Effect of Nanoparticles on Viscosity and Interfacial Tension of Aqueous Surfactant Solutions at High Salinity and High Temperature

Surfactant flooding has widely been used as one of the chemically enhanced oil recovery (EOR) techniques. Surfactants majorly influence the interfacial tension, γ, between oil and brine phase and control capillary number and relative permeability behavior and, thus, influence ultimate recovery. Additives, such as nanoparticles, are known to affect surfactant properties and are regarded as promising EOR agents. However, their detailed interactions with surfactants are not well understood. Thus, in this work, we examined the influence of silica nanoparticles on the ability of surfactants to lower γ and to increase viscosity at various temperatures and salinities. Results show that the presence of nanoparticles decreased γ between n-decane and various surfactant formulations by up to 20%. It was found that γ of nanoparticles–surfactant solutions passed through a minimum at 35 °C when salt was added. Furthermore, the viscosity of cationic surfactant solutions increased at specific salt (1.5 wt.%) and nanoparticle (0.05 wt.%) concentrations. Results illustrate that selected nanoparticles–surfactant formulations appear very promising for EOR as they can lower brine/n-decane interfacial tension and act as viscosity modifiers of the injected fluids.     

Hybrid self-inertia weight adaptive particle swarm optimisation with local search using C4.5 decision tree classifier for feature selection problems

ABSTRACT

Feature selection is an important task to improve the classifier’s accuracy and to decrease the problem size. A number of methodologies have been presented for feature selection problems using metaheuristic algorithms. In this paper, an improved self-adaptive inertia weight particle swarm optimisation with local search and combined with C4.5 classifiers for feature selection algorithm is proposed. In this proposed algorithm, the gradient base local search with its capacity of helping to explore the feature space and an improved self-adaptive inertia weight particle swarm optimisation with its ability to converge a best global solution in the search space. Experimental results have verified that the SIW-APSO-LS performed well compared with other state of art feature selection techniques on a suit of 16 standard data sets.     

Event-triggered decentralized adaptive fault-tolerant control of uncertain interconnected nonlinear systems with actuator failures

This paper investigates the event-triggered decentralized adaptive tracking problem of a class of uncertain interconnected nonlinear systems with unexpected actuator failures. It is assumed that local control signals are transmitted to local actuators with time-varying faults whenever predefined conditions for triggering events are satisfied. Compared with the existing control-input-based event-triggering strategy for adaptive control of uncertain nonlinear systems, the aim of this paper is to propose a tracking-error-based event-triggering strategy in the decentralized adaptive fault-tolerant tracking framework. The proposed approach can relax drastic changes in control inputs caused by actuator faults in the existing triggering strategy. The stability of the proposed event-triggering control system is analyzed in the Lyapunov sense. Finally, simulation comparisons of the proposed and existing approaches are provided to show the effectiveness of the proposed theoretical result in the presence of actuator faults.     

Ethanol absorption from CO2 using solutions of glycerol and glycols

CO₂ stripping during fermentation is a possible way of increasing the production of ethanol in distilleries. Ethanol, water, and carbon dioxide are the main compounds in the exiting gas mixture. To date, few studies have proposed the use of absorption to recover ethanol from this mixture or have considered different absorbents for this purpose. This work evaluates different absorbent solutions used to recover ethanol vapor from CO₂ gas. Glycols and glycerol can provide easier separation of ethanol than water, with lower distillation energy demand. A statistical experimental design was used to compare the mass transfers of ethanol from the gas phase to the liquid phase, using solutions of glycerol, ethylene glycol, and diethylene glycol. High concentrations of these substances did not favor the absorption of ethanol. The best results were achieved with 25% absorbent solutions, which offered better options than water for the recovery of ethanol from CO₂ gas in absorption columns and could increase the productivity of first generation bioethanol production.     

旋转式采煤工艺在煤矿生产中的应用

采煤工艺作为煤矿正常生产的核心,直接关系到综采作业的效率、煤炭回采率和综采作业的安全性,随着对综采作业要求的不断提高,旋转式采煤工艺在煤矿井下的应用范围也不断扩大。以黄家沟煤业1205综采作业面为分析对象,对旋转式采煤工艺在井下的应用情况进行了研究,为优化综采方法、提升综采作业效率和煤炭回采率奠定基础。     

Large neighborhood search for the bike request scheduling problem

This paper introduces an efficient algorithm for the bike request scheduling problem (BRSP). The BRSP is built around the concept of request, defined as the pickup or dropoff of a number of identical items (bikes) at a specific station, within a certain time window, and with a certain priority. The aim of the BRSP is to sequence requests on (and hence determine the routes of) a set of vehicles, in such a way that the sum of the priorities of the executed requests is maximized, all time windows are respected, and the capacity of the vehicles is not exceeded. The generation of the set of requests is explicitly not a part of the problem definition of the BRSP. The primary application of the BRSP, from which it derives its name, is to determine the routes of a set of repositioning vehicles in a bike sharing system, although other applications exist. The algorithm introduced in this paper is based on a set of related greedy randomized adaptive search procedure followed by variable neighborhood descent (GRASP + VND) operators embedded in a large neighborhood search (LNS) framework. Since this paper presents the first heuristic for the BRSP, a computational comparison to existing approaches is not possible. We therefore compare the solutions found by our LNS heuristic to those found by an exact solver (Gurobi). These experiments confirm that the proposed algorithm scales to realistic dimensions and is able to find near‐optimal solutions in seconds.     

A stiffness parameter and truncation error criterion for adaptive path following in structural mechanics

We explore a truncation error criterion to steer adaptive step length refinement and coarsening in incremental-iterative path following procedures, applied to problems in large-deformation structural mechanics. Elaborating on ideas proposed by Bergan and collaborators in the 1970s, we first describe an easily computable scalar stiffness parameter whose sign and rate of change provide reliable information on the local behavior and complexity of the equilibrium path. We then derive a simple scaling law that adaptively adjusts the length of the next step based on the rate of change of the stiffness parameter at previous points on the path. We show that this scaling is equivalent to keeping a local truncation error constant in each step. We demonstrate with numerical examples that our adaptive method follows a path with a significantly reduced number of points compared to an analysis with uniform step length of the same fidelity level. A comparison with Abaqus illustrates that the truncation error criterion effectively concentrates points around the smallest-scale features of the path, which is generally not possible with automatic incrementation solely based on local convergence properties.     

Learning bipartite Bayesian networks under monotonicity restrictions

ABSTRACT

Learning parameters of a probabilistic model is a necessary step in machine learning tasks. We present a method to improve learning from small datasets by using monotonicity conditions. Monotonicity simplifies the learning and it is often required by users. We present an algorithm for Bayesian Networks parameter learning. The algorithm and monotonicity conditions are described, and it is shown that with the monotonicity conditions we can better fit underlying data. Our algorithm is tested on artificial and empiric datasets. We use different methods satisfying monotonicity conditions: the proposed gradient descent, isotonic regression EM, and non-linear optimization. We also provide results of unrestricted EM and gradient descent methods. Learned models are compared with respect to their ability to fit data in terms of log-likelihood and their fit of parameters of the generating model. Our proposed method outperforms other methods for small sets, and provides better or comparable results for larger sets.     

镓锗铜萃余液综合利用研究与应用

本文针对镓锗铜综合回收系统产出的萃余液,设计通过中和氧化除杂、锌粉置换除铜镉、有机试剂除钴镍、纯碱法生产碱式碳酸锌、高温煅烧生产活性氧化锌和一步法生产元明粉的生产工艺流程,有效的将镓锗铜萃余液分类分离富集,产出具有价值的富集渣和工业产品,进一步提升综合回收经济效益。     

基于(火用)效率目标的环冷机余热回收系统操作参数优化

摘要：针对烧结环冷机余热回收利用率不高的难题，采用分析法建立了评价某钢铁厂烧结环冷机余热回收系统运行效率的效率模型。基于多孔介质模型、局部非热平衡方程、真实气体SRK方程建立环冷机内气固两相换热模型。通过CFD仿真模拟，探究料层高度、循环风机输入烟气温度、烧结矿底部入口风速三项可控环冷机运行工艺参数对系统效率的影响规律。结果表明，料层厚度在1～1.5 m区间每增加0.1 m，效率增加0.8%～1.1%；循环风温在100～140℃之间每增加10℃，效率增加1.4%～1.5%；烧结矿底部入口风速在0.9～1.9 m/s之间每增加0.1 m/s，效率降低0.18%～0.24%。在此基础上，基于工业运行数据建立效率正交试验优化模型，提高了该余热回收系统3.42%的效率。