Detection and quantification of valve stiction by the method of unknown input estimation

Valve stiction is one of the most common causes of oscillations in industrial process control loops. Such oscillations can degrade the overall performance of the loop and eventually the final product quality. The detection and quantification of valve stiction in industrial process control loops is thus important. From previous studies in the literature, a sticky valve has been shown to have a distinct signature of the stiction phenomena in its valve positioner data. However, the position of the modulating control valves is seldom available. We consider the problem of estimating the valve position as an unknown input estimation problem. In this work, we propose a novel application of the unknown input estimator in order to estimate the valve position given the process model and the data of the process variable and controller output. Using the estimated valve position, we can detect and also quantify the amount of stiction. We demonstrate the efficacy of the method through simulation examples where a sticky valve is deliberately introduced in the closed loop using a two-parameter stiction model available in the literature. Application of the proposed methodology to a laboratory scale flow control loop is presented. An industrial case study is also presented in which the algorithm accurately detects and quantifies stiction in the level control loop of a power plant.     

Seed reserves of chickpea in relation to molybdenum supply

Chickpea (Cicer arietinum L), cv K75 plants were grown till maturity at 0.00001, 0.0001, 0.001 (deficient), 0.02 (adequate), 0.2 (supranormal) and 2.0 (excess) mg dm^?3 Mo in refined sand. The pod and seed yield of chickpea were at a maximum at 0.2 mg dm^?3 Mo, which is ten times higher than the usual Mo requirement. The seed weight of chickpea was decreased more by low (<0.02 mg dm^?3) than excess (2 mg dm^?3) Mo. In chickpea seeds, the concentrations of starch, reducing, non‐reducing and total sugars were highest at 0.02 mg dm^?3 Mo and decreased by Mo stress (<>0.02–0.2 mg dm^?3). In comparison, the content of methionine, lysine, legumin, vicilin, total proteins, protein and non‐protein nitrogen in seeds of chickpea decreased variably both at low (<0.02 mg dm^?3) and high (>0.2 mg dm^?3) Mo. Both deficiency and excess of Mo deteriorated the quality of seeds by increasing the content of phenols, cysteine and albumin and decreasing that of methionine, lysine, legumin and vicilin protein fractions, apart from reducing the seed weight. The quality of seeds deteriorated more by deficiency than excess of Mo in chickpea. Copyright © 2004 Society of Chemical Industry     

Application of support vector regression for developing soft sensors for nonlinear processes

The field of soft sensor development has gained significant importance in the recent past with the development of efficient and easily employable computational tools for this purpose. The basic idea is to convert the information contained in the input–output data collected from the process into a mathematical model. Such a mathematical model can be used as a cost efficient substitute for hardware sensors. The Support Vector Regression (SVR) tool is one such computational tool that has recently received much attention in the system identification literature, especially because of its successes in building nonlinear blackbox models. The main feature of the algorithm is the use of a nonlinear kernel transformation to map the input variables into a feature space so that their relationship with the output variable becomes linear in the transformed space. This method has excellent generalisation capabilities to high‐dimensional nonlinear problems due to the use of functions such as the radial basis functions which have good approximation capabilities as kernels. Another attractive feature of the method is its convex optimization formulation which eradicates the problem of local minima while identifying the nonlinear models. In this work, we demonstrate the application of SVR as an efficient and easy‐to‐use tool for developing soft sensors for nonlinear processes. In an industrial case study, we illustrate the development of a steady‐state Melt Index soft sensor for an industrial scale ethylene vinyl acetate (EVA) polymer extrusion process using SVR. The SVR‐based soft sensor, valid over a wide range of melt indices, outperformed the existing nonlinear least‐square‐based soft sensor in terms of lower prediction errors. In the remaining two other case studies, we demonstrate the application of SVR for developing soft sensors in the form of dynamic models for two nonlinear processes: a simulated pH neutralisation process and a laboratory scale twin screw polymer extrusion process. A heuristic procedure is proposed for developing a dynamic nonlinear‐ARX model‐based soft sensor using SVR, in which the optimal delay and orders are automatically arrived at using the input–output data.     

Synthesis of a nano-crystalline solid acid catalyst from fly ash and its catalytic performance

The synthesis of nano-crystalline activated fly ash catalyst (AFAC) with crystallite size of 12 nm was carried out by chemical and thermal treatment of fly ash, a waste material generated from coal-burning power plants. Fly ash was chemically activated using sulfuric acid followed by thermal activation at 600 °C. The variation of surface and Physico-chemical properties of the fly ash by activation methods resulted in improved acidity and therefore, catalytic activity for acid catalyzed reactions. The AFAC was characterized by X-ray diffraction, FT-IR spectroscopy, N₂-adsorption–desorption isotherm, scanning electron microscopy, flame atomic absorption spectrophotometry and sulfur content by CHNS/O elemental analysis. It showed amorphous nature due to high silica content (81%) and possessed high BET surface area (120 m²/g). The catalyst was found to be highly active solid acid catalyst for liquid phase esterification of salicylic acid with acetic anhydride and methanol giving acetylsalicylic acid and methyl salicylate respectively. A maximum yield of 97% with high purity of acetylsalicylic acid (aspirin) and a very high conversion 87% of salicylic acid to methyl salicylate (oil of wintergreen) was obtained with AFAC. The surface acidity and therefore, catalytic activity in AFAC was originated by increased silica content, hydroxyl content and higher surface area as compared to fly ash. The study shows that coal generated fly ash can be converted into potential solid acid catalyst for acid catalyzed reactions. Furthermore, this catalyst may replace conventional environmentally hazardous homogeneous liquid acids making an ecofriendly; solvent free, atom efficient, solid acid based catalytic process. The application of fly ash to synthesize a solid acid catalyst finds a noble way to utilize this abundant waste material.     

Identification of uncertain nonlinear systems for robust fuzzy control

In this paper, we consider fuzzy identification of uncertain nonlinear systems in Takagi-Sugeno (T-S) form for the purpose of robust fuzzy control design. The uncertain nonlinear system is represented using a fuzzy function having constant matrices and time varying uncertain matrices that describe the nominal model and the uncertainty in the nonlinear system respectively. The suggested method is based on linear programming approach and it comprises the identification of the nominal model and the bounds of the uncertain matrices and then expressing the uncertain matrices into uncertain norm bounded matrices accompanied by constant matrices. It has been observed that our method yields less conservative results than the other existing method proposed by S?krjanc et al. (2005) [11] and [12]. With the obtained fuzzy model, we showed the robust stability condition which provides a basis for different robust fuzzy control design. Finally, different simulation examples are presented for identification and control of uncertain nonlinear systems to illustrate the utility of our proposed identification method for robust fuzzy control.     

Investigation on structural and photodetection properties of Gd2O3 thin films after annealing

Journal of Materials Science: Materials in Electronics - In the present study, the electron beam evaporation technique was employed to deposit gadolinium oxide thin films (Gd2O3-TF) on the silicon...     

A Position Synchronization Controller for Co-ordinated Links (COOL) Dual Robot Arm Based on Integral Sliding Mode: Design and Experimental Validation

Machine Intelligence Research - In this study, a simple position synchronization control algorithm based on an integral sliding mode is developed for dual-arm robotic manipulator systems. A...     

A comparison of simultaneous state and parameter estimation schemes for a continuous fermentor reactor

This article proposes a maximum likelihood algorithm for simultaneous estimation of state and parameter values in nonlinear stochastic state-space models. The proposed algorithm uses a combination of expectation maximization, nonlinear filtering and smoothing algorithms. The algorithm is tested with three popular techniques for filtering namely particle filter (PF), unscented Kalman filter (UKF) and extended Kalman filter (EKF). It is shown that the proposed algorithm when used in conjunction with UKF is computationally more efficient and provides better estimates. An online recursive algorithm based on nonlinear filtering theory is also derived and is shown to perform equally well with UKF and ensemble Kalman filter (EnKF) algorithms. A continuous fermentation reactor is used to illustrate the efficacy of batch and online versions of the proposed algorithms.     

Adaptive integral higher order sliding mode controller for uncertain systems

This paper proposes an adaptive integral higher order sliding mode (HOSM) controller for uncertain systems. Instead of a regular control input, the derivative of the control input is used in the proposed control law. The discontinuous sign function in the controller is made to act on the time derivative of the control input. The actual control signal obtained by integrating the derivative control signal is smooth and chattering free. The adaptive tuning law used in the proposed controller eliminates the need of prior knowledge about the upper bound of the system uncertainties. Stability and robustness of the proposed controller are proved by using the classical Lyapunov criterion. Simulation results demonstrate the advantages of the proposed control scheme.     

Lau optometer: A new subjective instrument to measure the astigmatism of the human eye

Abstract

We have developed an optometer to measure the ocular astigmatism of human eyes and tested its performance. In this instrument we make use of an optical field that has coherence only along specific meridians to form interference images of different orientations on the subject's retina. The subject sees fringes of different orientations sequentially, at a fixed vergence, by rotating an eyepiece consisting of a probe cylinder. From the angular positions of the eyepiece at which each of the fringes are seen best by the subject, his or her astigmatism is calculated. The instrument has the potential to probe meridional vision and accommodation characteristics of human eyes.