Frequency regularization of a linked wind–diesel system using dual structure fuzzy with ultra-capacitor

Microgrids (μ-grids) are gaining increased interest around the world for supplying cheap and clean energy. In this paper, a μ-grid comprising a wind turbine generator (WTG) and diesel generator (DG) is considered. It is one of most practical and demanding systems suitable for the present energy crisis in isolated or rural areas. However, wind energy is intermittent in nature while load demand changes frequently. Therefore, a µ-grid can experience large frequency and power fluctuations. The speed governor of the DG tries to minimize the frequency and power deviations in µ-grid though its operation is slow and cannot adequately minimize system deviations. The paper proposes a novel arrangement based on a dual structured fuzzy (DSF) whose structure changes according to the switching limit with a reduced rule base. It has the capability to switch between proportional and integral actions and hence improves the frequency regularization of the μ-grid. The proposed strategy is tested in a μ-grid and the results considering step load alteration, load alteration at different instants of time, nonstop changing load request are compared with some of the well published methods to validate the effectiveness and simplicity of the present design. In addition, it shows that ultra-capacitor establishment in a μ-grid has a positive impact in minimizing system deviations with DSF for the studied cases.     

Parametric optimization of multiwalled carbon nanotube-assisted electric discharge machining of Al-10%SiCp metal matrix composite by response surface methodology

The demand for miniaturized products having a glossy surface or nano-level surface is increasing exponentially in automobile, aerospace, biomedical, and semiconductor industries. The mirror-like surface finish has generated a need to develop advanced machining processes. The addition of powder particle into electric discharge machining (EDM) oil is considered a promising technique to achieve surface integrity at the miniaturization level. In this research, the Al–10%SiC_p metal matrix composite (MMC) has been machined after mixing the appropriate amount of multiwalled carbon nanotubes (MWCNTs) into the EDM dielectric fluid. An advanced experimental setup has been designed and fabricated in the laboratory for conducting the experiments. This proposed technology is called nano powder mixed electric discharge machining (NPMEDM). The input parameters of NPMEDM are also optimized using central composite rotatable design (CCRD) based on response surface methodology (RSM) in order to obtain the best surface finish and material removal rate (MRR). The MRR has been increased by 38.22% and surface finish has been improved by 46.06% after mixing the MWCNTs into the EDM dielectric fluid. The results indicate that the combination of parameters A5, B5, C5, and D5 might have produced maximum MRR, whereas A1, B1, C1, and D3 have produced minimum surface roughness (SR).     

Development and gamma-scintigraphy study of Hibiscus rosasinensis polysaccharide-based microspheres for nasal drug delivery

Objective: This work describes the application of natural plant polysaccharide as pharmaceutical mucoadhesive excipients in delivery systems to reduce the clearance rate through nasal cavity.

Methods: Novel natural polysaccharide (Hibiscus rosasinensis)-based mucoadhesive microspheres were prepared by using emulsion crosslinking method for the delivery of rizatriptan benzoate (RB) through nasal route. Mucoadhesive microspheres were characterized for different parameters and nasal clearance of technetium-99m (^99mTc)-radiolabeled microspheres was determined by using gamma-scintigraphy.

Results: Their Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) studies showed that the drug was stable during preparation of microspheres. Aerodynamic diameter of microspheres was in the range 13.23?±?1.83–33.57?±?3.69?µm. Change in drug and polysaccharide ratio influenced the mucoadhesion, encapsulation efficiency and in-vitro release property. Scintigraphs taken at regular interval indicate that control solution was cleared rapidly from nasal cavity, whereas microspheres showed slower clearance (p?Conclusion: Natural polysaccharide-based microspheres achieved extended residence by minimizing effect of mucociliary clearance with opportunity of sustained delivery for longer duration.     

Issues and strategies in composite fabrication via friction stir processing: A review

Friction stir processing (FSP) is an expeditiously emerging novel technique involving exterior layer modification, which enables one to successfully fabricate surface composites (SCs) as well as bulk composites of the metal matrix. SCs constitute an exclusive class of composites which exhibit improved surface properties while retaining the bulk properties unaltered. During initiative years, FSP was employed in development of SCs of light metal alloys like aluminum. But, nowadays, it has gained a shining role in the field of SC fabrication of various nonferrous alloys like aluminum, magnesium, copper, and even ferrous metals like steel etc. This article reviews the current trends, various issues, and strategies used to enhance the efficiency of the fabrication process of SCs. Factors involved in the process of SC fabrication are discussed and classified with a new approach. Also, variation of microstructural and mechanical characteristics with these factors is reviewed. In addition to a brief presentation on the interaction between various inputs and their effects on properties, a summary of literature on SC fabrication for different metals is tabulated with prominent results. Subsequently, shortfalls and future perspectives of FSP on SC fabrication domain are discussed.     

Machine Learning-Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Electrohydrodynamic (EHD) processes are promising techniques for manufacturing nanoscopic products with different shapes (such as thin films, nanofibers, 2D/3D nanostructures, and nanoparticles) and materials at a low cost using simple equipment. A key challenge in their adoption by nonexperts is the requirement of enormous time and resources in identifying the optimum design/process parameters for the underlying material and EHD system. Machine learning (ML) has made exciting advancements in predictive modeling of different processes, provided it is trained on high-quality datasets at appropriate volumes. This article extends the suitability of such ML-enabled approaches to a new technological domain of EHD spraying and drop-on-demand printing. Different ML models like ridge regression, random forest regression, support vector regression, gradient boosting regression, and multilayer perceptron are trained and their performance using evaluation metrics like RMSE and R2_score is examined. Tree-based algorithms like gradient boosting regression are found to be the most suitable technique for modeling EHD processes. The trained ML models show substantially higher accuracy (average error < 5%) in replicating these nonlinear processes as compared to previously reported scaling laws (average error ≈ 42%) and are well suited for predictive modeling/analysis of the underlying EHD system and process.     

Chaos in fractional ordered Liu system

Present paper deals with fractional version of a dynamical system introduced by C. Liu, L. Liu and T. Liu [C. Liu, L. Liu, T. Liu, A novel three-dimensional autonomous chaos system, Chaos Solitons Fractals 39 (4) (2009) 1950–1958]. Numerical investigations on the dynamics of this system have been carried out. Properties of the system have been analyzed by means of Lyapunov exponents. Furthermore the minimum effective dimensions have been identified for chaos to exist in commensurate and incommensurate orders. It is noteworthy that the results obtained are consistent with the analytical conditions given in the literature.     

Stability of a class of discrete-time nonlinear recursive observers

This work provides a framework for nominal and robust stability analysis for a class of discrete-time nonlinear recursive observers (DNRO). Given that the system has linear output mapping, local observability and Jacobian matrices satisfying certain conditions, the nominal and robust stability of the DNRO is defined by the property of estimation error dynamics and is analyzed using Lyapunov theory. Moreover, a simultaneous state and parameter estimation scheme is shown to be Input-to-State Stable (ISS), and adaptively reduce plant-model mismatch on-line. Three design strategies of the DNRO that satisfy the stability results are given as examples, including the widely used extended Kalman filter, extended Luenberger observer, and the fixed gain observer.     

Unknown input modeling and robust fault diagnosis using black box observers

A key issue that needs to be addressed while performing fault diagnosis using black box models is that of robustness against abrupt changes in unknown inputs. A fundamental difficulty with the robust FDI design approaches available in the literature is that they require some a priori knowledge of the model for unmeasured disturbances or modeling uncertainty. In this work, we propose a novel approach for modeling abrupt changes in unmeasured disturbances when innovation form of state space model (i.e. black box observer) is used for fault diagnosis. A disturbance coupling matrix is developed using singular value decomposition of the extended observability matrix and further used to formulate a robust fault diagnosis scheme based on generalized likelihood ratio test. The proposed modeling approach does not require any a priori knowledge of how these faults affect the system dynamics. To isolate sensor and actuator biases from step jumps in unmeasured disturbances, a statistically rigorous method is developed for distinguishing between faults modeled using different number of parameters. Simulation studies on a heavy oil fractionator example show that the proposed FDI methodology based on identified models can be used to effectively distinguish between sensor biases, actuator biases and other soft faults caused by changes in unmeasured disturbance variables. The fault tolerant control scheme, which makes use of the proposed robust FDI methodology, gives significantly better control performance than conventional controllers when soft faults occur. The experimental evaluation of the proposed FDI methodology on a laboratory scale stirred tank temperature control set-up corroborates these conclusions.     

Robust stability of nonlinear model predictive control based on extended Kalman filter

This work deals with state estimation and process control for nonlinear systems, especially when nonlinear model predictive control (NMPC) is integrated with extended Kalman filter (EKF) as the state estimator. In particular, we focus on the robust stability of NMPC and EKF in the presence of plant-model mismatch. The convergence property of the estimation error from the EKF in the presence of non-vanishing perturbations is established based on our previous work [1]. In addition, a so-called one way interaction is shown that the EKF error is not influenced by control action from the NMPC. Hence, the EKF analysis is still valid in the output-feedback NMPC framework, even though there is no separation principle for general nonlinear systems. With this result, we study the robust stability of the output-feedback NMPC under the impact of the estimation error. It turns out the output-feedback NMPC with EKF is Input-to-State practical Stable (ISpS). Finally, two offset-free strategies of output-feedback NMPC are presented and illustrated through a simulation example.     

Data reduction algorithm based on principle of distributional equivalence for fault diagnosis

Historical data based fault diagnosis methods exploit two key strengths of multivariate statistical approaches, viz.: (i) data compression ability, and (ii) discriminatory ability. It has been shown that correspondence analysis (CA) is superior to principal components analysis (PCA) on both these counts (Detroja, Gudi, Patwardhan, & Roy, 2006a), and hence is more suited for the task of fault detection and isolation (FDI). In this paper, we propose a CA based methodology for fault diagnosis that can facilitate significant data reduction as well as better discrimination. The proposed methodology is based on the principle of distributional equivalence (PDE). The PDE is a property unique to the CA algorithm and can be very useful in analyzing large datasets. The principle, when applied to historical data sets for FDI, can significantly reduce the data matrix size without significantly affecting the discriminatory ability of the CA algorithm. This can significantly reduce computational load during statistical model building. The data reduction ability of the proposed methodology is demonstrated using a simulation case study involving benchmark quadruple tank laboratory process. The proposed methodology when applied to experimental data obtained from the quadruple tank process also demonstrated data reduction capabilities of the principle of distributional equivalence. The above aspect has also been validated for large-scale data sets using the benchmark Tennessee Eastman process simulation case study.