Lattice-Boltzmann Modeling of Bacterial Chemotaxis in the Subsurface

The Lattice Boltzmann method (LBM) has been widely used because it is well-suited to model flow and transport in the complex geometries that are typical for subsurface porous media. Bacterial chemotaxis enables motile bacteria to move preferably toward chemoattractants that may be contaminants in the subsurface. This microbial phenomenon provides a valuable mechanism to enhance in situ bioremediation. Therefore, we developed Lattice Boltzmann (LB) models to study bacterial chemotaxis in the subsurface. A multiple-relaxation-time (MRT) LB model was developed to study the formation and migration of traveling bacterial waves caused by chemotaxis (chemotactic waves) in the absence of bacterial growth and decay. This model was validated by comparing simulations with experiments in which the chemotactic bacteria entered a tube filled with substrate due to chemotaxis. Simulations were performed to evaluate the effects of substrate diffusion, initial bacterial concentration, and hydrodynamic dispersion on the formation, shape, and propagation of such chemotactic waves. Wave formation requires a sufficiently high initial number of bacteria and a small substrate diffusion coefficient. Uniform flow does not affect the waves while shear flow does. Bacterial waves move both upstream and downstream when the flow velocity is small. However, the waves disappear once the velocity becomes large due to hydrodynamic dispersion. Generally waves can only be observed if the dimensionless ratio between a particularly defined coefficient, chemotactic sensitivity coefficient, and the effective diffusion coefficient of the bacteria exceeds a critical value, that is, when the biased movement due to chemotaxis overcomes the diffusion-like movement due to the random motility and hydrodynamic dispersion. Another two-relaxation-time (TRT) LB model was also introduced to simulate bacterial chemotaxis and other reactive transport. The TRT LB model can eliminate numerical diffusion by including a velocity correction term. One-dimensional solute transport with initial Gaussian and top hat distributions were investigated to evaluate the accuracy and stability of the TRT models with and without the velocity correction. The TRT model with the correction demonstrated better numerical accuracy and stability than that without the correction. When the velocity is small, the numerical diffusion can be neglected, and the TRT model without the correction attained very similar simulation results as the TRT model with the correction. However, it is necessary for the TRT model to include the velocity correction when the velocity is large. Since bacterial survival is a significant factor for contaminant remediation at contaminated sites, we studied the coupled effects of chemotaxis and growth on bacterial migration and contaminant remediation. The impacts of initial electron acceptor concentration on different bacteria and substrate systems were examined. The simulations showed that bacteria could form a growth/decay/motility wave due to a dynamic equilibrium between bacterial growth, decay and random motility, even though the bacteria perform no chemotaxis. We derived an analytical solution to estimate this growth/decay/motility wave speed. The initial electron acceptor concentration was shown to significantly affect the bacterial movement and substrate removal. The impact of chemotaxis on bacterial migration is determined by comparison of the chemotactic wave speed with the growth/decay/motility wave speed. When chemotaxis is too weak to allow for the formation of a chemotactic wave or its wave speed is less than half of the growth/decay/motility wave speed, it hardly enhances the bacterial propagation. However, chemotaxis significantly improves bacterial propagation once its wave speed exceeds the growth/decay/motility wave speed. The bacterial survival plays a crucial role in determining the efficiency of contaminant removal. If there is no growth, the traveling wave will move with a decreasing speed and finally terminates. Although chemotaxis has been widely observed to be able to improve contaminant degradation in laboratories, it is rarely reported to enhance bioremediation at field sites. We discuss this discrepancy based on our simulation findings and suggest operable measures to take advantage of chemotaxis in in situ bioremediation

Detailed quantitative description of fluvial reservoirs: A case study of L6-3 Layer of Sandgroup 6 in the second member of Shahejie Formation, Shengtuo Oilfield, China

 The steady development of the oil field is facing severe challenges due to the problems of small-layer division, unclear genesis period and unclear river channel distribution in the 4-6 sand formation in the second district of Shengtuo Oilfield. Based on the processing and optimization of logging data, this paper firstly divided the isochronous strata and established the high-resolution isochronous stratigraphic framework. Using the geo-statistics method in the stratigraphic framework, the sand bodies in each small layer were divided according to the principle of equal time of fluvial facies. On this basis, the distribution pattern of the sand bodies in each stage was simulated by the magnetic random walk model. The magnetic random walk model has obtained robust simulation results, which is consistent with the anatomy of reservoir architectures by experienced geologists. The results also show that the number of channels in each small-layer is different, while the overall distribution of NE direction is reflected. At present, the model can well simulate the position of the main channel line, but it cannot reflect the variation of the river width. The method of quantitative fine description based on logging data has great potential application in fluvial reservoir, especially the magnetic random walk model that can reveal the distribution of sand body in every stage. At the same time, the model can also reflect certain randomness and facilitate the uncertainty analysis of geological factors.Cited as: Li, J., Yan, K., Ren, H., Sun, Z. Detailed quantitative description of fluvial reservoirs: A case study of L6-3 Layer of Sandgroup 6 in the second member of Shahejie Formation, Shengtuo Oilfifield, China. Advances in Geo-Energy Research, 2020, 4(1): 43-53, doi: 10.26804/ager.2020.01.0

Assessment of Urban Transportation Metabolism from Life Cycle Perspective: A Multi-method Study

Abstract The goal of this study is to provide a multi-method based on the eco-thermodynamic framework to examine the environmental sustainability of urban public transportation systems. Urban transportation metabolism (UTM), as a metaphor of urban systematic research methodology for transportation system, has been proposed and combined with life cycle assessment (LCA). Results show that the most important factors in assessing the acceptability of a transportation system are not only the direct fuel consumption, and the energy and material costs of the vehicles, but also the energy and materials costs for the upstream and downstream side of the infrastructure construction and vehicle fuel

A Survey on Causal Reinforcement Learning

While Reinforcement Learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability. Interestingly, many researchers have leveraged insights from the causality literature recently, bringing forth flourishing works to unify the merits of causality and address well the challenges from RL. As such, it is of great necessity and significance to collate these Causal Reinforcement Learning (CRL) works, offer a review of CRL methods, and investigate the potential functionality from causality toward RL. In particular, we divide existing CRL approaches into two categories according to whether their causality-based information is given in advance or not. We further analyze each category in terms of the formalization of different models, ranging from the Markov Decision Process (MDP), Partially Observed Markov Decision Process (POMDP), Multi-Arm Bandits (MAB), and Dynamic Treatment Regime (DTR). Moreover, we summarize the evaluation matrices and open sources while we discuss emerging applications, along with promising prospects for the future development of CRL.Comment: 29 pages, 20 figure

Generalization bound for estimating causal effects from observational network data

Estimating causal effects from observational network data is a significant but challenging problem. Existing works in causal inference for observational network data lack an analysis of the generalization bound, which can theoretically provide support for alleviating the complex confounding bias and practically guide the design of learning objectives in a principled manner. To fill this gap, we derive a generalization bound for causal effect estimation in network scenarios by exploiting 1) the reweighting schema based on joint propensity score and 2) the representation learning schema based on Integral Probability Metric (IPM). We provide two perspectives on the generalization bound in terms of reweighting and representation learning, respectively. Motivated by the analysis of the bound, we propose a weighting regression method based on the joint propensity score augmented with representation learning. Extensive experimental studies on two real-world networks with semi-synthetic data demonstrate the effectiveness of our algorithm

A Predictive Analysis of China's Energy Security Based on Supply Chain Theory

AbstractChina's energy dependence on energy supply chain have been increasing rapidly in recent years. The long-term energy supply plays an important role to guarantee the energy security. Therefore, our emphasis placed on energy supply chain predictive analysis and security evaluation in China. In this study, a linked MARKAL-CGE-EIA model system is proposed to simulate the macro-level energy technology, macro-level economy and environmental impacts of China. The CGE module is used to produce a multi-sector simulation of economic growth and industrial structure change. A MARKAL module is used to analyze particular technologies within the energy system, given estimates of associated energy demand and the relative prices of fuel and other inputs. A third module of Environmental Impact is applied to make an analysis of pollutant emissions. The energy indicators are used to perform an assessment of the dynamic behavior and security trends of a national energy system's trajectory from 2000 to 2050. The results of our study will enable energy policy planners to understand these inter-linkages by addressing energy early-warming indicators and scenarios to the aggregate industrial sectors, the energy technology details, and environmental impacts