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The gas hydrate formation in pipelines of industries and chemical plants can cause various operational damages and can increase economic risks. Hence, the knowledge of hydrate formation conditions has become a critical research study to overcome the problems arising from the formation of hydrates. In this study, we applied an algorithm to develop an LSSVM model to predict the formation temperature of natural gas hydrate for a comprehensive range of data points. Total 188 experimental data points were applied from the literature for the development of the LSSVM model. The input parameter was finalized based on the structure of hydrates by each gas species. The results obtained by the LSSVM model have good accuracy as compared with empirical correlations available in the literature. This model gave the squared correlation coefficient (R2), and root mean square error of 0.9901 and 0.59974, respectively. The composition of gases may affect the phase equilibrium condition of gas hydrates. The applied algorithm revealed that the developed LSSVM model could become a good alternative for calculating the formation temperature of hydrate for the range of all data sets. The results showed that the proposed LSSVM model could be applicable for the prediction of hydrate formation temperature for all data points.

Resource constrained Internet-of-Things (IoT) devices are highly likely to be compromised by attackers, because strong security protections may not be suitable to be deployed. This requires an alternative approach to protect vulnerable components in IoT networks. In this article, we propose an integrated defense technique to achieve intrusion prevention by leveraging cyberdeception (i.e., a decoy system) and moving target defense (i.e., network topology shuffling). We evaluate the effectiveness and efficiency of our proposed technique analytically based on a graphical security model in a software-defined networking (SDN)-based IoT network. We develop four strategies (i.e., fixed/random and adaptive/hybrid) to address “when” to perform network topology shuffling and three strategies (i.e., genetic algorithm/decoy attack path-based optimization/random) to address “how” to perform network topology shuffling on a decoy-populated IoT network, and we analyze which strategy can best achieve a system goal, such as prolonging the system lifetime, maximizing deception effectiveness, maximizing service availability, or minimizing defense cost. We demonstrated that a software-defined IoT network running our intrusion prevention technique at the optimal parameter setting prolongs system lifetime, increases attack complexity of compromising critical nodes, and maintains superior service availability compared with a counterpart IoT network without running our intrusion prevention technique. Further, when given a single goal or a multi-objective goal (e.g., maximizing the system lifetime and service availability while minimizing the defense cost) as input, the best combination of “when” and “how” strategies is identified for executing our proposed technique under which the specified goal can be best achieved.

In this chapter, we propose a resource‐aware active defense framework for software‐defined networking (SDN)‐based Internet‐of‐Battle‐Things (IoBT) by leveraging the advanced features of deep reinforcement learning (DRL). The proposed framework aims to build a highly attack‐resistant network against both physical and cyberspace epidemic attacks. Since typically not all nodes are fully utilized to accomplish a mission, nodes with low utilities can be discarded when they have failed or have been compromised, instead of repairing or replacing them, for a resource‐constrained tactical network assigned with a time‐sensitive mission. However, highly critical and capable nodes should be protected with high priority in order to maximize system security and mission completion rate in the given tactical network. Considering severe resource constraints in IoBT consisting of highly heterogeneous entities under high hostility and network dynamics, we proposed two resource‐aware defense techniques: (1) a multilayer defense network architecture that can construct a network topology based on the importance levels of nodes to provide more security protection for more important nodes which can maximize both security and mission performance (e.g. service availability); and (2) a resource‐aware intrusion response framework that can determine an optimal response action(destruction, repair, or replacement) in response to a detected failure/attack. We conduct a comparative analysis of multiple DRL algorithms against baseline schemes to demonstrate the superiority of our proposed DRL‐based intrusion response strategies not only in system security (e.g. mean time to security failure, MTTSF) and performance (e.g. correct message delivery ratio for mission completion) but also in the accumulated reward obtainable by the system.

The present work focuses on developing a framework for accurate prediction of thermodynamic conditions for single-component hydrates, namely CH4, CO2, N2, H2S, and C2H6 (coded in MATLAB). For this purpose, an exhaustive approach is adopted by incorporating eight different equations of states, namely Peng–Robinson, van der Waals, Soave–Redlich–Kwong, Virial, Redlich–Kwong, Tsai-Teja, Patel, and Esmaeilzadeh–Roshanfekr, with the well-known van der Waals–Platteeuw model. Overall, for I–H–V phase region, the Virial and van der Waals equation of state gives the most accurate predictions with minimum AAD%. For Lw–H–V phase region, Peng–Robinson equation of state is found to yield the most accurate predictions with overall AAD of 3.36%. Also, genetic programming algorithm is adopted to develop a generalized correlation. Overall, the correlation yields quick estimation with an average deviation of less than 1%. The accurate estimation yields a minimal AAD of 0.32% for CH4, 1.93% for C2H6, 0.77% for CO2, 0.64% for H2S, and 0.72% for N2. The same correlation can be employed for fitting phase equilibrium data for other hydrates too. The tuning parameter, n, is to be used for fine adjustment to the phase equilibrium data. The findings of this study can help for a better understanding of phase equilibrium and cage occupancy behavior of different gas hydrates. The accuracy in phase equilibria is intimately related to industrial applications such as crude oil transportation, solid separation, and gas storage. To date, no single correlation is available in the literature that can accurately predict phase equilibria for multiple hydrate species. The novelty of the present work lies in both the accuracy and generalizability of the proposed correlation in predicting the phase equilibrium data. The genetic programming generalized correlation is convenient for performing quick equilibrium prediction for industrial applications.

Multichannel retailing has caused significant disruption in the market. The major credit for this disruption belongs to online retailing, which is proliferating at a high pace. With the immense gro...

Online apparel industry is suffering from a major issue of return, with a high rate of return for apparels that are sold online it becomes necessary to investigate the probable reasons of return in online apparel industry. The objective of the study is to develop a multi-criterion approach for evaluation of various factors that are responsible for the return of apparels purchased online in context of India. A total of 34 factors were identified through literature review and discussion with experienced experts from the fashion domain. In this study, best–worst method has been employed to prioritize and rank the factors for online return more effectively. Sensitivity analysis has been carried out to check the robustness of the proposed model of the study. The findings of the study show that fit and size variation, defects, found a better product (wisdom of purchase), wrong product delivery, lenient return policy and value for money were identified as crucial factors for online apparel return. The present study provides valuable research implications which can be used for retail policy improvements and also to online selling strategy.

The characteristics of the hydrate-bearing sediments affect the formation and dissociation of gas hydrate in sediments. The mineral composition, their dispersion, and chemical composition of hydrate-bearing sediment samples plays a dominant role in the hydrate stability condition and its economic development. In this paper, the physical properties of hydrate-bearing sediment of India are compared with each other. The sediment samples are taken from the Krishan-Godavari basin (Depth—127.5 and 203.2 mbsf), Mahanadi basin (Depth—217.4 mbsf), and Kerala-Konkan basin (Depth—217.4 mbsf). The saturation of the gas hydrate observed at these sites is between 3 and 50%. Particle size is an important parameter of the sediments because it provides information on the transportation and deposition of sediment and the deposition history. In the present study, we investigated the mineralogy of hydrate-bearing sediments by chemical analysis and X-ray Diffraction. XRD, FTIR, and Raman Spectroscopy distinguished the mineralogical behavior of sediments. Quartz is the main mineral (66.8% approx.) observed in the gas hydrate-bearing sediments. The specific surface area was higher for the sediment sample from the Mahanadi basin, representing the sediments’ dissipation degree. This characterization will give important information for the possible recovery of gas from Indian hydrate reservoirs by controlling the behavior of host sediment. SEM analysis shows the morphology of the sediments, which can affect the mechanical properties of the hydrate-bearing sediments. These properties can become the main parameters to consider for the design of suitable and economic dissociation techniques for gas hydrates formed in sediments.

In many real-world multiagent systems, agents must learn diverse tasks and coordinate with other agents. This paper introduces a method to allow heterogeneous agents to specialize and only learn complementary divergent behaviors needed for coordination in a shared environment. We use a hierarchical decomposition of diversity search and fitness optimization to allow agents to speciate and learn diverse temporally extended actions. Within an agent population, diversity in niches is favored. Agents within a niche compete for optimizing the higher level coordination task. Experimental results in a multiagent rover exploration task demonstrate the diversity of acquired agent behavior that promotes coordination.

Depressurization technique is considered as one of the most promising techniques in dissociating gas hydrates. However, since dissociation of hydrates is an endothermic process. Dissociation alone through depressurization is not a feasible technique due to limited heat transfer. The reduced heat transfer results in rapid cooling, thereby causing reduced permeability due to ice formation and re-formation of hydrates. The objective of the current study is to investigate the viability of depressurization under worst case scenario of suppressed heat transfer. The worst case scenario is simulated by employing Newman boundary of no heat flux from the surroundings. The novelty of the present work lies in investigating the gas production behavior using depressurization in a worst case scenario. For this purpose, a 2D model is applied for a 150 m × 150 m system. A production well is placed at the center of the domain. The depressurization is performed by the withdrawal of fluids from the production well. In order to determine the suitable depressurization rate, the withdrawal of fluids is carried out within a range of 0.01–0.6 kg/s. The overall cumulative production at the well (mass of CH4) is determined. In this study, we demonstrate that three major causes, namely ice formation, secondary hydrates and reservoir achieving steady state are responsible for stopping of gas production. Insights into the dissociation behavior of the cases analysed are obtained from the contours of gas, water, hydrate, pressure, equilibrium pressure, temperature, relative gas permeability, and relative water permeability.

This report presents the result of geochemical studies carried out on cores recovered from Krishna-Godavari Basin (K-G Basin) during the National Gas Hydrate Program Expedition 02 (NGHP-02). NGHP-02 targeted gas hydrate accumulations in sand facies in the K-G Basin off the east coast of the Indian Peninsula. Three areas (Area B, Area C and Area E) in the K-G Basin were investigated. During this expedition, 10 research sites in K-G basin were cored and samples for geochemistry analysis were collected. High TOC/TN ratios indicate an input from terrestrial organic matter. Analysis of gas samples from pressure cores also acquired during NGHP-02 were composed of mostly methane. Carbon Isotopic studies show that methane gas sampled from recovered gas hydrate samples were derived from biogenic (microbial) sources. This list of factors appears to indicate that the gas associated with the recovered hydrate-bearing core samples were derived from a microbial source. However, the biogeochemical processes responsible for generating the microbial gases found with the recovered gas hydrates may not have been limited to current limits off the gas hydrate stability zone. It is important to note here that microbial process likely extend to more than 1 km deep into the basin, so gas coming from below the GH stability zone can still be from a microbial source. In addition, the microbial gas within the stability zone was likely in part recycled from hydrate accumulations that has passed through the base of the gas hydrate stability zone as a product of sedimentation, with the dissociated microbial gas migration again vertically in the section, entering the gas hydrate stability zone and reforming gas hydrates. Although the organic carbon required for in situ methanogenesis and gas hydrate formation is abundant in the shallow sedimentary section of the KG-Basin, the faulted, fractured, and deformed stratigraphy observed in the KG- Basin coupled with high pore filling gas hydrate saturations at many sites, suggests that advective processes may be contributing to the supply of methane to the gas hydrates in the KG- Basin. The possible migration of gas from greater depths is also supported by seismic evidence of through-going migration-pathways that can connect the gas hydrate system with deeper petroleum systems. It is important to note here that the deep conventional gas accumulations in the basin also contain microbially sourced gas.