Measurements of dynamic behavior of a multistage flash water desalination system

This study focuses on measuring the process dynamics for the multistage flash desalination process (MSF) in an industrial unit with a capacity of 4546 m³/d. This is a novel addition to the literature because previous studies are limited to theoretical analysis of process dynamics or controller tuning, as well as conceptual design of conventional or advanced control systems. The measurements evaluate the performance of seven control loops, which include the pressure, temperature, and flow rate of the heating steam; the pressure of the vacuum ejector; and flow rates of the brine recycle, make-up seawater, and cooling seawater. All measurements start from steady-state conditions. The system is then set on manual where all control units are disengaged. Subsequently, only one control valve is adjusted by ± 15% of its steady-state setting. A total of 14 experiments were performed involving simultaneous measurements of the system variables. Measurements showed non-linear behavior where increasing or decreasing the valve settings did not provide similar trends. Analysis of results shows that one of the most sensitive variables is the distillate level in the last stage: the distillate trays either were flooded or became dry. The brine level in the last flashing stage was also found to be sensitive to valve settings where level increase resulted in higher product salinity. The results and analysis presented provide a better understanding in system fault analysis which could be caused by improper operating conditions. These data are essential to propose, design, and evaluate advanced/comprehensive control systems for the MSF process.     

Detection of cracks in refractory materials by an enhanced digital image correlation technique

This paper is devoted to the study of the fracture behaviour of two industrial refractory materials thanks to the development of a new technique of digital image correlation (DIC). DIC, already known as a helpful and effective tool for the measurement of displacement and deformation fields in materials, has been adapted to take into account displacement discontinuities as cracks. The material transformation, usually assumed homogeneous inside each DIC subset, is thus more complex, while each subset can be cut in two parts with different kinematics. By this way, it is possible to automatically find the fracture paths and follow the crack geometries (length, opening) during the loading with a higher spatial resolution than the one obtained by standard DIC. After having presented the principle of the new technique, its metrological performances are assessed from synthetic images and the choice of crack detection criterion is discussed. The capacity of this new technique is shown through a comparative study with standard DIC. Its application is led on magnesia-spinel refractory materials, specifically to highlight and to characterize the evolution of kinematic fields (displacement and strain) observed at the surface of sample during a wedge splitting test typically used to quantify the work of fracture. We show that refractories with aggregates of iron aluminate spinel present a fracture mechanism with crack branching and can dissipate more energy thanks to a longer crack network.     

An integrated model for assembly line re-balancing problem

In this paper, an integrated approach for assembly line rebalancing problem (IALRP) is proposed to quickly react and find an optimal rebalancing of the line when disruptive event occurs because of product demand changes. This model is motivated by real-life application of an automotive cable manufacturer which provides more realistic constraints. To solve the problem, we propose a genetic algorithm (GA) hybridised with a heuristic priority rule-based procedure. This hybridisation is used to add more rich seeds to the initial population and consequently to improve the convergence capability and performance of the GA. After the disturbance, we aim to find a rebalance with the proposed approach to maximise the line efficiency and distributing the idle time across the workstations as equally as possible. To evaluate the efficiency of the proposed algorithm, set of samples collected from the literature are used. The real case study and the experiment results show the proposed approach is very effective and competitive.     

Personalized teleoperation via intention recognition

ABSTRACT

One of the challenges of teleoperation is the recognition of a user’s intended commands, particularly in the manning of highly dynamic systems such as drones. In this paper, we present a solution to this problem by developing a generalized scheme relying on a Convolutional Neural Network (CNN) that is trained to recognize a user’s intended commands, directed through a haptic device. Our proposed method allows the interface to be personalized for each user, by pre-training the CNN differently according to the input data that is specific to the intended end user. Experiments were conducted using two haptic devices and classification results demonstrate that the proposed system outperforms geometric-based approaches by nearly 12%. Furthermore, our system also lends itself to other human–machine interfaces where intention recognition is required.     

Enhancing dependability through profiling in the collaborative internet of things

Multimedia Tools and Applications - The future of the Internet of Things (IoT) is the Collaborative Internet of Things (C-IoT) in which different IoT deployments collaborate to provide better...     

Robust feedback model predictive control of constrained uncertain systems

We propose a novel procedure for the solution to the problem of robust model predictive control (RMPC) of linear discrete time systems involving bounded disturbances and model-uncertainties along with hard constraints on the input and state. The RMPC (outer) controller – responsible for steering the uncertain system state to a designed invariant (terminal) set – has a mixed structure consisting of a state-feedback component as well as a control-perturbation. Both components are explicitly considered as decision variables in the online optimization and the nonlinearities commonly associated with such a state-feedback parameterization are avoided by adopting a sequential approach in the formulation. The RMPC controller minimizes an upper bound on an H₂/H_∞-based cost function. Moreover, the proposed algorithm does not require any offline calculation of (feasible) feedback gains for the computation of the RMPC controller. The optimal Robust Positively invariant set and the inner controller – responsible for keeping the state within the invariant set – are both computed in one step as solutions to an LMI optimization problem. We also provide conditions which guarantee the Lyapunov stability of the closed-loop system. Numerical examples, taken from the literature, demonstrate the advantages of the proposed scheme.     

A Framework for Using Unmanned Aerial Vehicles for Data Collection in Linear Wireless Sensor Networks

The wireless sensor network (WSN) technology have been evolving very quickly in recent years. Sensors are constantly increasing in sensing, processing, storage, and communication capabilities. In many WSNs that are used in environmental, commercial and military applications, the sensors are lined linearly due to the linear nature of the structure or area that is being monitored making a special class of these networks; We defined these in a previous paper as Linear Sensor Networks (LSNs), and provided a classification of the different types of LSNs. A pure multihop approach to route the data all the way along the linear network (e.g. oil, gas and water pipeline monitoring, border monitoring, road-side monitoring, etc.), which can extend for hundreds or even thousands of kilometers can be very costly from an energy dissipation point of view. In order to significantly reduce the energy consumption used in data transmission and extend the network lifetime, we present a framework for monitoring linear infrastructures using LSNs where data collection and transmission is done using Unmanned Aerial Vehicles (UAVs). The system defines four types of nodes, which include: sensor nodes (SNs), relay nodes (RNs), UAVs, and sinks. The SNs use a classic WSN multihop routing approach to transmit their data to the nearest RN, which acts as a cluster head for its surrounding SNs. Then, a UAV moves back and forth along the linear network and transport the data that is collected by the RNs to the sinks located at both ends of the LSN. We name this network architecture a UAV-based LSNs (ULSNs). This approach leads to considerable savings in node energy consumption, due to a significant reduction of the transmission ranges of the SN and RN nodes and the use of a one-hop transmission to communicate the data from the RNs to the UAV. Furthermore, the strategy provides for reduced interference between the RNs that can be caused by hidden terminal and collision problems, that would be expected if a pure multihop approach is used at the RN level. In addition, three different UAV movement approaches are presented, simulated, and analyzed in order to measure system performance under various network conditions.     

The effect of using different designs of solar stills on water distillation

Solar distillation is one of the important methods of utilizing the solar energy for the supply of potable water to small communities where the natural supply of fresh water is inadequate or of poor quality, and where sunshine is abundant. Solar energy utilization in two different types of solar stills is considered, and factors that influence the productivity of solar stills are discussed. The present investigation showed that the productivity of asymmetric greenhouse type still (ASGHT) having mirrors on its inside walls was higher than that of the symmetric greenhouse type still (SGHT) and more efficient. It was found that the distilled water output of the asymmetrical greenhouse type was 20% higher than that of symmetric greenhouse type. Performance characteristics of the two stills showed that the temperature at the water surface is closely related to the incident solar radiation, and the productivity of the stills can be increased with decreasing water depth, and by the addition of dye.     

Friction properties of OTS SAMs and silicon surface under water lubrication

The friction and wear properties of silicon surface covered with octadecyltrichlorosilane (OTS) self-assembled monolayers (SAMs) were investigated by a UMT-2 microtribometer with and without water as lubricant, and then compared with that of bare silicon surface. Dry friction measurement results show that OTS SAMs have a very low friction coefficient compared to bare silicon surface under lower sliding velocity and normal contact load. However, heavy wear occurs on OTS SAMs under higher contact stress and sliding velocity. Under water lubrication, OTS SAMs can prevent wear obviously and meanwhile present low coefficient of friction even under high velocities. The improved frictional and anti-wear property on OTS SAMs surface is attributed to the hydrophobic property of OTS and hydrodynamic effect of water. Furthermore, a wear critical phase diagram for OTS SAMs with and without water was proposed, which indicates that OTS SAMs working under water lubrication owns a wider range of available load and velocity to reduce friction and prevent wear. Funded by the National Natural Science Foundation of China (Nos. 50575123, 50275071, 50545035) and National Basic Research Program of China (“973” Program) (No. 2003CB716205)