for the effects on the mold surface temperature. This assessment was conducted by building the thermal-chemical curing model of composite laminates and utilizing the Sobol global sensitivity analysis (GSA) method. Additionally, the interactions among these factors were investigated to gain a comprehensive understanding of their combined effects. The results show that the sensitivity order of these factors is as follows: CHTC > MMT > TMF > TCL > TAL. Moreover, CHTC, MMT, and TMF are the main factors influencing mold surface temperature, as the sum of their first-order sensitivity indices accounts for over 97.3%. The influence of a single factor is more significant than that of the interaction between factors since the sum of the first-order sensitivity indices of the factors is more than 78.1%. This study will support the development of science-based guidelines for the thermal design of molds and associated heating equipment design.

Assessing the growing threat of heat stress in the North Africa and Arabian Peninsula region connected to climate change

Climate change exacerbates extreme heat events in the North Africa and Arabian Peninsula (NAAP) region, posing a significant threat to human health and well-being. This study proposes a novel approach for reliable mapping of population exposure in NAAP to different UTCI levels under future climate change scenarios by improved prediction of UTCI using machine learning (ML) methods. Daily mean UTCI was calculated using hourly ERA-HEAT data from 1979 to 2022. Sobol's global sensitivity analysis was used to identify the most influential meteorological variables affecting UTCI. Three ML models were trained to predict monthly UTCI based on these variables. The best-performing model was used to generate historical and future projections of UTCI for four CMIP6 climate models under Paris Agreement goal scenarios. Finally, the projected change in population exposure to mean UTCI was assessed by comparing historical and future projections. The results showed a significant influence of air temperature, wind speed, surface pressure, solar radiation, and humidity on UTCI in NAAP. Estimation of UTCI using these variables showed a higher performance of random forest (RF) (Kling-Gupta Efficiency = 0.84) compared to other models. The projection of UTCI from CMIP6 climatic model simulations using the RF model revealed a decrease in UTCI for the 1.5 °C temperature rise scenario by 0.25–0.75 °C, while an increase by 0.25–0.75 °C for the 2.0 °C temperature rise scenario in most of NAAP. The study revealed that even a modest increase of 0.5 °C in global warming could lead to a 20-day increase in the number of days with moderate thermal stress in Egypt and Sudan and a significant increase in the number of days with strong thermal stress in the Arabian Peninsula. The population exposure to heat stress would increase significantly even under the most ambitious Paris Agreement goals. These findings highlight the urgent need for adaptation and mitigation strategies to protect public health and well-being in the NAAP region from escalating risks.

Analysis of Factors Affecting Vacuum Formation and Drainage in the Siphon-Vacuum Drainage Method for Marine Reclamation

Traditional drainage methods for marine reclamation typically consume large amounts of energy and have a negative environmental impact. The siphon-vacuum drainage method (SVD) automatically forms a vacuum and drains using less energy. It has significant potential for research and application. In this study, a theoretical model is used to calculate the vacuum formation process and drainage rate. Qualitative analysis and global sensitivity analysis were conducted to investigate the effect of various factors in the SVD on vacuum formation and drainage. The qualitative analysis suggests that modifying the length and diameter of the siphon pipe and the thickness of the sealing soil column to increase the siphon rate can improve the vacuum degree and drainage efficiency. Sobol global sensitivity analysis reveals that the sealing soil column thickness is the main factor affecting the vacuum, with a first-order sensitivity index accounting for up to 79.48%. The impact of cylinder diameter and the local resistance coefficient (0.43%) can be almost neglected. A fitting equation for estimating the maximum achievable vacuum is provided. Calculations show that the vacuum formed by the SVD can reach over 80 kPa. This work can help optimize SVD design and advance environmentally friendly marine reclamation projects.

Exploring energetic, exergetic, economic and environmental (4E) performance of waste heat power generation in nuclear power plant systems: A perspective of pattern recognition

Combined cogeneration systems are employed to recover waste heat from the sCO2 cycle in nuclear power generation systems. However, dealing with their multidimensional and nonlinear design and optimization spaces is a complex challenge for designers. In this study, the combined cogeneration system is parameterized and modeled: 4E models including energetic, exergetic, economic and environmental are established for sCO2 combined Organic Rankine Cycle (sCO2/ORC) and trans-critical CO2 Cycle (sCO2/tCO2) respectively. Several decision variables and five performance indicators are selected. To gain a deeper understanding of the mechanisms and optimize the combined cogeneration system, pattern recognition methods including the Self-Organizing Map (SOM), an unsupervised competitive learning approach, and Statistical Sobol Global Sensitivity Analysis methods are proposed. These techniques help identify key parameters and system patterns within the design space. Then, the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA) is adopted to build a Pareto-optimal decision space, and the SOM and PCPs are proposed to visualize and mining based on pattern recognition and graph visualization interaction. The design parameters of the top cycle, P2, PRc, and x, have been confirmed as having a substantial impact on the system's performance. The multiple attributes, criterions and objectives problems in the combined cogeneration system are ultimately simplified to “Economy-Environment”, double objectives problem, and three trade-offs’ solutions are discussed: prioritizing economics, prioritizing the environment, and finding a balanced economic-environmental compromise. Pattern recognition method provides decision-makers with a fresh perspective for gaining a deeper understanding of the multi-criteria objectives space and potential trade-offs. The integration of pattern recognition into the thermodynamic, economic, and environmental models outlined in this paper represents a substantial and valuable advancement in the analysis and optimization of combined cogeneration cycles.

Study on the Hydraulic Response of an Open-Channel Water Transmission Project after Flow Switching

For the complex and changing water transfer conditions along the nullah water transfer project, it is of great significance to analyze the hydraulic response characteristics of the channel pool to ensure the safe and efficient operation of the project. In this paper, using the adjustable reservoir capacity of the channel pool, combined with the earliest and latest regulation time, a method is proposed to determine the optimal regulation time of water conservancy engineering structures through the hydraulic response characteristics. In order to validate the method, the hydraulic response characteristics of the channel pool during the whole regulation period were investigated using an actual river diversion project as an example. By using the Sobol global sensitivity analysis method, the sensitivity analysis of three variables (water transfer flow, upstream initial flow, and downstream water level) was conducted, and the results showed that the change of water transfer flow was the most-critical factor affecting the optimal regulation time. In order to quantitatively study the correlation between the water transfer flow and the optimal regulation time, the water transfer flow factor was coupled to the existing prediction model, and the results showed that the coupled model can efficiently predict the optimal regulation time, and the NSE coefficient can reach 0.98. In addition, the coupled model can be efficiently used to serve on-site dispatchers for accurately predicting the optimal regulation time and making the corresponding safety decisions.

Study on the solid-liquid separation mechanism of the inverting filter centrifuge’s dewatering process

Centrifugal dewatering is a common process used for solid-liquid separation in chemical industry. This process involves the intricate interaction between particles and liquid under centrifugal pressure. Accurate modeling and simulation of the kinetic process are key to optimizing the dewatering parameters. However, the feeding stage is often disregarded in most of the simulation studies, leading to obvious differences from the actual conditions in the dewatering behavior and initial solid concentration. In this study, a 1D-axisymmetric model is established to simulate the feeding and pre-dewatering stages of an inverting filter centrifuge. The simulation results at different rotating speeds agree with the experimental observations and show a significant difference compared to the model that ignores the feeding stage. Both phenomena demonstrate the method in this study is accurate and reliable. Furthermore, the effects of feeding parameters are investigated using the Sobol global sensitivity analysis method. This study provides crucial assistance for the parameters’ optimization and prediction of centrifugal dewatering.

A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA

Abstract. Geoscientific models are simplified representations of complex earth and environmental systems (EESs). Compared with physics-based numerical models, data-driven modeling has gained popularity due mainly to data proliferation in EESs and the ability to perform prediction without requiring explicit mathematical representation of complex biophysical processes. However, because of the black-box nature of data-driven models, their performance cannot be guaranteed. To address this issue, we developed a generalizable framework for improving the efficiency and effectiveness of model training and the reduction of model overfitting. This framework consists of two parts: hyperparameter selection based on Sobol global sensitivity analysis and hyperparameter tuning using a Bayesian optimization approach. We demonstrated the framework efficacy through a case study of daily edge-of-field (EOF) runoff predictions by a tree-based data-driven model using the extreme gradient boosting (XGBoost) algorithm in the Maumee domain, USA. This framework contributes towards improving the performance of a variety of data-driven models and can thus help promote their applications in EESs.

Sobol sensitivity analysis and multi-objective optimization of manifold microchannel heat sink considering entropy generation minimization

Manifold microchannel (MMC) heat sink has been widely employed as an efficient scheme for high heat flux cooling of electronic devices. The parametric optimization design of the MMC heat sink is the key factor that directly determines the thermal management performance. In this study, Sobol global sensitivity analysis is firstly applied to quantitatively evaluate the effects of four geometric design parameters and inlet volume flow rate on the MMC heat sink performance. A multi-objective optimization method combining space filling method, Back-Propagation Neural Network and NSGA-II is then established to minimum the thermal resistance, total pumping power and total entropy generation of MMC heat sink, in which TOPSIS method based on entropy weight is developed to select the best compromise solution from the Pareto-optimal solutions. Finally, the overall performance among different optimal solutions is compared to identify the effectiveness of the optimization results. The results show that the obtained optimum design parameters can provide more than 67% reduction in power consumption without scarifying thermal performance compared with the MMC heat sink published in the journal “Nature” by Erp et al. in 2020. In addition, the best compromise solution can be recommended as selectable for designer because it shows the best performance, which can boost the heat flux beyond 1000 W/cm2 with pumping power consumption 0.236 W. These results are of great significance to the design of advanced MMC heat sink for cooling electronic devices.

Determining the optimal operation and maintenance contract period of PV poverty alleviation projects based on real options and cooperative game: Evidence from rural China

China's photovoltaic poverty alleviation power stations (PPAPS) properly combine poverty alleviation and renewable power generation while also meeting rural energy demands. The fundamental problem is how to optimize the benefits of PPAPS during its limited lifetime. Using the post-operation of photovoltaic poverty alleviation power plants as a starting point, this paper proposes a model combining real option theory and cooperative game to determine the ideal contract period of service committed by the government to the operation and maintenance (O&M) company, thus providing a reference for both parties. The model's validity and applicability are verified by incorporating a PPAPS from Henan Province in China as an example; ultimately, the factors influencing the O&M contract period are ranked using the Sobol global sensitivity analysis. The results show that when the solar industry's environment is positive, the government is more inclined to sign long-term contracts with O&M companies. In contrast, the more the government invests in the facilities, the more likely both parties will sign short-term contracts. Furthermore, the O&M company's capital investment in the power station has a direct impact on the contract period. Finally, corresponding policy recommendations are proposed for future O&M contracts.

Towards operational atmospheric correction of airborne hyperspectral imaging spectroscopy: Algorithm evaluation, key parameter analysis, and machine learning emulators

Atmospheric correction of airborne hyperspectral imaging spectroscopy (AHIS) to obtain high-quality surface reflectance is the prerequisite for remote sensing applications. Over the last decades, different atmospheric correction methods have been developed based on radiative transfer models (RTMs), however, the relative performances of different algorithms are unclear. Automated operational atmospheric correction methods to process large-volume AHIS data in a high-accurate and high-throughput manner are still lacking. Therefore, this study proposed an operational atmospheric correction pipeline for deriving surface reflectance from AHIS data. To ensure the accuracy and efficiency of the pipeline, we focused on three specific aspects: (1) selecting a suitable RTM for the development of atmospheric lookup tables (LUTs) by comparing the commercial MODerate resolution atmospheric TRANsmission (MODTRAN) and open-sourced Library for Radiative TRANsfer (LibRadTRAN) models, where the widely-used software, Atmospheric/Topographic Correction for Airborne Imagery (ATCOR), was used as benchmarks; (2) identifying key atmospheric correction parameters and determining suitable sources for parameter retrievals including AHIS, Moderate Resolution Imaging Spectroradiometer (MODIS), and AErosol RObotic NETwork (AERONET); and (3) testing the performance of using machine learning emulators to speed up the RTM-based atmospheric correction. Results indicate that (1) atmospheric correction based on MODTRAN LUTs can produce surface reflectance accurately with mean absolute errors < 0.05 and cosine similarities > 0.98 compared to field measurements, which is comparable to the software ATCOR and slightly outperforms the LibRadTRAN LUTs; (2) sobol global sensitivity analysis demonstrates that in the atmospheric correction, visibility and water vapor are two key parameters that can be accurately derived from AHIS in contrast to MODIS or AERONET data; and (3) Random Forest emulators can produce accurate estimations of surface reflectance with mean absolute errors < 0.03 and cosine similarities > 0.98 for higher processing efficiency and determine a suitable set of wavelengths for retrieving atmospheric visibility and water vapor. The proposed atmospheric correction pipeline also improved the four-stream radiative transfer theory for airborne applications by considering adjacent effects from airborne surrounding pixels and can also be applied for atmospheric correction of hyperspectral data from spaceborne missions.

Study on properties and composition of ancient glass based on PCA and gradient lifting tree model

In order to help archaeologists better study ancient glass, uncover the mystery of the early Silk Road, and also better protect these cultural heritage.In this paper, the properties and composition of ancient glass are studied.All types of glass samples were analyzed by PCA principal component analysis and weighted summation, and the type characteristic equation was obtained. After calculating the candidate points iteratively in MATLAB, the optimal critical eigenvalue was set to 5, and the type judgment model was established.According to get the type of the characteristic equation, the selection is related components as high potassium and the composition of class division, choose negative correlation components as lead, barium and the composition of class division, through the PCA principal component analysis of two types of glass data are reduced to 3 d in the class we USES Kmeans clustering algorithm for two types of glass, For the sensitivity analysis of subclass partitioning, we used SOBOL global sensitivity analysis method, and obtained that principal component 1 would have the greatest impact on the model output.

Application of yaw dampers with frequency-selective damping to improve the locomotive adaptability to low/high conicity stability

High-speed locomotives are prone to the carbody or bogie hunting under extreme wheel-rail contact conditions, which can cause negative impacts on vehicle dynamics performance. The influence of key suspension parameters on the carbody lateral ride comfort and bogie lateral acceleration is investigated by the multi-objective optimization and Sobol global sensitivity analysis methods, which is based on the Radial Basis Function Neutral Network surrogate model. The results illustrate that the yaw damper damping is sensitive to the two hunting stabilities, but conventional yaw dampers with fixed damping fail to satisfy both the stability demands simultaneously. Therefore, this paper presents a damping-adjustable yaw damper integrating an FSD (frequency-selective damping) valve to guarantee the lateral stability especially in abnormal wheel-rail contact states, and the application effects of the FSD yaw damper for the locomotive are researched by dynamics simulations. The results show that the FSD yaw damper can improve the adaptability of the locomotive to both the low and high wheel-rail contact conicity service conditions compared with the conventional yaw dampers.

Efficient Sensitivity Analysis for Enhanced Heat Transfer Performance of Heat Sink with Swirl Flow Structure under One-Side Heating

Excellent heat transfer performance has increasingly become a key issue that needs to be solved urgently in the development process of large-scale fusion equipment. The study of heat transfer performance improvement to scientifically and reasonably determine the design parameters of the high heat flow (HHF) components of fusion reactors based on the efficient in-depth analysis of the heat transfer mechanism and its sensitive factors is of great significance. In this paper, a liquid-vapor two-phase flow model with subcooled boiling for a large length-diameter ratio swirl tube structure in the HHF calorimeter component is proposed to analyze the effects of key design parameters (such as inlet temperature of cooling water flow, swirl tube structure parameters, etc.) on its heat transfer performance. Then, considering the high computational cost of the liquid-vapor two-phase flow model, and in order to improve the efficiency of the sensitivity analysis of these design parameters, the polynomial response surface surrogate model of heat transfer performance function was constructed based on Latin hypercube sampling. On this basis, by combining the proposed surrogate model, the sensitivity index of each design parameter could be obtained efficiently using the Sobol global sensitivity analysis method. This method could greatly improve the calculation efficiency of the design parameter sensitivity analysis of HHF components in the fusion reactor, which provides vital guidance for the subsequent rapid design optimization of related components.

Modeling of true triaxial strength of rocks based on optimized genetic programming

The strength of a rock is the main factor affecting the stability of an engineered rock mass. As laboratory testing requires sophisticated equipment and considerable time to determine rock strength, prediction models are needed for establishing rock strength criteria. Genetic programming (GP) is a soft computing technology often used to address rock mechanics and engineering challenges. However, GP also has limitations, such as a long running time, complex individual growth without a corresponding fitness improvement, and difficulty in finding the optimal solution. Therefore, we conducted this study by applying a dynamic restriction on individual size, local search of the neighborhood of the optimal individual, and multithreaded evaluation to optimize GP and guarantee the accuracy of the results and to build a prediction model for the true triaxial strength involving different rock types. The results showed that the restriction dynamically changes to restrict the redundant bloat of strength individuals without a corresponding fitness improvement; using local search rules can effectively find individuals with high fitness, so the strength predicted by the system was in good agreement with the measured strength. We also found the predicted strength was suitable for fitting the rock strength criteria. Using this multithreaded evaluation sped up the operation of the algorithm and produced accurate predictions; and for complex problems, increasing the threads had a more pronounced effect on the runtime and fitness improvements. Based on the Sobol global sensitivity analysis, we analyzed the influence of each prediction parameter on the true triaxial strength of rocks. Combined with the statistical assessment indices involving sum of the absolute error, mean, a10-index, and regression determination coefficient, the predictions of the optimized GP model that we established in this study were more accurate than those of multiple regression analysis.

DYNAMICS OF AN SIRS EPIDEMIC MODEL WITH PERIODIC INFECTION RATE ON A SCALE-FREE NETWORK

Influenced by seasonal changes, the infection rate of many infectious diseases fluctuates in cycles. In this paper, we propose and investigate an SIRS model on a scale-free network. To model seasonality, we assume that the infection rate is periodic. The existence and positivity of solutions of the proposed model are proved and the basic reproduction number [Formula: see text] is defined. The global stability of steady states is determined by rigorous mathematical analysis. When [Formula: see text], the disease-free equilibrium [Formula: see text] is globally asymptotically stable. When [Formula: see text], the system has a unique positive periodic solution [Formula: see text], and [Formula: see text] is globally asymptotically stable. Numerical simulations are performed to support our theoretic results, and the effects of various parameters on the amplitude and mean of infected individuals are studied. The sensitivity of parameters of the basic reproduction number [Formula: see text] is solved by the Sobol global sensitivity analysis method, and the results show that the effects of the parameters [Formula: see text] and [Formula: see text] on [Formula: see text] are remarkable.

Probabilistic Stability Analysis of Deep Rock Tunnel Excavated by Mechanized Tunneling Considering Anisotropic Initial Stresses

Due to the complex geological conditions, the construction of underground tunnels in the rocks exhibiting creep behaviors is a great challenge and the structures’ long-term stability is of main importance. The excavation by tunnel boring machines (TBM) of a tunnel that is supported by a double flexible/concrete liner is considered a potential technical solution. However, the efficiency of such a support system on the stability at long-term of a tunnel must be verified in the real condition by considering both the effects of uncertainties of the time-dependent rock behavior and anisotropy of initial stress state. In this paper, the subset simulation and the Sobol global sensitivity analysis are chosen to estimate the failure probability of the tunnel supports and quantify the importance of different random parameters, respectively. The results reveal that considering the anisotropy of initial stresses increases the failure probability, especially in the concrete support elements. In addition, the parameters of initial stresses are the ones of main importance for both liners according to the Sobol indices. Therefore, the anisotropy of initial stresses and the related uncertainties should be considered for reliable tunnel designs.

Global Sensitivity Analysis of Earth-Moon Transfer Orbit Parameters Based on Sobol Method

In the process of Earth-Moon transfer orbit, many parameters are involved and the degree of influence varies among the parameters. How to accurately distinguish the influence relationship between different parameters is of great significance to engineering missions. Based on Sobol sequence sampling method and Sobol global sensitivity analysis method, a calculation process of global sensitivity analysis is proposed in this paper for high-fidelity Earth-Moon transfer orbit. A numerical simulation method is used to verify that the Sobol sequence sampling method has better convergence and higher precision than other sampling methods and has better adaptability in global sensitivity analysis. The effects of different state parameters and the combination of different parameters on the perilune parameters of Earth-Moon transfer orbit are given by simulation examples, which verifies the effectiveness and feasibility of the calculation process proposed in this paper. Simulation results show that the radial position and tangential velocity at the trans-lunar injection point are the main sensitivity parameters, and the other parameters have little effect on the results. The sensitivity of the orbital elements at the trans-lunar injection point to the perilune parameters is different and needs to be determined according to the specific parameters. The results of this study can provide important reference for future Earth-Moon transfer orbit design and related engineering missions.

Predicting remediation efficiency of LNAPLs using surrogate polynomial chaos expansion model and global sensitivity analysis

A multi-phase and multi-component numerical simulator assessed the removal efficiencies of light non-aqueous phase liquid (LNAPL) consisting of benzene, toluene, ethylbenzene, and xylene-p. Scenarios of the LNAPL-spilling, natural distribution, and remediation stages were designed in the full-physics numerical modeling. For LNAPLs remediation, a multi-phase extraction (MPE) and a steam injection technique were employed. The removal efficiencies of LNAPLs were computed by systematically varying 6 factors that determine the configuration of the remediation wells. Then, surrogate polynomial chaos expansion (PCE) models mathematically predicting the removal efficiencies were developed through 600 training datasets representing 4 scenario cases; different permeability and the location of SI well were considered in the scenario cases. The PCE models were utilized for Sobol global sensitivity analysis and stochastic Monte Carlo prediction. As a result, the depth of the MPE well was identified as the most significant factor in determining the removal efficiency of the LNAPLs. The removal efficiency was maximized when the MPE well was positioned 1.5 m below the groundwater table. Additionally, the contributions of influencing factors were significantly changed by the field permeability. This study proposed a general framework that efficiently predicts LNAPLs remediation efficiency and identifies key influencing factors by combining advanced numerical modeling, PCE-based surrogate modeling, and sensitivity analyses.