Solar/UV-induced photocatalytic degradation of three commercial textile dyes.

The photocatalytic degradation of three commercial textile dyes with different structure has been investigated using TiO(2) (Degussa P25) photocatalyst in aqueous solution under solar irradiation. Experiments were conducted to optimise various parameters viz. amount of catalyst, concentration of dye, pH and solar light intensity. Degradation of all the dyes were examined by using chemical oxygen demand (COD) method. The degradation efficiency of the three dyes is as follows: Reactive Yellow 17(RY17) > Reactive Red 2(RR2) > Reactive Blue 4 (RB4), respectively. The experimental results indicate that TiO(2) (Degussa P25) is the best catalyst in comparison with other commercial photocatalysts such as, TiO(2) (Merck), ZnO, ZrO(2), WO(3) and CdS. Though the UV irradiation can efficiently degrade the dyes, naturally abundant solar irradiation is also very effective in the mineralisation of dyes. The comparison between thin-film coating and aqueous slurry method reveals that slurry method is more efficient than coating but the problems of leaching and the requirement of separation can be avoided by using coating technique. These observations indicate that all the three dyes could be degraded completely at different time intervals. Hence, it may be a viable technique for the safe disposal of textile wastewater into the water streams.     

Reliability-based failure cause assessment of collapsed bridge during construction

Until now, in many forensic reports, the failure cause assessments are usually carried out by a deterministic approach so far. However, it may be possible for the forensic investigation to lead to unreasonable results far from the real collapse scenario, because the deterministic approach does not systematically take into account any information on the uncertainties involved in the failures of structures.Reliability-based failure cause assessment (reliability-based forensic engineering) methodology is developed which can incorporate the uncertainties involved in structural failures and structures, and to apply them to the collapsed bridge in order to identify the most critical failure scenario and find the cause that triggered the bridge collapse. Moreover, to save the time and cost of evaluation, an algorithm of automated event tree analysis (ETA) is proposed and possible to automatically calculate the failure probabilities of the failure events and the occurrence probabilities of failure scenarios. Also, for reliability analysis, uncertainties are estimated more reasonably by using the Bayesian approach based on the experimental laboratory testing data in the forensic report. For the applicability, the proposed approach is applied to the Hang-ju Grand Bridge, which collapsed during construction, and compared with deterministic approach.     

A KBES for integrated engineering

The expert process of engineering involves application of knowledge fundamental to the engineering process at one end, and of compiled knowledge in the form of heuristics gained through many years of experience at the other. Developments in Artificial Intelligence have made the process of computer aided engineering richer by enabling the simulation of this human process of engineering, which until now has been difficult to capture. Knowledge Based Expert Systems (KBES) in engineering developed so far have addressed only narrow domains of the entire process of engineering. This paper discusses a KBES shell (IES) that integrates various AI technologies, which makes it possible to implement new problem-solving strategies required for efficient handling of the entire engineering process. Issues in the integrated process of engineering are discussed first, in order to evolve a specification for the shell. The blackboard architecture is shown to be the ideal backbone for such a shell. The details of inference techniques, the knowledge based backtracking mechanism and the DBMS used in the IES are presented. The application of the IES is demonstrated using the domain of integrated engineering of steel industrial structures as an example. This application encompasses the engineering activities of conceptual design, detailed design, documentation and construction planning.     

Soft computing approach to fault diagnosis of centrifugal pump

Fault detection and isolation in rotating machinery is very important from an industrial viewpoint as it can help in maintenance activities and significantly reduce the down-time of the machine, resulting in major cost savings. Traditional methods have been found to be not very accurate. Soft computing based methods are now being increasingly employed for the purpose. The proposed method is based on a genetic programming technique which is known as gene expression programming (GEP). GEP is somewhat a new member of the genetic programming family. The main objective of this paper is to compare the classification accuracy of the proposed evolutionary computing based method with other pattern classification approaches such as support vector machine (SVM), Wavelet-GEP, and proximal support vector machine (PSVM). For this purpose, six states viz., normal, bearing fault, impeller fault, seal fault, impeller and bearing fault together, cavitation are simulated on centrifugal pump. Decision tree algorithm is used to select the features. The results obtained using GEP is compared with the performance of Wavelet-GEP, support vector machine (SVM) and proximal support vector machine (PSVM) based classifiers. It is observed that both GEP and SVM equally outperform the other two classifiers (PSVM and Wavelet-GEP) considered in the present study.     

Esterification of valeric acid over PTA supported mesoporous Al-SBA-15 as efficient solid acid catalysts

Journal of Porous Materials - Valeric acid can be produced by selective hydrogenation of biomass-derived levulinic acid. The present work aims to synthesize ethyl valerate (EV), a fuel, fuel...     

Silo music and silo quake: granular flow-induced vibration

Acceleration and sound measurements during granular discharge from silos are used to show that silo music is a sound resonance produced by silo quake. In tall and narrow silos, the latter is produced by stick-slip friction between the wall and the granular material. For the discharge rates studied, the occurrence of flow pulsations is determined primarily by the surface properties of the granular material and the silo wall. The measurements show that the pulsating motion of the granular material drives the oscillatory motion of the silo.     

A non-parametric method to determine basic probability assignment for classification problems

As an important tool for knowledge representation and decision-making under uncertainty, Dempster-Shafer evidence theory (D-S theory) has been used in many fields. The application of D-S theory is critically dependent on the availability of the basic probability assignment (BPA). The determination of BPA is still an open issue. A non-parametric method to obtain BPA is proposed in this paper. This method can handle multi-attribute datasets in classification problems. Each attribute value of the dataset sample is treated as a stochastic quantity. Its non-parametric probability density function (PDF) is calculated using the training data, which can be regarded as the probability model for the corresponding attribute. The BPA function is then constructed based on the relationship between the test sample and the probability models. The missing attribute values in datasets are treated as ignorance in the framework of the evidence theory. This method does not have the assumption of any particular distribution. As a result, it can be flexibly used in many engineering applications. The obtained BPA can avoid high conflict between evidence, which is desired in data fusion. Several benchmark classification problems are used to demonstrate the proposed method and to compare against existing methods. The constructed classifier based on the proposed method compares well to the state-of-the-art algorithms.