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.     

Textural pattern classification for oral squamous cell carcinoma

Despite being an area of cancer with highest worldwide incidence, oral cancer yet remains to be widely researched. Studies on computer‐aided analysis of pathological slides of oral cancer contribute a lot to the diagnosis and treatment of the disease. Some researches in this direction have been carried out on oral submucous fibrosis. In this work an approach for analysing abnormality based on textural features present in squamous cell carcinoma histological slides have been considered. Histogram and grey‐level co‐occurrence matrix approaches for extraction of textural features from biopsy images with normal and malignant cells are used here. Further, we have used linear support vector machine classifier for automated diagnosis of the oral cancer, which gives 100% accuracy.     

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     

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...     

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.     

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.     

Plant growth-promoting rhizobacteria (PGPR) and its mechanisms against plant diseases for sustainable agriculture and better productivity

Plant growth-promoting rhizobacteria (PGPR) are specialized bacterial communities inhabiting the root rhizosphere and the secretion of root exudates helps to, regulate the microbial dynamics and their interactions with the plants. These bacteria viz., Agrobacterium, Arthobacter, Azospirillum, Bacillus, Burkholderia, Flavobacterium, Pseudomonas, Rhizobium, etc., play important role in plant growth promotion. In addition, such symbiotic associations of PGPRs in the rhizospheric region also confer protection against several diseases caused by bacterial, fungal and viral pathogens. The biocontrol mechanism utilized by PGPR includes direct and indirect mechanisms direct PGPR mechanisms include the production of antibiotic, siderophore, and hydrolytic enzymes, competition for space and nutrients, and quorum sensing whereas, indirect mechanisms include rhizomicrobiome regulation via. secretion of root exudates, phytostimulation through the release of phytohormones viz., auxin, cytokinin, gibberellic acid, 1-aminocyclopropane-1-carboxylate and induction of systemic resistance through expression of antioxidant defense enzymes viz., phenylalanine ammonia lyase (PAL), peroxidase (PO), polyphenyloxidases (PPO), superoxide dismutase (SOD), chitinase and β-glucanases. For the suppression of plant diseases potent bio inoculants can be developed by modulating the rhizomicrobiome through rhizospheric engineering. In addition, understandings of different strategies to improve PGPR strains, their competence, colonization efficiency, persistence and its future implications should also be taken into consideration.

     

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.     

Uncertainty modelling of atmospheric dispersion by stochastic response surface method under aleatory and epistemic uncertainties

The parameters associated to a environmental dispersion model may include different kinds of variability, imprecision and uncertainty. More often, it is seen that available information is interpreted in probabilistic sense. Probability theory is a well-established theory to measure such kind of variability. However, not all available information, data or model parameters affected by variability, imprecision and uncertainty, can be handled by traditional probability theory. Uncertainty or imprecision may occur due to incomplete information or data, measurement error or data obtained from expert judgement or subjective interpretation of available data or information. Thus for model parameters, data may be affected by subjective uncertainty. Traditional probability theory is inappropriate to represent subjective uncertainty. Possibility theory is used as a tool to describe parameters with insufficient knowledge. Based on the polynomial chaos expansion, stochastic response surface method has been utilized in this article for the uncertainty propagation of atmospheric dispersion model under consideration of both probabilistic and possibility information. The proposed method has been demonstrated through a hypothetical case study of atmospheric dispersion.