Neural observer-based small fault detection and isolation for uncertain nonlinear systems

Small faults (some weak faults with a tiny magnitude) are difficult to detect and may cause severe problems leading to degrading the system performance. This paper proposes an approach to estimate, detect, and isolate small faults in uncertain nonlinear systems subjected to model uncertainties, disturbances, and measurement noise. A robust observer is developed to alleviate the lack of full state measurement. Using the estimated state, a dynamical radial basis function neural networks observer is designed in form of LMI problem to accurately learn the function of the inseparable mixture between modeling uncertainty and the small fault. By exploiting the knowledge obtained by the learning phase, a bank of observers is constructed for both normal and fault modes. A set of residues is achieved by filtering the differences between the outputs of the bank of observers and the monitored system output. Due to the noise dampening characteristics of the filters and according to the smallest residual principle, the small faults can be detected and isolated successfully. Finally, rigorous analysis is performed to characterize the detection and isolation capabilities of the proposed scheme. Simulation results are used to prove the efficacy and merits of the proposed approach.     

Mechanical,hydration, and durability modifications provided to mortar made with crushed sand and blended cements

This work aims to evaluate the characteristics of crushed sand (CS), resulting from limestone quarries, when it replaces natural sand (NS) in mortar, and to identify its performances with three types of cement and three W/C ratio. It is concluded that CS has a great effect on mortar properties; it accelerates the hydration of cement grains leading to a shorter setting time, increases the flexural and compressive strengths, generates more hydration heat, and improves the mortar durability. The properties of CS mortar can be predicted as simple relationships of those of NS with an acceptable correlation coefficient.     

Forecasting model for wind power integrating least squares support vector machine,singular spectrum analysis,deep belief network,and locality‐sensitive hashing

Accurate wind power prediction can alleviate the negative influence on power system caused by the integration of wind farms into grid. In this paper, a novel combination model is proposed with the purpose of enhancing short‐term wind power prediction precision. Singular spectrum analysis is utilized to decompose the original wind power series into the trend component and the fluctuation component. Then least squares support vector machine (LSSVM) is applied to forecast the trend component while deep belief network (DBN) is utilized to predict the fluctuation component. By this means, the performance advantages of LSSVM and DBN can be brought into full play. Moreover, the locality‐sensitive hashing search algorithm is introduced to cluster the nearest training samples to further improve forecasting accuracy. Besides, the effect of LSSVM based on different kernel functions and the number of the nearest samples is investigated. The simulation results show that the normalized root mean square errors of the proposed model based on linear kernel function from 1‐step to 3‐step forecasting are 2.13%, 5.03%, and 7.29%, respectively, which outperforms all the other comparison models. Therefore, it can be concluded that the proposed combination model provides a promising and effective alternative for short‐term wind power prediction.     

Effect of thermomechanical loads on the propagation of crack near the interface brittle/ductile

The purpose of this paper is to understand the combined effect of thermal and mechanical loading on the initiation and behaviour of sub-interface crack in the ceramic. In this study a 2D finite element model has been used to simulated mixed mode crack propagation near the bimaterial interface. The assembly ceramometalic is subjected simultaneously to thermomechanical stress field. The extent of a plastic zone deformation in the vicinity of the crack-tip has a significant influence on the rate of its propagation. The crack growth at the joint specimen under four-point bending (4PB) loading and the influence of residual stresses was also evaluated by the maximum tensile stress criterion. The J-integral at the crack tip is generally expressed by the thermomechanical local stresses. The results obtained show the effect of the temperature gradient ΔT, the size of the crack and the applied stresses on the J-integral.     

Separation in an inclined porous thermogravitational cell

This paper reports a theoretical and numerical study of species separation in a binary liquid mixture saturating a shallow porous layer heated from below or from above and inclined with respect to the vertical axis. It is shown that the separation can be increased using this configuration and the stability of the unicellular flow obtained in this case is investigated. The critical Rayleigh number obtained is much higher than the one leading to the maximum separation. Experiments performed with a solution of CuSO₄ give results which are almost in good agreement with the analytical and the numerical results.     

Solving a mixed integer linear program with times setup for the steel-continuous casting planning and scheduling problem

In this paper, we present a novel model that generalise the steelmaking continuous casting planning and scheduling problem. The model includes three stages: converters namely CV, refining stands namely RS and continuous castings namely CC. The obtained problem can be formulated as a mathematical program with typical constraints, namely the times setup constraints. We develop a mixed integer linear program (MILP) to schedule several sequences of charges for more than two continuous castings devices. The aim is: (i) on one hand, to schedule a list of several sequences that are already ordered for each continuous casting with times setup between two successive sequences, (ii) on the other hand, to determine the optimal order of the successive sequences. We use a benchmark and randomly generated instances to test the model and solved by mean of the commercial software solver Cplex v12.5. These problems span a variety of instances varying from small to large sized instances with different intrinsec parameters as the availability date for a converter CV or the variation intervals of the start time for the first charge at the continuous casting CC stage. We analyse the efficiency of the proposed model based on the CPU time and show the time limitations of the model for large instances. Also, we show the robustness of the model when modifying the initial (starting) state.     

Reducing the sulfur-dioxide binding power of sweet white wines by solid-phase extraction

The high sulfur-dioxide binding power of sweet white wines may be reduced by extracting the naturally present carbonyl compounds from wine that are responsible for carbonyl bisulphites formation. The carbonyl compounds mainly responsible for trapping SO₂ are acetaldehyde, pyruvic acid, and 2-oxoglutaric acid. The method employed was selective solid phase extraction, using phenylsulfonylhydrazine as a scavenging agent. The scavenging function was grafted onto a support prepared from raw materials derived from lignin. This approach is more acceptable to winemakers than the polymer media previously reported, as it reduces the possible contamination of wine to molecules already present in the wine making process.     

Rheological Properties and Phase Separation of Xanthan-Sodium Caseinate Mixtures Analyzed by a Response Surface Method

Effects of xanthan and sodium caseinate concentrations on the rheological properties of their mixture in an aqueous medium were investigated at neutral pH. It was deduced from the use of an experimental design methodology, the existence of a critical concentration of sodium caseinate, which depends on the xanthan quantity, and beyond which repulsive segregation interactions occur, generating a weakening of the elastic modulus and an embitterment of the colloidal system structure. In addition, it was observed that when the Casson viscosity value of the aqueous solution was above 0.1 Pa.s, phase separation of the system was observed. The phase diagram of the solution was established using a polynomial fit.     

Monte Carlo simulation to reveal the copper dissolution kinetics of an ion selective electrode based on copper sulfide

The present work aims at studying copper dissolution of a Cu²⁺ ion-selective electrode based on a CuS thin film. The electrode is prepared using electrochemical deposition of CuS on a silicon substrate. The obtained film exhibits an apparent cohesive granular structure with an average grain size of about 33 μm, a small porosity content (<4%) and a thickness of about 7.48 μm. The Cu²⁺ electrochemical response shows a nearly Nernstian behavior in the range of pCu 6–1. The copper dissolution is experimentally studied in a wide pH range. In order to quantitatively predict copper mass dissolution, an original numerical model is developed based on Monte Carlo simulation. Our main hypothesis is based on dissolution probability that triggers the whole dissolution process through solution/electrode surface exchanges. Several probability forms are suggested accounting for the real observed electrochemical kinetics. The experimental results show that, under a low pH, the dissolution process severely leads to the consumption of large material. Moreover, our predictions suggest a dissolution profile as a two-stage process irrespective of pH. Our numerical model is able to fit correctly the observed kinetics considering an exponential probability form under all pH conditions.